NATIONAL OPTICAL ASTRONOMY OBSERVATORY

NSF Research Experiences for Undergraduates Site Program in Astronomy at

Kitt Peak National Observatory (KPNO)

Annual Project Report 2009

(AST 0754223)

Submitted to

Robert Scott Fisher, Ph.D.

NSF, Program Director for Education and Special Programs

David Silva, Principal Investigator

November 5, 2009

TABLE OF CONTENTS

Annual Report 2009 REU Site Program at KPNO i

NSF RESEARCH EXPERIENCES FOR UNDERGRADUATES (REU)

Site Program in Astronomy at the

Kitt Peak National Observatory

D. Silva, Principal Investigator

K. Mighell, Site Director & Co-Principal Investigator

Annual Report for the 2009 REU Program at KPNO

Project Summary ...................................................................................................................................... 1

Program Activities and Participants ........................................................................................................ 2

Final Research Reports ............................................................................................................................ 5

Publications and Products ...................................................................................................................... 51

PROJECT SUMMARY

Annual Report 2009 REU Site Program at KPNO 1

The National Science Foundation Research Experiences for Undergraduates (REU) program is designed to encourage US college and university

students to pursue careers in science and engineering. REU site programs

make it possible for undergraduates to take part in independent research activities with professional scientists at major research institutions, usually

during the summer months. Every year since 1990, NSF has awarded

funding to the National Optical Astronomy Observatory (NOAO) to support an REU site program in astronomy at Kitt Peak National

Observatory (KPNO).

Six undergraduates were hired as full-time employees at NOAO in Tucson for a period of ten to twelve weeks, beginning at the end of May 2009, with the $82,863 allocated to the 2009 KPNO REU

program. The students spent the major portion of the program working as research assistants with

designated members of the NOAO scientific staff (REU “mentors”) on independent, supervised research projects. In the past, former KPNO REU participants have noted that the chance to attend

and participate at a major meeting of the American Astronomical Society (AAS) is one of the most

exciting aspects of our REU program. All six of our KPNO REU 2009 participants will be attending

the upcoming 215th AAS meeting which will be held at Washington, D.C. on January 3 – 7, 2010. While at the AAS meeting, these students will be presenting posters – all as the presenting author. Of

our six participants last year, two were women and four were men. The KPNO REU Site Director for

the 2009 program was Kenneth Mighell.

In addition to work on their particular research projects, the REU students attended weekly science

seminars and lectures by members of the NOAO scientific staff, gave periodic oral reports on the progress of their research to NOAO and visiting scientists, visited and toured nearby observatories

and research facilities, and planned and executed observing programs using the world-class

optical/infrared telescopes on Kitt Peak. Along with the students of the concurrent REU program at

the National Solar Observatory (NSO) in Tucson, the KPNO students also traveled to Sunspot, New Mexico, on a four-day field trip to visit the NSO facilities at Sacramento Peak and the National

Radio Astronomy Observatory’s Very Large Array.

At the end of the summer program, the KPNO students were required to write up the results of their

research work as concise scientific papers. The student papers are presented in this Annual Report

under the section Final Research Reports. All six of our 2008 KPNO REU 2008 participants presented posters at the 213th AAS meeting which was held at Long Beach, CA on January 4 – 8,

2009; their poster abstracts are reproduced below under the heading Publications and Products and

were published in the Bulletin of the American Astronomical Society, Vol. 41, No. 1, 2009.

Funding Acknowledgement

The NOAO/KPNO Research Experiences for Undergraduates (REU) Program is funded by the National Science Foundation Research Experiences for Undergraduates Program and the

Department of Defense ASSURE program through Scientific Program Order No. 13 (AST-

0754223) of the Cooperative Agreement No. AST-0132798 between the Association of

Universities for Research in Astronomy (AURA) and the NSF.

PROGRAM ACTIVITIES

Annual Report 2009 REU Site Program at KPNO 2

Recruitment

Last year, we had a total of 138 complete and 29 incomplete applicants to our program. From the

138 qualified applicants, six undergraduate students (two women and four men, 1 African American woman, 1 Hispanic man) were selected to participate in the 2009 REU site program at KPNO.

About four months before the application deadline, NOAO began recruitment for the program via

announcements on the KPNO REU web site (http://www.noao.edu/kpno/reu/), and by posters and

letters sent to colleges and universities across the US and Puerto Rico. In October 2008, approximately 900 posters and letters were mailed to US college and university Science Department

chairs and undergraduate advisors (principally in physics, astronomy, math, computer science, and

engineering programs); as well as to campus job placement and career counseling offices. Our 138 applicants were from 38 states (and the United Kingdom) and attended 116 schools; 54.3% of

applicants were women (59 out of 129; 9 gave no gender) and 54.3% were men (70 out of 129). Of the

120 qualified applicants who gave information about their race, we had 3 who classified themselves

as African-American (2.5%), 5 as Hispanic (4.2%), 2 as Mixed/Other (1.7%), 7 as Asian (5.8%), and 103 as Caucasian (85.8%). Of the 163 applicants who gave information about where they heard

about the KPNO REU program, 9 said from previous REU students (5.5%), 33 said their advisor

(20.2%), 18 said college or department staff member (11.0%), 16 said the REU poster on a bulletin board (9.8%), 73 said the KPNO REU Web site (44.9%), 1 learned of the program in a journal

(0.6%), and 13 said “other” (8.0%).

Participants

Each of the students selected for the program

was matched with an NOAO scientific staff member to work on a research project

previously proposed by them and evaluated by

the REU site director. In general, the scientific

projects most likely to be approved for the program are those that provide the greatest

opportunity for the REU assistant to make

substantial progress over the course of the summer, as well as those likely to result in an

eventual scientific publication in which the

student is a collaborator. Particular research

topics are assigned to particular students in consideration of the student’s background and

avowed scientific interests (as expressed in the

student’s application)

The six REU students, their college/university affiliations, and the NOAO scientists designated as their research advisors for the program are listed as follows:

REU Participant College/University NOAO Research Advisor(s)

Tahlia De Maio University of Colorado-Boulder W. Sherry

Davin Flateau University of Cincinnati S. Schuler

Erica Jones

Henderson

University of Louisiana J. Lotz

Evan Kaplan Vassar College G. Jacoby

Stephen Messenger University of Missouri A. Pope & A. Dey

Edward Montiel University of Arizona K. Mighell

Left to right: Stephen Messenger, Evan Kaplan,

Edward Montiel, Tahlia De Maio, Erica Jones,

and Davin Flateau.

PROGRAM ACTIVITIES

Annual Report 2009 REU Site Program at KPNO 3

Research Projects

The summer 2009 REU students at KPNO spent an average of 11 weeks working as full-time research assistants on projects ranging from the analysis of a stellar open cluster, a study of

elemental abundances in solar-type stars with planets, to a search for planetary nebulae in M31

globular clusters. In addition to work on their individual projects, the students collaborated in

designing observational programs carried out at the KPNO 2.1-m telescopes during 14 nights of observing on July 20 – 31, 2009 (an average of 5 observing nights per student). The observational

projects are an essential feature of the KPNO program, allowing each of the students to experience

first hand the process of designing and carrying out an original observing program at the national observatory.

Towards the end of the program, REU students are required to share their research findings with the

NOAO astronomical community through oral presentations and written reports. For many of the

students, this was their first experience presenting research results in a professional scientific

setting. The titles of the students’ research topics, presentations, and written reports are listed

below. The original written reports are presented in the following section.

• Tahlia De Maio: Low Mass Pre Main Sequence Members of the 25 Orionis Cluster

• Davin Flateau: Elemental Abundances in Solar Type Stars with Planets

• Erica Jones: Near IR Light Profiles of Massive Elliptical Galaxies at z ~ 1

• Evan Kaplan: The Search for Planetary Nebulae in M31 Globular Clusters

• Stephen Messenger: A Template-Independent Technique for Obtaining Photometric Redshift

Estimates for Dusty, Star-forming Galaxies

• Edward Montiel: Flickering Giants in the Ursa Minor Dwarf Spheroidal Galaxy

Scientific Lectures and Field Trips

In addition to the research project, two popular components of the KPNO REU program are the

weekly science lectures given by NOAO staff and the field trips to nearby observatories and non-

NOAO facilities. The lectures and field trips are designed to introduce the REU students to a broad

array of current scientific topics—including instrumentation issues—in O/IR ground-based, space-

based, and radio astronomy. The KPNO and NSO REU students visited the National Solar

Observatory facilities at Sacramento Peak, New Mexico, during July 3 – 7, 2009; they were given a

custom tour of NRAO’s Very Large Array by Rick Perley (EVLA Project Scientist) and they also

toured the nearby Apache Point Observatory. On July 10, the KPNO and NSO REU students

toured the University of Arizona’s Mirror Lab. The KPNO REU lectures are informal which allows

the students to interact and network with scientists working in many different areas of ground-based

& space-based astronomy, solar astronomy, and instrumentation. The topics of the 2009 REU

lecture series (June 2 – July 14, 2009) are listed on the next page.

PROGRAM ACTIVITIES

Annual Report 2009 REU Site Program at KPNO 4

• Bob Blum (NOAO Deputy Director): “Welcome to the National Optical Astronomy Observatory”

• Rachel Howe (NSO): “Introduction to Helioseismology”

• Connie Walker (NOAO): “Lighting and Astronomy”

• LeEllen Phelps (NSO): “Getting Ready to Build the Advanced Technology Solar Telescope”

• Jennifer Lotz (NOAO): “Collisions in the Cosmos”

• Frank Hill (NSO): “Space Weather”

• Steve Keil (NSO Director): “Solar Magnetism and the ATST”

• Alexei Pevtsov (NSO): “Magnetic Fields on the Sun: Where Do They Come from and Where Do They Go?”

• Knut Olsen (NOAO): “Stellar Populations”

• Jill Bechtold (University of Arizona): “The Astrophysics Graduate School Admission Process”

• Mike Merrill (NOAO): “Introduction to Infrared Astrophysics”

PROGRAM ACTIVITIES

Annual Report 2009 REU Site Program at KPNO 5

Low Mass Pre Main Sequence Members of the 25 Orionis Cluster

Tahlia De Maio KPNO REU 2009 and the University of Colorado- Boulder

Adviser: Dr. William Sherry (NSO)

ABSTRACT

The young, ~10Myr, cluster around the B star 25 Orionis in the OB1a sub-association of Orion

provides a prime sample of pre main sequence stars for the study of proto stellar evolution,

planetary disk evolution, and general stellar evolution in OB associations. Presented here is the

astrometry and photometry process for a large, deep, visual survey of the cluster associated with 25 Orionis.

INTRODUCTION

In 2007, Briceño et al. 2007), did a kinematic study of the stars within 1 degree of 25 Ori and found a kinematically distinct cluster associated with the B star, though the low mass, young cluster

members of the cluster associated with 25Ori were first characterized in Briceño et al. 2004.

Between the two studies he found a very strong association between the density of low mass

members and high mass members. It appears that the higher mass members have a higher density of low mass stars surrounding them, rather than a uniform distribution of low mass stars across the

association

Briceño also refined the age of the Orion 1a sub-association to around ~7-10 Myr.

Clusters of this age provide an important insight into OB association cluster evolution, though

relatively few surveys of these regions exist. Typically more diffuse than younger star forming regions, the low mass members in clusters around ~10Myr give insight into the timescales

associated with stellar accretion as well as flesh out our understanding of the evolution of low mass

pre main sequence stars. This project takes a deeper look at 25Ori and its associated cluster in the

Ori 1a sub-association with the goal of identifying low mass cluster members to further characterize the cluster radius, density distribution, and age.

DATA

The data for this project was taken over three nights in November of 2006 on the 2.3m BOK

telescope on Kitt Peak using 90Prime, Steward Observatory’s wide field imager. 90Prime itself consists of four thinned Lockheed 4096x4096 pixel CCDs with 30.2” per pixel. The four chips are

separated by 15.7 mm, or 7’ 41” and have a total field of view of 1.159° on a side, with 1.027° of

that being actively on the sky. Long and short exposures were taken in U, V, and I filters at all pointings- three dithers for each for the three fields. The dithers were chosen such that no gaps

remained in the images from the space between chips.

The catalogue data used to calibrate the 90Prime data was taken on photometric nights on the 0.9m

SMARTS telescope at CTIO in December of 2004. Unfortunately, the data collected during this

PROGRAM ACTIVITIES

Annual Report 2009 REU Site Program at KPNO 6

time was not expansive enough to encompass the entirety of the 90Prime data, thus preventing the

complete calibration of the 90Prime data. Additionally, images were only taken in V and I filters.

ANALYSIS

The first step in getting the data ready for analysis was to do detailed astrometry for the data set. Each of the images consisted of four chips measuring 31’ on a side and was centered at the focal

plane of the BOK 2.3 telescope on Kitt Peak. Due to the bullet shaped focal place, each chip is

tilted slightly to maximize the amount of chip at actual focus. Fitting coordinates to this configuration requires a fifth order polynomial fit, which can itself is an easily tunable parameter in

IRAF, but unfortunately requires a manual matching of 20-30 stars across each chip to the wcs in

IRAF. The IRAF script msctpeak was utilized, and though fairly straightforward and easy to use,

the volume of images, and therefore chips, needing the astrometry required several weeks to complete.

With the astrometry in hand, we then moved onto the photometry. Dr. Sherry modified his software, smarts_phot, which was used to do the photometry on the 0.9m fields, for 90Prime. 90Prime

however has enough unique quirks that the several problems were encountered. Many cosmic rays

and random fluctuation in the noise led to the misidentification of many stars. Thankfully, setting the threshold for minimum signal slightly higher easily solved this problem. More worrisome,

saturation levels of each of the chips varied widely, causing a significant number of saturated stars

to slip through the selection criteria. This problem was solved by manually combing through the

star fields finding and removing such stars which, not surprisingly, turned out to be extremely time consuming and labor intensive. Additionally, the photometry on the short exposures failed due to

aperture correction problems. Manual aperture correction was not an option in the software at the

time, though Dr. Sherry is now developing an interactive widget to solve this problem.

In the last two weeks of the program preliminary photometry of the 90Prime fields that had overlap

with the 0.9m fields was finished and the calibration process was started. The difference between the photometric V values and the instrumental 90Prime V values, as well as the difference in I

vales, showed a dependence on color, so a single correcting factor to calibrate the instrumental

colors was not possible. The trends were fitted with linear segments by hand in a crude ‘chi-by-eye’

test. An equation to correct for the color dependence in both V and I that depended only on the instrumental color, V-I was then created to calibrate the 90Prime fields.

A single, extremely preliminary, color magnitude diagram of V vs V_I was created from the calibrated 90Prime data. A very strong main sequence was present, and given a complete calibrated

catalog, it may have even been possible to visually pick out the cluster members out of the plot.

