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Abstract: Galaxy clusters are expected to be both dark matter (DM) reservoirs and storage rooms for the cosmic-ray protons (CRp) that accumulate along the cluster’s formation history. Accordingly, they are excellent targets to search for signals of DM annihilation and decay at γ-ray energies and are predicted to be sources of large-scale γ-ray emission due to hadronic interactions in the intracluster medium (ICM). In this paper, we estimate the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse γ-ray emission from the Perseus galaxy cluster. We first perform a detailed spatial and spectral modelling of the expected signal for both the DM and the CRp components. For each case, we compute the expected CTA sensitivity accounting for the CTA instrument response functions. The CTA observing strategy of the Perseus cluster is also discussed. In the absence of a diffuse signal (non-detection), CTA should constrain the CRp to thermal energy ratio X500 within the characteristic radius R500 down to about X500 < 0.003, for a spatial CRp distribution that follows the thermal gas and a CRp spectral index αCRp = 2.3. Under the optimistic assumption of a pure hadronic origin of the Perseus radio mini-halo and depending on the assumed magnetic field profile, CTA should measure αCRp down to about ∆αCRp ≃ 0.1 and the CRp spatial distribution with 10% precision, respectively. Regarding DM, CTA should improve the current ground-based γ-ray DM limits from clusters observations on the velocity- averaged annihilation cross-section by a factor of up to ∼ 5, depending on the modelling of DM halo substructure. In the case of decay of DM particles, CTA will explore a new region of the parameter space, reaching models with τχ > 10[sup]27 s for DM masses above 1 TeV. These constraints will provide unprecedented sensitivity to the physics of both CRp acceleration and transport at cluster scale and to TeV DM particle models, especially in the decay scenario.
Keywords: cosmic ray experiments, dark matter experiments, galaxy clusters, gamma ray experiments, very-high energy gamma rays, Cherenkov Telescope Array Observatory, Perseus galaxy cluster
Published in RUNG: 09.10.2024; Views: 601; Downloads: 1
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Abstract: Dark matter (DM), known to be a dominant matter component in the Universe, has been searched for extensively, yet remains undetected. One of the promising avenues of detecting a DM signal is to observe the so called ’DM sub-halos’ within our galaxy. These sub-halos, which are numerous within the Milky Way, are formed by the clustering of DM, as predicted by cosmological simulations, and most of them lack baryonic matter counterparts, making them challenging to detect. How- ever, the annihilation or decay of Weakly Interacting Massive Particles (WIMPs), a leading candidate for DM, within these sub-halos is expected to produce very high-energy (VHE) photons (called gamma-rays) at TeV energies, offering possible indirect DM detection. In this thesis, we focus on the Galactic Plane Survey (GPS) of the Cherenkov Tele- scope Array Observatory (CTAO), an upcoming ground-based gamma-ray obser- vatory, which promises unprecedented sensitivity and resolution in the detection of cosmic gamma-ray sources in the ∼ 30 GeV to ∼ 100 TeV energy range. As dark sub-halos are expected to appear as unidentified (point) sources in the CTAO GPS data, we employ a machine learning (ML)-based approach, the AutoSour- ceID framework, leveraging U-shaped networks (U-Nets) and Laplacian of Gaus- sian (LoG) filter, for automatic source detection and localization, and apply it to simulated GPS data. We establish detection thresholds for U-Nets trained on dif- ferently scaled counts (counts, square root or log of counts) and identify which approach offers best results (in terms of flux sensitivity and location accuracy). Our findings suggest that using log-scaled counts yields a factor of 1.7 lower flux threshold compared to counts alone. In addition, we compare our ML outcomes with traditional methods; however, this comparison is not straightforward, as ML and traditional approaches fundamentally differ in their methodologies and un- derlying assumptions. Nevertheless, The flux threshold obtained using log-scaled counts is comparable to that of the traditional likelihood-based detection method implemented in the Gammapy library, although further study is needed to estab- lish a more definitive comparison. These preliminary results also suggest that the flux threshold for detecting 90% of true sources with the ML approach is approx- imately two times lower than the sensitivity reported for the GPS in the CTAO publication. Although these results are not directly comparable due to differences in methodology, they hint that ML methods may offer superior performance in certain scenarios. Furthermore, we discuss the implications of our results on the sensitivity to DM sub-halos, improving it by a factor of 4, highlighting the possi- bility of detecting at least one sub-halo with a cross section approximately ⟨σv⟩ = 2.4 × 10−23 cm3 /s.
