@misc{10481/71386,
year = {2021},
month = {7},
url = {http://hdl.handle.net/10481/71386},
abstract = {The atmospheric depth of the air shower maximum X-max is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of X-max 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 X-max 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 X-max 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 X-max 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/cm(2) at energies above 2x10(19) eV.},
abstract = {United States Department of Energy (DOE)	DE-AC02-07CH11359 - DE-FR02-04ER41300 - DE- FG02-99ER41107 - DE-SC0011689},
organization = {Argentina - Comision Nacional de Energia Atomica},
organization = {Argentina - Agencia Nacional de Promocion Cientifica y Tecnologica (ANPCyT)},
organization = {Argentina - Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)},
organization = {Argentina - Gobierno de la Provincia de Mendoza},
organization = {Argentina - Municipalidad de Malargue},
organization = {Australian Research Council},
organization = {Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)
Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF)},
organization = {Brazil - Financiadora de Estudos e Projetos (FINEP)},
organization = {Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)},
organization = {Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)	2019/10151-2-2010/07359-6-1999/05404-3},
organization = {Brazil - Ministerio da Ciencia, Tecnologia, Inovacoes e Comunicacoes (MCTIC)},
organization = {Czech Republic Grant	MSMT CR LTT18004 - LM2015038 - LM2018102 - CZ.02.1.01/0.0/0.0/16_013/0001402 - CZ.02.1.01/0.0/0.0/18_046/0016010 - CZ.02.1.01/0.0/0.0/17_049/0008422},
organization = {France - Centre de Calcul IN2P3/CNRS},
organization = {Centre National de la Recherche Scientifique (CNRS)},
organization = {France - Conseil Regional Ile-de-France},
organization = {France - Departement Physique Nucleaire et Corpusculaire (PNC-IN2P3/CNRS)},
organization = {France - Departement Sciences de l'Univers (SDU-INSU/CNRS)},
organization = {French National Research Agency (ANR)	LABEX ANR-10-LABX-63 - ANR-11-IDEX-0004-02},
organization = {Federal Ministry of Education & Research (BMBF)},
organization = {German Research Foundation (DFG)},
organization = {Germany-Finanzministerium Baden-Wurttemberg},
organization = {Germany-Helmholtz Alliance for Astroparticle Physics (HAP)},
organization = {Germany-Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF)},
organization = {Germany-Ministerium fur Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen},
organization = {Germany-Ministerium fur Wissenschaft, Forschung und Kunst des Landes Baden-Wurttemberg},
organization = {Italy - Istituto Nazionale di Fisica Nucleare (INFN)},
organization = {Italy - Istituto Nazionale di Astrofisica (INAF)},
organization = {Italy - Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR)},
organization = {Italy - CETEMPS Center of Excellence},
organization = {Italy - Ministero degli Affari Esteri (MAE)},
organization = {Consejo Nacional de Ciencia y Tecnologia (CONACyT)	167733},
organization = {Mexico-Universidad Nacional Autonoma de Mexico (UNAM)},
organization = {Mexico-PAPIIT DGAPA-UNAM},
organization = {The Netherlands - Ministry of Education, Culture and Science},
organization = {Netherlands Organization for Scientific Research (NWO)},
organization = {The Netherlands - SURF Cooperative},
organization = {Poland - Ministry of Science and Higher Education	DIR/WK/2018/11},
organization = {Poland - National Science Centre	2013/08/M/ST9/00322 - 2016/23/B/ST9/01635 -HARMONIA 5-2013/10/M/ST9/00062 - UMO-2016/22/M/ST9/00198},
organization = {Portugal - Portuguese national funds},
organization = {Portugal - FEDER funds within Programa Operacional Factores de Competitividade through Fundacao para a Ciencia e a Tecnologia (COMPETE)},
organization = {Romania-Romanian Ministry of Education and Research},
organization = {Romania-Program Nucleu within MCI	PN19150201/16N/2019 - PN-19060102},
organization = {Romania-PNCDI III	PNIII-P1-1.2-PCCDI-2017-0839/19PCCDI/2018},
organization = {Slovenian Research Agency - Slovenia	P1-0031 - P1-0385 - I0-0033 - N1-0111},
organization = {Spain - Ministerio de Economia, Industria y Competitividad	FPA2017-85114-P - PID2019-104676GB-C32},
organization = {Spain - Xunta de Galicia	ED431C 2017/07},
organization = {Junta de Andalucia	SOMM17/6104/UGR - P18-FR-4314},
organization = {Spain - RENATA Red Nacional Tematica de Astroparticulas	FPA2015-68783-REDT},
organization = {Spain - Maria de Maeztu Unit of Excellence	MDM-2016-0692},
organization = {National Science Foundation (NSF)	0450696},
organization = {USA - Grainger Foundation},
organization = {USA - Marie Curie-IRSES/EPLANET},
organization = {USA - European Particle Physics Latin American Network},
organization = {USA - UNESCO},
organization = {Argentina - NDM Holdings and Valle Las Lenas},
organization = {The Netherlands - Dutch national e-infrastructure},
organization = {Spain - Feder Funds},
publisher = {IOP},
keywords = {Data analysis},
keywords = {Pattern recognition, cluster finding, calibration and fitting methods},
keywords = {Large detector systems for particle and astroparticle physics},
keywords = {Particle identification methods},
title = {Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory},
doi = {10.1088/1748-0221/16/07/P0701},
author = {Aab, A. and Carceller López, Juan Miguel and Bueno Villar, Antonio and Pierre Auger Collaboration},
}