Future work on this project will see the photometry of both the long and short exposure fields

working and uniform to generate a solid catalog of calibrated stars. V vs. V-I plots can then be generated and the pre-main sequence cluster members can thereby be identified as separate from the

defined main sequence. One this is done, all manor of science may be done on them, including

refining the age estimation of the cluster, finding the radius of the cluster and density distribution.

PROGRAM ACTIVITIES

Annual Report 2009 REU Site Program at KPNO 7

ACKNOWLEDGEMENT

I’d like to offer a special thanks to Bill Sherry for his invaluable experience and advice, also, for his sense of humor and patience at each obstacle this summer- not to mention his vast pool of

knowledge on British Sci-fi.

REFERENCES

Briceño et al., 2007, arxiv:astro-ph/07017v1 Briceño et al., 2004, arxiv:astro-ph/0410521v1

Figure 1 – Sample plot of V vs. absolute V-I. Two linear segments were chosen by eye with the break point at absolute V-I = 1.9.

PROGRAM ACTIVITIES

Annual Report 2009 REU Site Program at KPNO 8

Figure 2 – Sample plot of absolute V-I vs. instrumental V-I. A single linear line was chosen by eye

to use to be used for the relationship between the absolute and instrumental color.

Figure 3 – Plot of V vs V-I. The main sequence tread is clearly seen, despite being rather shaggy

from drawing from an unfiltered sample.

Elemental Abundances in Solar Type Stars with Planets

Davin FlateauKPNO REU 2009 and University of Cincinnati

Advisor: Simon Schuler (NOAO)

ABSTRACT

It has been observed that stars with known planets are more metal-rich than surrounding fieldstars. One explanation of this observation is that stars may have accreted hydrogen-depleted plan-etary matter into their atmospheres. In such a case, abundances of low-condensation temperatureelements are expected to be lower relative to abundances of high-condensation temperature ele-ments. In addition, the presence of 6Li, a fragile element that is destroyed in pre-main sequenceprocesses, could be a strong signal of accretion. High resolution spectra (R=120,000, S/N 500-850) were obtained for 10 solar-type stars with planets. As a first step toward analyzing thesestars for Tc trends and the presence of 6Li, e!ective temperature, surface gravity, metallicity, andmicroturbulence velocity values for the target stars were derived from Fe I and Fe lines via mea-surements of spectral equivalent widths. The derived stellar parameters are in good agreementwith the best known literature values for the target stars.

1. Introduction

Extrasolar planet detection and characteriza-tion has become a field of intense scientific andpublic interest in recent times. The first extraso-lar planet discovered around a solar-type star was51 Pegasi-b (Mayor & Queloz, 1995), with discov-eries continuing to today’s current count of 357.The characterization of the host stars that giverise to extrasolar planets is important in definingplanetary formation models, estimating galacticplanetary abundances, exploring stellar chemicalevolution and stellar atmospheres, and other at-tributes of extrasolar planet astronomy and stellarformation.

Gonzalez (1997) carried out spectroscopic in-vestigation of the four then-known stars with plan-ets, and revealed that the metallicity of thosestars was higher than those of field stars. Heexplained the relationship by the accretion ofhydrogen-depleted matter onto the star, previ-ously described by Lin et al. (1996). Gonzaleztheorized that one feature that may be presentin stars with planets that have accreted planetary

matter is a correlation between observed elementalabundances and the condensation temperatures ofthose elements.

As additional extrasolar planets were discov-ered, spectroscopic investigations into their hoststars continued. Santos et al. (2001) analyzedthe spectra of a sample of 23 stars with knownplanets or brown dwarfs along with a compari-son sample of 43 stars with no planets detected,and confirmed the relative higher metallicity forstars with planets. O!ering an alternative expla-nation for this observation, Santos attributed thehigher metal content to the chemistry of the star’sparental proto-stellar nebula rather than pollutionevents like accretion.

The story of the possible existence of 6Li insolar-type stars has been more turbulent. 6Liis a fragile isotope, destroyed at temperatures of2! 106 K through a proton capture process. Dur-ing the pre-main sequence of a solar-type star, thestar is entirely convective, transporting any 6Lideep into stellar interiors where it is destroyed.For solar mass stars with metallicities close to the

sun, 6Li is completely destroyed by the end of thisstage. The detection of this fragile isotope in solar-type stars would be a strong sign of planetary mat-ter accretion.

The 6Li spectral feature is an extremely weakabsorption feature at 6707.7A. This region is dom-inated by 7Li and Fe absorption lines, with com-plicating CN, Si, V and Ca features, all of whichmust be characterized to find the very faint signalof 6Li absorption.

6Li was seemingly found in the planetary hostHD 82943 by Israelian et al. (2001) who mea-sured 6Li/7Li =0.126±0.014. A followup studyby Reddy et al. (2002) cast doubt on this largeabundance, finding 6Li/7Li =0.0-0.3, or no 6Li. Is-raelian et al. (2003), after adopting a new linelistfor spectral synthesis and making other changes totheir analysis, reported a much lower but non-zeroabundance from his original finding, with 6Li/7Li=0.05±0.02. However, Ghezzi et al. (2009) ana-lyzed HD 82943 in a group of six stars with plan-ets in a search for 6Li, but found no traces of thespecies, perhaps finally closing the door on the ex-istence of this volatile isotope in that star.

A set of 7 solar-type stars with planets wereselected for this study: HD 20367, HD 40979, HD52265, HD 89744, HD 195019, HD 209458, andHD 217107, whose literature stellar parameters arelisted in Table 1. For this project, Fe abundancesof the target stars were measured for C, N, O, Si,Ti, Ca, and others coming in the near future. Allof the target stars were previously found to havenon-zero Tc trends; a representative plot of thistrend can be found for HD 20367 in Figure 1, takenfrom Ecuvillon et al. (2006). The presence of 6Lihas been previously investigated for HD 217107,where Ghezzi et al. (2009) found none.

2. Observations and Data Reduction

High resolution echelle spectra (R=120,000,S/N=500-850) for the program stars were ob-tained in 2007 from the Hobby-Eberly Telescope(HET) at the McDonald Observatory in Texas. Asolar spectrum was obtained through a scatteredsky observation from HET. A log of the HETobservations can be found in Table 2.

The HET spectra were reduced using the IRAFechelle package. The computer program SPEC-TRE (Sneden Ph.D. thesis 1973), designed to as-

sist in the normalization of spectra and measure-ment of equivalent widths, was used for these tasksfor the Fe lines. Equivalent widths were measuredby manually fitting a Gaussian profile to each Feline using the SPECTRE’s fitting functions.

3. Spectral Line List

The list of Fe lines to be evaluated in the spec-trum of each star was assembled using atomic datafrom the Vienna Atomic Line Database [VALD](Kupka et al. 2000, 2001, Ryabchikova et al.,1997, Piskunov et al., 1995). Lines that were un-resolvable in the solar spectrum due to blending,instrumental e!ects, cosmic rays or other factorswere removed from the line list. Fe I lines over "75 mA were also not measured in the spectra dueto their deviation from Gaussian profiles, resultingin increasing uncertainty in equivalent width mea-surements. A representative sample of the spectrafeaturing identified lines can be found in Figures2 and 3. The list of Fe spectral lines and theirmeasured equivalent widths for the program starsis found in Table 3.

4. Derivation of Stellar Parameters

Fe abundances relative to solar ([Fe/H]) werecalculated with MOOG (Sneden 1973), a com-puter program designed to generate abundancesfrom measured equivalent widths (W!) and atomicline data. E!ective temperature (Te!), microtur-bulence velocities (!), and the logarithm of thesurface gravity (log g) were derived by adjustingthese parameters as to force the correlation coe"-cients to zero for plots of [Fe/H] vs. lower excita-tion potential ("), [Fe/H] vs. reduced equivalentwidth (W!/#) , and [Fe I/H] vs [Fe II/H], respec-tively. Figures 4 and 5 are representative plots ofthe final correlation coe"cients using these meth-ods. In addition, the final measured line list fromeach star was initialy evaluated to ensure no cor-relation between lower excitation potential and re-duced equivalent width, ensuring a unique solutionto the stellar parameters with the final line list.

The stellar parameters derived from the Feabundances are found in Table 4, and generallyare in good agreement with the best known litera-ture values, with Te! within 1%, and log g within3.7%. However, microturbulent velocities di!eredin some cases by as much as much as 75%. Prelim-

inary analysis indicates this parameter to be some-what variable between spectroscopic and gravita-tional studies of some target stars. The metallicityof the stars computed separately from the Fe I andFe II abundances agree well, and this is one factorthat gives confidence in these derived velocities.Uncertainties in log g and [Fe/H] are to be calcu-lated in the near future.

5. Conclusion and Future Work

With the stellar parameters derived from high-resolution spectra, the abundances of C, N, O, Si,Ti, Ca and other elements can proceed, their rela-tive abundances calculated, and Tc trends for thestars evaluated. The presence of 6Li for these starswill also proceed, by line synthesis and the charac-terization of the weak, blended 6Li spectral featurenear 6707.7A.

Three additional spectra from solar-type starswith planets, HD 2039, HD 76700, and HD 75289obtained with the FEROS Spectrograph will beincluded in this study.

With the measured abundances for many ele-ments of ten solar-type stars with planets, thisstudy will provide strong constraints on the hy-potheses about planetary consumption by solar-type stars.

This project was supported by the NOAO/KPNOResearch Experiences for Undergraduates (REU)Program, which is funded by the National ScienceFoundation Research Experiences for Undergradu-ates Program and the Department of Defense AS-SURE program through Scientific Program Order13 (AST-07542223) of the Cooperative agreementNo. AST-0132798 between the Association of Uni-versities for Research in Astronomy (AURA) andthe NSF.

REFERENCES

Ecuvillon, A., Israelian, Santos, N.C., Mayor, M.,Gilli, G., 2006, A&A, 449, 809

Ghezzi, L., Cuhna, K., Smith, V.V., Margheim,S., Schuler, S., de Araujo, F. X., & de la Reza,R., 2009, AJ, 698, 451

Gonzalez, G., 1997, MNRAS, 285, 403

Gonzalez, G., Laws, C., Tyagi, S., Reddy, B. E.,2001, AJ, 121, 432

Israelian, G., Santos, N.C., Mayor, M., Rebolo,R., 2001, Nature, 411, 163

Israelian, G., Santos, N.C., Mayor, M., Rebolo,R., 2003, A&A, 405, 753

Kupka F., Ryabchikova T.A., Piskunov N.E.,Stempels H.C., Weiss W.W., 2000, Baltic As-tronomy, vol. 9, 590

Kupka F., Piskunov N.E., Ryabchikova T.A.,Stempels H.C., Weiss W.W., 1999, A&AS 138,119

Mayor, M., Queloz, D., Marcy, G., Butler, P.,Noyes, R., Korzennik, S., Krockenberger, M.,Nisenson, P., Brown, T., Kennelly, T., Row-land, C., Horner, S., Burki, G., Burnet, M.,Kunzli, M., 1995, IAU Circ., 6251, 1, Edited byMarsden, B. G.

Ryabchikova T.A., Piskunov N.E., Kupka F.,Weiss W.W., 1997, Baltic Astronomy, vol. 6,244

Piskunov N.E., Kupka F., Ryabchikova T.A.,Weiss W.W., Je!ery C.S., 1995, A&AS 112, 525

Lin, D. N. C., Bodenheimer, P., & Richardson, D.,1996, Nature, 380, 606

Reddy, B.E., Lambert, D.L., Laws, C., Gonzalez,G., 2002, MNRAS, 335, 1005

Santos, N. C., Israelian, G., & Mayor, M., 2001,A&A, 373, 1019

Santos, N. C., Israelian, G., & Mayor, M., 2004,A&A, 415, 1153

Sneden, C., 1973, Ph.D. thesis, University of Texas

Sneden, C., 1973, AJ, 184, 839

This 2-column preprint was prepared with the AAS LATEXmacros v5.2.

Table 1

Adopted Stellar Parameters

Te! log g ! MStar (K) (cm s!1) (km s!1) [Fe/H] (M") Ref

HD 20367 6138 4.53 1.22 0.17 1.2 1HD 40979 6145 4.31 1.29 0.21 1.2 1HD 52265 6162 4.29 1.23 0.27 1.2 2HD 89744 6234 3.98 1.62 0.22 1.4 1HD 195019 5842 4.32 1.27 0.08 1.1 1HD 209458 6063 4.38 1.02 0.04 1.1 2HD 217107 5646 4.31 1.06 0.37 0.97 1

References. — (1) Santos (2004); (2) Gonzalez (2001)

Table 2

Observation Log

Tint

Star Spectral Type V Observation Dates Na (s)

HD 20367 G0 6.41 2007 Mar 10 1 15602007 Sep 28 1 13422007 Oct 03 2 3120

HD 40979 F8 6.73 2007 Feb 27 2 21602007 Feb 28 2 2160

HD 52265 G0III-IV 6.31 2007 Feb 27 2 2880HD 89744 F7V 5.74 2007 Mar 05 1 1740HD 195019 G3IV-V 6.91 2007 May 10 2 2400

2007 May 14 2 2400HD 209458 G0V 7.65 2007 Jun 08 2 2480

2007 Jun 21 2 24802007 Jul 16 2 24802007 Aug 16 2 1480

HD 217107 G8IV 6.18 2007 Aug 10 2 2520

aNumber of exposures

Table 3

Selected Fe Lines and Measured Equivalent Widths

! LEP log gf Sun HD 20367 HD 40979 HD 52265 HD 89744 HD 195019 HD 209458 HD 217107(A) Ion (eV) (dex) (mA) (mA) (mA) (mA) (mA) (mA) (mA) (mA)

5522.447 Fe I 4.21 -1.55 43.1 40.0 43.9 45.2 · · · 48.7 37.1 · · ·

5543.936 4.22 -1.14 59.7 58.3 63.9 65.6 64.6 64.1 55.8 · · ·

5546.500 4.37 -1.31 49.8 48.1 53.8 52.1 53.5 55.0 · · · 71.65546.991 4.22 -1.91 26.9 22.6 27.6 27.8 27.4 30.8 19.9 54.45560.207 4.43 -1.19 51.5 48.7 52.6 55.0 54.6 54.6 45.0 70.55577.030 5.03 -1.55 11.4 10.5 11.0 12.8 13.3 12.6 8.5 21.95579.335 4.23 -2.40 9.6 8.6 12.0 11.9 10.1 10.8 8.3 · · ·

5587.574 4.14 -1.85 37.4 32.8 39.9 37.0 · · · 41.9 31.2 63.05646.684 4.26 -2.50 6.9 6.2 7.9 6.4 · · · 8.6 · · · 16.95651.469 4.47 -2.00 17.6 16.3 18.6 19.4 17.6 21.0 14.0 34.55652.318 4.26 -1.95 25.6 23.3 25.7 27.1 23.8 29.5 21.7 46.05661.346 4.28 -1.74 22.5 19.3 · · · 24.8 23.3 25.1 16.6 41.75667.518 4.18 -1.58 50.2 47.0 52.5 53.6 · · · · · · 42.8 · · ·

