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

Abbasi R, Ackermann M, Adams J, Aguilar JA, Ahlers M, Ahrens M, Alispach C, Alves AA, Amin NM, An R, Andeen K, Anderson T, Ansseau I, Anton G, Argüelles C, Axani S, Bai X, Balagopal AV, Barbano A, Barwick SW, Bastian B, Basu V, Baum V, Baur S, Bay R, Beatty JJ, Becker KH, Tjus JB, Bellenghi C, BenZvi S, Berley D, Bernardini E, Besson DZ, Binder G, Bindig D, Blaufuss E, Blot S, Böser S, Botner O, Böttcher J, Bourbeau E, Bourbeau J, Bradascio F, Braun J, Bron S, Brostean-Kaiser J, Burgman A, Busse RS, Campana MA, Chen C, Chirkin D, Choi S, Clark BA, Clark K, Classen L, Coleman A, Collin GH, Conrad JM, Coppin P, Correa P, Cowen DF, Cross R, Dave P, Clercq CD, DeLaunay JJ, Dembinski H, Deoskar K, Ridder SD, Desai A, Desiati P, de Vries KD, de Wasseige G, de With M, DeYoung T, Dharani S, Diaz A, Díaz-Vélez JC, Dujmovic H, Dunkman M, DuVernois MA, Dvorak E, Ehrhardt T, Eller P, Engel R, Evans J, Evenson PA, Fahey S, Fazely AR, Fiedlschuster S, Fienberg AT, Filimonov K, Finley C, Fischer L, Fox D, Franckowiak A, Friedman E, Fritz A, Fürst P, Gaisser TK, Gallagher J, Ganster E, Garrappa S, Gerhardt L, Ghadimi A, Glaser C, Glauch T, Glüsenkamp T, Goldschmidt A, Gonzalez JG, Goswami S, Grant D, Grégoire T, Griffith Z, Griswold S, Gündüz M, Haack C, Hallgren A, Halliday R, Halve L, Halzen F, Minh MH, Hanson K, Hardin J, Harnisch AA, Haungs A, Hauser S, Hebecker D, Helbing K, Henningsen F, Hettinger EC, Hickford S, Hignight J, Hill C, Hill GC, Hoffman KD, Hoffmann R, Hoinka T, Hokanson-Fasig B, Hoshina K, Huang F, Huber M, Huber T, Hultqvist K, Hünnefeld M, Hussain R, In S, Iovine N, Ishihara A, Jansson M, Japaridze GS, Jeong M, Jones BJ, Joppe R, Kang D, Kang W, Kang X, Kappes A, Kappesser D, Karg T, Karl M, Karle A, Katz U, Kauer M, Kellermann M, Kelley JL, Kheirandish A, Kim J, Kin K, Kintscher T, Kiryluk J, Klein SR, Koirala R, Kolanoski H, Köpke L, Kopper C, Kopper S, Koskinen DJ, Koundal P, Kovacevich M, Kowalski M, Krings K, Krückl G, Kurahashi N, Kyriacou A, Gualda CL, Lanfranchi JL, Larson MJ, Lauber F, Lazar JP, Leonard K, Leszczyńska A, Li Y, Liu QR, Lohfink E, Mariscal CJ, Lu L, Lucarelli F, Ludwig A, Luszczak W, Lyu Y, Ma WY, Madsen J, Mahn KB, Makino Y, Mallik P, Mancina S, Mariş IC, Maruyama R, Mase K, McNally F, Meagher K, Medina A, Meier M, Meighen-Berger S, Merz J, Micallef J, Mockler D, Momenté G, Montaruli T, Moore RW, Morik K, Morse R, Moulai M, Naab R, Nagai R, Naumann U, Necker J, Nguyên LV, Niederhausen H, Nisa MU, Nowicki SC, Nygren DR, Pollmann AO, Oehler M, Olivas A, O'Sullivan E, Pandya H, Pankova DV, Park N, Parker GK, Paudel EN, Peiffer P, de los Heros CP, Philippen S, Pieloth D, Pieper S, Pizzuto A, Plum M, Popovych Y, Porcelli A, Rodriguez MP, Price PB, Pries B, Przybylski GT, Raab C, Raissi A, Rameez M, Rawlins K, Rea IC, Rehman A, Reimann R, Renschler M, Renzi G, Resconi E, Reusch S, Rhode W, Richman M, Riedel B, Robertson S, Roellinghoff G, Rongen M, Rott C, Ruhe T, Ryckbosch D, Cantu DR, Safa I, Herrera SE, Sandrock A, Sandroos J, Santander M, Sarker S, Sarkar S, Satalecka K, Scharf M, Schaufel M, Schieler H, Schlunder P, Schmidt T, Schneider A, Schneider J, Schröder FG, Schumacher L, Sclafani S, Seckel D, Seunarine S, Sharma A, Shefali S, Silva M, Skrzypek B, Smithers B, Snihur R, Soedingrekso J, Soldin D, Spiczak GM, Spiering C, Stachurska J, Stamatikos M, Stanev T, Stein R, Stettner J, Steuer A, Stezelberger T, Stokstad RG, Stürwald T, Stuttard T, Sullivan GW, Taboada I, Tenholt F, Ter-Antonyan S, Tilav S, Tischbein F, Tollefson K, Tomankova L, Tönnis C, Toscano S, Tosi D, Trettin A, Tselengidou M, Tung CF, Turcati A, Turcotte R, Turley CF, Twagirayezu JP, Ty B, Elorrieta MA, Valtonen-Mattila N, Vandenbroucke J, van Eijk D, van Eijndhoven N, Vannerom D, van Santen J, Verpoest S, Vraeghe M, Walck C, Wallace A, Watson TB, Weaver C, Weindl A, Weiss MJ, Weldert J, Wendt C, Werthebach J, Weyrauch M, Whelan BJ, Whitehorn N, Wiebe K, Wiebusch CH, Williams DR, Wolf M, Woschnagg K, Wrede G, Wulff J, Xu XW, Xu Y, Yanez JP, Yoshida S, Yuan T, Zhang Z (2021)


Publication Type: Journal article

Publication year: 2021

Journal

Book Volume: 16

Article Number: P07041

Journal Issue: 7

DOI: 10.1088/1748-0221/16/07/P07041

Abstract

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.

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How to cite

APA:

Abbasi, R., Ackermann, M., Adams, J., Aguilar, J.A., Ahlers, M., Ahrens, M.,... Zhang, Z. (2021). A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory. Journal of Instrumentation, 16(7). https://doi.org/10.1088/1748-0221/16/07/P07041

MLA:

Abbasi, R., et al. "A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory." Journal of Instrumentation 16.7 (2021).

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