Abstract Details
| Name: Atul Pathania Affiliation: Bhabha Atomic Research Centre Conference ID: ASI2025_156 Title : Identification of gamma-ray pulsar candidates among the unassociated sources in the Fermi-LAT catalog using Random Forest Authors and Co-Authors : A. Pathania1,2, A. Tolamatti1,2, K. K. Singh1,2, S. K. Singh3, K. K. Yadav1,2, B. B. Singh4 Abstract Type : Poster Abstract Category : High Energy Phenomena, Fundamental Physics and Astronomy Abstract : More than 7100 high-energy gamma-ray sources are reported in the 4th Fermi-LAT catalogue (4FGL-DR4) using 14 years of all sky observations. However, exact astrophysical nature of a significant fraction (~34%) of these sources remains largely unknown and group of such astrophysical objects is referred to as unassociated sources. In this work, we employ Random Forest based machine learning algorithm to classify the unassociated sources into two broad classes viz. pulsars and active galactic nuclei. This study involves feature selection, hyper-parameter optimization and finally identifying the plausible pulsars and active galactic nuclei candidates. Pulsars being relatively faint sources in comparison to active galactic nuclei, only ~10% of total discovered pulsars are high energy gamma-ray pulsars with only 4 are being detected in the very high energy range (E > 30GeV ). Hence, the predicted pulsar candidates in the present work can set a pathway for other waveband observations, which can finally help in increasing the number of pulsar candidates for further populations studies. |