Upload
rimona
View
34
Download
3
Embed Size (px)
DESCRIPTION
Automated Classification of X-ray Sources. R. J. Hanisch, A. A. Suchkov, R. L. White Space Telescope Science Institute T. A. McGlynn, E. L. Winter, M. F. Corcoran NASA Goddard Space Flight Center W. Voges Max-Planck-Institute for Extraterrestrial Physics. - PowerPoint PPT Presentation
Citation preview
Automated Classificationof X-ray Sources
R. J. Hanisch, A. A. Suchkov, R. L. WhiteSpace Telescope Science Institute
T. A. McGlynn, E. L. Winter, M. F. Corcoran
NASA Goddard Space Flight Center
W. VogesMax-Planck-Institute for Extraterrestrial Physics
Supported by NASA’s Applied Information Systems Research Program, Grant NAG5-11019
IAU JD08, 2003-07-18 R. J. Hanisch et al. 2
ClassX
• ClassX is a Virtual Observatory prototype project aimed at the semi-automated classification of unidentified X-ray sources.
• ClassX draws from numerous on-line object catalogs using VO standard protocols (“cone search”, VOTable) to collect multi-wavelength position, flux, and source extent information.
• ClassX uses these data to train oblique decision tree classifiers, and then apply the classifiers to unidentified X-ray sources.
IAU JD08, 2003-07-18 R. J. Hanisch et al. 3
USNOA2
DSS
ClassX Overview
ClassifierNetwork
Post ClassificationAnalysis andVerification
ClassifierTraining
Classifier specifica-tions and statisticsClassifier specifica-tions and statistics
Data Pipeline
WGACAT
RASS
GSC2
2MASSNVSS
FIRST
ChandraXMM
SDSS
Initially use small-coverage resources for verification.
Use existing high-coverage resources to get information on user sources.
1
2 3
HST
IAU JD08, 2003-07-18 R. J. Hanisch et al. 4
What Kind of Classifier?
Classifiers can be distinguished along several orthogonal dimensions. Exploring all the dimensions is hard.
Different tasks may require different classifiers.
Classifier algorithm
Decision trees, oblique or otherwise
Neural networks
Nearest neighbor
Observed quantities
Fluxes, positions, colors, variability, spatial extent, …
X-ray, optical, IR, ...Training sets
WGACAT, ROSAT All Sky Survey, ...
ClassificationgranularityCoarse: Stellar vs. Extragalactic
Fine: A0 vs. B0…, AGN vs. QSO vs. galaxy
IAU JD08, 2003-07-18 R. J. Hanisch et al. 5
ClassX Performance
• Every output class needs substantial representation in the training set.
• Overlap between classes should be minimized.
• Classifier accuracy can be improved with additional information (i.e., flux in different bandpass), but not always!
• Stellar and extragalactic sources are easily distinguished.
X-ray, opt.X-ray, opt., IR
Very few stars classifiedas extragalactic sources
Almost no extra-galactic sources classified as stars
Small amount ofconfusion among
different stellar types
Extragalactic sourceclassifications moreambiguous owing to
class overlap
IAU JD08, 2003-07-18 R. J. Hanisch et al. 6
X-ray Stars in ρ Oph• 10X increase in
number of identified X-ray stars
• Dominance of late-type stars consistent with large pre-main-sequence population in active star formation region T Tauri-type stars
• Adds many new T Tauri candidates
IAU JD08, 2003-07-18 R. J. Hanisch et al. 7
X-ray Stars in the LMC• 10X increase in
number of identified early-type stars
• Dominance of early-type stars is consistent with expectations for stars at distance of LMC
• Many late-type X-ray stars suggest large population of PMS T Tauri stars in LMC star formation regions
IAU JD08, 2003-07-18 R. J. Hanisch et al. 8
X-ray Binaries
• WGACAT “stars” (type unknown) re-classified; most are indeed stars, most in direction of LMC/SMC
• 53 new XRB candidates; 50% increase in number known in WGACAT. These are mostly high-mass XRB candidates with bright optical counterparts.
mx1 (soft x-ray magnitude)
X-r
ay h
ardn
ess
rati
o
Additional XRBs?
IAU JD08, 2003-07-18 R. J. Hanisch et al. 9
Quasars and AGN• Nearly 20X increase in number of
QSO candidates, 3X increase in number of AGNs. ClassX differentiates reasonably well between QSOs and AGN.
• In contrast to QSO/AGN objects known in WGACAT, where dominant class is AGN, objects identified by ClassX are strongly dominated by QSOs. On average are much fainter in the X-rays, by more than 1 mag; also substantially redder in the optical.
• Of ClassX-classified QSOs in region of SDSS EDR, 60% are confirmed.
Knownsources
ClassXsources
10 R. J. Hanisch et al. IAU JD08, 2003-07-18
Summary
• Core technology of ClassX in place and working effectively.
• Suite of classifiers developed.• Initial results in areas of stellar X-ray sources…
– Pre-main-sequence stars, T Tauri stars readily identified in galactic star formation regions
– Large increase in numbers of both early- and late-type X-ray stars in LMC
– 50% increase in number of candidate X-ray binaries
• …and quasars/AGN– Identifying faint, high-redshift QSOs
• Pursuing further validation, e.g., through SDSS, HST, and Chandra observations
http://heasarc.gsfc.nasa.gov/classx/