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

2021· article· en· W3122562968 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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