5677.684 4.10 -2.70 7.3 5.4 6.2 7.5 6.7 7.8 5.5 14.95679.023 4.65 -0.92 58.6 58.9 61.1 61.8 59.2 62.5 52.6 · · ·

5680.240 4.19 -2.58 9.1 9.9 9.4 10.7 · · · 11.2 9.1 24.05731.762 4.26 -1.30 54.6 55.3 58.9 61.5 30.6 67.9 50.6 · · ·

5732.275 4.99 -1.56 14.0 14.7 · · · 18.6 · · · 19.0 10.9 24.05741.846 4.26 -1.85 30.1 28.0 30.5 31.7 30.6 35.0 26.6 28.45752.032 4.55 -1.18 52.6 53.7 56.2 58.7 58.0 57.5 50.7 · · ·

5775.081 4.22 -1.30 58.9 56.3 59.5 62.3 59.6 62.4 53.5 · · ·

5778.450 2.59 -3.48 21.9 16.6 19.6 19.4 16.5 25.2 14.8 42.26078.999 4.65 -1.12 45.6 43.6 49.6 51.3 48.8 48.5 42.1 67.76085.259 2.76 -3.10 42.0 32.7 34.6 38.3 33.1 46.4 30.0 70.86098.245 4.56 -1.88 17.3 14.4 17.0 18.0 16.8 17.2 13.0 31.86105.131 4.55 -2.05 11.4 9.6 15.1 15.0 10.7 13.1 8.6 · · ·

6151.617 2.18 -3.30 49.8 41.2 43.0 46.6 42.0 53.6 38.1 72.16159.368 4.61 -1.97 11.7 9.9 12.0 12.9 · · · 13.0 9.4 25.06165.360 4.14 -1.47 43.5 40.8 45.8 48.0 44.4 48.7 39.0 63.66173.336 2.22 -2.88 65.9 61.3 64.9 68.5 66.5 72.9 59.7 · · ·

6187.987 3.94 -1.72 46.6 42.4 46.3 48.2 44.3 52.2 38.5 71.66220.776 3.88 -2.46 19.7 15.7 21.2 20.1 · · · 21.8 14.2 37.56226.730 3.88 -2.22 28.5 25.3 30.7 29.8 27.6 32.6 23.3 49.96240.645 2.22 -3.23 46.0 39.1 44.3 46.6 43.9 51.1 37.4 70.66293.924 Fe I 4.84 -1.72 13.9 13.2 · · · 15.7 17.1 15.6 11.4 29.56380.743 4.19 -1.38 52.5 48.8 52.9 56.2 51.4 55.5 44.2 74.56392.538 2.28 -4.03 18.1 11.8 12.8 14.4 11.6 19.2 9.9 35.56496.467 4.79 -0.57 61.8 63.5 75.0 67.7 · · · 66.8 · · · · · ·

6498.945 0.96 -4.70 46.6 32.5 34.7 39.5 35.4 50.2 30.5 72.86597.557 4.79 -1.07 44.5 43.4 44.4 47.4 44.4 47.1 39.6 66.46608.024 2.28 -4.03 17.9 12.5 14.9 16.5 14.8 23.0 11.3 36.86627.540 4.55 -1.68 27.2 24.6 29.9 29.6 29.0 30.5 21.0 48.56646.932 2.61 -3.99 8.7 6.8 8.7 8.0 7.6 11.1 6.7 26.0

Table 3—Continued

! LEP log gf Sun HD 20367 HD 40979 HD 52265 HD 89744 HD 195019 HD 209458 HD 217107(A) Ion (eV) (dex) (mA) (mA) (mA) (mA) (mA) (mA) (mA) (mA)

6653.850 4.15 -2.52 9.2 9.3 11.0 11.0 9.6 11.8 8.2 22.36667.711 4.58 -2.11 9.5 8.1 · · · 10.2 · · · 11.4 6.3 20.66703.567 2.76 -3.16 36.6 30.0 33.0 35.6 30.6 40.8 27.1 61.46704.481 4.22 -2.66 6.6 6.1 6.6 6.5 · · · 7.3 5.0 13.86710.316 1.49 -4.88 15.7 10.7 10.5 12.0 11.5 17.3 9.0 36.76713.745 4.80 -1.60 19.8 19.4 22.5 23.4 19.5 23.3 16.8 39.26716.222 4.58 -1.92 15.5 13.5 15.3 17.2 15.4 17.7 11.8 32.86725.353 4.10 -2.30 16.6 15.3 17.3 17.7 15.9 19.3 12.4 35.16726.666 4.61 -1.13 44.8 45.8 48.1 50.4 46.0 50.5 41.5 69.56732.065 4.58 -2.21 8.3 6.7 8.3 · · · 7.9 8.5 6.1 17.16733.151 4.64 -1.58 26.3 23.8 27.5 28.7 26.7 28.6 21.5 46.66739.520 1.56 -4.79 11.2 7.3 8.7 9.2 7.5 13.1 6.3 27.76745.090 4.58 -2.16 8.1 6.8 7.5 · · · 7.6 · · · 5.5 18.36745.957 4.08 -2.77 6.9 6.2 6.3 7.4 7.2 7.4 4.8 16.56750.150 2.42 -2.62 72.0 68.3 69.8 73.7 71.4 78.3 63.7 · · ·

6752.716 4.64 -1.30 35.8 35.0 38.7 39.6 36.7 40.4 30.6 59.86777.408 4.19 -2.82 7.5 7.4 9.7 9.0 10.4 9.7 6.2 22.56786.856 4.19 -2.07 24.6 23.3 27.5 26.6 25.0 28.9 20.0 47.06793.259 4.08 -2.33 12.7 10.5 14.4 13.7 · · · 14.0 9.2 27.47114.549 2.69 -4.01 9.9 5.6 6.6 6.7 · · · 14.2 9.9 26.67284.835 4.14 -1.75 41.3 · · · 43.1 44.3 40.9 · · · 38.8 66.56149.249 Fe II 3.89 -2.88 36.0 47.5 54.4 55.8 69.4 45.9 44.9 45.76238.392 3.89 -2.75 42.9 57.8 62.5 65.0 76.8 55.4 52.7 60.46247.557 3.89 -2.44 52.1 67.8 80.0 74.5 96.7 62.4 64.8 61.06416.919 3.89 -2.88 41.9 50.2 54.6 56.3 66.2 48.4 46.4 52.56456.380 3.90 -2.19 62.2 78.5 87.1 85.4 103.5 73.5 74.3 71.3

Table 4

Derived Stellar Parameters of Target Stars

Te! Te! log g !Star (K) $ (cm s!1) (km s!1) [Fe/H]

HD 20367 6128 ±31 4.53 1.78±10 0.12HD 40979 6188 ±40 4.50 1.74±11 0.24HD 52265 6143 ±31 4.41 1.75±08 0.23HD 89744 6196 +34

!38 3.86 1.83±10 0.23HD 195019 5787 ±25 4.16 1.59±06 0.07HD 209458 6049 +33

!41 4.44 1.78±16 0.01HD 217107 5666 ±41 4.28 1.35±07 0.37

Fig. 1.— The Tc abundance trend for HD 20367found by Ecuvillon et al. (2006) through a least-squares fit, presented with a representative errorbar. Abundances of various elements relative tothe sun are shown by the data points.

Fe I

Fe I

Fe I

Fe I

Fe I

Fe I

Fe I

Fe I

Ca I

Si I

Fig. 2.— HET Spectra for HD 20367, showing theregion surrounding the 6Li feature around 6707.7A. Spectral lines of interest are identified.

Li ?!

Li "

Fig. 3.— HET Spectra for HD 20367, showingdetail around the 6Li feature around 6707.7 A.

Fig. 4.— Determination of the e!ective tempera-ture for HD 89744 by forcing a zero slope in the re-lation between lower excitation potential and cal-culated Fe abundances relative to the sun.

Fig. 5.— Determination of the microturbulent ve-locity for HD 89744 by forcing a zero slope in therelation between reduced equivalent widths andcalculated Fe abundances relative to the sun.

Near IR Light Profiles of Massive Elliptical Galaxies at z!1

Erica D JonesKPNO REU 2009 and Louisiana State University

Advisor: Jennifer Lotz (NOAO)

1. Introduction

Massive elliptical galaxies are some of the mostdi!cult galaxy populations to explain with galaxyevolution models. Local ellipticals contain overhalf of the stars in the local universe. These mas-sive ellipticals are also rare and lack active stellarformation. Observations have shown that thesegalaxies evolve in size but not mass. Explana-tions of these findings lead to merger-driven galaxyevolution models, although there have been manyproposed theories. Because of the complexity ofthese objects, they are very interesting to study,and there has been a lot of work in trying to un-derstand them.

Recent observations of massive elliptical galax-ies at z"1 have revealed that these objects aresmall and compact. These findings are typicallybased on the analysis and profiling done with rest-frame blue light at z!1 and NIR (near infrared)at z!2. In the Trujillo et al. 2009 paper, theGALFIT code (Peng et al. 2002) is used to fit aSersic profile to the data selection of galaxies in Iband and V band. From these fits, they find thatmassive spheroid-like galaxies at z!1.5 are fourtimes smaller than local galaxies. Sersic profilingin conjunction with the GALFIT code (Peng etal. 2002) is the most common method for fittinggalaxies with z"1. Van Dokkum et al. 2008 usethis same method and they also find that galaxieswith z!2.3 are much smaller than local galaxies.Another method used for looking at the light fromthese distant galaxies is comparing surface densityprofiles to local elliptical galaxy profiles. Naab etal. 2009 show density profiles for galaxies pro-duced by a simulation. It is stated that centralmass densities are mainly dominated by in situstars, and it is shown that the stellar mass densityfor galaxies at z=1 at large radii is less than thatof local galaxies.

Theorists have made galaxy evolution models toexplain the observed phenomenon. An acceptedmodel for massive elliptical galaxies is that theystart o" as massive compact objects and evolveinto the larger local elliptical galaxies. The ’pu"-ing up’ of these galaxies is accounted for througha series of minor mergers. These local massiveellipticals are mainly composed of older star pop-ulations and lack new stellar formation. As theseare older, redder objects, a better way to analyzethese is looking at them at longer wavelengths be-cause it is easier to see light at large radii, and itis less biased by recent star formation.

2. Data Selection

Our data is taken from AEGIS, the All-wavelength Extended Groth strip InternationalSurvey. We chose data based on four importantcriteria. Our data has spectroscopic redshift be-tween 0.6 and 1.2 with these numbers obtainedfrom the DEEP2 survey. We have data from bothHST ACS I band (8000 angstroms) and NIC3 Hband (16000 angstroms). The I band data has aresolution of 0.03” per pixel, and the H band has0.2” per pixel. The I band simulated psf has aFWHM of 0.06”, and the H band simulated psfhas a FWHM of 0.14”. The galaxies have stel-lar mass estimates Mstar"3e10 Msun based onground based optical colors. These galaxies alsohave ACS I band Sersic profile n#2.5 measuredusing the GALFIT program (Peng et al. 2002).This Sersic profile indicates that these are indeedbulge-like galaxies. Our resulting list consists ofeleven galaxies.

3. Data Analysis

We have taken the ACS I band and NIC3 Hband images and cut out smaller postage stampswith the galaxy approximately in the center. We

use the IRAF task ELLIPSE to find the best fitellipticity, position angle, and center of the galaxy.ELLIPSE produces a table of intensities, elliptic-ity, and more important data. We fit a Sersic pro-file to these galaxies using the following equation:

io $ e(2n!0.331)( r

reff)(

1n

!1)

.

We have written two programs to find the bestfit central intensity, io, e"ective radius, reff , andSersic index, n. We wrote our own version of Sersicprofile fitting code, galfit, and gal powell.

Galfit calculates !2 for a grid of io, reff , and nvalues. The minimum !2 value is found within thisgrid, and the corresponding io, reff , and n valuesare the best fit for the Sersic profiles. Galfit pro-duces a three dimensional array of values. Holdingthe best fit io constant, we have a two dimensionalarray of n and reff . The result is similar when wehold n and re" constant. This allows us to lookat confidence intervals around the best fit points.A Sersic model is created as the program loopsthrough io, reff , and n, and the final model withthe best fit parameters is convolved with a psf.An IDL function is run on the convolved modeland used to get intensities similar to those in theoutput table produced by IRAF ELLIPSE.

Gal powell uses the powell function (Press etal. 2002) to find the best fit Sersic profile andthe minimum !2 is also calculated. The powellmethod works by minimizing the multidimensionalSersic equation given a starting point vector of io,reff , and n. A Sersic model and convolved modelare created in this program as well as an arrayof intensities. The !2 is better because while thegalfit programs steps through grid of given values,the powell function allows for the points betweenthese steps to be tested, ultimately producing abest fit with greater precision. The powell functiononly returns the best fit, so there are no confidenceintervals produced in this program.

We run IRAF ELLIPSE on ACS I band andNIC3 H and J band images and use the outputto create color profiles for the galaxy. The I bandimages are convolved and rebinned to match thepixel scale and resolution of the H band data,which is from 0.03” per pixel to 0.20” per pixel.We then align the I and H images before creat-ing color gradients. The I-H color gradients areused to produce a stellar mass density profile. A

stellar mass density profile is produced using theintensities from IRAF ELLIPSE and the intensi-ties from the models in the programs, which areused to compare the actual data to the model.

4. Results

Figure 1 is a plot produced by the galfit pro-gram for the H band data for galaxy 1. There arethree plots with confidence intervals at fixed io,reff , and n corresponding to the best fit value ofthe these three parameters. There is also a surfacebrightness profile, which is the intensity versus ra-dius. Although the Sersic index, n, and intensity,io, cannot be constrained within confidence inter-vals, the e"ective radius, reff , is very small, evenwithin error bars. The plot of intensities showsthe actual data(blue) and the data from the mod-els produced in galfit(green) and gal powell(red).These show that the models are good fits to theactual data. The models were created with thebest fit parameters, but as shown in the plot of iovs reff , the re" of 0.50”, which corresponds to thephysical size of 4-5 kpc and similar to what youwould see for local elliptical galaxies, is within thefirst confidence interval and therefore within 68percent of the small best fit reff of 0.20”.

Figure 2 is a similar plot for the I band data.We see much smaller confidence intervals, and thereff is consistent with the H band data. Also, theintensity plot reveals that the models are a goodfit to the data. Although, the reff of 0.50”, 4-5kpc and similar to local galaxy sizes, is still withinthree confidence intervals of the best fit. Figures 3and 4 show the color gradients seen for this galaxy.Figure 3 reveals a slightly bluer center, but thegradient is very weak. Figure 4 reveals a veryflat gradient. The findings of weak and mostlyflat gradients imply a galaxy of a uniform stellarage population. The color gradient from Figure3 is used to create the stellar mass density pro-files, adopting Bruzual and Charlot 2003 modelsto convert color to a stellar mass light ratio.

