Abstract
The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.
| Original language | English |
|---|---|
| Article number | 164332 |
| Journal | Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment |
| Volume | 977 |
| DOIs | |
| Publication status | Published - 2020 Oct 11 |
| Externally published | Yes |
Keywords
- Data analysis
- Detector
- KDE
- MC
- Monte Carlo
- Neutrino
- Neutrino mass ordering
- Smoothing
- Statistics
- VLVνT
ASJC Scopus subject areas
- Nuclear and High Energy Physics
- Instrumentation
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Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments. / Aartsen, M. G.; Ackermann, M.; Adams, J. et al.
In: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 977, 164332, 11.10.2020.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments
AU - Aartsen, M. G.
AU - Ackermann, M.
AU - Adams, J.
AU - Aguilar, J. A.
AU - Ahlers, M.
AU - Ahrens, M.
AU - Alispach, C.
AU - Andeen, K.
AU - Anderson, T.
AU - Ansseau, I.
AU - Anton, G.
AU - Argüelles, C.
AU - Arlen, T. C.
AU - Auffenberg, J.
AU - Axani, S.
AU - Backes, P.
AU - Bagherpour, H.
AU - Bai, X.
AU - Balagopal, A. V.
AU - Barbano, A.
AU - Barwick, S. W.
AU - Bastian, B.
AU - Baum, V.
AU - Baur, S.
AU - Bay, R.
AU - Beatty, J. J.
AU - Becker, K. H.
AU - Becker Tjus, J.
AU - BenZvi, S.
AU - Berley, D.
AU - Bernardini, E.
AU - Besson, D. Z.
AU - Binder, G.
AU - Bindig, D.
AU - Blaufuss, E.
AU - Blot, S.
AU - Bohm, C.
AU - Börner, M.
AU - Böser, S.
AU - Botner, O.
AU - Böttcher, J.
AU - Bourbeau, E.
AU - Bourbeau, J.
AU - Bradascio, F.
AU - Braun, J.
AU - Bron, S.
AU - Brostean-Kaiser, J.
AU - Burgman, A.
AU - Buscher, J.
AU - Busse, R. S.
AU - Carver, T.
AU - Chen, C.
AU - Cheung, E.
AU - Chirkin, D.
AU - Choi, S.
AU - Clark, K.
AU - Classen, L.
AU - Coleman, A.
AU - Collin, G. H.
AU - Conrad, J. M.
AU - Coppin, P.
AU - Correa, P.
AU - Cowen, D. F.
AU - Cross, R.
AU - Dave, P.
AU - De Clercq, C.
AU - DeLaunay, J. J.
AU - Dembinski, H.
AU - Deoskar, K.
AU - De Ridder, S.
AU - Desiati, P.
AU - de Vries, K. D.
AU - de Wasseige, G.
AU - de With, M.
AU - DeYoung, T.
AU - Diaz, A.
AU - Dáz-Vélez, J. C.
AU - Dujmovic, H.
AU - Dunkman, M.
AU - Dvorak, E.
AU - Eberhardt, B.
AU - Ehrhardt, T.
AU - Eller, P.
AU - Engel, R.
AU - Evans, J. J.
AU - Evenson, P. A.
AU - Fahey, S.
AU - Fazely, A. R.
AU - Felde, J.
AU - Filimonov, K.
AU - Finley, C.
AU - Fox, D.
AU - Franckowiak, A.
AU - Friedman, E.
AU - Fritz, A.
AU - Gaisser, T. K.
AU - Gallagher, J.
AU - Ganster, E.
AU - Garrappa, S.
AU - Gerhardt, L.
AU - Ghorbani, K.
AU - Glauch, T.
AU - Glüsenkamp, T.
AU - Goldschmidt, A.
AU - Gonzalez, J. G.
AU - Grant, D.
AU - Griffith, Z.
AU - Griswold, S.
AU - Günder, M.
AU - Gündüz, M.
AU - Haack, C.
AU - Hallgren, A.
AU - Halliday, R.
AU - Halve, L.
AU - Halzen, F.
