The Einstein@Home Gamma-Ray Pulsar Survey II. Source Selection, Spectral Analysis and Multi-wavelength Follow-up

TL;DR

This paper reports the discovery and analysis of 13 gamma-ray pulsars from the Einstein@Home survey, using machine learning for source selection, spectral analysis, and multi-wavelength follow-up, revealing new radio-quiet pulsars and modeling their emission profiles.

Contribution

It introduces a systematic approach combining machine learning, gamma-ray spectral analysis, and follow-up observations to discover and characterize new gamma-ray pulsars.

Findings

13 new gamma-ray pulsars discovered.

Faint radio pulsations detected in two pulsars.

Most pulsars are likely radio-quiet.

Abstract

We report on the analysis of 13 gamma-ray pulsars discovered in the Einstein@Home blind search survey using Fermi Large Area Telescope (LAT) Pass 8 data. The 13 new gamma-ray pulsars were discovered by searching 118 unassociated LAT sources from the third LAT source catalog (3FGL), selected using the Gaussian Mixture Model (GMM) machine learning algorithm on the basis of their gamma-ray emission properties being suggestive of pulsar magnetospheric emission. The new gamma-ray pulsars have pulse profiles and spectral properties similar to those of previously-detected young gamma-ray pulsars. Follow-up radio observations have revealed faint radio pulsations from two of the newly-discovered pulsars, and enabled us to derive upper limits on the radio emission from the others, demonstrating that they are likely radio-quiet gamma-ray pulsars. We also present results from modeling the gamma-ray pulse profiles and radio profiles, if available, using different geometric emission models of pulsars. The high discovery rate of this survey, despite the increasing difficulty of blind pulsar searches in gamma rays, suggests that new systematic surveys such as presented in this article should be continued when new LAT source catalogs become available.