Figure 5 shows the stellar mass density profilesfor the I band and H band data for all galaxies.Although the programs reveal a small reff , thisplot shows that, for H band data, light is visibleout to at least 16 kpc which is consistent with whatwe find for local galaxies. We run into problemswith I band data because of noise, so we cannot

quite see what’s going on at large radii. Figure 6is the same plot with error bars.

Figure 7 is not the actual data. This plot isproduced by simulated profiles from the modelsproduced in the program. Although the reff arevery small, this plot is consistent with what wesee for local galaxies with reff that are 4 and 5times larger. Using a reff of 0.50” to produce themodels, Figure 8 shows that the resulting densityprofiles are not di"erent for what we see for theactual data and for local galaxies. This figure alsoshows the e"ects of the psf. As expected, the ma-jor di"erence is in the inner most points, and thewings of the galaxy are not very di"erent revealingthat the psf is not contributing significantly to thelight we see out to large radii in Figure 5.

5. Discussion and Conclusion

The results for these galaxies at z!1 revealsmall sizes from the output of the programs fit-ting Sersic profiles although the stellar mass den-sity profiles are similar to what we see for localgalaxies. This contradiction can be explained bylooking more closely at the galfit program thatwas written and programs similar to it. Becauseof the background noise in the data, the error barsfor measured intensities are very big at larger radiiresulting in a program that actually only fits theinner most points. Because of limitations in ourdata, the programs that are finding minimum !2

values and fitting Sersic profiles are actually onlyfitting the points at smaller radii and producingvery small reff . Also, given the same best fit Ser-sic index, n, and a di"erent io, the choice of a reff

equivalent to that of local galaxies produces a plot(Figure 8) that is remarkably similar to plots withvery small reff (Figure 7). Therefore, a ”best fit”models with a small reff is not significantly betterthan a model with reff similar to local galaxies.

Basing size measurements mainly on Sersic pro-files may not be the best way to categorize thesemassive elliptical galaxies. Even with changes inthe size of these galaxies, we see models that are”good” fits to the data. Although the stellar massdensity profiles for the H band data are along thelines of what is seen with local galaxies, we runinto problems due to limitations in data for the Iband data, such as a large signal to noise ratio.Although the plots in Figure 5 do not show evi-

dence of light out to at least 10 kpc for the I banddata, the background noise in the data outweighsany real data. In the NIR data, light at even largerradii (!16kpc) is visible. Hopkins et al. 2009 alsoreveal the lack of light for galaxies with z"2 atlarge radii, but with better, deeper high resolu-tion data, we can find out if these galaxies areindeed small and compact or if there actually isstellar mass out to these large radii and that thesegalaxies at z!1 are not very di"erent from localgalaxies.

6. Future Work

As stated before, there are limitations with thedata we used to profile these galaxies. Givenhigher resolution data, we can look into reproduc-ing these results with better precision. Anotheridea is to look at these galaxies in the X-ray tofind if the slightly bluer centers are caused by ac-tive galactic nuclei (AGNs). As we have showndrawbacks to relying solely on the Sersic profil-ing of galaxies, a good idea would be to look intonew methods to categorize these galaxies. If theseresults are reproduced given new data and possi-bly new profiling techniques, we can then focus onmodifying galaxy evolution models to explain andunderstand our observations.

7. Acknowledgements

Jones was supported by the NOAO/KPNOResearch Experiences for Undergraduates (REU)Program which is funded by the National ScienceFoundation Research Experiences for Undergrad-uates Program and the Department of DefenseASSURE program through Scientific ProgramOrder No. 13 (AST-07542223) of the Cooper-ative Agreement No. AST-0132798 between theAssociation of Universities for Research in Astron-omy (AURA) and the NSF. Special thanks to TodLauer, Ben Weiner, and Michael Cooper for usefuldiscussions.

REFERENCES

Hopkins et al. 2009, MNRAS, arXiv:0903.2479v1Naab et al. 2009, ApJL, in press, arXiv:0903.1636Press et al. 2002, Numerical Recipes in C++Trujillo, et al. 2007, MNRAS, 382, 109van Dokkum et al. 2008, ApJ, 677, L5

Table 1

Sersic profile best fits for entire data selection.

Gal ID ell(h) ell(i) pa(h) pa(i) xc(h) yc(h) xc(i) yc(i) n(h) n(i) i(h) i(i) reh rei chi2(h) chi2(i) reh rei z(”) (”) chi2(h) chi2(i) (kpc) (kpc) z

1 0.18 0.18 -5 68 43.5 43.5 204 204 12.7 2.54 6.6 0.015 0.2 0.35 0.143 4.025 1.635 2.861 1.102 0.26 0.27 -79 -33 42.0 41.5 203 199 13.3 3.15 0.4 0.012 1.1 0.75 0.269 5.102 8.052 5.490 0.753 0.18 0.30 -59 -10 21.5 21.0 203 205 1.41 3.76 1.486 0.002 0.34 0.92 0.64 4.677 2.8197 7.6297 1.204 0.26 0.25 -82 -45 21.0 22.0 194 208 1.536 2.956 1.38 0.0125 0.244 0.30 0.215 2.348 1.977 2.431 1.055 0.23 0.45 66 -74 26.0 27.0 204 203 1.99 2.9 0.901 0.069 0.38 0.25 1.29 2.479 2.7686 1.821 0.746 0.28 0.40 52 -77 31.0 32.5 205 204 6.4 2.58 3.701 0.025 0.26 0.35 1.55 2.836 1.895 2.843 0.747 0.14 0.20 0 63 32.0 32.0 197 201 2.67 2.989 1.019 0.024 0.535 0.45 2.225 9.369 3.914 3.292 0.748 0.06 0.11 45 -87 43.0 43.0 202 202 1.45 2.052 0.805 0.17 0.435 0.425 1.568 4.39 3.4715 3.392 0.999 0.39 0.53 -54 89 32.0 31.5 206 201 6.862 2.536 11.726 0.08 0.2 0.33 4.605 5.896 1.349 2.226 0.6110 0.46 0.35 64 -69 27.5 27.0 201 200 1.1 1.903 1.087 4.654 0.6 0.95 1.53 4.654 4.795 7.592 0.9911 0.15 0.26 -41 20 31.5 32.0 206 196 5.869 2.067 34.196 0.149 0.1 0.214 7.272 1.219 0.7405 1.5847 0.77

Fig. 1.— This plot consists of confidence intervalsand the surface brightness profile for galaxy 1 inH band (rest-frame r-i light). The red points arefrom the gal powell program, and the green pointsare from the galfit program. The upper left plothas Sersic index, n, versus e"ective radius, reff .The bottom left plot has the intensity, io, versusn. The top right plot has io versus reff . Theseconfidence interval plots show that n and io are notvery well constrained, but the best fit reff is verysmall even within three sigmas. A reff of 0.50”,which is 4-5 kpc and comparable to local ellipticalgalaxy sizes, is within one sigma of the best fit andtherefore within 68 percent of the best fit. Thebottom right plot is the surface brightness profilewith intensity versus radius. This plot shows thatthe best fits from galfit and gal powell are goodfits for the real data (blue).

Fig. 2.— This plot consists of confidence intervalsand the surface brightness profile for galaxy 1 in Iband (rest-frame blue light). The maroon pointsare from the gal powell program, and the orangepoints are froom the galfit program. The upperleft plot has Sersic index, n, versus e"ective ra-dius, reff . The bottom left plot has the intensity,io, versus n. The top right plot has io versus reff .These confidence interval plots show smaller con-fidence intervals than what is seen with the I banddata, and the reff is consistent with the reff forH band data within three sigmas. These plots alsoshow a very small reff for this galaxy. The bot-tom right plot is the surface brightness profile withintensity versus radius. This plot shows that thebest fits from galfit and gal powell are also goodfits for the real data (blue).

Fig. 3.— This plot is the I-H color profile forgalaxy 1. The plot has I-H versus radius. TheI band data was convolved and rebinned to matchthe resolution of the H band data which is 0.2” perpixel. This plot reveals a very weak color gradi-ent with galaxy 1 possibly having a slightly bluercenter.

Fig. 4.— This plot consists of the J-H color pro-file for galaxy 1. The plot has J-H versus radius.The color gradient is very flat, revealing a uniformstellar age population.

Fig. 5.— This plot is the stellar mass density pro-file for the entire data selection for I and H banddata. The plots are stellar mass density versus r

14 .

The I band data shows light out to at least 10 kpcfrom the galaxy centers, but the background noiseeliminates the possibility of seeing light at largerradii. The H band data shows light out to at least16 kpc, which is similar to what is seen for localmassive ellipticals. These plots reveal that usingnear infrared data allows us to see more light fromthese galaxies at z ! 1.

Fig. 6.— This plot is the stellar mass densityprofile for the entire data selection including er-ror bars. The error bars are small, indicating thatthe results stated for Figure 5 are accurate.

Fig. 7.— This plot is the stellar mass density pro-file produced by simulated profile models. Thisplot reveals that the simulated models producedin the gal powell program fit the data well.

Fig. 8.— This is the stellar mass density profilefor one galaxy produced by simulated profile mod-els before convolution(black) and after convolutionwith a psf(red). This plot reveals that the psfdoes not contribute significantly to the wings ofthe galaxies but does change the shape for the in-ner most radii. This plot is also produced with areff of 0.50”, and the outcome is extremely simi-lar the the plots produced for data with a reff of0.2”. This reveals that when using Sersic profiling,a large reff of 0.50” is just as ’good’ as a fit witha small reff .

The Search for Planetary Nebulae in M31 Globular Clusters

Evan Kaplan KPNO REU 2009 and Vassar College

Advisor: Dr. George Jacoby (WIYN/NOAO)

Collaborators: Laura Fullton (Nagravision, Lausanne)

Myung Gyoon Lee (Seoul National University) Orsola De Marco (AMNH/MQ)

Robin Ciardullo (Penn State) John Feldmeier (Youngstown State) Ho Seong Hwang (Korea Institute)

Kim Herrmann (Lowell Observatory) James Davies (NOAO)

Abstract

The formation of planetary nebulae (PNe) is traditionally thought to occur during the late stages of stellar evolution in individual low mass stars. However, the presence of PNe in Milky Way (MW) globular clusters (GCs) suggests other methods may be required, such as interacting binaries or mergers. Using medium resolution spectra of 455 GCs in M31, we searched for chemical and kinematic tracers of GC PNe. The results yielded 10 – 14 very likely candidate PNe; statistically proportional to the amount found in MW GCs. However, a lack of PNe from x-ray GCs does not support the interacting binary theory and needs to be further examined. During this project, the velocities of 405 M31 GCs were also confirmed, and a list was compiled of 115 newly found probable and possible MW PNe. Keywords: planetary nebulae, globular clusters, M31, Andromeda, stellar evolution

1. Introduction

The formation of PNe is conventionally believed to occur during the late stages of stellar evolution in individual low mass stars. Considered the death of a star, PNe are the outer layers of a star shed off and excited by a hot central core left behind. The lifetime of a PN is between 10,000 – 50,000 years; relatively short compared to the lifespan of a star. Despite this, thousands of PNe have been observed throughout the galaxy just due to the sheer numbers of stars in the MW. In fact, PNe have been observed in the most unlikely of places, GCs. GCs are home to some of the oldest stars in the galaxy, so the only main sequence stars left in them are

<1M!

. According to the pAGB track (see Fig. 1), a 1.0M!

star will take about 5,000 – 10,000 years to

become hot enough (30,000K) to ionize the gas it shed off, thus forming a PN. However, a 0.8M!

star will take 100,000 – 200.000 years to reach the same temperature. By then, the outer layers of the star would have already dissipated into the ISM before they could be ionized and become a PN (Vassiliadis & Wood 1994).

Figure 1: The pAGB track for a 1.0M

! and 0.8M

!star (time on track is in 10 years).

Since PNe have been observed in GCs, there must either be another way to form them other than the textbook theory or stellar evolution occurs faster in low mass stars than previously believed. If stellar evolution did occur more rapidly (whereby every star goes through a PN phase), we should see approximately 16 PNe in MW GCs. To date, many fewer than 16 (only 4) PNe have been observed, so stellar evolution is not violated, but another process must also be triggering the formation of PNe to see any at all (Jacoby et al 1997). One emerging hypothesis is the creation of a PN from interacting binaries such as common envelope binaries (CEBs) or mergers (De Marco 2009). Interacting binaries occur when a secondary star orbits in the envelope of a primary, larger star. Here, the main sequence secondary interacts with an evolving primary star, not yet a white dwarf. The interaction serves to speed up the evolutionary process, in a variety of ways. First, mass transfer is possible if the secondary is orbiting within the outer atmosphere of the primary. The primary grows to

an apparent mass larger than 1 M!

and goes through a PN phase, while preventing the secondary from becoming a PN because of mass loss (Soker 1997). Second, the two stars can merge if the secondary’s orbit is close enough. This results in a more massive star that can go through a PN phase such as a blue straggler (which are seen in many GCs). Third, the presence of the secondary can induce a faster evolutionary time scale of the future central star in the primary, allowing heating of the primary’s nebula by the central star after ejection (Iben & Livio 1993). One signature of a significant close binary population is strong x-ray emission. Dumping of stellar material onto the secondary surface (mass transfer) causes a 10 K hotspot that emits x-ray radiation (Grindlay et al. 1995; Bailyn 1996; Margon & Bolte 1987). Therefore, one would expect to see more PNe in GCs where x-rays have been detected if close binary stars are a pre-requisite to form PNe in old populations. In the MW, two of the four GCs with a known PN are in fact from the 14 GCs with strong x ray sources. Also, one of the two remaining PNe is from one of the eight GCs with weak x-ray sources. Of the 133 known MW GCs, the probability of finding three of the four GC PNe from GCs with x-ray sources is remote, 0.022 (Jacoby et al. 1997). However, a sample size of 2-3 objects is small.

