Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory

Aab, A.A.AabAbreu, P.P.AbreuAGLIETTA, MARCOMARCOAGLIETTAAlbury, J. M.J. M.AlburyAllekotte, I.I.AllekotteAlmela, A.A.AlmelaAlvarez-Muñiz, J.J.Alvarez-MuñizAlves Batista, R.R.Alves BatistaAnastasi, G. A.G. A.AnastasiAnchordoqui, L.L.AnchordoquiAndrada, B.B.AndradaAndringa, S.S.AndringaAramo, C.C.AramoAraújo Ferreira, P. R.P. R.Araújo FerreiraArteaga Velázquez, J. C.J. C.Arteaga VelázquezAsorey, H.H.AsoreyAssis, P.P.AssisAvila, G.G.AvilaBadescu, A. M.A. M.BadescuBakalova, A.A.BakalovaBalaceanu, A.A.BalaceanuBarbato, F.F.BarbatoBarreira Luz, R. J.R. J.Barreira LuzBecker, K. H.K. H.BeckerBellido, J. A.J. A.BellidoBerat, C.C.BeratBertaina, M. E.M. E.BertainaBertou, X.X.BertouBiermann, P. L.P. L.BiermannBister, T.T.BisterBiteau, J.J.BiteauBlazek, J.J.BlazekBleve, C.C.BleveBoháčová, M.M.BoháčováBoncioli, D.D.BoncioliBonifazi, C.C.BonifaziBonneau Arbeletche, L.L.Bonneau ArbeletcheBorodai, N.N.BorodaiBotti, A. M.A. M.BottiBrack, J.J.BrackBretz, T.T.BretzBrichetto Orchera, P. G.P. 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O.KlagesKleifges, M.M.KleifgesKleinfeller, J.J.KleinfellerKöpke, M.M.KöpkeKunka, N.N.KunkaLago, B. L.B. L.LagoLang, R. G.R. G.LangLangner, N.N.LangnerLeigui de Oliveira, M. A.M. A.Leigui de OliveiraLenok, V.V.LenokLetessier-Selvon, A.A.Letessier-SelvonLhenry-Yvon, I.I.Lhenry-YvonLo Presti, D.D.Lo PrestiLopes, L.L.LopesLópez, R.R.LópezLu, L.L.LuLuce, Q.Q.LuceLucero, A.A.LuceroLundquist, J. P.J. P.LundquistMachado Payeras, A.A.Machado PayerasMancarella, G.G.MancarellaMandat, D.D.MandatManning, B. C.B. C.ManningManshanden, J.J.ManshandenMantsch, P.P.MantschMarafico, S.S.MaraficoMariazzi, A. G.A. G.MariazziMariş, I. C.I. C.MarişMarsella, G.G.MarsellaMartello, D.D.MartelloMartinez, H.H.MartinezMartínez Bravo, O.O.Martínez BravoMastrodicasa, M.M.MastrodicasaMathes, H. J.H. J.MathesMatthews, J.J.MatthewsMatthiae, G.G.MatthiaeMayotte, E.E.MayotteMazur, P. O.P. 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M.SchäferScherini, V.V.ScheriniSchieler, H.H.SchielerSchimassek, M.M.SchimassekSchimp, M.M.SchimpSchlüter, F.F.SchlüterSchmidt, D.D.SchmidtScholten, O.O.ScholtenSchovánek, P.P.SchovánekSchröder, F. G.F. G.SchröderSchröder, S.S.SchröderSchulte, J.J.SchulteSciutto, S. J.S. J.SciuttoScornavacche, M.M.ScornavaccheSEGRETO, ALBERTOALBERTOSEGRETOSehgal, S.S.SehgalShellard, R. C.R. C.ShellardSigl, G.G.SiglSilli, G.G.SilliSima, O.O.SimaŠmída, R.R.ŠmídaSommers, P.P.SommersSoriano, J. F.J. F.SorianoSouchard, J.J.SouchardSquartini, R.R.SquartiniStadelmaier, M.M.StadelmaierStanca, D.D.StancaStanič, S.S.StaničStasielak, J.J.StasielakStassi, P.P.StassiStreich, A.A.StreichSuárez-Durán, M.M.Suárez-DuránSudholz, T.T.SudholzSuomijärvi, T.T.SuomijärviSupanitsky, A. D.A. D.SupanitskyŠupík, J.J.ŠupíkSzadkowski, Z.Z.SzadkowskiTaboada, A.A.TaboadaTapia, A.A.TapiaTaricco, C.C.TariccoTimmermans, C.C.TimmermansTkachenko, O.O.TkachenkoTobiska, P.P.TobiskaTodero Peixoto, C. J.C. J.Todero PeixotoTomé, B.B.ToméTravaini, A.A.TravainiTravnicek, P.P.TravnicekTrimarelli, C.C.TrimarelliTrini, M.M.TriniTueros, M.M.TuerosUlrich, R.R.UlrichUnger, M.M.UngerVaclavek, L.L.VaclavekVacula, M.M.VaculaValdés Galicia, J. F.J. F.Valdés GaliciaValore, L.L.ValoreVarela, E.E.VarelaVarma K. C., V.V.Varma K. C.Vásquez-Ramírez, A.A.Vásquez-RamírezVeberič, D.D.VeberičVentura, C.C.VenturaVergara Quispe, I. D.I. D.Vergara QuispeVerzi, V.V.VerziVicha, J.J.VichaVink, J.J.VinkVorobiov, S.S.VorobiovWahlberg, H.H.WahlbergWatanabe, C.C.WatanabeWatson, A. A.A. A.WatsonWeber, M.M.WeberWeindl, A.A.WeindlWiencke, L.L.WienckeWilczyński, H.H.WilczyńskiWinchen, T.T.WinchenWirtz, M.M.WirtzWittkowski, D.D.WittkowskiWundheiler, B.B.WundheilerYushkov, A.A.YushkovZapparrata, O.O.ZapparrataZas, E.E.ZasZavrtanik, D.D.ZavrtanikZavrtanik, M.M.ZavrtanikZehrer, L.L.ZehrerZepeda, A.A.ZepedaThe Pierre Auger collaboration2025-02-262025-02-2620211748-0221http://hdl.handle.net/20.500.12386/36267The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2 × 1019 eV.ELETTRONICOenDeep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger ObservatoryArticle10.1088/1748-0221/16/07/P070192-s2.0-85111106773https://iopscience.iop.org/article/10.1088/1748-0221/16/07/P07019https://api.elsevier.com/content/abstract/scopus_id/851111067732021JInst..16P7019AFIS/05 - ASTRONOMIA E ASTROFISICA