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Graph Neural Networks for low-energy event classification & reconstruction in IceCube

Journal of Instrumentation IOP Publishing 17:11 (2022) p11003

Authors:

R Abbasi, M Ackermann, J Adams, N Aggarwal, JA Aguilar, M Ahlers, M Ahrens, JM Alameddine, AA Alves, NM Amin, K Andeen, T Anderson, G Anton, C Argüelles, Y Ashida, S Athanasiadou, S Axani, X Bai, A Balagopal V., M Baricevic, SW Barwick, V Basu, R Bay, JJ Beatty, K-H Becker, J Becker Tjus, J Beise, C Bellenghi, S Benda, S BenZvi, D Berley, E Bernardini, DZ Besson, G Binder, D Bindig, E Blaufuss, S Blot, F Bontempo, JY Book, J Borowka, C Boscolo Meneguolo, S Böser, O Botner, J Böttcher, E Bourbeau, J Braun, B Brinson, J Brostean-Kaiser, RT Burley, RS Busse, MA Campana, EG Carnie-Bronca, C Chen, Z Chen, D Chirkin, K Choi, BA Clark, L Classen, A Coleman, GH Collin, A Connolly, JM Conrad, P Coppin, P Correa, S Countryman, DF Cowen, R Cross, C Dappen, P Dave, C De Clercq, JJ DeLaunay, D Delgado López, H Dembinski, K Deoskar, A Desai, P Desiati, KD de Vries, G de Wasseige, T DeYoung, A Diaz, JC Díaz-Vélez, M Dittmer, H Dujmovic, MA DuVernois, T Ehrhardt, P Eller, R Engel, H Erpenbeck, J Evans, PA Evenson, KL Fan, AR Fazely, A Fedynitch, N Feigl, S Fiedlschuster, AT Fienberg, C Finley, L Fischer, D Fox, A Franckowiak, E Friedman, A Fritz, P Fürst, TK Gaisser, J Gallagher, E Ganster, A Garcia, S Garrappa, L Gerhardt, A Ghadimi, C Glaser, T Glauch, T Glüsenkamp, N Goehlke, JG Gonzalez, S Goswami, D Grant, SJ Gray, T Grégoire, S Griswold, C Günther, P Gutjahr, C Haack, A Hallgren, R Halliday, L Halve, F Halzen, H Hamdaoui, M Ha Minh, K Hanson, J Hardin, AA Harnisch, P Hatch, A Haungs, K Helbing, J Hellrung, F Henningsen, L Heuermann, S Hickford, C Hill, GC Hill, KD Hoffman, K Hoshina, W Hou, T Huber, K Hultqvist, M Hünnefeld, R Hussain, K Hymon, S In, N Iovine, A Ishihara, M Jansson, GS Japaridze, M Jeong, M Jin, BJP Jones, D Kang, W Kang, X Kang, A Kappes, D Kappesser, L Kardum, T Karg, M Karl, A Karle, U Katz, M Kauer, JL Kelley, A Kheirandish, K Kin, J Kiryluk, SR Klein, A Kochocki, R Koirala, H Kolanoski, T Kontrimas, L Köpke, C Kopper, DJ Koskinen, P Koundal, M Kovacevich, M Kowalski, T Kozynets, E Krupczak, E Kun, N Kurahashi, N Lad, C Lagunas Gualda, MJ Larson, F Lauber, JP Lazar, JW Lee, K Leonard, A Leszczyńska, M Lincetto, QR Liu, M Liubarska, E Lohfink, C Love, CJ Lozano Mariscal, L Lu, F Lucarelli, A Ludwig, W Luszczak, Y Lyu, WY Ma, J Madsen, KBM Mahn, Y Makino, S Mancina, W Marie Sainte, IC Mariş, S Marka, Z Marka, M Marsee, I Martinez-Soler, R Maruyama, T McElroy, F McNally, JV Mead, K Meagher, S Mechbal, A Medina, M Meier, S Meighen-Berger, Y Merckx, J Micallef, D Mockler, T Montaruli, RW Moore, R Morse, M Moulai, T Mukherjee, R Naab, R Nagai, U Naumann, A Nayerhoda, J Necker, M Neumann, H Niederhausen, MU Nisa, SC Nowicki, A Obertacke Pollmann, M Oehler, B Oeyen, A Olivas, R Orsoe, J Osborn, E O'Sullivan, H Pandya, DV Pankova, N Park, GK Parker, EN Paudel, L Paul, C Pérez de los Heros, L Peters, TC Petersen, J Peterson, S Philippen, S Pieper, A Pizzuto, M Plum, Y Popovych, A Porcelli, M Prado Rodriguez, B Pries, R Procter-Murphy, GT Przybylski, C Raab, J Rack-Helleis, M Rameez, K Rawlins, Z Rechav, A Rehman, P Reichherzer, G Renzi, E Resconi, S Reusch, W Rhode, M Richman, B Riedel, EJ Roberts, S Robertson, S Rodan, G Roellinghoff, M Rongen, C Rott, T Ruhe, L Ruohan, D Ryckbosch, D Rysewyk Cantu, I Safa, J Saffer, D Salazar-Gallegos, P Sampathkumar, SE Sanchez Herrera, A Sandrock, M Santander, S Sarkar, S Sarkar, M Schaufel, H Schieler, S Schindler, B Schlueter, T Schmidt, J Schneider, FG Schröder, L Schumacher, G Schwefer, S Sclafani, D Seckel, S Seunarine, A Sharma, S Shefali, N Shimizu, M Silva, B Skrzypek, B Smithers, R Snihur, J Soedingrekso, A Søgaard, D Soldin, C Spannfellner, GM Spiczak, C Spiering, M Stamatikos, T Stanev, R Stein, T Stezelberger, T Stürwald, T Stuttard, GW Sullivan, I Taboada, S Ter-Antonyan, WG Thompson, J Thwaites, S Tilav, K Tollefson, C Tönnis, S Toscano, D Tosi, A Trettin, CF Tung, R Turcotte, JP Twagirayezu, B Ty, MA Unland Elorrieta, K Upshaw, N Valtonen-Mattila, J Vandenbroucke, N van Eijndhoven, D Vannerom, J van Santen, J Vara, J Veitch-Michaelis, S Verpoest, D Veske, C Walck, W Wang, TB Watson, C Weaver, P Weigel, A Weindl, J Weldert, C Wendt, J Werthebach, M Weyrauch, N Whitehorn, CH Wiebusch, N Willey, DR Williams, M Wolf, G Wrede, J Wulff, XW Xu, JP Yanez, E Yildizci, S Yoshida, S Yu, T Yuan, Z Zhang, P Zhelnin, The IceCube collaboration
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Searches for Neutrinos from Gamma-Ray Bursts Using the IceCube Neutrino Observatory

