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1.
Detection of gamma-ray sources and search for dark matter signals with Cherenkov Telescope Array surveys : dissertation
Veronika Vodeb, 2024, doctoral dissertation

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: 213; Downloads: 4
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2.
AutoSourceID-Classifier : star-galaxy classification using a convolutional neural network with spatial information
F. Stoppa, Saptashwa Bhattacharyya, R. Ruiz de Austri, P. Vreeswijk, S. Caron, Gabrijela Zaharijas, S. Bloemen, G. Principe, D. Malyshev, Veronika Vodeb, 2023, original scientific article

Abstract: Aims: Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. Methods: The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results. Results: We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
Keywords: astronomical databases, data analysis, statistics, image processing
Published in RUNG: 12.12.2023; Views: 764; Downloads: 4
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3.
AutoSourceID-FeatureExtractor : optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation
F. Stoppa, R. Ruiz de Austri, P. Vreeswijk, Saptashwa Bhattacharyya, S. Caron, S. Bloemen, Gabrijela Zaharijas, G. Principe, Veronika Vodeb, P. J. Groot, E. Cator, G. Nelemans, 2023, original scientific article

Abstract: Aims: In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data. Methods: The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment. Results: We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.
Keywords: data analysis, image processing, astronomical databases
Published in RUNG: 08.11.2023; Views: 736; Downloads: 7
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4.
Performance study update of observations in divergent mode for the Cherenkov Telescope Array
A. Donini, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, published scientific conference contribution

Abstract: Due to the limited field of view (FoV) of Cherenkov telescopes, the time needed to achieve target sensitivity for surveys of the extragalactic and Galactic sky is large. To optimize the time spent to perform such surveys, a so-called “divergent mode” of the Cherenkov Telescope Array Observatory (CTAO) was proposed as an alternative observation strategy to the traditional parallel pointing. In the divergent mode, each telescope points to a position in the sky that is slightly offset, in the outward direction, from the original center of the field of view. This bring the advantage of increasing the total instantaneous arrays’ FoV. From an enlarged field of view also benefits the search for very-high-energy transient sources, making it possible to cover large sky regions in follow-up observations, or to quickly cover the probability sky map in case of Gamma Ray Bursts (GRB), Gravitational Waves (GW), and other transient events. In this contribution, we present the proposed implementation of the divergent pointing mode and its first preliminary performance estimation for the southern CTAO array.
Keywords: Cherenkov Telescope Array, CTAO, divergent mode, very-high-energy transient sources
Published in RUNG: 26.09.2023; Views: 854; Downloads: 5
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5.
Expected exclusion limits to TeV dark matter from the perseus cluster with the Cherenkov Telescope Array
Rémi Adam, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, published scientific conference contribution

Abstract: Clusters of galaxies are the largest gravitationally-bound structures in the Universe. They are composed of galaxies and gas (approximately 15% of the total mass) mostly dark matter (DM, accounts up to 85% of the total mass). If the DM is composed of Weakly Interacting Massive Particles (WIMPs), galaxy clusters represent one of the best targets to search for gamma-ray signals induced by the decay of WIMPs, with masses around the TeV scale. Due to its sensitivity and energy range of operation (from 20 GeV to 300 TeV), the Cherenkov Telescope Array (CTA) Observatory has a unique opportunity to test WIMPs with masses close to the unitarity limit. This will complement the searches for DM from other gamma-ray observatories as well as direct and collider experiments. The CTA Observatory is planning to search for gamma-ray emission, either its origin may be cosmic-ray (CR) or DM related, in the Perseus galaxy cluster during the first years of operation. In this poster, we will present the software created to perform the analysis using the ctools software and the corresponding results.
Keywords: Cherenkov Telescope Array, CTA, dark matter, standard model, dwarf spheroidal galaxies
Published in RUNG: 26.09.2023; Views: 849; Downloads: 4
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6.
Sensitivity of the Cherenkov Telescope Array to the gamma-ray emission from neutrino sources detected by IceCube
Olga Sergijenko, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, published scientific conference contribution

Abstract: Gamma-ray observations of the astrophysical neutrino sources are fundamentally important for understanding the underlying neutrino production mechanism. We investigate the Cherenkov Telescope Array (CTA) ability to detect the very-high-energy (VHE) gamma-ray counterparts to the neutrino-emitting Active Galaxies. The CTA performance under different configurations and array layouts is computed based on the neutrino and gamma-ray simulations of steady and transient types of sources, assuming that the neutrino events are detected with the IceCube neutrino telescope. The CTA detection probability is calculated for both CTA sites taking into account the visibility constraints. We find that, under optimal observing conditions, CTA could observe the VHE gamma-ray emission from at least 3 neutrino events per year.
Keywords: Cherenkov Telescope Array, IceCube neutrino telescope, neutrinos, neutrino sources
Published in RUNG: 26.09.2023; Views: 950; Downloads: 6
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7.
Variability studies of active galactic nuclei from the long-term monitoring program with the Cherenkov Telescope Array
G. Grolleron, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, published scientific conference contribution

