A Statistical Approach to Recognizing Source Classes for Unassociated Sources in the Second Fermi-LAT Catalog.

We have developed a new and innovative technique to classify Fermi sources based solely on their observed gamma-ray properties. Our technique, based on Classification Trees, uses the properties of known objects to build a classification analysis which provides the probability for an unidentified source to belong to a given astronomical class (Pulsar, AGN,...).

We have applied this technique to the second Fermi-LAT source catalog (2FGL), and computed a classification probability for each unidentified source. This provides a clearer picture of the unidentified source population and extends the number of interesting candidate objects, thus helping the community in scheduling multiwavelength observations.