Astrophysical Journal 939:2 (2022)

Authors:

R Abbasi, M Ackermann, J Adams, JA Aguilar, M Ahlers, M Ahrens, JM Alameddine, AA Alves, NM Amin, K Andeen, T Anderson, G Anton, C Argüelles, Y Ashida, S Athanasiadou, S Axani, X Bai, AV Balagopal, SW Barwick, V Basu, S Baur, R Bay, JJ Beatty, KH Becker, JB Tjus, J Beise, C Bellenghi, S Benda, S Benzvi, D Berley, E Bernardini, DZ Besson, G Binder, D Bindig, E Blaufuss, S Blot, M Boddenberg, F Bontempo, JY Book, J Borowka, S Böser, O Botner, J Böttcher, E Bourbeau, F Bradascio, J Braun, B Brinson, S Bron, J Brostean-Kaiser, RT Burley, RS Busse, MA Campana, EG Carnie-Bronca, C Chen, Z Chen, D Chirkin, K Choi, BA Clark, K Clark, L Classen, A Coleman, GH Collin, A Connolly, JM Conrad, P Coppin, P Correa, DF Cowen, R Cross, C Dappen, P Dave, CD Clercq, JJ Delaunay, DD López, H Dembinski, K Deoskar, A Desai, P Desiati, KDD Vries, GD Wasseige, T Deyoung, A Diaz, JC Díaz-Vélez, M Dittmer, H Dujmovic, MA Duvernois, T Ehrhardt, P Eller, R Engel, H Erpenbeck, J Evans, PA Evenson, KL Fan, AR Fazely, A Fedynitch, N Feigl, S Fiedlschuster, AT Fienberg, C Finley, L Fischer, D Fox

Abstract:

Gamma-ray bursts (GRBs) are considered as promising sources of ultra-high-energy cosmic rays (UHECRs) due to their large power output. Observing a neutrino flux from GRBs would offer evidence that GRBs are hadronic accelerators of UHECRs. Previous IceCube analyses, which primarily focused on neutrinos arriving in temporal coincidence with the prompt gamma-rays, found no significant neutrino excess. The four analyses presented in this paper extend the region of interest to 14 days before and after the prompt phase, including generic extended time windows and targeted precursor searches. GRBs were selected between 2011 May and 2018 October to align with the data set of candidate muon-neutrino events observed by IceCube. No evidence of correlation between neutrino events and GRBs was found in these analyses. Limits are set to constrain the contribution of the cosmic GRB population to the diffuse astrophysical neutrino flux observed by IceCube. Prompt neutrino emission from GRBs is limited to 21% of the observed diffuse neutrino flux, and emission on timescales up to 104 s is constrained to 24% of the total diffuse flux.
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Search for quantum gravity using astrophysical neutrino flavour with IceCube

Nature Physics Springer Nature 18:11 (2022) 1287-1292
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Searching for axion forces with precision precession in storage rings

(2022)

Authors:

Prateek Agrawal, David E Kaplan, On Kim, Surjeet Rajendran, Mario Reig
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Cosmic inflation and genetic algorithms

Progress of Physics Wiley 71:1 (2022) 2200161

Authors:

Steve A Abel, Andrei Constantin, Thomas R Harvey, Andre Lukas

Abstract:

Large classes of standard single-field slow-roll inflationary models consistent with the required number of e-folds, the current bounds on the spectral index of scalar perturbations, the tensor-to-scalar ratio, and the scale of inflation can be efficiently constructed using genetic algorithms. The setup is modular and can be easily adapted to include further phenomenological constraints. A semi-comprehensive search for sextic polynomial potentials results in O (300,000) viable models for inflation. The analysis of this dataset reveals a preference for models with a tensor-to-scalar ratio in the range 0.0001 ≤ r ≤ 0.0004. We also consider potentials that involve cosine and exponential terms. In the last part we explore more complex methods of search relying on reinforcement learning and genetic programming. While reinforcement learning proves more difficult to use in this context, the genetic programming approach has the potential to uncover a multitude of viable inflationary models with new functional forms.

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