In order to improve statistics and find more evidence for the binary theory, we examined the GCs in Andromeda (M31). M31 has a larger sample of GCs (roughly 3 – 4x more than in the MW), so as a result, we can test the hypothesis more convincingly. If the probability of finding PNe in M31 is proportional to that of the MW, we expect to find 12 -16 GCs with PNe, and of those, 6-8 PNe from x-ray clusters. 2. Methods

Observations were taken in October 2008 at the WIYN telescope using the Hydra fiber fed spectrograph. 455 M31 clusters were observed between wavelengths of 4600 – 5400Å with 1Å resolution. The majority of the data was already reduced before I worked with it. First, the spectra underwent normal Hydra reductions and were sky subtracted. Then, they were normalized around the 5000Å region of counts on the spectra. Next, the data were Doppler corrected due to the redshift for each of M31 GCs, and finally, a template of a GC without a PN was matched to each of the spectrum of each GC based on its chemical compositions and age and then subtracted. Then, a list of candidate PNe was determined by examining line ratios, PNe velocities, and GC velocities. 2.1. Line Ratios Between the wavelengths of 4600 – 5400Å, tracers of PNe are [OIII] emission lines at 5007Å and 4959Å, and an H line at 4861Å is expected (see Fig. 2). In addition, when selecting potential PN candidates certain criteria must be observed. Most importantly, the [OIII]5007 line should be present. Then, you should see the [OIII] 4959 line at one-third strength to the 5007 line. Next, if H is present, it should be no more than half that of [OIII] 5007. This is a low ratio compared to that of PNe observed outside of GCs. Typically, they have an [OIII]5007/H ratio of 5 – 15. However, since GCs are metal poor, a lower ratio is permitted. We chose 2 as our cutoff, because it is the lowest ratio observed in a known GC PN (Ps1 in M15).

Figure 2: Spectrum of a known PN. From left to right are the H 4861Å, [OIII]4959Å, and

[OIII]5007Å characteristic emission line tracers.

Line ratios were measured for all clusters that showed the above PN emission characteristics. This was done by examining the amount of flux each line had above the continuum, and then dividing the appropriate fluxes ([OIII]5007/H ) by each other. In addition, the instrumental set up was designed so that the spectrograph was most sensitive at 5007Å. Therefore, the H flux was multiplied by a factor of 1.44 before it was used in the ratios. 2.2. PN velocities Velocities of a PN's emission lines should not exceed the escape velocity of its GC. Escape velocity is roughly the square root of 5-10 times the cluster absorption line velocity dispersion (typically 5-20 km/s [Strader et al 2009]) squared, or about 12-60 km/s, but can be as high as 100 km/s. The Doppler shift of the [OIII] and H lines were measured for candidate PNe using a simple IRAF script (Jacoby 2008). This script used the redshift equation to determine radial velocities of the candidate PNe. In addition, the location of the Mgb line (5183.6A) was measured to check the accuracy of the template subtraction and GC Doppler correction applied during the reduction steps. The velocity of the [OIII] 5007 line was most important, and a spectrum was rejected if this line had a velocity greater than 100 km/s from the GC velocity. 2.3. GC velocities Lee 2008 and Strader et al 2009 measured the velocities of GCs in M31. Our cluster velocities were measured by evaluating the redshift of the Mgb line (5183.6Å) in the pre-Doppler corrected spectra. Then, the velocities of all the clusters were plotted against the results of Lee and Strader (see Fig. 3). A 1:1 linear relationship was expected between the two data sets. This was mostly observed, except for a few anomalies that may be caused in part by instrument malfunction or bookkeeping errors.

Figure 3: M31 GC velocity comparisons with Lee 2008 and Strader et al 2009.

3. Results & Discussion After examining the criteria for selecting objects as candidate PNe, 10 – 14 objects out of the 455 GCs were discovered and classified as very likely candidate PNe candidate (see Appendix). A very likely candidate PN is one that demonstrates a strong [OIII] 5007 emission with a [OIII] 4959 line at one-third strength, an [OIII]5007/H ratio greater than 2 and a radial velocity difference less than 100 km/s. Many objects were rejected due to low S/N, strong H emission, velocities greater than 100 km/s or large Doppler shifts of the Mgb line. Low S/N objects can either be caused by bright clusters or faint PNe, whereby the PN signal is too low to accurately discern from the background. Often times, strong [OIII] 5007 lines and [OIII] 4959 lines at one-third strength were observed. However, strong H emissions caused us to reject these candidates in case of confusion with diffuse low excitation gas emission throughout M31 (Ciardullo et al 1988). Also, a Doppler shift greater than 100 km/s is a strong indicator that the object is not a member of the GC observed. Since that velocity is greater than the escape velocity of the GC, the object could be a PN perhaps just in the line of sight of our observations, or it is just another example of diffuse gas throughout M31. Finally, spectra with large Doppler shifts of the Mgb line in the pre-template subtracted images were rejected, because they are an indicator of an incorrect Doppler correction on the GC usually due to low S/N. In addition to the above complications, spectra were examined for tracers of supernova remnants and symbiotics, as they can be confused with PNe. 10 – 14 very likely candidate PNe is within the statistical hypothesis proportional to MW GC PNe. Because there are 4 known PNe in MW GCs, 12 – 16 GC PNe were predicted for M31 since it has 3-4x as many GCs. In spite of this, the results do not support the binary theory for the formation of GC PNe, because none of the candidates come from x-ray clusters. It was expected that 6 – 8 of the PNe would come from x-ray sources. However, due to distance (M31 is ~750 kpc away) we may not detect x-rays from clusters, leading us to falsely categorize the source of some PNe. In addition to searching for GC PNe in M31, the velocities of 405 clusters were confirmed with data presented by Lee and Strader. This was helpful in determining the accuracy of our observations and resulting measurements. 4. Conclusions & Future Work

The discovery of 10 – 14 very likely PNe candidates in M31 GCs is a promising advance towards formulating a GC PN formation theory. However, the lack of PNe from x-ray clusters needs to be examined. Chemical abundances from high resolution spectroscopy is the next step in both confirming these objects are PNe and providing a more detailed check on suspect PNe. In addition, new and archived HST images in [OIII] should be used for on/off band comparisons to further confirm the existence of PNe. Simulations of GC PN spectra will allow us to determine the detection limit of PNe in M31 GCs (see Fig. 4). In addition, follow up work is needed on possible galactic nuclei mislabeled as GCs, as evidenced by line broadening observed in a number GC spectra. Also, H fluxes tended to be greater in x-ray and faint clusters. This may call for revisions on the [OIII]5007/H ratio limitations. Finally, the confirmation of 405 M31 GC velocities allows for more detailed kinematic studies of the GCs and, in turn, M31 structure.

Figure 4: Simulation of a PN GC spectrum. The first image on the top left is a GC spectrum without a

PN. The image below it is a medium strength PN spectrum. The middle image shows the result of combining and Doppler correcting the two left images. The right image is the final product normalized

and template subtracted. 5. Side Project

In addition to searching for PNe in M31 GCs, I also compiled a list of 115 newly found probable and possible PNe in the MW. These PNe were discovered by amateur astronomers using Digital Sky Survey images. The list will be used to organize observing runs (WIYN 3.5m September 10 – 13, 2009) and provide public access to information on PNe. 6. Acknowledgements

Kaplan was supported by the NOAO/KPNO Research Experience for Undergraduates (REU) Program, which is funded by the National Science Foundation Research Experience for Undergraduates Program and the Department of Defense ASSURE program through Scientific Program Order No. 13 (AST-07542223) of the Cooperative Agreement No. AST-0132798 between the Association of Universities for Research in Astronomy (AURA) and the NSF. The author would also like to thank Dr. George Jacoby for his support and advice throughout the project and JC for waterlemons.

7. References Bailyn, C.D. 1996, in The Origins, Evolutions, and Sestinies of Binary Stars in Clusters, ASP Conf.

Ser. 90, edited by E. Milone and J.-C.Merrilliod (ASP, San Francisco), p. 320 Ciardullo, R., Rubin, V.C., Jacoby, G.H., Ford, H.C., & Ford Jr., W.K. 1988, AJ, 95, 438 De Marco, O. 2009, PASP, 121, 316 Grindlay, J.E., Cool, A.M., Callanan, P.J., Bailyn, C.D., Cohn, H.N., & Lugger, P.M. 1995, ApJ, 455,

L47 Iben Jr., I., & Livio, M. 1993, PASP, 105, 1373 Jacoby, G.H., Morse, J.A., Fullton, L.K., Kwitter, K.B., & Henry, R.B.C. 1997, AJ, 114, 2611 Lee, M.G., et al. 2008, ApJ, 674, 886 Margon, B., & Bolte, M. 1987, ApJ, 321, 261 Soker, N. 1997, ApJS, 112, 487 Strader, J., Smith, G.H., Larsen, S., Brodie, J.P., & Huchra, J.P. 2009, AJ, 138, 547 Vassiliadis, E., & Wood, P.R., 1994, ApJS, 92, 125

8. Appendix

Spectra and information table on the 14 candidate PNe.

Lee ID |GC – PN| Velocity

Difference (km/s)

[OIII]5007/H

6170 9 3.3 90277 11 2.4 5735 22 3.3 2040 4 no H 7448 12 no H 9223 17 3.3 1612 6 no H 5606 5 no H

4131 6 no H 6257 29 no H

8793 33 2.7 8566 32 no H

3967 49 no H 7050 51 no H

A Template-Independent Technique for Obtaining Photometric

Redshift Estimates for Dusty, Star-forming Galaxies

Stephen J. MessengerUniversity of Missouri and National Optical Astronomy Observatories, Tucson, AZ 85719

sjm7w6@mail.missouri.edu

Alexandra Pope, Arjun DeyNational Optical Astronomy Observatories, Tucson, AZ 85719

pope@noao.edu, dey@noao.edu

ABSTRACT

Spectroscopic redshift determinations of high redshift galaxies require the devotion of a largeamount of time and resources. These factors drive the need for alternative methods which wouldbe able to calculate accurate photometric redshifts for galaxies. One field that would really beginto thrive upon the creation of such a method is the field of submm galaxies. A promising avenuefor creating such a method involves using the 1.6 µm stellar bump of star-forming SMGs toobtain accurate photometric redshifts. The samples and data required to develop this methodalready exist. Specifically, the spectroscopic SMG sample from Chapman et al. (2005) and thespectroscopically confirmed DOGs in Bootes can be used to train/test this method. We proposea SMG redshift determination formula that includes IRAC fluxes of 3.6, 4.5, and 5.8 µm alongwith the MIPS flux of 24 µm. One of the powerful aspects of this technique is that it is atemplate-independent method of determining photometric redshift estimates. We have been ableto train our technique such that it produces relatively accurate photometric redshift estimates.We have applied this redshift estimator to many SMG sources that do not have spectroscopicredshifts, such as those in SHADES. We have also applied our technique to approximately 1750DOGs that do not have spectroscopic redshifts. The tests we have run and the results we haveobtained give us much confidence that our method can be used as a quick and easy way to obtainphotometric redshifts for submm galaxies. Once accurate redshift estimates are obtained, sciencewill be able to dive further into SMG studies concerning star formation rates, stellar masses, etc.

Subject headings: galaxies: — Photometric Redshift Estimates, Sub-millimeter galaxies, Template-

Independent

1. Introduction

About ten years ago, a new class of galaxies,sub-millimeter (submm) galaxies, emerged fromdeep surveys of the sky with the Sub-millimeterCommon User Bolometer Array (SCUBA) on theJames Clerk Maxwell Telescope (JCMT) (Hollandet al. 1999; Smail et al. 1997; Hughes et al. 1998;Barger et al. 1998; Eales et al. 1999). Thesegalaxies, which occupy a redshift range of around

0.5 to 4, provide key insight into galactic proper-ties such as stellar masses, dust mass, and star-formation rates (Chapman et al. 2005; Pope etal. 2006). Due to their wide redshift range, an-alyzing large samples of these galaxies providesone way to study the evolution of this population.These galaxies are quite numerous compared tolocal dust-obscured galaxies; hence, probing theirproperties will provide for much understanding asto how galaxies form, evolve, and interact. Dis-

covering characteristics of submm galaxies is notstraight forward and can be hampered without theknowledge of certain parameters of the system.One of the most important parameters that onemust obtain, in order to study these galaxies, isredshift. Without redshift measurements, muchscience is held back from achieving its full poten-tial.

Obtaining redshifts for these galaxies hasproven to be a di!cult task. One must eitherdevote a large amount of telescope time, suchthat spectroscopic redshift-determining observa-tions can be taken, or one must create methodsthat can circumvent the need for time-consumingobservations, hopefully with high levels accuracyamong the methods. Since the demand for tele-scope time is, as usual, quite high, creating al-ternative methods to determine redshifts has un-dertaken an even higher importance than firstanticipated.

1.1. Star-forming Submm Galaxies

Submm galaxies can be split into two cate-gories: primarily star-forming galaxies and thosewith a significant AGN contribution. It is impor-tant to separate submm galaxies into these twocategories because di"erent physical mechanismscontrol each one. While submm galaxies can beseparated into two groups, about seventy to eightypercent of them fall into the star-forming galax-ies group, while the other twenty to thirty per-cent are AGN dominated (Alexander et al. 2005;Pope et al. 2008).

The research presented here specifically in-volves star-forming submm galaxies. One methodthat has become reliable in determining redshiftsinvolves using the stellar bump of star-formingsubmm galaxies (Sawicki 2002; Pope et al. 2006).The reason for our separation of star-formingsubmm galaxies and AGN dominated submmgalaxies becomes apparent at this point since theAGN dominated galaxies do not exhibit a clearstellar bump and thus cannot be constrained byone. The stellar bump of the star-forming submmgalaxies provides a promising mechanism fromwhich to determine redshifts to these galaxies (e.g.Pope et al. 2006). In Pope et al. (2006), theyapplied such a model to a small training set ofgalaxies, which consisted of ten galaxies from theGreat Observatories Origins Deep Survey-North

(GOODS-N) survey. In that study, they werelimited by how many sources had spectroscopicredshifts because, without spectroscopic redshifts,they could not properly train a sample. Thereare now many more submm galaxies with Spitzerphotometry and spectroscopic redshifts. Thesenew data has allowed us to refine this method. Inthis paper, we present a new technique that incor-porates many more sources (48) into the trainingset. As compared to the Pope et al. (2006) model,we have increased the amount of training sourcesby approximately a factor of five. The additionalsources have allowed us to strengthen the e"ec-tive nature of the technique and create a muchmore comprehensive model. Our new techniquecan now be used to determine estimates for theredshifts of star-forming submm galaxies.

2. Data

We have used several di"erent samples for train-ing, testing, and applying this technique. For red-shift determinations, the specific wavelengths ofinterest are 3.6, 4.5, 5.8, 8.0, and 24 µm. The 3.6— 8.0 µm measurements are taken using the In-fraRed Array Camera (IRAC) (Fazio et al. 2004)on Spitzer, while the 24 µm measurements aretaken with the Multiband Imaging Photometer forSpitzer (MIPS) (Rieke et al. 2004). As will be de-scribed later, we have run tests to insure that weare using a homogeneous sample, even though thesources come from di"erent surveys.

2.1. Sub-millimeter Sources with Spectro-

scopic Redshifts

The GOODS-N field contains observations frommany instruments (such as powerful ground-basedobservations, the Hubble Telescope, Chandra X-ray telescope, and the Spitzer Space Telescope)over approximately 160 square arcminutes in theHubble Deep Field North. This project uses the 35GOODS-N SMGs from Pope et al. (2006). Popeet al. (2006) has IRAC and MIPS data for the 35sources. The addition of spectroscopic redshiftsfor some of the 35 sources allowed us to includethose sources within our training set. Further-more, we were able to apply our redshift deter-mination method to the GOODS-N SMGs thatdid not have spectroscopic redshifts. Further de-tails concerning the GOODS-N sample and obser-

vations can be found in Pope et al. (2006).

