Title: Automated%20Classification%20of%20X-ray%20Sources
1Automated 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 NASAs Applied Information Systems
Research Program, Grant NAG5-11019
2ClassX
- 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.
3ClassX 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
4What 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, ...
5ClassX Performance
X-ray, opt.
X-ray, opt., IR
- 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.
Almost no extra- galactic sources classified as
stars
Small amount of confusion among different stellar
types
Very few stars classified as extragalactic sources
Extragalactic source classifications
more ambiguous owing to class overlap
6X-ray Stars in ? 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
7X-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
8X-ray Binaries
Additional XRBs?
X-ray hardness ratio
mx1 (soft x-ray magnitude)
- 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.
9Quasars 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
10Summary
- 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/