AU - Hanson, K.
AU - Haungs, A.
AU - Hebecker, D.
AU - Heereman, D.
AU - Heix, P.
AU - Helbing, K.
AU - Hellauer, R.
AU - Henningsen, F.
AU - Hickford, S.
AU - Hignight, J.
AU - Hill, G. C.
AU - Hoffman, K. D.
AU - Hoffmann, R.
AU - Hoinka, T.
AU - Hokanson-Fasig, B.
AU - Hoshina, K.
AU - Huang, F.
AU - Huber, M.
AU - Huber, T.
AU - Hultqvist, K.
AU - Hünnefeld, M.
AU - Hussain, R.
AU - In, S.
AU - Iovine, N.
AU - Ishihara, A.
AU - Japaridze, G. S.
AU - Jeong, M.
AU - Jero, K.
AU - Jones, B. J.P.
AU - Jonske, F.
AU - Joppe, R.
AU - Kang, D.
AU - Kang, W.
AU - Kappes, A.
AU - Kappesser, D.
AU - Karg, T.
AU - Karl, M.
AU - Karle, A.
AU - Katori, T.
AU - Katz, U.
AU - Kauer, M.
AU - Kelley, J. L.
AU - Kheirandish, A.
AU - Kim, J.
AU - Kintscher, T.
AU - Kiryluk, J.
AU - Kittler, T.
AU - Klein, S. R.
AU - Koirala, R.
AU - Kolanoski, H.
AU - Köpke, L.
AU - Kopper, C.
AU - Kopper, S.
AU - Koskinen, D. J.
AU - Kowalski, M.
AU - Krings, K.
AU - Krückl, G.
AU - Kulacz, N.
AU - Kurahashi, N.
AU - Kyriacou, A.
AU - Lanfranchi, J. L.
AU - Larson, M. J.
AU - Lauber, F.
AU - Lazar, J. P.
AU - Leonard, K.
AU - Leszczyńska, A.
AU - Leuermann, M.
AU - Liu, Q. R.
AU - Lohfink, E.
AU - Lozano Mariscal, C. J.
AU - Lu, L.
AU - Lucarelli, F.
AU - Lünemann, J.
AU - Luszczak, W.
AU - Lyu, Y.
AU - Ma, W. Y.
AU - Madsen, J.
AU - Maggi, G.
AU - Mahn, K. B.M.
AU - Makino, Y.
AU - Mallik, P.
AU - Mallot, K.
AU - Mancina, S.
AU - Mandalia, S.
AU - Mariş, I. C.
AU - Maruyama, R.
AU - Mase, K.
AU - Maunu, R.
AU - McNally, F.
AU - Meagher, K.
AU - Medici, M.
AU - Medina, A.
AU - Meier, M.
AU - Meighen-Berger, S.
AU - Menne, T.
AU - Merino, G.
AU - Meures, T.
AU - Micallef, J.
AU - Mockler, D.
AU - Momenté, G.
AU - Montaruli, T.
AU - Moore, R. W.
AU - Morse, R.
AU - Moulai, M.
AU - Muth, P.
AU - Nagai, R.
AU - Naumann, U.
AU - Neer, G.
AU - Niederhausen, H.
AU - Nisa, M. U.
AU - Nowicki, S. C.
AU - Nygren, D. R.
AU - Obertacke Pollmann, A.
AU - Oehler, M.
AU - Olivas, A.
AU - O'Murchadha, A.
AU - O'Sullivan, E.
AU - Palczewski, T.
AU - Pandya, H.
AU - Pankova, D. V.
AU - Park, N.
AU - Peiffer, P.
AU - Pérez de los Heros, C.
AU - Philippen, S.
AU - Pieloth, D.
AU - Pinat, E.
AU - Pizzuto, A.
AU - Plum, M.
AU - Porcelli, A.
AU - Price, P. B.
AU - Przybylski, G. T.
AU - Raab, C.
AU - Raissi, A.
AU - Rameez, M.
AU - Rauch, L.
AU - Rawlins, K.
AU - Rea, I. C.
