Systematic search for gamma-ray emitting molecular clouds in the vicinity of supernova remnants

TL;DR

This paper develops a systematic approach to identify gamma-ray emitting molecular clouds near supernova remnants, aiming to better understand cosmic ray propagation and particle acceleration mechanisms in our galaxy.

Contribution

It introduces a new strategy combining theoretical models and catalog data to find potential gamma-ray sources around SNRs using current IACT systems.

Findings

Identified parameter space for detectable gamma-ray emitting MCs.

Proposed a method to constrain cosmic ray diffusion coefficients.

First results from the systematic search are presented.

Abstract

Observations of very-high-energy (VHE) gamma-ray emission from supernova remnants (SNR) established them as sources of accelerated particles up to energies of 100 TeV. The dominant process - leptonic or hadronic - responsible for the VHE emission is still not proven for most of the SNRs. Molecular clouds (MCs) in the vicinity of SNRs provide increased amount of target material for accelerated particles escaping the SNRs, thus making MCs potential gamma-ray sources. The predicted gamma-ray flux for MCs offset from the SNR shock depends on the applied diffusion model for VHE particles and the SNR and MC properties, which encounter large uncertainties. While the the average galactic diffusion coefficient is estimated, the spatially resolved propagation properties of VHE cosmic rays are unknown. gamma-ray emitting MCs provide a unique possibility to derive new information on the propagation of VHE particles through the ISM and on the acceleration of hadrons at SNRs. We present in this paper a strategy and first results of a systematic search for TeV gamma-ray emitting MCs in the vicinity of SNRs in the galactic plane region. Based on a theoretical model and current SNR and MC catalogues the detectable parameter space (e.g. diffusion coefficient, SNR age) for current Imaging Atmospheric Cherenkov Telescope systems (IACTs) is obtained. This allows to identify potential gamma-ray emitting regions.