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Abstract: The Pierre Auger Observatory, located near the town Malargüe in the province of Mendoza, Argentina, is the largest cosmic-ray detector in existence, covering an area of 3000 sq. km. The upgraded Observatory, in Phase II of operations, consists of a surface array of 1660 stations combining water Cherenkov, scintillator, and radio detectors. A subset of stations also includes underground muon detectors. Additionally, fluorescence detectors located at four sites overlook the array. The science goals for the enhanced Observatory include the measurement of the properties of ultra-high-energy cosmic rays with large statistics and high sensitivity to the primary composition. The Observatory is also sensitive to photons and neutrinos at the highest energies, allowing it to participate in multi-messenger studies. The Auger Offline Framework provides the tools to perform detailed simulations, using the Geant 4 toolkit, of all components of the Observatory and the analysis of both data and simulated events. It proved to have the flexibility needed to evolve during the lifetime of the Observatory, to accommodate new sub-detectors and, recently, changes to the station readout electronics. A new challenge is interfacing the framework with Machine Learning tools for both the development and execution of neural-network-based algorithms. Independent of the framework, CORSIKA 7 is used to simulate particles, fluorescence light, and radio signals produced by air showers. The production of simulations is coordinated centrally to provide standard libraries for analyses and to optimize the use of computing resources. We will describe the evolution and status of the Offline Framework and the tools used to coordinate the simulation efforts. We will also discuss the challenges of the massive simulation efforts and the resources consumed to provide the simulation libraries required by the Collaboration.
Keywords: ultra-high-energy cosmic rays, Pierre Auger Observatory, air-shower simulations, extensive air showers, detector simulations
Published in RUNG: 29.05.2025; Views: 392; Downloads: 6
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Abstract: 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.
Keywords: 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
Published in RUNG: 16.05.2025; Views: 390; Downloads: 6
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Abstract: As part of the upgrade of the Pierre Auger Observatory, known as AugerPrime, the Underground Muon Detector is being deployed in the low-energy extension of the Surface Detector. It comprises an array of 30 m[sup]2 plastic scintillator muon counters, buried 2.3 meters underground near the water-Cherenkov detectors, allowing for direct measurement of the muonic component of air showers in the energy range of 10[sup]16.5 − 10[sup]19 eV. To achieve an extended dynamic range, the detector operates in two modes: the binary mode, which is optimized for low muon densities, and the ADC mode, designed for high muon densities. In this contribution, we present the latest improvements to the calibration procedure of the ADC mode and to the data reconstruction of the binary mode. We assess their performance with simulations.
Keywords: ultra-high-energy cosmic rays (UHECRs), extensive air showers, Pierre Auger Observatory, AugerPrime upgrade, Auger underground muon detector (UMD), muonic air-shower component, detector calibration, data reconstruction
Published in RUNG: 30.04.2025; Views: 662; Downloads: 8
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Abstract: The depth of the maximum of an air shower development, Xmax, as observed with fluorescence telescopes, is among the most sensitive observables for studying the interaction characteristics and primary composition of ultra-high-energy cosmic rays. However, precise measurement of the interaction cross section remains challenging, as standard analyses often rely on assumptions about the composition, which are closely tied to the validity of specific hadronic interaction models. In this work, we discuss a method for the simultaneous estimation of the proton-proton interaction cross section and primary mass composition, addressing the limitations of separate measurements. The inclusion of the Xmax scale into the fit further accounts for systematic uncertainties in the data and theoretical uncertainties in particle production. The performance of the method is evaluated using simulations that include detector responses under realistic conditions and with a particular focus on assessing the systematic uncertainties of the fit.
Keywords: ultra-high-energy cosmic rays, Pierre Auger Observatory, extensive air showers, air shower maximum depth
Published in RUNG: 28.03.2025; Views: 598; Downloads: 11
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Abstract: We report an investigation of the mass composition of cosmic rays with energies from 3 to 100 EeV (1 EeV = 10[sup]18 eV) using the distributions of the depth of shower maximum Xmax. The analysis relies on ∼50,000 events recorded by the surface detector of the Pierre Auger Observatory and a deep-learning-based reconstruction algorithm. Above energies of 5 EeV, the dataset offers a 10-fold increase in statistics with respect to fluorescence measurements at the Observatory. After cross-calibration using the fluorescence detector, this enables the first measurement of the evolution of the mean and the standard deviation of the Xmax distributions up to 100 EeV. Our findings are threefold: (i) The evolution of the mean logarithmic mass toward a heavier composition with increasing energy can be confirmed and is extended to 100 EeV. (ii) The evolution of the fluctuations of Xmax toward a heavier and purer composition with increasing energy can be confirmed with high statistics. We report a rather heavy composition and small fluctuations in Xmax at the highest energies. (iii) We find indications for a characteristic structure beyond a constant change in the mean logarithmic mass, featuring three breaks that are observed in proximity to the ankle, instep, and suppression features in the energy spectrum.
Keywords: ultra-high-energy cosmic rays, UHECRs, extensive air showers, Pierre Auger Observatory, UHECR mass composition, depth of shower maximum, fluorescence detector, surface detector, deep learning
Published in RUNG: 20.01.2025; Views: 948; Downloads: 8
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