# Identification of Gamma Ray Pulsar Candidates in the \\emph{Fermi}-LAT 4FGL-DR4 Unassociated Sources Using Supervised Machine Learning

Source: [Identification of Gamma Ray Pulsar Candidates in the \\emph{Fermi}-LAT 4FGL-DR4 Unassociated Sources Using Supervised Machine Learning](<https://www.sota2.com/research/paper/68fa4c331e209131ce94b484>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T08:41:05.252Z

Authors: A. Pathania, K. K. Singh, S. K. Singh, A. Tolamatti, B. B. Singh, K. K. Yadav

Published: 2025-10-09T08:58:54.000Z

## Abstract

The Large Area Telescope (LAT) on board the \\emph{Fermi} Gamma-ray Space Telescope has been continuously providing good quality   survey data of the entire sky in the high energy range from 30 MeV to 500 GeV and above since August 2008. A succession of gamma-ray   source catalogs is published after a comprehensive analysis of the \\emph{Fermi}--LAT data. The most recent release of data in the   fourth \\emph{Fermi}--LAT catalog of gamma-ray sources (4FGL-DR4), based on the first 14 years of observations in the energy band   50 MeV-1 TeV, contains 7195 sources. A large fraction ($\\sim$ 33\\%) of this population has no known counterparts in the lower wave   bands. Such high energy gamma-ray sources are referred to as unassociated or unidentified. An appropriate classification of these   objects into known type of gamma-ray sources such as the active galactic nuclei or pulsars is essential for population studies and   pointed multi-wavelength observations to probe the radiative processes. In this work, we perform a detailed classification of the   unassociated sources reported in the 4FGL-DR4 catalog using two supervised machine learning techniques-Random Forest and   Extreme Gradient Boosting. We mainly focus on the identification of new gamma-ray pulsar candidates by making use of different   observational features derived from the long-term observations with the \\emph{Fermi}--LAT and reported in the incremental 4FGL-DR4 catalog.   We also explore the effects of data balancing approach on the classification of the \\emph{Fermi}--LAT unassociated sources.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.08654>)

[PDF](<https://arxiv.org/pdf/2510.08654.pdf>)