Chapman et al. (2005) used Keck 1 to obtainspectroscopic redshifts for submm galaxies thathave a radio counterpart. The existence of ra-dio counterparts for these sources was imperativebecause radio counterparts provide precise posi-tions needed for spectroscopic observations. Onechallange in studying SMGs is that, while manysources have been discovered, many have unknownredshifts due to them not having a detectable radiocounterpart. After using the radio counterpartsto discover the positions for the sources, spectro-scopic data can be taken to discover the redshiftfor each source.

There were a total of 150 SMG sources detectedat 850 µm in Chapman et al. (2005). Of those 150sources, 98 of them had radio counterparts. Chap-man et al. (2005) were able to obtain spectro-scopic redshifts for 73 of the total 98 radio SMGsobserved.

While Chapman et al. (2005) provided ex-tremely valuable spectroscopic redshift data, noIRAC or MIPS data existed for these sources atthat time. Recently, however, Hainline et al.(2009) supplemented the data presented in Chap-man et al. (2005) by obtaining IRAC and MIPSfluxes for the 73 sources (with spectroscopic red-shifts) listed in Chapman et al. (2005). The pub-lication of these data provided a crucial step for-ward in the field of submm galaxies. The combi-nation of these two data sets gave us one of thelargest samples of SMGs from which to develop amethod of determining redshifts.

2.2. Spitzer-selected Dust-Obscured Galax-

ies with Spectroscopic Redshifts

Using the Spitzer Space Telescope, Dey et al.(2008) discovered a new class of galaxies, whichthey termed Dust-Obscured Galaxies (DOGs),within the bootes field of the NOAO Deep Wide-Field Survey (Jannuzi & Dey 1999). This set ofgalaxies had been previously undetected becausethey do not emit strongly in optical wavelengths(Dey et al. 2008). As defined in Dey et al. (2008),in order to be considered a DOG, a source mustpass the two following criterion.

S24µm ! 300µJy (1)

(R " [24]) ! 14Vega magnitudes (2)

These conditions naturally constrain the red-shift range that the DOGs inhabit. The upperlimit for the redshift range is determined by Eq.1. This criterion is due completely to an obser-vational limit which prevents DOGs at redshiftgreater than 3 from being detected with a fluxvalue at 24 µm greater than 300 µJy. The lowerlimit to the redshift is determined by the limitingcase of Eq. 2. To satisfy Eq. 2, a source must befaint in the optical as compared to its flux at 24µm. The limiting case of this criterion requires aredshift to be greater than approximately 1.

Dey et al. (2008) measured spectroscopic red-shifts for approximately 100 of the approximatetotal of 2600 DOGs and determined that many ofthe DOGs had a redshift close to 2. As expected,this distribution of redshifts falls very nicely intothe range that we are trying to fit. The IRAC datafor DOGs are available from the Spitzer Deep,Wide-Field Survey (SDWFS) (Ashby et al. 2009).The MIPS data comes from Dey et al. (2008).Since IRAC and MIPS data was also obtainedfor these sources, they provided a good sampleof additional sources that could be added to ourredshift fitting. For our project, the majorityof DOGs at these bright 24 µm fluxes fall intothe AGN dominated category. However, it didprovide approximately 20 additional dusty star-forming galaxies that we could add to our sample.This addition to our training set helped to fill ina redshift range that previously had fewer sourcesfrom the SMG data set. After the addition of theDOGs, we have a comprehensive set of sources touse for our redshift range, which is approximately0.5 to 2.7.

2.3. Sub-millimeter Galaxies and Dust-Obscured Galaxies without Spectro-

scopic Redshifts

After establishing our training set, we com-piled a list of SMGs with unknown spectroscopicredshifts, so that we could apply our methodto determine photometric redshifts. One set ofuseful sources came from Dye et al. (2008).These sources came from the SCUBA HAlf DegreeExtragalactic Survey (SHADES). Mortier et al.(2005) describe the reasons that drive the necessityfor research concerning the SHADES sources. Oneadvantage of SHADES is that it provides a siz-able collection of like submm sources with multi-

wavelength follow-up data (Dye et al. 2008). Dyeet al. (2008) described sixty sources detected bySCUBA within the Lockman Hole field, which con-sists of approximately 320 square arc minutes onthe sky. Dye et al. (2008) present IRAC fluxesat 3.6, 4.5, 5.8, and 8.0 µm for the LockmanHole SHADES sources. Details concerning coun-terpart identification and data can be found in Dyeet al. (2008). For the Lockman Hole SHADESsources, MIPS data at 24 µm is given in Ivison etal. (2007); details concerning the MIPS data canbe found in that paper. For the sixty LockmanHole SHADES sources, Dye et al. (2008) providephotometric redshift estimates from template fit-ting along with an uncertainty range for the red-shifts. Unfortunately, the uncertainty range forthe photometric redshifts is large in some cases.Of the sixty sources presented, our technique canbe applied to forty-four of them. As the redshiftestimates for these galaxies improve, science canprobe deeper into the nature of these galaxies.

We also compiled a list of DOGs from whichwe could apply our technique. Out of the ap-proximately 2500 DOGs in Dey et al. (2008)that did not have spectroscopic redshifts, our tech-nique could be applied to approximately 1750 ofthem, which is approximately seventy percent ofthe DOGs. This sample gave us a great opportu-nity to apply our technique to an enormous num-ber of sources, one of the largest to date.

2.4. Sample Selection

As previously stated, SMGs can be separatedinto two categories: 1) those that exhibit a stel-lar bump (i.e. star-forming galaxies) and 2) thosewhose spectrum contains a significant AGN con-tribution. Our technique only works for sourcesthat exhibit the stellar bump feature. In orderto separate the two types of sources within thesample, we chose to use sources only if their colorS8.0/S4.5 was less than 2. This technique has beenshown to work at separating out SMGs and DOGswith significant AGN emission (Pope et al. 2008).A general explanation as to why that criterioneliminates the majority of the AGN dominatedsources can be found by looking at rest framespectral energy distributions (SEDs) of the indi-vidual sources. If a simple power-law fits the fivefluxes (located at 3.6, 4.5, 5.8, 8.0, and 24 µm)in a rest frame SED, then the source has signifi-

cant AGN contribution and does not show a clearstellar bump. This criterion does a very good jobat removing most of the AGN dominated sources;however, a few sources had to be removed by in-dividual source inspection using rest frame SEDplots.

Given that the Chapman et al. (2005) andHainline et al. (2009) sample is the largest andmost comprehensive to date (with spectroscopicredshifts), it provides the best means from whichto test for biases within our criterion. For thesources from Chapman et al. (2005) and Hain-line et al. (2009), Fig. 1a shows three spectro-scopic redshift distributions. As described previ-ously, our method for calculating redshifts only in-cludes sources that satisfy the criterion S8.0/S4.5

< 2.0. As seen in Fig. 1a, this criterion re-moves some sources from our training set. Remov-ing sources from a training set could cause biaseswithin the set; however, the S8.0/S4.5 < 2.0 crite-rion does not create any biases because it removessources relatively evenly throughout the redshiftrange. In addition, for the sources from Chapmanet al. (2005) and Hainline et al. (2009), Fig. 1bshows that the criterion e"ectively separates thestar-forming sources from the sources with signifi-cant AGN contribution. Similar comparisons havebeen shown in Ivision et al. (2004) and Pope etal. (2008). The density of star-forming sourcesin Fig. 1b is higher than the AGN sources, whichgives evidence to the previous statement that star-forming sources constitute a majority of the totalamount of galaxies in this regime. The combina-tion of Figs. 1 a and b illustrates that our crite-rion does not create any biases within the trainingsample.

3. Template-independent Photometric Red-

shift Technique

We used our training set of 48 sources to createa method from which we could calculate photo-metric redshifts. We developed Eq. 3 to be ourstarting point. It consists of a linear combinationof the logarithms of five fluxes (in units of µJy)(See Eq. 3).

zIR = A + B # log(S3.6) + C # log(S4.5)

+D # log(S5.8) + E # log(S8.0) + F # log(S24.0) (3)

Our redshift technique first involved using our

Fig. 1.— TBD a) Comparison of redshift distribution for star-forming source criterion. b) Color-Color plotshowing the relationship between the Chapman et al. (2005) and Hainline et al. (2009) sample and thestar-forming criterion.

training set to solve Eq. 3 for its coe!cients A,B, C, D, E, and F. In order to obtain values forthe coe!cients and errors for both the coe!cientsand the photometric redshifts, we used a jack-knifing technique. The technique involved ran-domly omitting one of our sources from the fit-ting each time. We ran the jackknifing technique10000 times, which allowed for robust estimatesof the coe!cients and errors. After determiningthe best coe!cients, we used the IRAC and MIPSdata from the training set to calculate photometricredshifts for the sources in the training set. Thephotometric redshifts of these sources were thencompared to their spectroscopic redshifts, in or-der to analyze the quality of the redshift fits.

3.1. Testing our Training Set

Since we were using sources from di"erent sam-ple groups for our training set (i.e. GOODS-N,Chapman et al. 2005, Hainline et al. 2009, andDOGs), we wanted to check for consistency withinthe training sample. One way that we tested forconsistency involved randomly using only half ofour training set (which we denote as the trainingset in Fig. 2) to calculate the coe!cients of Eq. 3.We then used those coe!cients to solve for photo-metric redshifts for the other half of the trainingset, which we denote as the testing set in Fig. 2.We also used the coe!cients to calculate photo-metric redshifts for the training set. This test pro-duced photometric redshifts that were consistent

Fig. 2.— Comparison of spectroscopic and photo-metric redshifts for the test that included only halfof the training set. Asterisks denote the testing set(sources not included in the training set) whereasdiamonds represent sources used in training thecoe!cients. There appears to be no systematicbiases in our results.

with the spectroscopic redshifts of the sources (seeFig. 2). This test verified that, even though ourtraining set was created from di"erent catalogs,we are dealing with a fairly homogeneous sample.

When including both SMG and DOG sourceswithin the training set, we noticed that it ap-peared that the SMGs were dominating the red-shift fits. Even though we had already tested forconsistency in the training sample, we wanted to

verify that we did not include a set of sources thatwould harm the overall fitting. In order to analyzethis condition, we ran trials using solely the SMGsources to calculate the coe!cients of Eq 3. Wethen calculated photometric redshifts using thosecoe!cients. We also calculated a value for the re-duced !2.

By doing such, we obtained a worse redshift fit-ting and a higher reduced !2 value than the trialswhich used both the SMGs and DOGs in the train-ing set. We also conducted the same calculationsusing solely the DOGs in the training set and re-ceived both a worse redshift fitting and reduced!2 value. This test showed that, even though theSMGs have more of an impact than the DOGs inthe redshift fits, the DOGs still improve the fitsand reduce the !2 value a significant amount. Wenote that one possible reason for the SMGs dom-inating the fits is due to the combined trainingset consisting of more SMG sources relative to thenumber of DOGs. However, we do maintain thatthe DOGs improve the fits and are, thus, veryimportant to include. Besides improving our re-duced !2 value, another importance for includingthe DOGs in the fits can be understood by analyz-ing their spectroscopic redshifts. The DOGs fill inthe redshift range z $ 1.7 to z $ 2.2. This range isvery important to our overall fitting capability be-cause we do not have as many SMG sources withinthis range, though we do have higher populationsof SMGs outside of this range. The tight fitting ofthe DOGs within this redshift range helps to filla small redshift gap and strengthen our techniquefor redshift fitting throughout our entire redshiftrange.

3.2. Limitations of this Method

The redshift range that our technique appliesto is 0.5 to 2.7. Even though this range mayseem at first to be small, it proves quite usefulbecause it includes the redshift “desert” that ex-ists around the redshift of 1.5. One of the otherlimitations is that this technique can only be ap-plied to star-forming sources with a clear stellarbump. Another method will need to be devel-oped in order to obtain photometric redshifts forsources with significant AGN contribution. How-ever, since star-forming sources dominate submmgalaxies, the majority of submm sources could usethis technique.

4. Results

4.1. Best Redshift Fit Formula

After justifying the inclusion of the DOGswithin the training sample, we combined themwith the SMG sources to solve for the coe!cientsin Eq. 3. At this point, we manipulated Eq. 3in order to test for its fitting capability. We firsttested the redshift fitting by using all five fluxes(as represented in Eq. 3). Upon calculating thecoe!cients, we noticed that the coe!cient corre-sponding to the 8.0 µm flux was approximatelyequal to zero in all trials run using all five fluxes.This result has shown that the 8.0 µm flux doesnot provide a significant contribution to the red-shift fitting with these coe!cients. The apparentlack of influence of the 8.0 µm flux has been atr-ributed to its influence being absorbed within theother coe!cients of Eq. 3. Perhaps, if we hadmore high redshift (z > 3) sources in our trainingset, the 8.0 µm flux would become more impor-tant. With this result in mind, we chose to modifyEq. 3 by leaving out one of the flux terms, suchthat we would only use four fluxes in the redshiftfitting. After trying all combinations of fluxes, weconcluded that the combination that produces thelowest reduced !2 value involves the fluxes fromthe wavelengths of 3.6, 4.5, 5.8, and 24.0 µm (seeEq. 4).

z = A + B # log(S3.6) + C # log(S4.5)

+D # log(S5.8) + E # log(S24.0) (4)

Using Eq. 4, we found the best-fit coe!cientsto have values of 2.73, -4.46, 2.56, 2.35, and -0.83for the coe!cients A, B, C, D, and E, respectively.We used these coe!cients to calculate photomet-ric redshifts for the training sample. Figure 3shows the relationship between the spectroscopicredshifts and photometric redshifts for the train-ing set. As seen in Fig. 3, the simple combinationof fluxes in Eq. 4 provides a remarkably accurateestimate of the redshift.

4.2. Redshift Estimates for Sub-millimeterGalaxies and Dust Obscured Galaxies

without Spectroscopic Redshifts

After obtaining the best-fit coe!cients to es-timate redshifts, we now apply our method tosources with unknown spectroscopic redshifts. We

Fig. 3.— Comparison of spectroscopic and ourtemplate-independent photometric redshifts forthe entire training sample. The diagonal line in-dicates a one-to-one relation.

applied Eq. 4 and the best-fit coe!cients to theSHADES Lockman SMG sample. Our calculatedphotometric redshifts for the SHADES sample arelisted in Table 1.

