GollumFit: An IceCube Open-Source Framework for Binned-Likelihood Neutrino Telescope Analyses
Computer Physics Communications Elsevier (2026) 110195
Authors:
R Abbasi, M Ackermann, J Adams, SK Agarwalla, JA Aguilar, M Ahlers, JM Alameddine, NM Amin, K Andeen, C Argüelles, Y Ashida, S Athanasiadou, SN Axani, R Babu, X Bai, AV Balagopal, M Baricevic, SW Barwick, S Bash, V Basu, R Bay, JJ Beatty, J Becker Tjus, P Behrens, J Beise, C Bellenghi, B Benkel, S BenZvi, D Berley, E Bernardini, DZ Besson, E Blaufuss, L Bloom, S Blot, F Bontempo, JY Book Motzkin, C Boscolo Meneguolo, S Böser, O Botner, J Böttcher, J Braun, B Brinson, Z Brisson-Tsavoussis, RT Burley, D Butterfield, MA Campana, K Carloni, J Carpio, S Chattopadhyay, N Chau, Z Chen, D Chirkin, BA Clark, A Coleman, P Coleman, GH Collin, A Connolly, JM Conrad, R Corley, DF Cowen, C De Clercq, JJ DeLaunay, D Delgado, T Delmeulle, S Deng, A Desai, P Desiati, KD de Vries, G de Wasseige, T DeYoung, JC Díaz-Vélez, A Diaz, S DiKerby, M Dittmer, A Domi, L Draper, L Dueser, H Dujmovic, D Durnford, K Dutta, MA DuVernois, T Ehrhardt, L Eidenschink, A Eimer, P Eller, E Ellinger, D Elsässer, R Engel, H Erpenbeck, W Esmail, J Evans, PA Evenson, KL Fan, K Fang, K Farrag, AR Fazely, A Fedynitch, N Feigl, C Finley, L Fischer, D Fox, A Franckowiak, S Fukami, P Fürst, J Gallagher, E Ganster, A Garcia, M Garcia, G Garg, E Genton, L Gerhardt, A Ghadimi, C Glaser, T Glüsenkamp, JG Gonzalez, S Goswami, A Granados, D Grant, SJ Gray, S Griffin, S Griswold, KM Groth, D Guevel, C Günther, P Gutjahr, C Ha, C Haack, A Hallgren, L Halve, F Halzen, L Hamacher, M Ha Minh, M Handt, K Hanson, J Hardin, AA Harnisch, P Hatch, A Haungs, J Häußler, K Helbing, J Hellrung, L Hennig, L Heuermann, R Hewett, N Heyer, S Hickford, A Hidvegi, C Hill, GC Hill, R Hmaid, KD Hoffman, S Hori, K Hoshina, M Hostert, W Hou, T Huber, K Hultqvist, R Hussain, K Hymon, A Ishihara, W Iwakiri, M Jacquart, S Jain, O Janik, M Jeong, M Jin, BJP Jones, N Kamp, D Kang, X Kang, A Kappes, L Kardum, T Karg, M Karl, A Karle, A Katil, M Kauer, JL Kelley, M Khanal, A Khatee Zathul, A Kheirandish, H Kimku, J Kiryluk, C Klein, SR Klein, Y Kobayashi, A Kochocki, R Koirala, H Kolanoski, T Kontrimas, L Köpke, C Kopper, DJ Koskinen, P Koundal, M Kowalski, T Kozynets, N Krieger, J Krishnamoorthi, T Krishnan, K Kruiswijk, E Krupczak, A Kumar, E Kun, N Kurahashi, N Lad, C Lagunas Gualda, L Lallement Arnaud, M Lamoureux, MJ Larson, F Lauber, JP Lazar, K Leonard Deholtona, A Leszczyńska, J Liao, YT Liu, M Liubarska, C Love, L Lu, F Lucarelli, W Luszczak, Y Lyu, J Madsen, E Magnus, KBM Mahn, Y Makino, E Manao, S Mancina, A Mand, W Marie Sainte, IC Mariş, S Marka, Z Marka, L Marten, I Martinez-Soler, R Maruyama, F Mayhew, F McNally, JV Mead, K Meagher, S Mechbal, A Medina, M Meier, Y Merckx, L Merten, J Mitchell, L Molchany, T Montaruli, RW Moore, Y Morii, R Morse, A Mosbrugger, M Moulai, D Mousadi, T Mukherjee, R Naab, M Nakos, U Naumann, J Necker, L Neste, M Neumann, H Niederhausen, MU Nisa, K Noda, A Noell, A Novikov, A Obertacke Pollmann, V O’Dell, A Olivas, R Orsoe, J Osborn, E O’Sullivan, V Palusova, H Pandya, A Parenti, N Park, V Parrish, EN Paudel, L Paul, C Pérez de los Heros, T Pernice, J Peterson, A Pizzuto, M Plum, A Pontén, V Poojyam, Y Popovych, M Prado Rodriguez, B Pries, R Procter-Murphy, GT Przybylski, L Pyras, C Raab, J Rack-Helleis, N Rad, M Ravn, K Rawlins, Z Rechav, A Rehman, I Reistroffer, E Resconi, S Reusch, CD Rho, W Rhode, B