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

2021· article· en· W3122562968 on OpenAlexafffund

Bibliographic record

VenueJournal of Instrumentation · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsSnolabUniversity of AlbertaInstitute of Particle Physics
FundersOffice of Experimental Program to Stimulate Competitive ResearchJapan Society for the Promotion of ScienceDeutsches Elektronen-SynchrotronNatural Sciences and Engineering Research Council of CanadaOffice of Polar ProgramsCollege of Engineering, Michigan State UniversityHelmholtz Alliance for Astroparticle PhysicsInstitute for Global Prominent Research, Chiba UniversityRWTH Aachen UniversityChiba UniversityKnut och Alice Wallenbergs StiftelseVillum FondenNational Research Foundation of KoreaFonds Wetenschappelijk OnderzoekMarsden FundBundesministerium für Bildung und ForschungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science FoundationBelgian Federal Science Policy OfficeDeutsche ForschungsgemeinschaftMichigan State UniversityNational Research FoundationWestern Canada Research GridFonds De La Recherche Scientifique - FNRSPolarforskningssekretariatetUniversity of OxfordCompute CanadaMarquette UniversityUniversity of Wisconsin-MadisonU.S. Department of EnergyVetenskapsrådet
KeywordsConvolutional neural networkObservatoryDetectorNeutrinoCascadeNeutrino detectorArtificial neural network

Abstract

fetched live from OpenAlex

A convolutional neural network based cascade reconstruction for the IceCube Neutrino Observatory, The IceCube collaboration, Abbasi, R., Ackermann, M., Adams, J., Aguilar, J.A., Ahlers, M., Ahrens, M., Alispach, C., Alves, A.A., Amin, N.M., An, R., Andeen, K., Anderson, T., Ansseau, I., Anton, G., Argüelles, C., Axani, S., Bai, X., Balagopal V., A., Barbano, A., Barwick, S.W., Bastian, B., Basu, V., Baum, V., Baur, S., Bay, R., Beatty, J.J., Becker, K.-H., Becker Tjus, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D.Z., 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, R.S., Campana, M.A., Chen, C., Chirkin, D., Choi, S., Clark, B.A., Clark, K., Classen, L., Coleman, A., Collin, G.H., Conrad, J.M., Coppin, P., Correa, P., Cowen, D.F., Cross, R., Dave, P., Clercq, C.D., DeLaunay, J.J., Dembinski, H., Deoskar, K., Ridder, S.D., Desai, A., Desiati, P., de Vries, K.D., de Wasseige, G., de With, M., DeYoung, T., Dharani, S., Diaz, A., Díaz-Vélez, J.C., Dujmovic, H., Dunkman, M., DuVernois, M.A., Dvorak, E., Ehrhardt, T., Eller, P., Engel, R., Evans, J., Evenson, P.A., Fahey, S., Fazely, A.R., Fiedlschuster, S., Fienberg, A.T., Filimonov, K., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Friedman, E., Fritz, A., Fürst, P., Gaisser, T.K., Gallagher, J., Ganster, E., Garrappa, S., Gerhardt, L., Ghadimi, A., Glaser, C., Glauch, T., Glüsenkamp, T., Goldschmidt, A., Gonzalez, J.G., Goswami, S., Grant, D., Grégoire, T., Griffith, Z., Griswold, S., Gündüz, M., Haack, C., Hallgren, A., Halliday, R., Halve, L., Halzen, F., Minh, M.H., Hanson, K., Hardin, J., Harnisch, A.A., Haungs, A., Hauser, S., Hebecker, D., Helbing, K., Henningsen, F., Hettinger, E.C., Hickford, S., Hignight, J., Hill, C., Hill, G.C., Hoffman, K.D., 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, G.S., Jeong, M., Jones, B.J.P., 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, J.L., Kheirandish, A., Kim, J., Kin, K., Kintscher, T., Kiryluk, J., Klein, S.R., Koirala, R., Kolanoski, H., Köpke, L., Kopper, C., Kopper, S., Koskinen, D.J., Koundal, P., Kovacevich, M., Kowalski, M., Krings, K., Krückl, G., Kurahashi, N., Kyriacou, A., Lagunas Gualda, C., Lanfranchi, J.L., Larson, M.J., Lauber, F., Lazar, J.P., Leonard, K., Leszczyńska, A., Li, Y., Liu, Q.R., Lohfink, E., Lozano Mariscal, C.J., Lu, L., Lucarelli, F., Ludwig, A., Luszczak, W., Lyu, Y., Ma, W.Y., Madsen, J., M. Mahn, K.B., Makino, Y., Mallik, P., Mancina, S., Mariş, I.C., 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, R.W., Morik, K., Morse, R., Moulai, M., Naab, R., Nagai, R., Naumann, U., Necker, J., Nguyễn, L.V., Niederhausen, H., Nisa, M.U., Nowicki, S.C., Nygren, D.R., Obertacke Pollmann, A., Oehler, M., Olivas, A., O'Sullivan, E., Pandya, H., Pankova, D.V., Park, N., Parker, G.K., Paudel, E.N., Peiffer, P., Pérez de los Heros, C., Philippen, S., Pieloth, D., Pieper, S., Pizzuto, A., Plum, M., Popovych, Y., Porcelli, A., Prado Rodriguez, M., Price, P.B., Pries, B., Przybylski, G.T., Raab, C., Raissi, A., Rameez, M., Rawlins, K., Rea, I.C., 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., Rysewyk Cantu, D., Safa, I., Sanchez Herrera, S.E., Sandrock, A., Sandroos, J., Santander, M., Sarkar, S., Sarkar, S., Satalecka, K., Scharf, M., Schaufel, M., Schieler, H., Schlunder, P., Schmidt, T., Schneider, A., Schneider, J., Schröder, F.G., 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, G.M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stein, R., Stettner, J., Steuer, A., Stezelberger, T., Stokstad, R.G., Stürwald, T., Stuttard, T., Sullivan, G.W., 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, C.F., Turcati, A., Turcotte, R., Turley, C.F., Twagirayezu, J.P., Ty, B., Unland Elorrieta, M.A., 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, T.B., Weaver, C., Weindl, A., Weiss, M.J., Weldert, J., Wendt, C., Werthebach, J., Weyrauch, M., Whelan, B.J., Whitehorn, N., Wiebe, K., Wiebusch, C.H., Williams, D.R., Wolf, M., Woschnagg, K., Wrede, G., Wulff, J., Xu, X.W., Xu, Y., Yanez, J.P., Yoshida, S., Yuan, T., Zhang, Z.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.242
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations56
Published2021
Admission routes2
Has abstractyes

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