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
UCS_classList.pdf3FGL_BFLAP_classLIST_.pdf
Please contact the authors before any use of these data