Abstract

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.

Highlights

  • Understanding the origin of Galactic cosmic rays (CRs) continues to be a very active research area, with many open questions (Blasi 2013; Grenier, Black & Strong 2015)

  • 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

  • Using current catalogues of known supernova remnant (SNR) and interstellar clouds, we explore which combinations of accelerators and clouds are the most promising targets to look for evidence of PeVatron activity with current instruments, such as H.E.S.S. or HAWC, and for future detectability with Cherenkov Telescope Array (CTA), LHAASO, or SWGO

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Summary

INTRODUCTION

Understanding the origin of Galactic cosmic rays (CRs) continues to be a very active research area, with many open questions (Blasi 2013; Grenier, Black & Strong 2015). Cosmic rays at PeV energies, would produce ∼100 TeV gamma rays These CRs are sufficiently energetic to escape the SNR shock and travel through the interstellar medium (ISM). Interstellar clouds provide suitable targets for CR interactions generating pions (among other particles) that may subsequently produce a detectable gamma-ray flux through the decay of neutral pions. We quantify which combinations of accelerator and cloud properties achieve detectable levels of gamma-ray flux under standard assumptions We present this information through plots demonstrating the favourable phase space for interstellar clouds, in an analogous manner to the well-known phase space for astrophysical sites of CR acceleration (Hillas 1984). This enables us to explore the phase space of different accelerator and interstellar cloud properties and the resulting dependence of the gamma-ray flux at a given energy on these properties, assuming the same initial particle spectrum

Particle spectrum reaching the target
Gamma-ray flux produced: two-step model
Step 1
Step 2
Resulting gamma-ray spectrum
Accelerator target distribution
DEPENDENCEOFGAMMA - R AY F L U X ABOVE 100 TEV ON SYSTEM PROPERTIES
Variation in system properties
Diffusion coefficient variation
Predicting gamma rays above 100 TeV from SNR–cloud pairs
Total gamma-ray spectrum per cloud
Including contributions from ‘invisible’ SNRs traced by pulsars
Systematic uncertainties due to model assumptions
DISCUSSION
Potentially bright clouds overlapping HGPS sources
Potentially bright clouds observable by HAWC
Regions outside the HGPS
Limitations of the model
Applications of the results
Findings
Further work and next steps
CONCLUSIONS
Full Text
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