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Analysis Methods for Gamma-ray Astronomy

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arxiv 2309.02966 v1 pith:OKX64PFD submitted 2023-09-06 astro-ph.IM astro-ph.HE

classification astro-ph.IMastro-ph.HE
keywords gamma-rayanalysisastronomydataenergiesfieldimagingmethods
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abstract

The launch of the Fermi satellite in 2008, with its Large Area Telescope (LAT) on board, has opened a new era for the study of gamma-ray sources at GeV ($10^9$ eV) energies. Similarly, the commissioning of the third generation of imaging atmospheric Cherenkov telescopes (IACTs) - H.E.S.S., MAGIC, and VERITAS - in the mid-2000's has firmly established the field of TeV ($10^{12}$ eV) gamma-ray astronomy. Together, these instruments have revolutionised our understanding of the high-energy gamma-ray sky, and they continue to provide access to it over more than six decades in energy. In recent years, the ground-level particle detector arrays HAWC, Tibet, and LHAASO have opened a new window to gamma rays of the highest energies, beyond 100 TeV. Soon, next-generation facilities such as CTA and SWGO will provide even better sensitivity, thus promising a bright future for the field. In this chapter, we provide a brief overview of methods commonly employed for the analysis of gamma-ray data, focusing on those used for Fermi-LAT and IACT observations. We describe the standard data formats, explain event reconstruction and selection algorithms, and cover in detail high-level analysis approaches for imaging and extraction of spectra, including aperture photometry as well as advanced likelihood techniques.

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  1. Machine Learning in Gamma Astronomy

    astro-ph.IM 2025-01 unverdicted

    This paper reviews deep learning approaches for Imaging Atmospheric Cherenkov Telescope data, covering classification, parameter reconstruction, and generative modeling, with a focus on references.

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