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Autoclassification of the Variable 3XMM Sources Using the Random Forest Machine Learning Algorithm | | Sean A. Farrell
; Tara Murphy
; Kitty K. Lo
; | | Date: |
12 Sep 2015 | | Abstract: | In the current era of large surveys and massive data sets, autoclassification
of astrophysical sources using intelligent algorithms is becoming increasingly
important. In this paper we present the catalog of variable sources in the
Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the
Random Forest machine learning algorithm. We used a sample of manually
classified variable sources from the second data release of the XMM-Newton
catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ~92%. We
also evaluated the effectiveness of identifying spurious detections using a
sample of spurious sources, achieving an accuracy of ~95%. Manual investigation
of a random sample of classified sources confirmed these accuracy levels and
showed that the Random Forest machine learning algorithm is highly effective at
automatically classifying 3XMM sources. Here we present the catalog of
classified 3XMM variable sources. We also present three previously unidentified
unusual sources that were flagged as outlier sources by the algorithm: a new
candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an
eclipsing 5 hr binary system coincident with a known Cepheid. | | Source: | arXiv, 1509.3714 | | Services: | Forum | Review | PDF | Favorites | |
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