Identifying New $\gamma$-Ray Sources in All-Sky Surveys Based on Fermipy's Advanced Algorithm

TL;DR

This paper presents a novel, efficient algorithmic approach for identifying new gamma-ray sources in all-sky surveys using Fermi LAT data, significantly expanding the catalog of known sources and analyzing their properties.

Contribution

The paper introduces an advanced, parallelized method leveraging Fermipy algorithms and Galactic diffuse models to efficiently discover and analyze new gamma-ray sources across the entire sky.

Findings

Identified 1379 new gamma-ray sources with >4sigma significance

Detected 497 sources with >5sigma significance

Characterized 21 extended, 23 spectral curved, and 44 variable sources

Abstract

We employ an efficient method for identifying gamma-ray sources across the entire sky, leveraging advanced algorithms from Fermi p y, and cleverly utilizing the Galactic diffuse background emission model to partition the entire sky into 72 regions,thereby greatly enhancing the efficiency of discovering new sources throughout the sky through multi-threaded parallel computing. After confirming the reliability of the new method, we applied it for the first time to analyze data from the Fermi Large Area Telescope encompassing approximately 15.41yr of all sky surveys. Through this analysis, we successfully identified 1379 new sources with levels exceeding 4sigma, of which 497 sources exhibited higher significance levels exceeding 5sigma. Subsequently, we performed a systematic analysis of the spatial extension, spectra, and light variation characteristics of these newly identified sources. We identified 21 extended sources and 23 sources exhibiting spectral curvature above 10GeV. Additionally, we identified 44 variable sources above 1GeV.