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11.
Telescope Array 10-Year Monocular Spectrum Measurement
D. Bergman, Jon Paul Lundquist, 2022, published scientific conference contribution

Abstract: Telescope Array (TA) is the largest cosmic ray detector in the northern hemisphere. We present a measurement of the cosmic ray energy spectrum for energies above 10^17.5 eV using fluorescence telescopes in monocular mode. A novel weather classification scheme using machine learning was used to select data parts with good weather to ensure the quality of the fluorescence data. The data from the Black Rock Mesa (BR) and Long Ridge (LR) fluorescence telescope stations were analysed separately in monocular mode, with the calculated fluxes combined into a single spectrum. The 10-year monocular combined cosmic ray energy spectrum is in excellent agreement with previous measurements from the northern hemisphere. We present fits of the combined spectrum to a series of broken power law models. A thrice-broken power law is observed to be the best fit considering the Poisson deviance per degrees of freedom. The three breaks suggest an additional feature of the spectrum between the previously observed Ankle at 10^18.7 eV and the GZK suppression at 10^19.8 eV.
Keywords: Telescope Array, indirect detection, fluorescence detection, ultra-high energy, cosmic rays, energy spectrum, composition, machine learning, weather classification
Published in RUNG: 02.10.2023; Views: 572; Downloads: 5
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12.
Event-by-event reconstruction of the shower maximum Xmax with the Surface Detector of the Pierre Auger Observatory using deep learning
J. Glombitza, Andrej Filipčič, Jon Paul Lundquist, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2022, published scientific conference contribution

Abstract: The measurement of the mass composition of ultra-high energy cosmic rays constitutes a prime challenge in astroparticle physics. Most detailed information on the composition can be obtained from measurements of the depth of maximum of air showers, Xmax, with the use of fluorescence telescopes, which can be operated only during clear and moonless nights. Using deep neural networks, it is now possible for the first time to perform an event-by-event reconstruction of Xmax with the Surface Detector (SD) of the Pierre Auger Observatory. Therefore, previously recorded data can be analyzed for information on Xmax, and thus, the cosmic-ray composition. Since the SD operates with a duty cycle of almost 100% and its event selection is less strict than for the Fluorescence Detector (FD), the gain in statistics with respect to the FD is almost a factor of 15 for energies above 10^19.5 eV. In this contribution, we introduce the neural network particularly designed for the SD of the Pierre Auger Observatory. We evaluate its performance using three different hadronic interaction models, verify its functionality using Auger hybrid measurements, and find that the method can extract mass information on an event level.
Keywords: Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, neural network, machine learning
Published in RUNG: 29.09.2023; Views: 593; Downloads: 5
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13.
Mass composition of Telescope Array's surface detectors events using deep learning
I. Kharuk, Jon Paul Lundquist, 2022, published scientific conference contribution

Abstract: We report on an improvement of deep learning techniques used for identifying primary particles of atmospheric air showers. The progress was achieved by using two neural networks. The first works as a classifier for individual events, while the second predicts fractions of elements in an ensemble of events based on the inference of the first network. For a fixed hadronic model, this approach yields an accuracy of 90% in identifying fractions of elements in an ensemble of events.
Keywords: Telescope Array, indirect detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, deep learning, machine learning, neural networks
Published in RUNG: 29.09.2023; Views: 649; Downloads: 4
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14.
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: 527; Downloads: 6
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15.
Detecting and characterizing pulsar halos with the Cherenkov Telescope Array Observatory
Christopher 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: 525; Downloads: 7
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16.
Performance update of an event-type based analysis for the Cherenkov Telescope Array
J. 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: 539; Downloads: 6
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Narratives need not end well; nor say it all
Sara Andreetta, Davide Spalla, Alessandro Treves, 2023, short scientific article

Abstract: To fully embrace situations of radical uncertainty, we argue that the theory should abandon the requirements that narratives, in general, must lead to affective evaluation, and that they have to explain (and potentially simulate) all or even the bulk of the current decisional context. Evidence from studies of incidental learning show that narrative schemata can bias decisions while remaining fragmentary, insufficient for prediction, and devoid of utility values.
Keywords: conviction narrative theory, Hebbian learning, schemata, memory, neuroscience
Published in RUNG: 10.05.2023; Views: 907; Downloads: 2
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