After determining photometric redshifts for theSHADES sample, we wanted to test if any biasesexist in our method. We compared our SHADESsample against the Chapman et al. (2005) sam-ple through plotting a redshift distribution. Fig-ure 4 shows that the calculated redshifts for theSHADES sources fall within the range of theChapman et al. (2005) sources. However, our red-shift estimate appears to uncover more redshifts inthe redshift desert where spectroscopy is particu-larly di!cult. Furthermore, we do not find a largenumber of sources at z > 2.75, indicating the peakfor SMGs may be more modest than previouslythought.

Figure 4 also contains a redshift distributionfor the GOODS-N SMG sources without spectro-scopic redshifts. These sources fall nicely into therange of the Chapman et al. (2005) sample aswell; however, we are limited in our analysis ofthe GOODS-N sources because there are very fewGOODS-N sources (10) without spectroscopic red-shifts from which to compare.

We also applied our method to approximately1750 DOGs in the Bootes field. Fig. 5 com-pares the redshift distribution of the DOGs withspectroscopic redshifts to the DOGs with newly-

Fig. 4.— Redshift distribution comparison ofsources from SHADES vs. spectroscopic redshiftsfrom Chapman et al. (2005). The dashed line in-dicates the spectroscopic redshifts of the sourcesfrom Chapman et al. (2005), while the solidline indicates the photometric redshifts from theSHADES sources. The dash-dot line indicatesphotometric redshifts for the GOODS-N samplewithout spectroscopic redshifts.

calculated photometric redshifts. Since there werenot equivalent numbers of sources with and with-out spectroscopic redshifts, the two samples havebeen normalized to each other such that they bothhave the same number of sources in Fig. 5. Thiscomparison shows that our photometric redshiftestimates have a distribution similar to the DOGswith spectroscopic redshifts. Our normalization ofthe DOGs with spectroscopic redshifts also showsthat there should be a significant population ofDOGs within the redshift desert domain. Further-more, we predict that over 800 of the DOGs in ourset surround this range. These estimates could in-dicate that the redshift desert is not as barren asoriginally thought.

5. Conclusions

Measuring redshifts for distant galaxies hasproven itself not an easy task. Without the avail-ability of large amounts of telescope time, one willnot be able to obtain spectroscopic redshifts forsubmm galaxies. Since large amounts of telescopetime on 8 m class telescopes is not readily avail-able, we must develop alternative methods in or-der to calculate the much needed redshift of many

Fig. 5.— Redshift distribution comparison ofDOGs with spectroscopic redshifts vs. DOGs withphotometric redshift estimates. These distribu-tions are normalized to have an equivalent numberof sources.

submm galaxies. The stellar bump of star-formingSMGs can be used to estimate photometric red-shifts for SMGs with unknown spectroscopic red-shifts. The technique developed here has provenitself to be powerful and extremely useful in pho-tometric redshift determination. One of the bestaspects to this method is that it is a very inexpen-sive way to obtain redshift estimates. This tech-nique can be applied quickly and with relative easeto large samples of submm galaxies when Spitzerdata is available. For example, the Herschel andSCUBA-2 surveys, when operational, will discoverthousands of SMG sources. Our technique can beused very e"ectively to obtain very decent red-shift estimates for these sources. Once the red-shift estimates are obtained, investigators will beable to use the estimates to probe many interestingaspects of SMGs, such as star-formination rates,stellar masses, etc.

In the future, after the discovery of moresubmm galaxies and spectroscopic redshifts forthose galaxies, we will be able to refine our tech-nique such that we obtain even more precise pho-tometric redshifts. As more submm sources withspectroscopic redshifts become available, we willbe able to extend our method out to redshiftsmuch greater than our current limit of approxi-mately 2.7. We hope to be able to extend ourmethod out to redshifts of at least 4, if not farther,in the near future. In conclusion, our technique

will provide others with a very useful redshift de-termination tool that they can add to their toolboxfor their future studies.

6. Acknowledgements

Messenger was supported by the NOAO/KPNOResearch Experiences for Undergraduates (REU)Program which is funded by the National ScienceFoundation Research Experience for Undergrad-uates Program and the Department of DefenseASSURE program through Scientific Program Or-der No. 13 (AST-07542223) of the CooperativeAgreement No. AST-0132798 between the Asso-ciation of Universities for Research in Astronomy(AURA) and the NSF.

REFERENCES

Alexander, D. M. et al. 2005, ApJ, 632, 736

Ashby, M. L. N. et al. 2009, ApJ, 701, 428

Barger, A. J. et al. 1998, Nature, 394, 248

Chapman, S. C. et al. 2005, ApJ, 622, 772

Dey, A. et al. 2008, ApJ, 677, 943

Dye, S. et al. 2008, MNRAS, 386, 1107

Eales, S. et al. 1999, ApJ, 515, 518

Fazio, G. G. et al. 2004, ApJ, 154, 10

Hainline, L. et al. 2009, ApJ, 699, 1610

Holland, W. S. et al. 1999, MNRAS, 303, 659

Hughes, D. H. et al. 1998, Nature, 394, 241

Ivison, R. J. et al. 2004, ApJS, 154, 124

Ivison, R. J. et al. 2007, MNRAS, 380, 199

Jannuzi, B. T. & Dey, A. 1999, The Hy-RedshiftUniverse: Galaxy Formation and Evolution atHigh Redshift, 193, 258

Mortier, A. M. J. et al. 2005, MNRAS, 363, 563

Pope, A. et al. 2006, MNRAS, 370, 1185

Pope, A. et al. 2008, ApJ, 689, 127

Rieke, G. H. et al. 2004, ApJS, 154, 25

Sawicki, M. 2002, ApJ, 124, 3050

Smail, I., Ivison, R., & Blain, A. 1997, ApJ, 490,L5

This 2-column preprint was prepared with the AAS LATEXmacros v5.2.

Table 1

Photometric Redshift Estimates for SHADES Lockman Sample. Typical errors onredshift are "z = 0.2 or "z/(1+z)=0.08

Name RA Dec. Photometric Redshift

Lock850.002 10:52:57.084 57:21:02.82 1.72Lock850.003 10:52:38.299 57:24:35.76 1.64Lock850.003b 10:52:38.401 57:24:39.50 1.53Lock850.004 10:52:04.079 57:26:58.52 1.96Lock850.004b 10:52:04.226 57:26:55.46 1.79Lock850.005 10:53:02.696 57:18:21.95 1.29Lock850.006 10:52:04.013 57:25:24.20 2.14Lock850.009 10:52:15.636 57:25:04.26 1.04Lock850.009b 10:52:15.730 57:25:01.70 0.53Lock850.011 10:51:29.160 57:24:06.80 0.89Lock850.012 10:52:27.579 57:25:12.46 1.80Lock850.013 10:51:31.770 57:31:41.20 0.40Lock850.014 10:52:30.717 57:22:09.56 1.63Lock850.015 10:53:19.271 57:21:08.45 2.38Lock850.016 10:51:51.690 57:26:36.09 1.87Lock850.017 10:51:58.018 57:18:00.27 1.38Lock850.019 10:52:36.090 57:31:19.60 1.89Lock850.021 10:52:56.790 57:30:37.90 2.08Lock850.022 10:51:37.090 57:33:16.90 1.77Lock850.023 10:52:14.976 57:31:53.62 0.79Lock850.024 10:52:00.445 57:20:40.16 1.14Lock850.026 10:52:40.698 57:23:09.96 2.29Lock850.028 10:52:57.667 57:30:58.71 1.44Lock850.031 10:52:15.989 57:16:19.34 1.50Lock850.034 10:52:14.202 57:33:28.30 2.34Lock850.037b 10:51:24.595 57:23:31.08 1.34Lock850.038 10:53:07.060 57:24:31.60 1.43Lock850.040 10:52:01.721 57:19:17.00 2.36Lock850.041 10:51:59.760 57:24:24.94 1.02Lock850.041b 10:52:00.248 57:24:21.69 1.99Lock850.043 10:52:56.561 57:23:52.80 1.64Lock850.043b 10:52:56.576 57:23:58.62 1.39Lock850.048 10:52:56.030 57:32:42.30 0.48Lock850.052 10:52:45.808 57:31:19.86 0.27Lock850.052b 10:52:46.160 57:31:20.20 0.43Lock850.053 10:52:40.290 57:19:24.40 1.37Lock850.063 10:51:54.261 57:25:02.55 1.98Lock850.064 10:52:52.320 57:32:33.00 0.91Lock850.067 10:52:08.870 57:23:56.30 1.31Lock850.073 10:51:41.992 57:22:17.52 1.45Lock850.076 10:51:49.101 57:28:40.28 0.53Lock850.079 10:51:52.594 57:21:24.43 1.84Lock850.081 10:52:31.523 57:17:51.67 2.76Lock850.083 10:53:07.17 57:28:40.00 0.43Lock850.087 10:51:53.365 57:17:30.05 2.30

Flickering Giants in the Ursa Minor Dwarf Spheroidal Galaxy?

Edward J. Montiel

KPNO REU 2009 and University of Arizona

and

Kenneth J. Mighell

National Optical Astronomy Observatory

1. Introduction

Variability in stars has been well known and studied phenomena in astronomy for hundreds

of years. As the field has advanced and become more sophisticated both the number of variable

stars and mechanisms for this phenomenon have increased. Today, the instability strip on the

Hertzsprung-Russell (HR) diagram has been defined and populated with numerous classes of stars

exhibiting magnitude fluctuations.

Expanding on the research in this area, Mighell and Rodeder (2004) attempted to find if there

were any RR Lyraes in the Ursa Minor (UMi) dwarf spheroidal (dSph) galaxy. They obtained two

HST epochs of archival WFPC2 data taken in the F555W (!V) and F814W (!I) filters. They found

no RR Lyraes in their observations; instead reported the detection of nine red giant variables ex-

hibiting low-amplitude brightness fluctuations on 10 minute timescales (Mighell & Roederer 2004),

called flickering red giants (FRGs). Their work was limited due to only having seven observations

between both epochs, which led to very short baselines making it di!cult to find explanations for

what had been observed. In this work, we expand upon the work of Mighell and Roderer by moving

to a new area of the UMi dSph field with more observations and longer baselines. We report on

finding 8 more candidate FRGs and 3 additional giants exhibiting these brightness fluctuations on

10 minute timescales among 3 epochs of HST observations.

2. Procedure

Our data consisted of three sets of archival WFPC2 observations taken in the UMi dSph field.

All of these WFPC2 observations were taken with the F606W (broadband V filter) and all were of

equal exposure time (160 s). The first set of observations (epoch 1), from the program GO-7341(PI:

Olszewski), U50J0101R through U50J0114R were obtained on 1999 March 13 and consisted of 40

observations. The second set of observations (epoch 2), from the program GO-8776 (PI: Olszewski),

consisted of 36 observations U65Q0601R through U65Q0610R were obtained roughly 2 years later

on 2001 March 11. The final set of 36 observations (epoch 3), from the program GO-9239 (PI

– 2 –

Olszewski), U6D90601M through U6D90610M were obtained again nearly 2 years later on 2003

March 01. The observations are of the same area of the sky, since their original intent was to

provide accurate astrometry for the UMi dSph for Olszewski and his co-investigators (Piatek et al.

2005). The data were retrieved from the Canadian Astronomy Data Centre (CADC) in June 2009.

The photometric reductions were performed using the HSTphot stellar photometry package

(Dolphin 2000) which was specifically designed to analyze HST WFPC2 observations of stellar pop-

ulations. The first image of each epoch (1999: U50J0101R, 2001: U65Q0601R, 2003: U6D90601M)

was used as the reference image for HSTphot. After determining the relative image o"sets with

respect to the reference image, we were able to run HSTphot on all the observations in a given

epoch simultaneously. Since these observations only used one filter, HSTphot provided flight system

magnitudes (F606W).

In order to find flickering stars in these observations, we used selection criteria that were similar

to those of Mighell and Roederer (2004). First we required that HSTphot consider an object to be

a good stellar candidate (“class 1”). Secondly, we required that the rms errors for all observations

be "0.1 mag (S/N ! 10). When HSTphot encounters cosmic rays or bad pixels it will report a

magnitude of 99.999. We required that all observations in a given epoch were good measurements.

These very conservative conditions enabled us to take only the best photometry as determined by

HSTphot. We found 69, 72, and 91 stars respectively in the 2000, 2002, and 2003 epochs.

With our subsets in place, our next goal was to calculate !2 per epoch for all the stars contained

in each to create our list of candidates exhibiting flickering behavior. The equation used was

!2F606W #

NF606W!

j=1

(F606Wj$ < F606W >)2

("2F606W j

+ 0.012)

where NF606W is the number of observations for a given epoch, F606Wj is the jth observation,

< F606W > is the average magnitude for a given star determined by HSTphot, and "2F606W j the

corresponding rms measurement error for the jth observation. A small error of 0.01 mag was added

in quadrature to account for a minimum image-to-image photometric scatter of 0.1 mag (Mighell

& Roederer 2004, and references therein). The threshold level for being considered a candidate

at the 95 percent confidence level was 54.56 for 2000 (# = 39) and 49.77 for 2002 and 2004 (# =

35) (Abramowitz and Stegun 1964, p. 985). We found 26, 33, and 29 candidates for each epoch,

respectively.

Before we could accurately classify the candidates we needed to have additional color infor-

mation, which we did not have from just having F606W measurements. To do this we needed to

match the stars in our subsets to another data set with multi-band photometry. These colors were

obtained when we matched our candidates to the V, I photometry taken by Bellazzini et al. (2002).

In that paper Bellazzini et al. stars which were observed a 25 % 25 arc minute area of the UMi

dSph in which the Olszewski fields were contained. The matching process began by taking the chip

number, x-coordinate, and y-coordinate provided by HSTphot and and we determined the right

– 3 –

ascension (RA) and declination (Dec) of our candidates using the IRAF/STSDAS metric task. For

any stars to be considered matches we set a tolerance of 1!! or less. As an added precaution the

matches between epochs were visually checked for accuracy. From this process a total yield of 24

stars, 14 flickering and 10 constant, were matched and found in all 3 epochs. There were also 34

stars, flickering and constant, that appeared in two epochs or only one. See Figures 1 and 2.

3. Analysis and Discussion

For the purposes of completeness, we studied in greater detail the stars that we found that

could be matched to all three epochs. There is at least one potential foreground star (F606W &

18 mag and V-I & 0.6) and two background galaxies (V-I " 1.7) in our observed region. Mighell

and Roederer (2004) discussed specifically the nature of low amplitude variability in red giants.

Their reductions yielded a set of 373 total stars, between the two epochs they worked with, and

they found that 65 of these were red giants. Nine red giants (14%) were reported to be candidates

displaying low amplitude variability. Our data and reductions yielded 16 red giants and we found

that 8 (50%) flickered. In addition, we have found 3 additional giants that sit along the horizontal

branch of the UMi dSph galaxy color magnitude diagram.

