Tuning parametric models of the atmospheric muon flux in MUPAGE to data from the KM3NeT detector

• Main Authors: Fearraigh B. O., Ageron M., Aiello S., Albert A., Alshamsi M., Alves Garre S., Aly Z., Ambrosone A., Ameli F., Andre M., Androulakis G., Anghinolfi M., Anguita M., Anton G., Ardid M., Ardid S., Assal W., Aublin J., Bagatelas C., Baret B., Basegmez du Pree S., Bendahman M., Benfenati F., Berbee E., van den Berg A. M., Bertin V., Beurthey S., van Beveren V., Biagi S., Billault M., Bissinger M., Boettcher M., Bou Cabo M., Boumaaza J., Bouta M., Boutonnet C., Bouvet G., Bouwhuis M., Bozza C., Branzas H., Bruijn R., Brunner J., Bruno R., Buis E., Buompane R., Busto J., Caiffi B., Caillat L., Calvo D., Campion S., Capone A., Carduner H., Carretero V., Castaldi P., Celli S., Cereseto R., Chabab M., Champion C., Chau N., Chen A., Cherubini S., Chiarella V., Chiarusi T., Circella M., Cocimano R., Coelho J. A. B., Coleiro A., Colomer Molla M., Colonges S., Coniglione R., Cosquer A., Coyle P., Cresta M., Creusot A., Cruz A., Cuttone G., D'Amico A., Dallier R., De Martino B., De Palma M., Di Palma I., Diaz A. F., Diego-Tortosa D., Distefano C., Domi A., Donzaud C., Dornic D., Dorr M., Drouhin D., Eberl T., Eddyamoui A., van Eeden T., van Eijk D., El Bojaddaini I., Eljarrari H., Elsaesser D., Enzenhofer A., Espinosa V., Fermani P., Ferrara G., Filipovic M. D., Filippini F., Fransen J., Fusco L. A., Gajanana D., Gal T., Garcia Mendez J., Garcia Soto A., Garcon E., Garufi F., Gatius C., Geisselbrecht N., Gialanella L., Giorgio E., Gozzini S. R., Gracia R., Graf K., Grella G., Guderian D., Guidi C., Guillon B., Gutierrez M., Haefner J., Hallmann S., Hamdaoui H., van Haren H., Heijboer A., Hekalo A., Hennig L., Henry S., Hernandez-Rey J. J., Hofestadt J., Huang F., Idrissi Ibnsalih W., Ilioni A., Illuminati G., James C. W., Janezashvili D., Jansweijer P., de Jong M., de Jong P., Jung B. J., Kadler M., Kalaczynski P., Kalekin O., Katz U. F., Kayzel F., Keller P., Khan Chowdhury N. R., Kistauri G., van der Knaap F., Kooijman P., Kouchner A., Kreter M., Kulikovskiy V., Labalme M., Lagier P., Lahmann R., Lamare P., Lamoureux M., Larosa G., Lastoria C., Laurence J., Lazo A., Le Breton R., Le Guirriec E., Le Stum S., Lehaut G., Leonardi O., Leone F., Leonora E., Lerouvillois C., Lesrel J., Lessing N., Levi G., Lincetto M., Lindsey Clark M., Lipreau T., LLorens Alvarez C., Lonardo A., Longhitano F., Lopez-Coto D., Lumb N., Maderer L., Majumdar J., Manczak J., Margiotta A., Marinelli A., Marini A., Markou C., Martin L., Martinez-Mora J. A., Martini A., Marzaioli F., Mastroianni S., Melis K. W., Miele G., Migliozzi P., Migneco E., Mijakowski P., Miranda L. S., Mollo C. M., Mongelli M., Moussa A., Muller R., Musico P., Musumeci M., Nauta L., Navas S., Nicolau C. A., Nkosi B., Zywucka N., O'Sullivan M., Orlando A., Ottonello G., Ottonello S., Palacios Gonzalez J., Papalashvili G., Papaleo R., Pastore C., Paun A. M., Pavalas G. E., Pellegrini G., Pellegrino C., Perrin-Terrin M., Pestel V., Piattelli P., Pieterse C., Pisanti O., Poire C., Popa V., Pradier T., Pratolongo F., Probst I., Puhlhofer G., Pulvirenti S., Quemener G., Randazzo N., Rapicavoli A., Razzaque S., Real D., Reck S., Riccobene G., Rigalleau L., Romanov A., Rovelli A., Royon J., Salesa Greus F., Samtleben D. F. E., Sanchez Losa A., Sanguineti M., Santangelo A., Santonocito D., Sapienza P., Schmelling J., Schnabel J., Schneider M. F., Schumann J., Schutte H. M., Seneca J., Sgura I., Shanidze R., Sharma A., Sinopoulou A., Spisso B., Spurio M., Stavropoulos D., Steijger J., Stellacci S. M., Taiuti M., Tatone F., Tayalati Y., Tenllado E., Tezier D., Thakore T., Theraube S., Thiersen H., Timmer P., Tingay S., Tsagkli S., Tsourapis V., Tzamariudaki E., Tzanetatos D., Valieri C., Van Elewyck V., Vasileiadis G., Versari F., Viola S., Vivolo D., de Wasseige G., Wilms J., Wojaczynski R., de Wolf E., Yousfi T., Zavatarelli S., Zegarelli A., Zito D., Zornoza J. D., Zuniga J.
• Other Authors: Bianca Keilhauer, Alexander Kappes, Fearraigh, B. O., Ageron, M., Aiello, S., Albert, A., Alshamsi, M., Alves Garre, S., Aly, Z., Ambrosone, A., Ameli, F., Andre, M., Androulakis, G., Anghinolfi, M., Anguita, M., Anton, G., Ardid, M., Ardid, S., Assal, W., Aublin, J., Bagatelas, C., Baret, B., Basegmez du Pree, S., Bendahman, M., Benfenati, F., Berbee, E., van den Berg, A. M., Bertin, V., Beurthey, S., van Beveren, V., Biagi, S., Billault, M., Bissinger, M., Boettcher, M., Bou Cabo, M., Boumaaza, J., Bouta, M., Boutonnet, C., Bouvet, G., Bouwhuis, M., Bozza, C., Branzas, H., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Caillat, L., Calvo, D.
• Format: Conference Object
• Language: English
• Published: Sissa Medialab Srl 2022
• Subjects: Orca
• Online Access: https://hdl.handle.net/11591/489430

The muons produced by cosmic ray interactions in the upper atmosphere constitute the most abundant signal for underwater neutrino detectors such as KM3NeT (the Cubic Kilometre Neutrino Telescope), which is currently being deployed in the Mediterranean Sea at two distinct locations. Situated at different depths, the KM3NeT/ARCA and KM3NeT/ORCA detectors experience a different flux of muons, and thus are uniquely positioned to study their evolution and propagation from cosmic ray showers. It is imperative to the main physics goals of the experiment that the atmospheric muon background is modelled correctly, which aids in benchmarking and understanding the detector response to the constant flux of these particles. In this study, the data from the KM3NeT/ORCA detector is used and compared with the Monte Carlo (MC) prediction from the MUPAGE (MUons from PArametric formulas: a fast GEnerator for neutrino telescopes) software package, which generates the energy spectrum, lateral distribution, and muon multiplicity of muon bundles according to a specific parametrisation at different depths below sea level. This parametrisation consists of many free parameters which can be tuned such that simulated physical observables in the detector agree with those measured in data. In this way, improvements to the data-MC agreement are achieved by quantitatively comparing the level of agreement between simulated and measured observables in the KM3NeT detector.