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Seeking the faint extremes: Detection and characterisation of extragalactic soft-spectrum gamma-ray sources and exploring methods to enhance their detection with machine learning in the 50 GeV-50 TeV energy range
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering.ORCID iD: 0000-0003-2946-1313
2022 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

This thesis deals with an observational study of Blazars, strong gamma-ray sources with Very High Energies (VHE) located far outside our Galaxy. Blazars are a class of galaxies that contain a supermassive black hole that is actively consuming large quantities of matter, a process that results in the liberation of tremendous amounts of energy that then powers the emission of huge bulks of charged particles that get accelerated almost to the speed of light.

The details of the extreme processes involved are still very uncertain, and more observational studies are still required to discriminate between the various theories. Because it takes a lot of energy to create VHE gamma-rays, they are tightly coupled to the most energy-rich places in Blazars.

This means that observations of gamma rays directly probe the central engine responsible for the enormous amounts of radiation we detect.

Direct studies have been carried out with the H.E.S.S. observatory, an Imaging Atmospheric Cherenkov Telescope which uses our atmosphere as an integral part of its detector and is able to detect gamma-ray photons with energies from over 50 GeV up to tens of TeV.

Using H.E.S.S., seven new sources of gamma-rays in the VHE regime were carefully studied in this thesis, significantly expanding the collection of known sources of TeV photons.

Computer studies were also performed exploring the possibility of using deep learning to improve the sensitivity of ALTO, a newly-proposed observatory belonging to an emerging class of gamma-ray instruments, the particle detector arrays.

Place, publisher, year, edition, pages
Växjö: Linnaeus University Press, 2022. , p. 203
Series
Linnaeus University Dissertations ; 464
Keywords [en]
GeV gamma-rays, TeV gamma-rays, AGN, blazar, deep learning, observational high-energy astronomy
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics, Astroparticle Physics
Identifiers
URN: urn:nbn:se:lnu:diva-116309DOI: 10.15626/LUD.464.2022ISBN: 9789189709379 (print)ISBN: 9789189709386 (electronic)OAI: oai:DiVA.org:lnu-116309DiVA, id: diva2:1697902
Public defence
2022-10-14, Weber (K1009V), Georg Lückligs väg 8, Växjö, 21:42 (English)
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Available from: 2022-09-23 Created: 2022-09-22 Last updated: 2024-03-07Bibliographically approved

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Bylund, Tomas

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CiteExportLink to record
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