AU - Reimann, R.
AU - Relethford, B.
AU - Renschler, M.
AU - Renzi, G.
AU - Resconi, E.
AU - Rhode, W.
AU - Richman, M.
AU - Robertson, S.
AU - Rongen, M.
AU - Rott, C.
AU - Ruhe, T.
AU - Ryckbosch, D.
AU - Rysewyk, D.
AU - Safa, I.
AU - Sanchez Herrera, S. E.
AU - Sandrock, A.
AU - Sandroos, J.
AU - Santander, M.
AU - Sarkar, S.
AU - Satalecka, K.
AU - Schaufel, M.
AU - Schieler, H.
AU - Schlunder, P.
AU - Schmidt, T.
AU - Schneider, A.
AU - Schneider, J.
AU - Schröder, F. G.
AU - Schulte, L.
AU - Schumacher, L.
AU - Sclafani, S.
AU - Seckel, D.
AU - Seunarine, S.
AU - Shefali, S.
AU - Silva, M.
AU - Snihur, R.
AU - Soedingrekso, J.
AU - Soldin, D.
AU - Söldner-Rembold, S.
AU - Song, M.
AU - Spiczak, G. M.
AU - Spiering, C.
AU - Stachurska, J.
AU - Stamatikos, M.
AU - Stanev, T.
AU - Stein, R.
AU - Steinmüller, P.
AU - Stettner, J.
AU - Steuer, A.
AU - Stezelberger, T.
AU - Stokstad, R. G.
AU - Stößl, A.
AU - Strotjohann, N. L.
AU - Stürwald, T.
AU - Stuttard, T.
AU - Sullivan, G. W.
AU - Taboada, I.
AU - Tenholt, F.
AU - Ter-Antonyan, S.
AU - Terliuk, A.
AU - Tilav, S.
AU - Tollefson, K.
AU - Tomankova, L.
AU - Tönnis, C.
AU - Toscano, S.
AU - Tosi, D.
AU - Trettin, A.
AU - Tselengidou, M.
AU - Tung, C. F.
AU - Turcati, A.
AU - Turcotte, R.
AU - Turley, C. F.
AU - Ty, B.
AU - Unger, E.
AU - Unland Elorrieta, M. A.
AU - Usner, M.
AU - Vandenbroucke, J.
AU - Van Driessche, W.
AU - van Eijk, D.
AU - van Eijndhoven, N.
AU - van Santen, J.
AU - Verpoest, S.
AU - Vraeghe, M.
AU - Walck, C.
AU - Wallace, A.
AU - Wallraff, M.
AU - Wandkowsky, N.
AU - Watson, T. B.
AU - Weaver, C.
AU - Weindl, A.
AU - Weiss, M. J.
AU - Weldert, J.
AU - Wendt, C.
AU - Werthebach, J.
AU - Whelan, B. J.
AU - Whitehorn, N.
AU - Wiebe, K.
AU - Wiebusch, C. H.
AU - Wille, L.
AU - Williams, D. R.
AU - Wills, L.
AU - Wolf, M.
AU - Wood, J.
AU - Wood, T. R.
AU - Woschnagg, K.
AU - Wrede, G.
AU - Wren, S.
AU - Xu, D. L.
AU - Xu, X. W.
AU - Xu, Y.
AU - Yanez, J. P.
AU - Yodh, G.
AU - Yoshida, S.
AU - Yuan, T.
AU - Zöcklein, M.
