2022MNRAS.515.1807C

The latest Fermi-LAT gamma-ray catalogue, 4FGL-DR3, presents a large fraction of sources without clear association to known counterparts, i.e. unidentified sources (unIDs). In this paper, we aim to classify them using machine learning algorithms, which are trained with the spectral characteristics of associated sources to predict the class of the unID population. With the state-of-the-art CATBOOST algorithm, based on gradient boosting decision trees, we are able to reach a 67 per cent accuracy on a 23-class data set. Removing a single of these classes - blazars of uncertain type - increases the accuracy to 81 per cent. If interested only in a binary AGN/pulsar distinction, the model accuracy is boosted up to 99 per cent. Additionally, we perform an unsupervised search among both known and unID population, and try to predict the number of clusters of similar sources, without prior knowledge of their classes. The full code used to perform all calculations is provided as an interactive PYTHON notebook.