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Using interstellar clouds to search for Galactic PeVatrons: gamma-ray signatures from supernova remnants


Mitchell, A M W; Rowell, G P; Celli, S; Einecke, S (2021). Using interstellar clouds to search for Galactic PeVatrons: gamma-ray signatures from supernova remnants. Monthly Notices of the Royal Astronomical Society, 503(3):3522-3539.

Abstract

Interstellar clouds can act as target material for hadronic cosmic rays; gamma rays subsequently produced through inelastic proton–proton collisions and spatially associated with such clouds can provide a key indicator of efficient particle acceleration. However, even in the case that particle acceleration proceeds up to PeV energies, the system of accelerator and nearby target material must fulfil a specific set of conditions in order to produce a detectable gamma-ray flux. In this study, we rigorously characterize the necessary properties of both cloud and accelerator. By using available supernova remnant (SNR) and interstellar cloud catalogues, we produce a ranked shortlist of the most promising target systems, those for which a detectable gamma-ray flux is predicted, in the case that particles are accelerated to PeV energies in a nearby SNR. We discuss detection prospects for future facilities including CTA, LHAASO and SWGO; and compare our predictions with known gamma-ray sources. The four interstellar clouds with the brightest predicted fluxes >100 TeV identified by this model are located at (l,b)  = (330.05, 0.13), (15.82, −0.46), (271.09, −1.26), and (21.97, −0.29). These clouds are consistently bright under a range of model scenarios, including variation in the diffusion coefficient and particle spectrum. On average, a detectable gamma-ray flux is more likely for more massive clouds; systems with lower separation distance between the SNR and cloud; and for slightly older SNRs.

Abstract

Interstellar clouds can act as target material for hadronic cosmic rays; gamma rays subsequently produced through inelastic proton–proton collisions and spatially associated with such clouds can provide a key indicator of efficient particle acceleration. However, even in the case that particle acceleration proceeds up to PeV energies, the system of accelerator and nearby target material must fulfil a specific set of conditions in order to produce a detectable gamma-ray flux. In this study, we rigorously characterize the necessary properties of both cloud and accelerator. By using available supernova remnant (SNR) and interstellar cloud catalogues, we produce a ranked shortlist of the most promising target systems, those for which a detectable gamma-ray flux is predicted, in the case that particles are accelerated to PeV energies in a nearby SNR. We discuss detection prospects for future facilities including CTA, LHAASO and SWGO; and compare our predictions with known gamma-ray sources. The four interstellar clouds with the brightest predicted fluxes >100 TeV identified by this model are located at (l,b)  = (330.05, 0.13), (15.82, −0.46), (271.09, −1.26), and (21.97, −0.29). These clouds are consistently bright under a range of model scenarios, including variation in the diffusion coefficient and particle spectrum. On average, a detectable gamma-ray flux is more likely for more massive clouds; systems with lower separation distance between the SNR and cloud; and for slightly older SNRs.

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Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Physics Institute
Dewey Decimal Classification:530 Physics
Scopus Subject Areas:Physical Sciences > Astronomy and Astrophysics
Physical Sciences > Space and Planetary Science
Uncontrolled Keywords:Space and Planetary Science, Astronomy and Astrophysics
Language:English
Date:31 March 2021
Deposited On:27 May 2021 04:55
Last Modified:25 Jun 2024 01:40
Publisher:Oxford University Press
ISSN:0035-8711
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/mnras/stab667
  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)