N1 - Funding Information: The IceCube collaboration gratefully acknowledges the support from the following agencies and institutions: USA – U.S. National Science Foundation -Office of Polar Programs, U.S. National Science Foundation -Physics Division, Wisconsin Alumni Research Foundation , Center for High Throughput Computing (CHTC) at the University of Wisconsin-Madison , Open Science Grid (OSG), Extreme Science and Engineering Discovery Environment (XSEDE), U.S. Department of Energy-National Energy Research Scientific Computing Center , Particle astrophysics research computing center at the University of Maryland , Institute for Cyber-Enabled Research at Michigan State University , and Astroparticle physics computational facility at Marquette University ; Belgium – Funds for Scientific Research (FRS-FNRS and FWO), FWO Odysseus and Big Science programmes , and Belgian Federal Science Policy Office (Belspo); Germany – Bundesministerium für Bildung und Forschung (BMBF), Deutsche Forschungsgemeinschaft (DFG), Helmholtz Alliance for Astroparticle Physics (HAP), Initiative and Networking Fund of the Helmholtz Association , Deutsches Elektronen Synchrotron (DESY), and High Performance Computing cluster of the RWTH Aachen ; Sweden – Swedish Research Council , Swedish Polar Research Secretariat , Swedish National Infrastructure for Computing (SNIC), and Knut and Alice Wallenberg Foundation ; Australia – Australian Research Council ; Canada – Natural Sciences and Engineering Research Council of Canada , Calcul Québec , Compute Ontario , Canada Foundation for Innovation , WestGrid , and Compute Canada ; Denmark – Villum Fonden , Danish National Research Foundation (DNRF), Carlsberg Foundation ; New Zealand – Marsden Fund ; Japan – Japan Society for Promotion of Science (JSPS) and Institute for Global Prominent Research (IGPR) of Chiba University ; Korea – National Research Foundation of Korea (NRF); Switzerland – Swiss National Science Foundation (SNSF); United Kingdom – Department of Physics, University of Oxford . Funding Information: The IceCube collaboration gratefully acknowledges the support from the following agencies and institutions: USA ? U.S. National Science Foundation-Office of Polar Programs, U.S. National Science Foundation-Physics Division, Wisconsin Alumni Research Foundation, Center for High Throughput Computing (CHTC) at the University of Wisconsin-Madison, Open Science Grid (OSG), Extreme Science and Engineering Discovery Environment (XSEDE), U.S. Department of Energy-National Energy Research Scientific Computing Center, Particle astrophysics research computing center at the University of Maryland, Institute for Cyber-Enabled Research at Michigan State University, and Astroparticle physics computational facility at Marquette University; Belgium ? Funds for Scientific Research (FRS-FNRS and FWO), FWO Odysseus and Big Science programmes, and Belgian Federal Science Policy Office (Belspo); Germany ? Bundesministerium f?r Bildung und Forschung (BMBF), Deutsche Forschungsgemeinschaft (DFG), Helmholtz Alliance for Astroparticle Physics (HAP), Initiative and Networking Fund of the Helmholtz Association, Deutsches Elektronen Synchrotron (DESY), and High Performance Computing cluster of the RWTH Aachen; Sweden ? Swedish Research Council, Swedish Polar Research Secretariat, Swedish National Infrastructure for Computing (SNIC), and Knut and Alice Wallenberg Foundation; Australia ? Australian Research Council; Canada ? Natural Sciences and Engineering Research Council of Canada, Calcul Qu?bec, Compute Ontario, Canada Foundation for Innovation, WestGrid, and Compute Canada; Denmark ? Villum Fonden, Danish National Research Foundation (DNRF), Carlsberg Foundation; New Zealand ? Marsden Fund; Japan ? Japan Society for Promotion of Science (JSPS) and Institute for Global Prominent Research (IGPR) of Chiba University; Korea ? National Research Foundation of Korea (NRF); Switzerland ? Swiss National Science Foundation (SNSF); United Kingdom ? Department of Physics, University of Oxford. Publisher Copyright: © 2020 Elsevier B.V.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.
AB - The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.
KW - Data analysis
KW - Detector
KW - KDE
KW - MC
KW - Monte Carlo
KW - Neutrino
KW - Neutrino mass ordering
KW - Smoothing
KW - Statistics
KW - VLVνT
UR - http://www.scopus.com/inward/record.url?scp=85087620956&partnerID=8YFLogxK
U2 - 10.1016/j.nima.2020.164332
DO - 10.1016/j.nima.2020.164332
M3 - Article
AN - SCOPUS:85087620956
SN - 0168-9002
VL - 977
JO - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
JF - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
M1 - 164332
ER -