Automatic classification of time-variable X-ray sources

Journal Article · Thu May 01 04:00:00 EDT 2014 · Astrophysical Journal

DOI:https://doi.org/10.1088/0004-637X/786/1/20· OSTI ID:22357039

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M. ^[1]

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1. Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, NSW 2006 (Australia)

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

• OSTI ID: 22357039
• Journal Information: Astrophysical Journal, Journal Name: Astrophysical Journal Journal Issue: 1 Vol. 786; ISSN ASJOAB; ISSN 0004-637X
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
ACCURACY
ALGORITHMS
CLASSIFICATION
COSMIC X-RAY SOURCES
DETECTION
MINING
RANDOMNESS
TRAINING
TRANSIENTS
WAVELENGTHS
X RADIATION
X-RAY SPECTRA