Automated Classification of X-ray Sources

1. Automated Classificationof 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 Supported by NASA’s Applied Information Systems Research Program, Grant NAG5-11019

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. R. J. Hanisch et al.

3. ClassX Overview Use existing high-coverage resources to get information on user sources. Initially use small-coverage resources for verification. WGACAT SDSS RASS 1 HST USNOA2 Chandra DSS XMM GSC2 2 3 2MASS NVSS FIRST 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, ... Classification granularity Coarse: Stellar vs. Extragalactic Fine: A0 vs. B0…, AGN vs. QSO vs. galaxy Training sets WGACAT, ROSAT All Sky Survey, ... R. J. Hanisch et al.

5. ClassX Performance X-ray, opt. X-ray, opt., IR Small amount of confusion among different stellar types Almost no extra- galactic sources classified as stars • 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. Very few stars classified as extragalactic sources Extragalactic source classifications more ambiguous owing to class overlap 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 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 R. J. Hanisch et al.

8. X-ray Binaries Additional XRBs? • 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. X-ray hardness ratio mx1 (soft x-ray magnitude) 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. Known sources ClassX sources R. J. Hanisch et al.

10. 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/ R. J. Hanisch et al.