Mighell and Roederer left the question of periodicity in these objects up to speculation due

to their lack of observations and hence much shorter baselines. Our data provides us with a

better ability to investigate into this issue. The importance of determining the presence or lack of

periodicity is that it will allows us to say if there is a global phenomenon (for example, pulsations

or spots rotating across the surface) powering what we have observed. However, if this variability

were found to be powered by pulsations, then it is important explain where the power is coming

from to drive them, since we are looking on 10 minute timescales. We used the Fortran program

PDM (Stellingwerf 1978) to g through our light curves and pull out any significant frequencies, if

they are present. The program was run on the individual subsets to see if there were any pulsators

in our field, and from this we learned that every star in the set appears to be aperiodic. This result

means that the observed variability is random in nature, and other explanations must be sought.

One possible explanation for the observed stellar phenomenon is what is known in the solar

community as “chromospheric activity”. This term invokes the possibility of having witnessed

flaring events on these giants. Solar flares at their strongest of a power of about 1029 ergs while the

Sun outputs on the order of 1033 ergs normally. This corresponds to only an increase of around a

0.01% at the upper extreme. This raises questions that have been traditionally outside the scope

of previous research. If we assume that bigger stars have bigger flares, is it possible that a very

energetic flare could be behind the flickering? Before this can be answered, the first question that

needs to be resolved is what is the strength of magnetic fields for giants–especially red giants Not

much work has been done into this research topic, which makes determining if “chromospheric

activity” is the correct explanation problematic.

– 4 –

Other possible, but unlikely, solutions to these anomalies is if there are any problems with

WFPC2 instrument onboard HST or the photometric reduction package HSTphot. The primary

push to looking for instrumentation or software errors stems from a few instances where we have a

significant drop in magnitude in certain epochs. Since 25 October 2005 the astronomical community

has been aware of what has been called the “WF4 anomaly”. The anomaly is described as images

taken with WF4 tend to have a low to zero bias (Biretta and Gonzaga 2005). It was detected for

observations dating back to March 2002, which a"ects our third data set, and has been announced

to only impacted the WF4 CCD; leaving the others fine. Just four red giants (2 flickering and 2

constant) are found on WF4, and does not a"ect any of the time based resolution of our data. Two

other disturbing results was finding that all objects !21 magnitude or brighter flickers, and in some

instances that the star experienced a dimming as a part of its flickering. These results could also

be attributed to instrument errors with WFPC2, however this e"ect can be because of problems

with HSTphot. However, HSTphot has had an excellent publication record: it has been referenced

in at least 211 refereed papers (out of 234 total).

4. Future Work and Conclusion

At this time it is di!cult to say which if any of the proposed explanations for our results is

correct. What is easy to see is that more research needs to be put into this topic. If flickering giants

do exist, we suggest that time resolved near-IR spectra be taken of nearby giants at a large telescope

(an 8-m class facility like the Gemini observatories). By looking for Zeeman line splitting in specific

lines (calcium, hydrogen, and potassium) the strength of the magnetic fields on giants could be

measured. In addition, if low amplitude variability on short time scales is more common than

previously though then upcoming all sky digital surveys like Pan-STARRS, LSST, and SkyMapper

should be able to find discover many more objects like these. We propose as a test on WFPC2

and into the validity of this phenomenon is to go through the archival data to find in other dSph

galaxies to see if the results can be repeated. A final test on WFPC2 and HSTphot would be

to re-image our same area with the Wide Field Camera 3 in the visible and near-IR allowing the

spectral energy distribution to be found. This is the strongest test on whether or not flickering

giants exist as it would have be done with a new instrument and need a di"erent reduction pipeline.

Building upon the work of Mighell and Roederer (2004), a very conservative statistical analysis

of more archival WFPC2 observations with the aid of HSTphot has yielded eight new candidate

FRGs with three more variable candidate giants. If these results are reliable then it appears that

low amplitude brightness fluctuations in stars are more common than previously thought since

50% of our red giants flicker. At this time we are lacking a clear defined explanation, as there

are additional factors that need to be resolved before moving forward. It would be helpful if more

research were conducted into the stellar structure and evolution of normal giants. Furthermore,

any of the proposed future work would greatly improve the probability of finding an explanation

for or against the results.

– 5 –

5. References

Abramowitz, M., & Stegun, I., eds. 1964, Handbook of Mathematical Functions with Formulas,

Graphs, and Mathematical Tables (Washington, DC: NBS)

Bellazzini, M., Ferraro, F. R., Origlia, L., Pancino, E., Monaco, L., & Oliva, E. 2002, AJ, 124, 3222

Biretta, J., & Gonzaga, S. 2005, Instrument Science Report WFPC2 2005-002 (December 14, 2005),

STScI

Dolphin, A. E. 2000, PASP, 112, 1383

Mighell, K. J., & Roederer, I. U. 2004, ApJ, 617, L41

Piatek, S., Pryor, C., Bristow, P., Olszewski, E. W., Harris, H. C., Mateo, M., Minniti, D., &

Tinney, C. G. 2005, AJ, 130, 95

Stellingwerf, R. F. 1978, ApJ, 224, 953

– 6 –

Fig. 1.— Color Magnitude Drawing of the UMI dSph galaxy.

– 7 –

Fig. 2.— Light curves of our objects.

PUBLICATIONS AND PRODUCTS

Annual Report 2009 REU Site Program at KPNO 51

KPNO REU Students at the 213

rd AAS Meeting

January 2009

The opportunity to present the findings of their original research at the most important national

meeting of US astronomy is arguably one of the most prized benefits enjoyed by KPNO REU

students. All six of the 2008 summer students attended the 213rd

meeting of the American

Astronomical Society (AAS) January 4 – 8, 2009 at Long Beach, California. The students presented

posters incorporating aspects of their REU summer research projects. Abstracts of the REU papers

were published in the Bulletin of the American Astronomical Society, Vol. 41, No. 1, 2009. The

abstracts of the REU student posters are reproduced below.

Site Characterization of El Peñón: The Site of the Large Synoptic Survey Telescope

Taylor S. Chonis (University of Nebraska-Lincoln Dept. of Physics and Astronomy) , C. F. Claver

(NOAO), J. Sebag (NOAO)

El Peñón is located at the southwest end of the Cerro Pachón ridge in northern Chile. Since its

selection as the LSST observatory site, detailed measurements of wind and atmospheric seeing have

been conducted to help determine design and operating parameters. The wind measurements are

made at 4 elevations (5, 12, 20, 30 meters) using ultrasonic 3-axis anemometers. The atmospheric

seeing is monitored with a Differential Image Motion Monitor (DIMM). We have studied

correlations in the wind speed and direction at the different elevations and with the atmospheric

seeing. From the wind-elevation correlations, we find evidence for a surface turbulence layer up to

a minimum of 12 meters above the local topography. Knowing where the boundary layer is will

affect the overall height of telescope and the summit building. In examining the correlation of

image quality from the DIMM with wind directions and speed, we found a surprising result: there

appears to be a weak preference for better seeing when the wind is coming from the south, rather

than from the northeast as expected. We also find that this correlation appears to be independent of

wind speed below 30 meters. This information will be used in site design analysis along with

performance modeling of the LSST. This research was supported by the NOAO/KPNO Research

Experiences for Undergraduates (REU) Program which is funded by the National Science

Foundation Research Experiences for Undergraduates Program and the Department of Defense

ASSURE program through Scientific Program Order No. 13 (AST-0754223) of the Cooperative

Agreement No. AST-0132798 between the Association of Universities for Research in Astronomy

(AURA) and the NSF.

Chonis, T.S., Claver, C.F., & Sebag, T. 2009, BAAS, 41, 371 (Poster 460.24)

PUBLICATIONS AND PRODUCTS

Annual Report 2009 REU Site Program at KPNO 52

Collinder 121: Analyzing It's Pre-main Sequence Population

Matthew Henderson (NOAO), W. Sherry (NSO)

OB associations represent excellent opportunities to study the end products of stellar accretion.

These diffuse groups of stars, whose lifespans are limited by the dispersive forces that scatter them

as they move through the galaxy, are thought to be the structures in which the majority of all stars

form. We present a VI photometric census of the regions surrounding some of the stars identified by

de Zeeuw et al. (1999) to be part of Collinder 121 (CMa OB2). Orion OB1 includes a clustered

population of low-mass, pre-main sequence stars present in the regions around the OB stars that

define the group. This study demonstrates whether a clustered population of low-mass, PMS stars

exists in CMa OB2 and determines if the spatial pattern observed in Orion can be extended to

another OB association in the solar neighborhood. Matt Henderson's research was supported by the

NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the

National Science Foundation Research Experiences for Undergraduates Program and the

Department of Defense ASSURE program through Scientific Program Order No. 13 (AST-

0754223) of the Cooperative Agreement No. AST-0132798 between the Association of Universities

for Research in Astronomy (AURA) and the NSF.

Henderson, M, & Sherry, W. 2009, BAAS, 41, 224 (Poster 414.03)

Deep Photometry of the Open Cluster IC4651

Tiffany Meshkat (NOAO), C. Claver

(NOAO), K. Mighell

(NOAO)

We present a preliminary analysis of deep CCD photometry of the Galactic open star cluster IC

4651. We seek to identify the white dwarf cluster members in order to determine the age, distance,

metallicity of the star cluster and to test stellar evolutionary theory. Claver observed IC 4651 at the

CTIO 4-meter telescope in Chile on May 7 - 10, 1997. The star cluster was covered by four camera

fields with small overlapping regions; one field was observed each night. Long and short exposures

were obtained with U, B, V, and I filters in each field. Two of the nights were photometric with

subarsecond seeing, and the remaining two nights were not photometric. Stellar photometry was

obtained using the DAOPHOT procedure within IRAF data reduction and analysis system.

Calibrated photometry in all 4 fields was obtained using IRAF’s PHOTCAL package with the

DAOPHOT instrumental magnitudes and standard star fit files. We made calibrated color-

magnitude diagrams and color-color diagrams in all four fields. The distance modulus was

determined to be (m-M) = 10.015 magnitudes, making IC4651 approximately 1000 pc away. The V

vs. V-I color-magnitude diagram of IC 4651 is bracketed by the 1.5 Gyr and 2.0 Gyr Yonsei-Yi

isochrones. White dwarf candidates were determined by selecting stars near the cooling sequences

for DA 0.5 and 0.9 solar-mass white dwarfs; several of these white dwarf candidates fit the cooling

sequences within one standard deviation errorbars. Meshkat's research was supported by the

NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the

National Science Foundation Research Experiences for Undergraduates Program and the

Department of Defense ASSURE program through Scientific Program Order No. 13 (AST-

0754223) of the Cooperative Agreement No. AST-0132798 between the Association of Universities

for Research in Astronomy (AURA) and the NSF.

Meshkat, T., Claver, C., & Mighell, K. 2009, BAAS, 41, 321 (Poster 442.12)

PUBLICATIONS AND PRODUCTS

Annual Report 2009 REU Site Program at KPNO 53

Analyzing the Effects of Scattered Light on Stellar Photometry

Ashley Stewart (University of Arkansas, Fayetteville), J. Glaspey (NOAO)

The CCD detectors that we use today are very good at converting most of the light gathered by a

telescope into an electronic signal without adding much noise to the process. What this means for

observers is that they are now only limited by the optical quality of the telescope in how well they

can measure the brightness of faint sources. Our goal was to quantify the affect of the scattered light

component of telescope images, usually dominated by the quality of primary mirror coating, using

empirically derived models of stellar Point Spread Functions for ground based imagers. We

analyzed archived MOSAIC data from the W-Project, which used the Blanco 4m telescope in Cerro

Tololo, primarily because it covered the same fields over multiple years using the same telescope,

instruments, filters, and exposure times. From this data set, we selected stars using Source Extractor

and performed the photometry with the IRAF package DAOPHOT. This gave an estimate of the

magnitudes and magnitude errors of the stars in the fields which could be compared for data taken

before and after primary mirror re-aluminizing. Here, we will present the findings of our project and

discuss what future work could be done to improve the quality of science obtained from ground

based telescopes. Stewart's research was supported by the NOAO/KPNO Research Experiences for

Undergraduates (REU) Program, which is funded by the National Science Foundation Research

Experiences for Undergraduates Program and the Department of Defense ASSURE program

through Scientific Program Order No. 13 (AST-0754223) of the Cooperative Agreement No. AST-

0132798 between the Association of Universities for Research in Astronomy (AURA) and the NSF.

Stewart, A., & Glaspey, J. 2009, BAAS, 41, 428 (Poster 474.03)

Tuesday, January 6, 2009

[443.02] Investigating Galaxy Merger Signatures with IGNITE

Matthew J. Zagursky (University of Maryland), J. M. Lotz (NOAO)

I present the software package IGNITE, the Interacting Galaxy Non-Interactive Tail Extractor. Its

purpose is to locate tidal tails and quantify their morphological and photometric properties. I

demonstrate the effective use of IGNITE on the case galaxy of NGC 2623 and report the

photometric and morphological signatures of tidal tails in this galaxy. The future of the IGNITE

package is a merge with other successful software packages aimed at quantifying merging galaxies

to further enhance the accuracy of the quantitative measurements of morphologies and photometry

profiles of merging galaxy candidates.

Zagursky, M. J., & Lotz, J. M. 2009, BAAS, 41, 324 (Poster 443.02)

PUBLICATIONS AND PRODUCTS

Annual Report 2009 REU Site Program at KPNO 54

Monday, January 5, 2009

A Wavelet Time Series Analysis of Aperiodic Variable Stars in the Kepler Field

Timothy Arnold (The Ohio State University) , K. Mighell (NOAO), S. Howell (NOAO)

The variable sky offers insights into the physical mechanisms of astronomical objects and can be

used as a useful tool for many other purposes like the determination of distance with standard

candles. Periodic variables were the first to be classified, understood, and used. Many variable but

aperiodic light curves are discarded or insufficiently analyzed because of the apparent uselessness

of the information contained in these data. Many contemporary projects (e.g. the Large Synoptic

Survey Telescope, PanSTARRS, the Kepler mission) aim to map the transient sky, and recently

methods of time series analysis have become increasingly advanced. It would be advantageous to

discover identifying information in the large number of variable but ostensibly aperiodic light

curves. We use a wavelet analysis, based on a weighted projection of time series data on to basis

functions, to analyze aperiodic variable stars in the Burrell-Optical-Kepler Survey (BOKS). Using

the Weighted Wavelet Z-Transform detailed in Foster 1996, we find that variable but aperiodic stars

in our sample offer few characteristic properties that would be useful for further classification.

Arnold's research was supported by the NOAO/KPNO Research Experiences for Undergraduates

(REU) Program which is funded by the National Science Foundation Research Experiences for

Undergraduates Program and the Department of Defense ASSURE program through Scientific

Program Order No. 13 (AST-0754223) of the Cooperative Agreement No. AST-0132798

between the Association of Universities for Research in Astronomy (AURA) and the NSF.

NOAO NEWSLETER (DECEMBER 2008)

Annual Report 2009 REU Site Program at KPNO 55