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Abstract: Gamma rays serve as important messengers in modern astrophysics, offering insights into the most energetic processes in the cosmos. Advancements in gamma-ray astronomy, facilitated by international scientific collaboration, have expanded its reach and capabilities. The Fermi-Large Area Telescope (Fermi-LAT) has so far contributed immensely to our understanding of the gamma-ray sky at GeV energies, surveying numerous source classes. At the same time, ground-based observatories like H.E.S.S., MAGIC, VERITAS, HAWC, and LHASSO, enable the exploration of high-energy (HE) phenomena across various energy scales, reaching the PeV range. The collective data from Fermi-LAT and ground-based instruments provide a comprehensive picture of cosmic phenomena across diverse energy regimes. Efforts to catalog HE gamma-ray sources have resulted in the detection of several thousand sources at GeV, including Pulsar Wind Nebulae (PWNe), Supernova Remnants (SNRs), pulsars, blazars, and Gamma-Ray Bursts (GRBs), with the observational capability to study their spectral and spatial morphology enhancing our understanding of their origin and evolution. Looking ahead, the Cherenkov Telescope Array (CTA) represents the next frontier in ground-based gamma-ray astronomy. Operating at very high energies (VHE) between 20 GeV and 300 TeV, CTA's improved sensitivity, angular resolution, and expanded field of view (FoV) promise enhanced imaging of extended sources and performance of large-scale surveys. CTA's Key Science Projects (KSPs) include the Extragalactic (EGAL) survey, a survey of a quarter of the extragalactic sky, and the Galactic Plane Survey (GPS), a survey of the entire Galactic Plane (GP). The KSPs will receive dedicated observation time and careful planning to ensure the optimization of their scientific output. As CTA is currently entering the construction phase, simulations are being extensively employed to predict its response to various signals, playing a vital role in comprehending CTA's response and sensitivity to different signals. The derived predictions are paving the way for estimating the CTA's scientific output, informing the observational strategy, and ensuring its success in maximizing the contribution to HE gamma-ray astronomy. In this thesis, I contribute to assessing the sensitivity of the CTA surveys, particularly the GPS and the EGAL survey, to diverse astrophysical sources and signals. Focusing on the GPS, I delve into understanding the detectability of pulsar halos, which emit multi-TeV gamma rays, the detection of which was recently reported by the HAWC Observatory. The study involves a spatial-spectral likelihood analysis, evaluating sensitivity to simple Gaussian extended sources and physically modeled sources. Employing a template-fitting approach, I analyze CTA's GPS sensitivity to extended sources and explore the prospects for pulsar halo detection and characterization. A preliminary population study addresses the visibility of pulsar halos to CTA's GPS and explores the angular sensitivity to extended sources. The thesis sets the detectability prospects of pulsar halos with CTA and investigates what fraction of the preliminary pulsar halo population CTA will be able to probe. The thesis extends its exploration into the persistent mystery of dark matter (DM), a fundamental puzzle in cosmology. The search for DM signals remains a vigorous pursuit in the physics community, utilizing various astrophysical messengers resulting from DM particle annihilation or decay. I investigate the potential of CTA's GPS to detect dark sub-halos within our galaxy, utilizing a similar approach as in the sensitivity assessment to pulsar halos, applied to recent sub-halo population simulations. Furthermore, the thesis addresses the intricate task of disentangling DM components from astrophysical contributions in the observed gamma-ray sky. In terms of the EGAL survey, employing advanced statistical methods such as the cross-correlation technique, I explore the prospects of using CTA's EGAL survey to correlate the Extragalactic Gamma-ray Background (EGRB) with galaxy catalogs, providing insights into DM properties. While traditional methods rely on likelihood analysis with background subtraction or template fitting, the emergence of supervised machine learning (ML) offers a novel, potentially more effective approach for cataloging the sky. The thesis touches upon the usability of ML in the high and VHE gamma-ray sky. My study focuses on CTA's GPS and utilizes deep-learning-based algorithms in a detection pipeline for the automatic classification of extended sources from gamma-ray data. As CTA stands at the forefront of gamma-ray astronomy as the next-generation observatory, the research presented in this thesis contributes a small step towards answering the open questions about pulsar halos and DM, showcasing the potential breakthroughs that may emerge from CTA's observations. The detailed likelihood analysis performed aims to advance our understanding of these enigmas, from the physical intricacies of pulsar halos to the elusive nature of DM, driven by curiosity about the continuous exploration of the Universe's mysteries.
