| 1. |
- Aharonian, F., et al.
(författare)
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A deep spectromorphological study of the ϒ-ray emission surrounding the young massive stellar cluster Westerlund 1
- 2022
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Ingår i: Astronomy and Astrophysics. - : EPD Sciences. - 0004-6361 .- 1432-0746. ; 666
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Tidskriftsartikel (refereegranskat)abstract
- Context. Young massive stellar clusters are extreme environments and potentially provide the means for efficient particle acceleration. Indeed, they are increasingly considered as being responsible for a significant fraction of cosmic rays (CRs) that are accelerated within the Milky Way. Westerlund 1, the most massive known young stellar cluster in our Galaxy, is a prime candidate for studying this hypothesis. While the very-high-energy gamma-ray source HESS J1646-458 has been detected in the vicinity of Westerlund 1 in the past, its association could not be firmly identified. Aims. We aim to identify the physical processes responsible for the gamma-ray emission around Westerlund 1 and thus to understand the role of massive stellar clusters in the acceleration of Galactic CRs better. Methods. Using 164 h of data recorded with the High Energy Stereoscopic System (H.E.S.S.), we carried out a deep spectromorphological study of the gamma-ray emission of HESS J1646-458. We furthermore employed H I and CO observations of the region to infer the presence of gas that could serve as target material for interactions of accelerated CRs. Results. We detected large-scale (similar to 2 degrees diameter) gamma-ray emission with a complex morphology, exhibiting a shell-like structure and showing no significant variation with gamma-ray energy. The combined energy spectrum of the emission extends to several tens of TeV, and it is uniform across the entire source region. We did not find a clear correlation of the gamma-ray emission with gas clouds as identified through H I and CO observations. Conclusions. We conclude that, of the known objects within the region, only Westerlund 1 can explain the majority of the gamma-ray emission. Several CR acceleration sites and mechanisms are conceivable and discussed in detail. While it seems clear that Westerlund 1 acts as a powerful particle accelerator, no firm conclusions on the contribution of massive stellar clusters to the flux of Galactic CRs in general can be drawn at this point.
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| 2. |
- Becherini, Yvonne, et al.
(författare)
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The CoMET multiperspective event tracker for wide field-of-view gamma-ray astronomy
- 2022
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Ingår i: Proceedings of Science. - : Scuola Internazionale Superiore di Studi Avanzati (SISSA).
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Konferensbidrag (refereegranskat)abstract
- The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
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| 3. |
- Senniappan, Mohanraj, et al.
(författare)
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Expected performance of the ALTO particle detector array designed for 200 GeV - 50 TeV gamma-ray astronomy
- 2022
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Ingår i: Proceedings of Science. - : Scuola Internazionale Superiore di Studi Avanzati (SISSA).
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Konferensbidrag (refereegranskat)abstract
- The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.
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