Steve Kuhlmann Argonne National Laboratory...II. Weak Lensing III. Baryon Acoustic Oscillations IV. Supernovae • Two multiband surveys: 5000 deg2 grizY to 24th mag 15 deg2repeat

Challenges For Future Supernova SurveysChallenges For Future Supernova SurveysSteve Kuhlmann

Argonne National Laboratory

IntroductionIntroduction

Dark Energy Survey OverviewDark Energy Survey Overview

Simulations of the DES SN SurveySimulations of the DES SN Survey

Supernova Photometric Classification ChallengeSupernova Photometric Classification Challenge

IntroductionIntroductionThe most obvious challenge for supernova surveys such as The most obvious challenge for supernova surveys such as

Pan-STARRS, DES, and LSST is the vast number of Pan-STARRS, DES, and LSST is the vast number of candidates exceeding spectroscopic resources.candidates exceeding spectroscopic resources.

J. Bernstein, D. Cinabro, R. Kessler, S. Kuhlmann, LSST Science BookJ. Bernstein, D. Cinabro, R. Kessler, S. Kuhlmann, LSST Science Book


Several other issues deserve mention:Several other issues deserve mention:• Combining SN samples in different photometry systems.• Brightness correlations with host galaxy properties such as metallicity (recent evidence from CfA, SNLS, SDSS)• SN Evolution with z (completely due to host galaxy changes?)• Reliability of UV fluxes?• Continuing to improve our knowledge of dust systematics, preferably with combined optical/IR measurements.

All worthy topics, but the main topic for this All worthy topics, but the main topic for this presentation is photometric typing for SNIa presentation is photometric typing for SNIa







The Dark Energy SurveyThe Dark Energy Survey• Study Dark Energy using

4 complementary* techniques:I. Cluster CountsII. Weak LensingIII. Baryon Acoustic OscillationsIV. Supernovae

• Two multiband surveys:5000 deg2 grizY to 24th mag15 deg2 repeat (SNe)

• Build new 3 deg2 cameraand Data management system

Survey 2011-2016 (525 nights)Response to NOAO AO

Blanco 4-meter at CTIO

*in systematics & in cosmological parameter degeneracies*geometric+structure growth: test Dark Energy vs. Gravity

Four Probes of Dark Energy• Galaxy Clusters

• ~100,000 clusters to z>1• ~10,000 with SZE measurements from SPT• Sensitive to growth of structure and geometry

• Weak Lensing• Shape measurements of 300 million galaxies • Sensitive to growth of structure and geometry

• Baryon Acoustic Oscillations• 300 million galaxies to z = 1 and beyond• Sensitive to geometry

• Supernovae• 15 sq deg time-domain survey• ~3000 well-sampled SNe Ia to z ~1• Sensitive to geometry

Dark Energy Survey Science ProgramDark Energy Survey Science Program

Forecast Constraints on DE Equation of State

Thousands of

The Dark Energy SurveyThe Dark Energy Survey

DES InstitutionsDES Institutions

Blanco 4-meter at CTIO

Camera being replaced

FermilabUniversity of Illinois at Urbana-ChampaignUniversity of ChicagoLawrence Berkeley LaboratoryNOAO/CTIODES Spain consortiumDES United Kingdom consortiumUniversity of MichiganOhio State UniversityUniversity of PennsylvaniaDES Brazil consortiumArgonne National LaboratorySouth Bay consortiumUniversitats-Sternwarte Munich

Project Director: Josh FriemanProject Director: Josh Frieman

Dark Energy Camera (First Light Oct 2011)Dark Energy Camera (First Light Oct 2011)Shutter Imager

85 Science-Grade CCDs tested, need 72 inc. spares

CageHexapods

1st of 5 lenses

Full-Scale Telescope Simulator

Filters and Changerdecamlaba.fnal.gov Live!

Mosaic Image from Prototype DECam Mosaic Image from Prototype DECam

Not using Not using Science-Grade Science-Grade

CCDs...CCDs...

Project Manager Project Manager Brenna FlaugherBrenna Flaugher







Described in R. Kessler et al., PASP V121 (2009) Described in R. Kessler et al., PASP V121 (2009)

Public URL: http://www.sdss.org/supernova/SNANA.htmlPublic URL: http://www.sdss.org/supernova/SNANA.html

Used by SDSS, DES, LSST and the “Supernova Photometric Used by SDSS, DES, LSST and the “Supernova Photometric Classification Challenge”.Classification Challenge”.

Software suite for simulating and fitting SN light curves Software suite for simulating and fitting SN light curves

Uses various Ia simulation and fitting models (MLCS2k2, SALT-II, ...) Uses various Ia simulation and fitting models (MLCS2k2, SALT-II, ...)

