Characterizing the Gamma-ray Emission from Low-Luminosity AGN

TL;DR

This study analyzes 14.4 years of Fermi-LAT data to characterize gamma-ray emission from low-luminosity AGN, detecting one new source and exploring emission mechanisms and contributions from jets and star formation.

Contribution

It provides the first detection of a new LLAGN in gamma rays, applies stacking to subthreshold sources, and releases a Python library for population analysis.

Findings

Detected one new LLAGN in gamma rays.

Subthreshold emission is consistent with star formation, but jet contributions are possible.

Detected LLAGN likely dominated by jet emission, explained by synchrotron self-Compton models.

Abstract

A majority of the active galactic nuclei (AGN) in the local Universe are classified as low-luminosity AGN (LLAGN), having bolometric luminosities $\lesssim 10^{42} \ \mathrm{erg \ s^{-1}}$. Although high-energy gamma-ray emission is predicted from both the jets and disks of LLAGN, to date only four have been detected by the Fermi Large Area Telescope (Fermi-LAT). In this work, we therefore conduct a comprehensive study of all the LLAGN from the Palomar spectroscopic survey of bright, northern galaxies, including both subthreshold and detected gamma-ray sources, using 14.4 years of LAT data. Our analysis results in a new detection of one LLAGN, as well as a detection of the subthreshold population using a stacking technique. We find that the signal from the subthreshold sample is consistent with being dominated by star-formation activity, although a contribution from compact jets or a mixed contribution from jetted and non-jetted systems is also feasible. On the other hand, the individually detected LLAGN are likely dominated by jet emission. We perform detailed spectral modeling for a subset of these sources and find that the gamma-ray signal can be explained by synchrotron self-Compton radiation, if the inner jet emission region is weakly magnetized with its total energy density being strongly particle dominated, and only slowly moving. With this work we also publicly release our Python-based stacking library for analyzing subthreshold source populations with the LAT, based on a proven technique used in numerous studies.