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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: 428; Downloads: 6
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Abstract: Since its full commissioning in 2008, the Pierre Auger Observatory has consistently demonstrated its scientific productivity. A major upgrade of the Surface Detector array (SD) improves the capabilities of measuring the different components of extensive air showers. One of the elements of the upgrade consists of new Scintillator Surface Detectors (SSD) placed on top of the Water-Cherenkov stations of the SD. At the Observatory, the integration of the SSD components and their deployment in the array is well advanced. In this paper, the main challenges and characteristics of the construction and installation will be reviewed. Started in 2016, an Engineering Array of twelve upgraded stations has been taking data in the field. In March 2019, a preproduction array of 77 SSDs started data acquisition with an adapted version of non-upgraded electronics. It is collecting events and proving the goodness of SSD design. Since December 2020, the upgraded electronics boards are being deployed in the field together with the photomultiplier tubes, increasing the number of SSD detectors, which are taking data continuosly with good stability. In this paper, the-long term performance of a subset of stations acquiring data for more than two years will be discussed. The data collected so far demonstrate the quality of the new detectors and the physics potential of the upgrade project
Keywords: Pierre Auger Observatory, indirect detection, surface detection, ground array, scintillator surface detectors, ultra-high energy, cosmic rays
Published in RUNG: 04.10.2023; Views: 2453; Downloads: 8
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Keywords: ultra-high energy cosmic rays, Pierre Auger Observatory, AugerPrime Observatory Upgrade, Scintillator Surface Detectors (SSDs), SSD Production and Quality Control
Published in RUNG: 24.07.2020; Views: 4024; Downloads: 75
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Abstract: The data collected with the Pierre Auger Observatory have led to a number of surprising discoveries. While a strong suppression of the particle flux at the highest energies has been established unambiguously, the dominant physics processes related to this suppression could not be identified. Within the energy range covered by fluorescence detector observations with sufficient statistics, an unexpected change of the depth of maximum distribution is found. Using LHC-tuned interaction models these observations can be understood as a correlated change of the fluxes of different mass groups. On the other hand, they could also indicate a change of hadronic interactions above the energy of the ankle. Complementing the water Cherenkov detectors of the surface array with scintillator detectors will, mainly through the determination of the muonic shower component, extend the composition sensitivity of the Auger Observatory into the flux suppression region. The upgrade of the Auger Observatory will allow us to estimate the primary mass of the highest energy cosmic rays on a shower-by-shower basis. In addition to measuring the mass composition the upgrade will open the possibility to search for light primaries at the highest energies, to perform composition-selected anisotropy studies, and to search for new phenomena including unexpected changes of hadronic interactions. After introducing the physics motivation for upgrading the Auger Observatory the planned detector upgrade is presented. In the second part of the contribution the expected performance and improved physics sensitivity of the upgraded Auger Observatory are discussed.
Keywords: Pierre Auger Observatory, ultra-high energy cosmic rays, elemental composition sensitivity, Auger upgrade (AugerPrime), muonic shower component, scintillator detectors
Published in RUNG: 03.03.2016; Views: 5839; Downloads: 226
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