Classification of Fermi-LAT sources with deep learning using energy and time spectra

TL;DR

This paper introduces a deep learning approach using energy and time spectra to classify uncertain gamma-ray sources in the Fermi-LAT catalogue, achieving performance comparable to traditional methods and aiding in source identification.

Contribution

It presents a novel deep neural network framework, including recurrent networks, for gamma-ray source classification directly from spectral data, without relying on handcrafted features.

Findings

Neural networks classify sources with performance comparable to traditional methods.

1050 sources predicted as Active Galactic Nuclei, 78 as Galactic pulsars.

The approach effectively identifies high-confidence candidates for follow-up observations.

Abstract

Despite the growing number of gamma-ray sources detected by Fermi-LAT, about one third of the sources in each survey remains of uncertain type. We present a new deep neural network approach for the classification of unidentified or unassociated gamma-ray sources in the last release of the Fermi-LAT catalogue (4FGL-DR2) obtained with 10 years of data. In contrast to previous work, our method directly uses the measurements of the photon energy spectrum and time series as input for the classification, instead of specific, human-crafted features. Dense neural networks, and for the first time in the context of gamma-ray source classification recurrent neural networks, are studied in depth. We focus on the separation between extragalactic sources, i.e.\ Active Galactic Nuclei, and Galactic pulsars, and on the further classification of pulsars into young and millisecond pulsars. Our neural network architectures provide powerful classifiers, with a performance that is comparable to previous analyses based on human-crafted features. Our benchmark neural network predicts that of the sources of uncertain type in the 4FGL-DR2, 1050 are Active Galactic Nuclei and 78 are Galactic pulsars, with both classes following the expected sky distribution and the clustering in the variability-curvature plane. We investigate the problem of sample selection bias by testing our architectures against a cross-match test data set using an older catalogue, and propose a feature selection algorithm using autoencoders. Our list of high-confidence candidate sources labelled by the neural networks provides a set of targets for further multiwavelength observations addressed to identify their nature. The deep neural network architectures we develop can be easily extended to include specific features, as well as multiwavelength data on the source photon energy and time spectra coming from different instruments.