Simulates many effects: Host galaxy and Milky Way dust, CTIO sky Simulates many effects: Host galaxy and Milky Way dust, CTIO sky fluctuations, CCD and filter characteristics, realistic cadences, zeropoint fluctuations, CCD and filter characteristics, realistic cadences, zeropoint fluctuations, color fluctuations, …fluctuations, color fluctuations, …

Also simulates and fits non-Ia SNeAlso simulates and fits non-Ia SNe

SNANA: SNANA: SSuperuperNNova ova ANAANAlysis lysis Simulation Forecasts for DES SN SurveySimulation Forecasts for DES SN Survey

Dark Energy Survey SupernovaeDark Energy Survey Supernovae

• ~3000 well-measured Ia

• Much better red-sensitivity with LBNL CCDs

• >100/year overlap with IR bands from VIDEO

• 1% photometry goal, good overlap with SDSS filters and survey area as low-z Hubble-diagram anchor

Expected SNe in 15 sq. deg. surveyExpected SNe in 15 sq. deg. survey

T. Hufford et al. T. Hufford et al. (2010AAS...21543033H)(2010AAS...21543033H)

Dark Energy Survey SupernovaeDark Energy Survey Supernovae Improved color determination compared to Improved color determination compared to

SDSS/SNLS with better z-band sensitivitySDSS/SNLS with better z-band sensitivity

Simulated and Fit SNIa with SALT-II

Dark Energy Survey SupernovaeDark Energy Survey Supernovae >100 per year SNe overlap with valuable IR information from VIDEO >100 per year SNe overlap with valuable IR information from VIDEO

J. Bernstein et al. J. Bernstein et al. (2010AAS...21537007B)(2010AAS...21537007B)








arXiv:1001.5210

• Compile a blind sample of Ia+Core-Collapse, similar to full 5-year DES simulation sample.

• Provide a training sample of 1256 confirmed SNe.

• Improve the non-Ia template knowledge with unpublished, contributed, confirmed SNe. 41 new templates from SDSS, SNLS, and CSP. Thanks!


• Uses SNANA, and DES simulation technology such as griz filters, sky fluctuations from Essence data, DES realistic cadence including CTIO community time and weather, etc.

• Redshift range is 0-1.1.

• Challenge time-frame was Jan 29 to Jun 1.

• Loose cuts applied: 1 filter with SNR>5, at least 5 epochs

Input RatesInput Rates• Use SDSS rate for Ia

• For Core-Collapse, use Star Formation Rate (1+z)3.6 for z-dependence and SNLS CC/Ia to normalize.

• Increased Core-Collapse Rate by x1.3, 1 standard deviation above SNLS measurement, to make more difficult.

• Use Smartt et al. for relative Core-Collapse fractions:

TemplatesTemplates

Blind Sample for the ChallengeBlind Sample for the Challenge

Participants in the ChallengeParticipants in the Challenge

THANKS! Note multiple algorithms from some contributors.10 groups, 13 entries with host photo-z, 9 entries with no host-z

Four Typing Techniques Represented Four Typing Techniques Represented • Fit to a SNIa model.

• Compare light curves to Ia and non-Ia templates and use Bayesian probabilities to determine SN type.

• Determine best Hubble diagram from training sample and constrain unconfirmed SNe to be consistent with Ia interpretation and cosmology.

• Fit training light curves with parametric functionsand test unconfirmed SNe with statistical techniques.

SN Photometric Classification Challenge ResultsSN Photometric Classification Challenge Results

Confirmed=TrainingUnconfirmed=Blind

Pseudo-Purity and Figure of MeritPseudo-Purity and Figure of Merit

Cosmology effects non-linear with Purity, should Cosmology effects non-linear with Purity, should pay a bigger price for mis-tag rate uncertainty. pay a bigger price for mis-tag rate uncertainty.

Pseudo-Purity=Pseudo-Purity=

WWIaIa

falsefalse is a penalty. If mis-tag rate solely determined is a penalty. If mis-tag rate solely determined from 20% sample spectra, from 20% sample spectra, W W

IaIafalse false = 6. (1+1/0.2)= 6. (1+1/0.2)

But other ways of determining mis-tag rates such as But other ways of determining mis-tag rates such as fitting distance modulus tails may reduce the penalty significantly.fitting distance modulus tails may reduce the penalty significantly.

Using WUsing WIaIa

falsefalse = 3 = 3

FOM = Efficiency * Pseudo-Purity (best FOM for cosmology?)FOM = Efficiency * Pseudo-Purity (best FOM for cosmology?)

Challenge Results: Pseudo-Purity and Figure of MeritChallenge Results: Pseudo-Purity and Figure of MeritAnalysis of these results just beginningAnalysis of these results just beginning

Confirmed=TrainingUnconfirmed=Blind

1) Large variations btw algorithms.•2) Large redshift variations.

Comparison of Two ContributionsComparison of Two Contributions

Effi

cien

cy

Pur

ityFO

M“SNANA cuts” applied tighter SNR cuts to sample, losing efficiency at high-z.

Sako used all events and his typer gave similar purity at high-z, hence better FOM.

Conclusions and Future Conclusions and Future

Challenge very useful exercise (at least for us!)

Large improvement in non-Ia templates.

Contributors have the answers and will hopefully perform a careful post-mortem and contribute to a summary paper.

No plans to hold a second challenge, but maintain the web site and upgrade the samples as better knowledge

and templates become available. Also include more samples in addition to the DES.

Documents

Steve Kuhlmann Argonne National Laboratory...II. Weak Lensing III. Baryon Acoustic Oscillations IV. Supernovae • Two multiband surveys: 5000 deg2 grizY to 24th mag 15 deg2repeat