[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125337-en":3,"doc-seo-125337-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},125337,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning-based analyses using surface detector data of the Pierre Auger Observatory - Conference paper","Machine learning-based algorithms are presented to infer high-level extensive air shower observables and ultimately the mass composition of ultra-high-energy cosmic rays. The Pierre Auger Observatory’s surface detectors record complex spatio-temporal shower footprints with near-continuous uptime, enabling data-driven reconstruction where analytical approaches struggle. Focus is placed on using footprints measured by Water-Cherenkov and Surface Scintillator detectors to determine mass-sensitive observables such as shower-maximum depth and muon-related quantities, with results evaluated on simulations and improved reconstruction performance on measured data.","Machine learning-based analyses using surface detector data of the Pierre Auger Observatory  \nSteffen Hahn0􀂖􀀃 for the Pierre Auger Collaboration1  \n0 Karlsruhe Institute of Technology -Institute for Astroparticle Physics, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany  \n1 Full author list: [http://www.auger.org/archive/authors_2024_11.html](http://www.auger.org/archive/authors_2024_11.html)  \nE-mail: [spokesperson@auger.org](spokesperson@auger.org)  \nThe 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 WCDsand 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.  \n7th International Symposium on Ultra High Energy Cosmic Rays (UHECR2024)  \n17.– 21. November 2024  \nMalargüe, Mendoza, Argentina  \n􀀃Speaker  \n© Copyright owned by the author(s) under the terms of the Creative Commons  \nAttribution-NonCommercial-NoDerivatives 4 .0 International License (CC BY-NC-ND 4 .0) .  \nAll rights for text and data mining, AI training, and similar technologies for commercial purposes, are reserved.  \nISSN 1824-8039. Published by SISSA Medialab. [https://pos.sissa.it/](https://pos.sissa.it/)  \nPoS(UH ECR2024)091  \n1. Introduction  \nCosmic rays above an energy of 1 EeV are commonly referred to as ultra-high-energy cosmic rays (UHECRs) . Due to their low flux [1], the direct detection of UHECRs is not feasible. However, when UHECRs interact with the atmosphere, they produce a cascade of secondary particles commonly referred to as extensive air showers (EAS) . The Pierre Auger Observatory is the largest detector for the study of extensive air showers induced by UHECRs. It is located in Argentina, in the Province of Mendoza, and is designed to simultaneously detect EAS using a hybrid technique. One of the main scientific goals of the Pierre Auger Observatory is to understand the physics behind the sources of UHECRs. Having orders of magnitude higher energy than any particle accelerated by human-made devices, the origin of UHECRs must be attributed to the most extreme processes in the Universe. To gain insight into these processes and to potentially identify them, an important piece of information is the mass composition of UHECRs. Analyses in this regime are based on so-called ‘mass-sensitive observables’ (MSOs) . Essentially, MSOs are properties of EAS that st","cbCair6HZB1eHz6K","https://ap.wps.com/l/cbCair6HZB1eHz6K","pdf",1553110,1,"English","en",105,"# Introduction\n## Ultra-high-energy cosmic rays and extensive air showers\n## Mass-sensitive observables and shower maximum\n## Surface detector footprints and AugerPrime detectors\n## Motivation for data-driven machine learning approaches","[{\"question\":\"Why is determining UHECR mass composition difficult?\",\"answer\":\"Ultra-high-energy cosmic rays cannot be measured directly, so their mass must be inferred indirectly from shower observables. This inference is non-trivial and requires statistically mass-sensitive information.\"},{\"question\":\"What role do surface detector footprints play in the analyses?\",\"answer\":\"The surface detector records the shower footprint at ground level, providing spatio-temporal information. This footprint is complex but highly informative, and it can be exploited using machine learning to improve reconstruction.\"},{\"question\":\"Which detectors’ footprint data are emphasized?\",\"answer\":\"The analyses particularly focus on footprints detected by the Water-Cherenkov detectors and the Surface Scintillator Detectors of the Surface Detector system.\"}]","Machine learning-based analyses using surface detector data of the Pierre Auger Observatory - Conference paper | PDF",1785898261,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-based-analyses-using-surface-detector-data-of-the-pierre-auger-observatory-conference-paper","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-based-analyses-using-surface-detector-data-of-the-pierre-auger-observatory-conference-paper/125337/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is determining UHECR mass composition difficult?","Question",{"text":74,"@type":75},"Ultra-high-energy cosmic rays cannot be measured directly, so their mass must be inferred indirectly from shower observables. This inference is non-trivial and requires statistically mass-sensitive information.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What role do surface detector footprints play in the analyses?",{"text":79,"@type":75},"The surface detector records the shower footprint at ground level, providing spatio-temporal information. This footprint is complex but highly informative, and it can be exploited using machine learning to improve reconstruction.",{"name":81,"@type":72,"acceptedAnswer":82},"Which detectors’ footprint data are emphasized?",{"text":83,"@type":75},"The analyses particularly focus on footprints detected by the Water-Cherenkov detectors and the Surface Scintillator Detectors of the Surface Detector system.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]