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1.
Machine learning-based analyses using surface detector data of the Pierre Auger Observatory
Steffen Hahn, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2025, objavljeni znanstveni prispevek na konferenci

Opis: The Pierre Auger Observatory is the largest detector for the study of extensive air showers induced by ultra-high-energy cosmic rays (UHECRs). Its hybrid detector design allows the simultaneous observation of different parts of the shower evolution using various detection techniques. To accurately understand the physics behind the origin of UHECRs, it is essential to determine their mass composition. However, since UHECRs cannot be measured directly, estimating their masses is highly non-trivial. The most common approach is to analyze mass-sensitive observables, such as the number of secondary muons and the atmospheric depth of the shower maximum. An intriguing part of the shower to estimate these observables is its footprint. The shower footprint is detected by ground-based detectors, such as the Water-Cherenkov detectors (WCDs) of the Surface Detector (SD) of the Observatory, which have an uptime of nearly 100%, resulting in a high number of observed events. However, the spatio-temporal information stored in the shower footprints is highly complex, making it very challenging to analyze the footprints using analytical and phenomenological methods. Therefore, the Pierre Auger Collaboration utilizes machine learning-based algorithms to complement classical methods in order to exploit the measured data with unprecedented precision. In this contribution, we highlight these machine learning-based analyses used to determine high-level shower observables that help to infer the mass of the primary particle, with a particular focus on analyses using the shower footprint detected by the WCDs and the Surface Scintillator Detectors (SSD) of the SD. We show that these novel methods show promising results on simulations and offer improved reconstruction performance when applied to measured data.
Ključne besede: ultra-high-energy cosmic rays (UHECRs), extensive air showers, Pierre Auger Observatory, surface detector, Water-Cherenkov detectors (WCDs), Surface Scintillator Detectors (SSDs), UHECR mass composition, air-shower footprint, machine learning
Objavljeno v RUNG: 16.05.2025; Ogledov: 396; Prenosov: 6
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2.
Reconstruction of muon number of air showers with the surface detector of the Pierre Auger Observatory using neural networks
Steffen Traugott Hahn, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2023, objavljeni znanstveni prispevek na konferenci

Opis: To understand the physics of cosmic rays at the highest energies, it is mandatory to have an accurate knowledge of their mass composition. Since the mass of the primary particles cannot be measured directly, we have to rely on the analysis of mass-sensitive observables to gain insights into this composition. A promising observable for this purpose is the number of muons at the ground relative to that of an air shower induced by a proton primary of the same energy and inclination angle, commonly referred to as the relative muon number �μ. Due to the complexity of shower footprints, the extraction of �μ from measurements is a challenging task and intractable to solve using analytic approaches. We, therefore, reconstruct �μ by exploiting the spatial and temporal information of the signals induced by shower particles using neural networks. Using this data-driven approach permits us to tackle this task without the need of modeling the underlying physics and, simultaneously, gives us insights into the feasibility of such an approach. In this contribution, we summarize the progress of the deep-learning-based approach to estimate �μ using simulated surface detector data of the Pierre Auger Observatory. Instead of using single architecture, we present different network designs verifying that they reach similar results. Moreover, we demonstrate the potential for estimating �μ using the scintillator surface detector of the AugerPrime upgrade.
Ključne besede: ultra-high energy cosmic rays, Pierre Auger Observatory, AugerPrime, surface detector
Objavljeno v RUNG: 23.01.2024; Ogledov: 2494; Prenosov: 32
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