TY - JOUR

T1 - A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory

AU - Abbasi, R.

AU - Ackermann, M.

AU - Adams, J.

AU - Aguilar, J. A.

AU - Ahlers, M.

AU - Ahrens, M.

AU - Alispach, C.

AU - Alves, A. A.

AU - Amin, N. M.

AU - An, R.

AU - Andeen, K.

AU - Anderson, T.

AU - Ansseau, I.

AU - Anton, G.

AU - Argüelles, C.

AU - Axani, S.

AU - Bai, X.

AU - Balagopal, A. V.

AU - Barbano, A.

AU - Barwick, S. W.

AU - Bastian, B.

AU - Basu, V.

AU - Baum, V.

AU - Baur, S.

AU - Bay, R.

AU - Beatty, J. J.

AU - Becker, K. H.

AU - Tjus, J. Becker

AU - Bellenghi, C.

AU - BenZvi, S.

AU - Berley, D.

AU - Bernardini, E.

AU - Besson, D. Z.

AU - Binder, G.

AU - Bindig, D.

AU - Blaufuss, E.

AU - Blot, S.

AU - Böser, S.

AU - Botner, O.

AU - Böttcher, J.

AU - Bourbeau, E.

AU - Bourbeau, J.

AU - Bradascio, F.

AU - Braun, J.

AU - Bron, S.

AU - Brostean-Kaiser, J.

AU - Engel, R.

AU - Koskinen, D. J.

AU - Rameez, M.

AU - Stuttard, T.

AU - The IceCube collaboration

N1 - Funding Information: The IceCube collaboration acknowledges the significant contributions to this manuscript from Mirco H?nnefeld. The authors gratefully acknowledge the support from the following agencies and institutions: U.S.A. - U.S. National Science Foundation-Office of Polar Programs, U.S. National Science Foundation-Physics Division, U.S. National Science Foundation-EPSCoR, Wisconsin Alumni Research Foundation, Center for High Throughput Computing (CHTC) at the University of Wisconsin-Madison, Open Science Grid (OSG), Extreme Science and Engineering Discovery Environment (XSEDE), Frontera computing project at the Texas Advanced Computing Center, U.S. Department of Energy-National Energy Research Scientific Computing Center, Particle astrophysics research computing center at the University of Maryland, Institute for Cyber-Enabled Research at Michigan State University, and Astroparticle physics computational facility at Marquette University; Belgium - Funds for Scientific Research (FRS-FNRS and FWO), FWO Odysseus and Big Science programmes, and Belgian Federal Science Policy Office (Belspo); Germany - Bundesministerium f?r Bildung und Forschung (BMBF), Deutsche Forschungsgemeinschaft (DFG), Helmholtz Alliance for Astroparticle Physics (HAP), Initiative and Networking Fund of the Helmholtz Association, Deutsches Elektronen Synchrotron (DESY), and High Performance Computing cluster of the RWTH Aachen; Sweden - Swedish Research Council, Swedish Polar Research Secretariat, Swedish National Infrastructure for Computing (SNIC), and Knut and Alice Wallenberg Foundation; Australia - Australian Research Council; Canada - Natural Sciences and Engineering Research Council of Canada, Calcul Qu?bec, Compute Ontario, Canada Foundation for Innovation, WestGrid, and Compute Canada; Denmark - Villum Fonden and Carlsberg Foundation; New Zealand - Marsden Fund; Japan - Japan Society for Promotion of Science (JSPS) and Institute for Global Prominent Research (IGPR) of Chiba University; Korea - National Research Foundation of Korea (NRF); Switzerland - Swiss National Science Foundation (SNSF); United Kingdom - Department of Physics, University of Oxford. Publisher Copyright: © 2021 IOP Publishing Ltd and Sissa Medialab

PY - 2021/7

Y1 - 2021/7

N2 - Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hexagonally shaped kernels is presented. The presented method is robust towards systematic uncertainties in the simulation and has been tested on experimental data. In comparison to standard reconstruction methods in IceCube, it can improve upon the reconstruction accuracy, while reducing the time necessary to run the reconstruction by two to three orders of magnitude.

AB - Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hexagonally shaped kernels is presented. The presented method is robust towards systematic uncertainties in the simulation and has been tested on experimental data. In comparison to standard reconstruction methods in IceCube, it can improve upon the reconstruction accuracy, while reducing the time necessary to run the reconstruction by two to three orders of magnitude.

KW - Calibration

KW - Cluster finding

KW - Data analysis

KW - Fitting methods

KW - Neutrino detectors

KW - Pattern recognition

U2 - 10.1088/1748-0221/16/07/P07041

DO - 10.1088/1748-0221/16/07/P07041

M3 - Journal article

AN - SCOPUS:85112103983

VL - 16

JO - Journal of Instrumentation

JF - Journal of Instrumentation

SN - 1748-0221

IS - 7

M1 - P07041

ER -