1. Sensitivity to keV-MeV dark matter from cosmic-ray scattering with current and the upcoming ground-based arrays CTA and SWGOIgor 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: 581; Downloads: 6
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2. Detecting and characterizing pulsar halos with the Cherenkov Telescope Array ObservatoryChristopher Eckner, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, published scientific conference contribution Abstract: The recently identified source class of pulsar halos may be populated and bright enough at TeV energies to constitute a large fraction of the sources that will be observed with the Cherenkov Telescope Array (CTA), especially in the context of the planned Galactic Plane Survey (GPS). In this study, we examine the prospects offered by CTA for the detection and characterization of such objects. CTA will cover energies from 20 GeV to 300 TeV, bridging the ranges already probed with the Fermi Large Area Telescope and High Altitude Water Cherenkov Observatory, and will also have a better angular resolution than the latter instruments, thus providing a complementary look at the phenomenon. From simple models for individual pulsar halos and their population in the Milky Way, we examine under which conditions such sources can be detected and studied from the GPS observations. In the framework of a full spatial-spectral likelihood analysis, using the most recent estimates for the instrument response function and prototypes for the science tools, we derive the spectral and morphological sensitivity of the CTA GPS to the specific intensity distribution of pulsar halos. From these, we quantify the physical parameters for which pulsar halos can be detected, identified, and characterized, and what fraction of the Galactic population could be accessible. We also discuss the effect of interstellar emission and data analysis systematics on these prospects.
Keywords: Cherenkov Telescope Array, CTA, very-high-energy gamma-ray astroparticle physics, instrument response functions, machine learning
Published in RUNG: 26.09.2023; Views: 578; Downloads: 7
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3. Performance update of an event-type based analysis for the Cherenkov Telescope ArrayJ. Bernete, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, published scientific conference contribution 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: 595; Downloads: 6
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4. The Instrument Response Function Format for the Cherenkov Telescope ArrayJohn E. Ward, Andrej Filipčič, Gašper Kukec Mezek, Samo Stanič, Serguei Vorobiov, Lili Yang, Gabrijela Zaharijas, Danilo Zavrtanik, Marko Zavrtanik, 2015, published scientific conference contribution Keywords: Cherenkov Telescope Array (CTA), public observatory, instrument response functions
Published in RUNG: 02.03.2017; Views: 3542; Downloads: 177
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