Riedel, A Rifaie, EJ Roberts, S Robertson, M Rongen, A Rosted, C Rott, T Ruhe, L Ruohan, I Safa, J Saffer, D Salazar-Gallegos, P Sampathkumar, A Sandrock, G Sanger-Johnson, M Santander, S Sarkar, J Savelberg, P Savina, P Schaile, M Schaufel, H Schieler, S Schindler, L Schlickmann, A Schneider, B Schlüter, F Schlüter, N Schmeisser, T Schmidt, FG Schröder, L Schumacher, S Schwirn, S Sclafani, D Seckel, L Seen, M Seikh, S Seunarine, PA Sevle Myhr, R Shah, S Shefali, N Shimizu, M Silva, B Skrzypek, R Snihur, J Soedingrekso, A Søgaard, D Soldin, P Soldin, G Sommani, C Spannfellner, GM Spiczak, C Spiering, J Stachurska, M Stamatikos, T Stanev, T Stezelberger, T Stürwald, T Stuttard, GW Sullivan, I Taboada, S Ter-Antonyan, A Terliuk, A Thakuri, M Thiesmeyer, WG Thompson, J Thwaites, S Tilav, K Tollefson, S Toscano, D Tosi, A Trettin, MA Unland Elorrieta, AK Upadhyay, K Upshaw, A Vaidyanathan, N Valtonen-Mattila, J Vandenbroucke, T Van Eeden, N van Eijndhoven, J van Santen, J Vara, F Varsi, J Veitch-Michaelis, M Venugopal, M Vereecken, S Vergara Carrasco, S Verpoest, D Veske, A Vijai, J Villarreal, C Walck, N Wandkowsky, A Wang, E Warrick, C Weaver, P Weigel, A Weindl, AY Wen, C Wendt, J Werthebach, M Weyrauch, N Whitehorn, CH Wiebusch, DR Williams, L Witthaus, M Wolf, G Wrede, XW Xu, JP Yanez, E Yildizci, S Yoshida, R Young, F Yu, S Yu, T Yuan, A Zegarelli, S Zhang, Z Zhang, P Zhelnin, P Zilberman, M Zimmerman
Abstract:
We present GollumFit, a framework designed for performing binned-likelihood analyses on neutrino telescope data. GollumFit incorporates model parameters common to any neutrino telescope and also model parameters specific to the IceCube Neutrino Observatory. We provide a high-level overview of its key features and how the code is organized. We then discuss the performance of the fitting in a typical analysis scenario, highlighting the ability to fit over tens of nuisance parameters. We present some examples showing how to use the package for likelihood minimization tasks. This framework uniquely incorporates the particular model parameters necessary for neutrino telescopes, and solves an associated likelihood problem in a time-efficient manner. PROGRAM Program title: GollumFit Documentation website: https://docs.icecube.aq/gollumfit/main/index.html Developer's repository link: https://github.com/icecube/GollumFit Licensing provisions: GNU Lesser General Public License 2.1 (LGPL) Programming language: C++, Python Nature of problem: Statistical analysis of data from neutrino telescope experiments is often complex and computationally demanding, owing to the need to optimize a likelihood function over many parameters that describe sources of systematic uncertainties and quantities of interest. Solution method: We introduce a framework that performs binned-likelihood optimization, whose performance can handle the number of parameters typical for a neutrino telescope analysis. We highlight a method to perform event-by-event reweighting to incorporate the experimental parameters. In particular, for neutrino telescopes the parameters that incorporate the uncertainties in the atmospheric neutrino flux are common across all experiments and analyses, and are implemented in our framework. The framework has been designed to be easily extendable in the number of observable dimensions and fit parameters. Finally, we use an automatic differentiation package to achieve computational speed in the likelihood optimization.
