A search for dark matter among Fermi-LAT unidentified sources with systematic features in machine learning

Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93.3 per cent ± 0.7 per cent performance. Other ML evaluation parameters, such as the True Negative and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the degeneracy between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs ; The work of VG and MASC was supported by the grants PROYECTOS DE I+D DE «GENERACIÓN DE CONOCIMIENTO PGC2018-095161-B-I00, CENTRO DE EXCELENCIA “SEVERO OCHOA” CEX2020-001007-S, Proyectos I+D+i PID2021-125331NB-I00 all funded by Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033 and by ‘European Regional Development Fund (ERDF) A way of making Europe’. VG’s contribution to this work has been supported by Juan de la Cierva-Formación ...