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Reconstruction of muon number of air showers with the surface detector of the Pierre Auger Observatory using neural networks

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Date
2024
Author
Hahn S.T.; Abdul Halim A.; Abreu P.; Aglietta M.; Allekotte I.; Almeida Cheminant K.; Almela A.; Aloisio R.; Alvarez-Muñiz J.; Ammerman Yebra J.; Anastasi G.A.; Anchordoqui L.; Andrada B.; Andringa S.; Aramo C.; Araújo Ferreira P.R.; Arnone E.; Arteaga Velázquez J.C.; Asorey H.; Assis P.; Avila G.; Avocone E.; Badescu A.M.; Bakalova A.; Balaceanu A.; Barbato F.; Bartz Mocellin A.; Bellido J.A.; Berat C.; Bertaina M.E.; Bhatta G.; Bianciotto M.; Biermann P.L.; Binet V.; Bismark K.; Bister T.; Biteau J.; Blazek J.; Bleve C.; Blümer J.; Boháčová M.; Boncioli D.; Bonifazi C.; Bonneau Arbeletche L.; Borodai N.; Brack J.; Brichetto Orchera P.G.; Briechle F.L.; Bueno A.; Buitink S.; Buscemi M.; Büsken M.; Bwembya A.; Caballero-Mora K.S.; Cabana-Freire S.; Caccianiga L.; Caracas I.; Caruso R.; Castellina A.; Catalani F.; Cataldi G.; Cazon L.; Cerda M.; Cermenati A.; Chinellato J.A.; Chudoba J.; Chytka L.; Clay R.W.; Cobos Cerutti A.C.; Colalillo R.; Coleman A.; Coluccia M.R.; Conceição R.; Condorelli A.; Consolati G.; Conte M.; Convenga F.; Correia dos Santos D.; Costa P.J.; Covault C.E.; Cristinziani M.; Cruz Sanchez C.S.; Dasso S.; Daumiller K.; Dawson B.R.; de Almeida R.M.; de Jesús J.; de Jong S.J.; de Mello Neto J.R.T.; De Mitri I.; de Oliveira J.; de Oliveira Franco D.; de Palma F.; de Souza V.; De Vito E.; Del Popolo A.; Deligny O.; Denner N.; Deval L.; di Matteo A.; Dobre M.; Dobrigkeit C.; D’Olivo J.C.; Domingues Mendes L.M.; dos Anjos J.C.; dos Anjos R.C.; Ebr J.; Ellwanger F.; Emam M.; Engel R.; Epicoco I.; Erdmann M.; Etchegoyen A.; Evoli C.; Falcke H.; Farmer J.; Farrar G.; Fauth A.C.; Fazzini N.; Feldbusch F.; Fenu F.; Fernandes A.; Fick B.; Figueira J.M.; Filipčič A.; Fitoussi T.; Flaggs B.; Fodran T.; Fujii T.; Fuster A.; Galea C.; Galelli C.; García B.; Gaudu C.; Gemmeke H.; Gesualdi F.; Gherghel-Lascu A.; Ghia P.L.; Giaccari U.; Giammarchi M.; Glombitza J.; Gobbi F.; Gollan F.; Golup G.; Gómez Berisso M.; Gómez Vitale P.F.; Gongora J.P.; González J.M.; González N.; Goos I.; Góra D.; Gorgi A.; Gottowik M.; Grubb T.D.; Guarino F.; Guedes G.P.; Guido E.; Hahn S.; Hamal P.; Hampel M.R.; Hansen P.; Harari D.; Harvey V.M.; Haungs A.; Hebbeker T.; Hojvat C.; Hörandel J.R.; Horvath P.; Hrabovský M.; Huege T.; Insolia A.; Isar P.G.; Janecek P.; Johnsen J.A.; Jurysek J.; Kääpä A.; Kampert K.H.; Keilhauer B.; Khakurdikar A.; Kizakke Covilakam V.V.; Klages H.O.; Kleifges M.; Knapp F.; Kunka N.; Lago B.L.; Langner N.; Leigui de Oliveira M.A.; Lema-Capeans Y.; Lenok V.; Letessier-Selvon A.; Lhenry-Yvon I.; Lo Presti D.; Lopes L.; Lu L.; Luce Q.; Lundquist J.P.; Machado Payeras A.; Majercakova M.; Mandat D.; Manning B.C.; Mantsch P.; Marafico S.; Mariani F.M.; Mariazzi A.G.; Mariş I.C.; Marsella G.; Martello D.; Martinelli S.; Martínez Bravo O.; Martins M.A.; Mastrodicasa M.; Mathes H.J.; Matthews J.; Matthiae G.; Mayotte E.; Mayotte S.; Mazur P.O.; Medina-Tanco G.; Meinert J.; Melo D.; Menshikov A.; Merx C.; Michal S.; Micheletti M.I.; Miramonti L.; Mollerach S.; Montanet F.; Morejon L.; Morello C.; Müller A.L.; Mulrey K.; Mussa R.; Muzio M.; Namasaka W.M.; Negi S.; Nellen L.; Nguyen K.; Nicora G.; Niculescu-Oglinzanu M.; Niechciol M.; Nitz D.; Nosek D.; Novotny V.; Nožka L.; Nucita A.; Núñez L.A.; Oliveira C.; Palatka M.; Pallotta J.; Panja S.; Parente G.; Paulsen T.; Pawlowsky J.; Pech M.; Pekala J.; Pelayo R.; Pereira L.A.S.; Pereira Martins E.E.; Perez Armand J.; Pérez Bertolli C.; Perrone L.; Petrera S.; Petrucci C.; Pierog T.; Pimenta M.; Platino M.; Pont B.; Pothast M.; Pourmohammad Shahvar M.; Privitera P.; Prouza M.; Puyleart A.; Querchfeld S.; Rautenberg J.; Ravignani D.; Reininghaus M.; Ridky J.; Riehn F.; Risse M.; Rizi V.; Rodrigues de Carvalho W.; Rodriguez E.; Rodriguez Rojo J.; Roncoroni M.J.; Rossoni S.; Roth M.; Roulet E.; Rovero A.C.; Ruehl P.; Saftoiu A.; Saharan M.; Salamida F.; Salazar H.; Salina G.; Sanabria Gomez J.D.; Sánchez F.; Santos E.M.; Santos E.; Sarazin F.; Sarmento R.; Sato R.; Savina P.; Schäfer C.M.; Scherini V.; Schieler H.; Schimassek M.; Schimp M.; Schlüter F.; Schmidt D.; Scholten O.; Schoorlemmer H.; Schovánek P.; Schröder F.G.; Schulte J.; Schulz T.; Sciutto S.J.; Scornavacche M.; Segreto A.; Sehgal S.; Shivashankara S.U.; Sigl G.; Silli G.; Sima O.; Simon F.; Smau R.; Šmída R.; Sommers P.; Soriano J.F.; Squartini R.; Stadelmaier M.; Stanca D.; Stanič S.; Stasielak J.; Stassi P.; Strähnz S.; Straub M.; Suárez-Durán M.; Suomijärvi T.; Supanitsky A.D.; Svozilikova Z.; Szadkowski Z.; Tapia A.; Taricco C.; Timmermans C.; Tkachenko O.; Tobiska P.; Todero Peixoto C.J.; Tomé B.; Torrès Z.; Travaini A.; Travnicek P.; Trimarelli C.; Tueros M.; Unger M.; Vaclavek L.; Vacula M.; Valdés Galicia J.F.; Valore L.; Varela E.; Vásquez-Ramírez A.; Veberič D.; Ventura C.; Vergara Quispe I.D.; Verzi V.; Vicha J.; Vink J.; Vlastimil J.; Vorobiov S.; Watanabe C.; Watson A.A.; Weindl A.; Wiencke L.; Wilczyński H.; Wittkowski D.; Wundheiler B.; Yue B.; Yushkov A.; Zapparrata O.; Zas E.; Zavrtanik D.; Zavrtanik M.