Keywords: high-energy gamma-ray astronomy, astroparticle physics, Cherenkov Telescope Array, pulsar halos, dark matter, dissertations
Published in RUNG: 06.06.2024; Views: 393; Downloads: 5
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Abstract: A wealth of astrophysical and cosmological observational evidence shows that the matter content of the universe is made of about 85% of non-baryonic dark matter. Huge experimental efforts have been deployed to look for the direct detection of dark matter via their scattering on target nucleons, their production in colliders, and their indirect detection via their annihilation products. Inelastic scattering of high-energy cosmic rays off dark matter particles populating the Milky Way halo would produce secondary gamma rays in the final state from the decay of the neutral pions produced in such interactions, providing a new avenue to probe dark matter properties. We compute here the sensitivity for H.E.S.S.-like observatory, a current-generation ground-based Cherenkov telescopes, to the expected gamma-ray flux from collisions of Galactic cosmic rays and dark matter in the center of the Milky Way. We also derive sensitivity prospects for the upcoming Cherenkov Telescope Array (CTA) and Southern Wide-field Gamma-ray Observatory (SWGO). The expected sensitivity allows us to probe a poorly-constrained range of dark matter masses so far, ranging from keV to sub-GeV, and provide complementary constraints on the dark matter-proton scattering cross section traditionally probed by deep underground direct dark matter experiments.
Keywords: Cherenkov Telescope Array, CTA, very-high-energy gamma-ray astroparticle physics, instrument response functions, machine learning
Published in RUNG: 26.09.2023; Views: 1082; Downloads: 6
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Abstract: The Cherenkov Telescope Array (CTA) will be the next-generation observatory in the field of very-high-energy (20 GeV to 300 TeV) gamma-ray astroparticle physics. The traditional approach to data analysis in this field is to apply quality cuts, optimized using Monte Carlo simulations, on the data acquired to maximize sensitivity. Subsequent steps of the analysis typically use the surviving events to calculate one set of instrument response functions (IRFs) to physically interpret the results. However, an alternative approach is the use of event types, as implemented in experiments such as the Fermi-LAT. This approach divides events into sub-samples based on their reconstruction quality, and a set of IRFs is calculated for each sub-sample. The sub-samples are then combined in a joint analysis, treating them as independent observations. In previous works we demonstrated that event types, classified using Machine Learning methods according to their expected angular reconstruction quality, have the potential to significantly improve the CTA angular and energy resolution of a point-like source analysis. Now, we validated the production of event-type wise full-enclosure IRFs, ready to be used with science tools (such as Gammapy and ctools). We will report on the impact of using such an event-type classification on CTA high-level performance, compared to the traditional procedure.
Keywords: Cherenkov Telescope Array, CTA, very-high-energy gamma-ray astroparticle physics, instrument response functions, machine learning
Published in RUNG: 26.09.2023; Views: 1136; Downloads: 7
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Keywords: dark matter, high energy physics, astrophysical observations, cosmological observations, Large Synoptic Survey Telescope (LSST)
Published in RUNG: 03.05.2022; Views: 1775; Downloads: 42
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Abstract: Despite their discovery already more than a century ago, Cosmic Rays (CRs) still did not divulge all their properties yet. Theories about the origin of ultra-high energy (UHE, > 10^18 eV) CRs predict accompanying primary photons. The existence of UHE photons can be investigated with the world’s largest ground-based experiment for detection of CR-induced extensive air showers (EAS), the Pierre Auger Observatory, which offers an unprecedented exposure to rare UHE cosmic particles. The discovery of photons in the UHE regime would open a new observational window to the Universe, improve our understanding of the origin of CRs, and potentially uncloak new physics beyond the standard model. The novelty of the presented work is the development of a "real-time" photon candidate event stream to a global network of observatories, the Astrophysical Multimessenger Observatory Network (AMON). The stream classifies CR events observed by the Auger surface detector (SD) array as regards their probability to be photon nominees, by feeding to advanced machine learning (ML) methods observational air shower parameters of individual CR events combined in a multivariate analysis (MVA). The described straightforward classification procedure further increases the Pierre Auger Observatory’s endeavour to contribute to the global effort of multi-messenger (MM) studies of the highest energy astrophysical phenomena, by supplying AMON partner observatories the possibility to follow-up detected UHE events, live or in their archival data.
Keywords: astroparticle physics, ultra-high energy cosmic rays, ultra-high energy photons, extensive air showers, Pierre Auger Observatory, multi-messenger, AMON, machine learning, multivariate analysis, dissertations
Published in RUNG: 27.10.2021; Views: 3262; Downloads: 175
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