Abstract: Blazars are active galactic nuclei (AGN) with a relativistic jet oriented toward the observer. This jet is composed of accelerated particles which can display emission over the entire electromagnetic spectrum. Spectral variability has been observed on short- and long-time scales in AGN, with a power spectral density (PSD) that can show a break at frequencies below the well-known red-noise process. This break frequency in the PSD has been observed in X-rays to scale with the accretion regime and the mass of the central black hole. It is expected that a break could also be seen in the very-high-energy gamma rays, but constraining the shape of the PSD in these wavelengths has not been possible with the current instruments. The Cherenkov Telescope Array (CTA) will be more sensitive by a factor of five to ten depending on energy than the current generation of imaging atmospheric Cherenkov telescopes, therefore it will be possible with CTA to reconstruct the PSD with a high accuracy, bringing new information about AGN variability. In this work, we focus on the AGN long-term monitoring program planned with CTA. The program is proposed to begin with early-start observing campaigns with CTA precursors. This would allow us to probe longer time scales on the AGN PSD.
Keywords: Cherenkov Telescope Array, CTA, dark matter, standard model, dwarf spheroidal galaxies
Published in RUNG: 26.09.2023; Views: 1010; Downloads: 7
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8.
Pybkgmodel - a background modelling toolbox for the CTA
Marcel C. Strzys, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, published scientific conference contribution

Abstract: Despite the advancement in background rejection techniques, observation of the very-high-energy gamma-ray sky by imaging atmospheric Cherenkov telescopes (IACTs) are subject to an irreducible background from gamma-like hadron- or electron-induced air showers. The determination of this residual background is crucial for accurate spectral and spatial measurements. The Cherenkov Telescope Array (CTA) will become the next generation of IACTs. To unveil its full potential, the improved reconstruction performance of CTA needs to be coupled with a reliable background estimate across the entire field of view. This may become especially important in the case of the planned surveys of large areas of the sky. In this contribution we will present pybkgmodel, an open-source python software package de-veloped for CTA. It aims at providing in a consistent way the various background modelling methods, based on the experience from current IACTs such as H.E.S.S, MAGIC, and VERITAS. It is designed as a toolbox allowing a user to easily choose the optimal reconstruction approach for various target regions or a combination of several algorithms. We will introduce the design of the package as well as demonstrate its functionality using data for the CTA Large-Sized Telescope prototype (LST-1).
Keywords: Cherenkov Telescope Array, CTA, dark matter, standard model, dwarf spheroidal galaxies
Published in RUNG: 26.09.2023; Views: 901; Downloads: 6
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9.
Dark matter searches in dwarf spheroidal galaxies with the Cherenkov Telescope Array
F. G. Saturni, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, published scientific conference contribution

Abstract: Dark matter (DM) is one of the major components in the Universe. However, at present its existence is still only inferred through indirect astronomical observations. DM particles can annihilate or decay, producing final-state Standard Model pairs that subsequently annihilate into high-energy �-rays. The dwarf spheroidal galaxies (dSphs) in the Milky Way DM halo have long been considered optimal targets to search for annihilating DM signatures in GeV-to-TeV �-ray spectra due to their high DM densities (hence high astrophysical factors), as well as the expected absence of intrinsic �-ray emission of astrophysical origin. For such targets, it is important to compute the amount of DM in their halos in a consistent way to optimize the �-ray data analysis. Such estimates directly affect the observability of DM signals in dSphs, as well as the DM constraints that can be derived in case of null detection. In this contribution, we present the results on the sensitivity of the Cherenkov Telescope Array (CTA) for DM annihilation and decay searches using planned observations of the Milky Way dSphs. We select the most promising targets among all presently known dwarf satellites, providing new determinations of their expected DM signal. This study shows an improvement of approximately an order of magnitude in sensitivity compared to current searches in similar targets. We also discuss the results in terms of cuspy and cored DM models, and investigate the sensitivity obtained by the combination of observations from different dSphs. Finally, we explore the optimal strategies for CTA observations of dSphs.
Keywords: Cherenkov Telescope Array, CTA, dark matter, standard model, dwarf spheroidal galaxies
Published in RUNG: 26.09.2023; Views: 953; Downloads: 4
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10.
Sensitivity to keV-MeV dark matter from cosmic-ray scattering with current and the upcoming ground-based arrays CTA and SWGO
Igor Reis, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, published scientific conference contribution

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: 867; Downloads: 6
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