Citación

       
TY - GEN T1 - Reconstruction of muon number of air showers with the surface detector of the Pierre Auger Observatory using neural networks Y1 - 2024 UR - http://hdl.handle.net/11407/8873 PB - et al.; Institute for Cosmic Ray Research (ICRR) Univeristy of Tokyo; International Union of Pure and Applied Physics (IUPAP); JPS; Nagoya Convention and Visitors Bureau; Nagoya University AB - 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 Rµ. Due to the complexity of shower footprints, the extraction of Rµ from measurements is a challenging task and intractable to solve using analytic approaches. We, therefore, reconstruct Rµ 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 Rµ 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 Rµ using the scintillator surface detector of the AugerPrime upgrade. © Copyright owned by the author(s) under the terms of the Creative Commons. ER - @misc{11407_8873, author = {}, title = {Reconstruction of muon number of air showers with the surface detector of the Pierre Auger Observatory using neural networks}, year = {2024}, abstract = {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 Rµ. Due to the complexity of shower footprints, the extraction of Rµ from measurements is a challenging task and intractable to solve using analytic approaches. We, therefore, reconstruct Rµ 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 Rµ 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 Rµ using the scintillator surface detector of the AugerPrime upgrade. © Copyright owned by the author(s) under the terms of the Creative Commons.}, url = {http://hdl.handle.net/11407/8873} }RT Generic T1 Reconstruction of muon number of air showers with the surface detector of the Pierre Auger Observatory using neural networks YR 2024 LK http://hdl.handle.net/11407/8873 PB et al.; Institute for Cosmic Ray Research (ICRR) Univeristy of Tokyo; International Union of Pure and Applied Physics (IUPAP); JPS; Nagoya Convention and Visitors Bureau; Nagoya University AB 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 Rµ. Due to the complexity of shower footprints, the extraction of Rµ from measurements is a challenging task and intractable to solve using analytic approaches. We, therefore, reconstruct Rµ 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 Rµ 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 Rµ using the scintillator surface detector of the AugerPrime upgrade. © Copyright owned by the author(s) under the terms of the Creative Commons. OL Spanish (121)
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Abstract
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 Rµ. Due to the complexity of shower footprints, the extraction of Rµ from measurements is a challenging task and intractable to solve using analytic approaches. We, therefore, reconstruct Rµ 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 Rµ 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 Rµ using the scintillator surface detector of the AugerPrime upgrade. © Copyright owned by the author(s) under the terms of the Creative Commons.
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http://hdl.handle.net/11407/8873
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