Keywords: Cherenkov Telescope Array Observatory, dark matter, sub-halos, machine learning, gamma-rays, master's thesis
Published in RUNG: 06.09.2024; Views: 774; Downloads: 12
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Abstract: Imaging Atmospheric Cherenkov Telescope (IACT) systems are used in high-energy astrophysics to detect and study gamma-ray sources in the universe. These telescopes measure properties of cosmic gamma rays using Cherenkov radiation emitted by secondary particles produced after interacting with the Earth’s atmosphere. Atmospheric monitoring is crucial for the proper operation of IACT systems. A number of remote sensing and in situ methods are used to understand and correct the effects of the atmosphere on the propagation of Cherenkov radiation from its origin to the telescope. Failure to do so greatly decreases IACT performance, particularly, their energy resolution and threshold. Lidar systems can be used to measure atmospheric optical depth profiles, which are essential for IACT calibration. The main aim of this thesis is to demonstrate that a prototype lidar for the north-ern site of the Cherenkov Telescope Array Observatory (CTAO), which is a next-generation IACT now under construction, has hardware and software capabilities that would lead to the construction of CTAO Raman lidar. Due to specific require-ments of the CTAO, a Raman lidar, which can provide better atmospheric parameter accuracy, will be used for the first time in an IACT system. In 2021-2022 the BRL was deployed to the future CTAO-North site at the Roque de los Muchachos observatory, La Palma, for testing in its actual operating conditions. During a year and a half long test period, it underwent a number of tests, including manual and remote operation under various environmental conditions. These included hot summers, cold winters, rain, snow, ice, and strong winds, but also some more exotic events, such as desert sand intrusions (Calima) and volcanic eruptions. The latter two are presented in this work as test cases for atmospheric characterization. Since the main goal of a CTAO lidar is the optical depth measurements, the BRL does not have the full set of features for aerosol characterization. While it can yield the ˚Angstr¨om exponent and lidar ratio, it does not provide depolarization informa-tion, which is one of the key components for aerosol typing. As the atmosphere at La Palma was found to be more complex than initially anticipated, we present the benefits of including depolarization capability by using a conceptually similar Raman lidar system specifically designed for aerosol characterization. We present the results of a campaign focused on the determination of bioaerosol capabilities to act as cloud condensation nuclei and ice-nucleating particles in mixed-phase clouds, where depo-larization information was used to determine the thermodynamic phase of the cloud.
Keywords: Cherenkov Telescope Array Observatory, Raman Lidar, atmospheric optical depth, aerosol characterization, Cumbre Vieja, Calima
Published in RUNG: 23.10.2023; Views: 1920; Downloads: 51
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Abstract: The Cherenkov Telescope Array (CTA) will be the next generation ground-based very-high-energy gamma-ray observatory, constituted by tens of Imaging Atmospheric Cherenkov Telescopes at two sites once its construction and commissioning are finished. Like its predecessors, CTA relies on Instrument Response Functions (IRFs) to relate the observed and reconstructed properties to the true ones of the primary gamma-ray photons. IRFs are needed for the proper reconstruction of spectral and spatial information of the observed sources and are thus among the data products issued to the observatory users. They are derived from Monte Carlo simulations, depend on observation conditions like the telescope pointing direction or the atmospheric transparency and can evolve with time as hardware ages or is replaced. Producing a complete set of IRFs from simulations for every observation taken is a time-consuming task and not feasible when releasing data products on short timescales. Consequently, interpolation techniques on simulated IRFs are investigated to quickly estimate IRFs for specific observation conditions. However, as some of the IRFs constituents are given as probability distributions, specialized methods are needed. This contribution summarizes and compares the feasibility of multiple approaches to interpolate IRF components in the context of the pyirf python software package and IRFs simulated for the Large-Sized Telescope prototype (LST-1). We will also give an overview of the current functionalities implemented in pyirf.
Keywords: Cherenkov Telescope Array, CTA, ground-based very-high-energy gamma-ray observatory, pyirf
Published in RUNG: 26.09.2023; Views: 1935; Downloads: 5
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Abstract: The upcoming Cherenkov Telescope Array (CTA) ground-based gamma-ray observatory will open up our view of the very high energy Universe, o˙ering an improvement in sensitivity of 5-10 times that of previous experiments. NectarCAM is one of the proposed cameras for the Medium-Sized Telescopes (MST) which have been designed to cover the core energy range of CTA, from 100 GeV to 10 TeV. The final camera will be capable of GHz sampling and provide a field of view of 8 degrees with its 265 modules of 7 photomultiplier each (for a total of 1855 pixels). In order to validate the performance of NectarCAM, a partially-equipped prototype has been constructed consisting of only the inner 61-modules. It has so far undergone testing at the integration test-bench facility in CEA Paris-Saclay (France) and on a prototype of the MST structure in Adlershof (Germany). To characterize the performance of the prototype, Monte Carlo simulations were conducted using a detailed model of the 61 module camera in the CORSIKA/sim_telarray framework. This contribution provides an overview of this work including the comparison of trigger and readout performance on test-bench data and trigger and image parameterization performance during on-sky measurements.
Keywords: Cherenkov Telescope Array, ground-based gamma-ray observatory, NectarCAM, Medium-Sized Telescopes
Published in RUNG: 18.09.2023; Views: 1580; Downloads: 5
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