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G.Chiaro, D.Salvetti, G.La Mura, D.J. Thompson

In 3FGL catalog more than 52 % of the FERMI LAT sources are still unclassified. Learning machine technique is one of the most interesting approach for screening and ranking sources, according with their predicted  source class. This study is a part of a wider analysis of unclassified sources listed the Fermi LAT 4-Year Point Source Catalog (3FGL) as described in http://adsabs.harvard.edu/abs/2016ApJ...820....8S , ( Paper1 ) and  http://adsabs.harvard.edu/abs/2016MNRAS.462.3180C (Paper2). The final result of this study suggests a new zoo for 3FGL sources where the percentage of uncertain sources drops from 52%  to 10 % . and opens new planning for mutiwavelenght observational campaings

Fig. 1 3FGL including classification as in Paper1 and Paper2  before this study

 

Fig.2  3FGLzoo where in black the results of  this study 

 

We looked into the literature and BZCat  in order to obtain a validation of our statistic by optical spectra.

We found very high consistency of our statistic prevision with the in optical spectra published in:

Alvarez Crespo, N., et al. 2016a, ApSS, 361, 316

Alvarez Crespo, N., et al. 2016b, AJ,151,32

Alvarez Crespo, N., et al. 2016c, AJ,151,95

Landoni M. et al., 2015, Aj,149,163

Massaro E. et al, 2015, ASS, 357,75

Massaro F. et al., 2014, AJ, 148,112

Massaro F. et al., 2015a, ApJS, 217,2

Massaro F. et al., 2015b, AA, 575, 124

Ricci F. et al, 2015, AJ, 149, 160

 

Paper is in progress

 

Here we enclose

a)      BCUs Classification List

b)     UCS/AGNs Classification List

 

 

 

 

Please contact the authors before any use of these data

 

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