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J/ApJ/813/28
 Autoclassification of the variable 3XMM sources (Farrell+, 2015)
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img(gal)
1.J/ApJ/813/28/table43XMM variable source classifications using the Random Forest machine learning algorithm (RF) (2876 rows)

 
Query by Constraints constraints help applied on Columns (Output Order: + - )
ShowSortColumn  Constraint Explain   (UCD)
  recno  Record number assigned by the VizieR team. Should Not be used for identification. (meta.record)
  3XMM (char) 3XMM IAU designation (JHHMMSS.s+DDMMSS) (meta.id;meta.main)
  P(AGN)  RF classifier probability; AGN source class (stat.fit.goodness)
  P(CV)  RF classifier probability; CV source class (stat.fit.goodness)
  P(GRB)  RF classifier probability; GRB source class (stat.fit.goodness)
  P(STAR)  RF classifier probability; STAR source class (stat.fit.goodness)
  P(ULX)  RF classifier probability; ULX source class (stat.fit.goodness)
  P(XRB)  RF classifier probability; XRB source class (stat.fit.goodness)
  P(Max)  Max RF classifier probability over all source classes (stat.fit.goodness)
  P(Spur)  RF classifier probability that the source is spurious (stat.fit.goodness)
  Cl (char) Source class assigned by RF classifier (Note 1)   (src.class)
  Out   [-1/2891] Outlier measure of the source (Note 2)   (stat.likelihood)
  Margin   [-0.6/1] Classification margin of the source (Note 3)   (stat.fit.goodness)
  _RA deg Positions from 3XMM names (right ascension part) (pos.eq.ra;meta.main)
  _DE deg Positions from 3XMM names (declination part) (pos.eq.dec;meta.main)

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