2023MNRAS.521.6195M

In this paper, we develop multiclass classification of Fermi-large area telescope (LAT) gamma-ray sources using machine learning with hierarchical determination of classes. One of the main challenges in the multiclass classification of the Fermi-LAT sources is that the size of some of the classes is relatively small, for example with less than 10 associated sources belonging to a class. In this paper, we propose a hierarchical structure for the determination of the classes. This enables us to have control over the size of classes and to compare the performance of the classification for different numbers of classes. In particular, the class probabilities in the two-class case can be computed either directly by the two-class classification or by summing probabilities of children classes in multiclass classification. We find that the classifications with few large classes have comparable performance with classifications with many smaller classes. Thus, on one hand, the few-class classification can be recovered by summing probabilities of classification with more classes while, on the other hand, the classification with many classes gives a more detailed information about the physical nature of the sources. As a result of this work, we construct three probabilistic catalogues, which are available online. This work opens up a possibility to perform population studies of sources including unassociated sources and to narrow down searches for possible counterparts of unassociated sources, such as active galactic nuclei, pulsars, or millisecond pulsars.