Data-driven model validation for neutrino-nucleus cross section measurements

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mismodeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

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Publication Details

Journal
Physical review. D/Physical review. D.
Published
2025-05-15
DOI
https://doi.org/10.1103/physrevd.111.092010
Citations
2
Primary Topic
Neutrino Physics Research
Type
article
Field-Weighted Citation Impact
3.74

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article

Data-driven model validation for neutrino-nucleus cross section measurements

E. Gramellini, O. Alterkait, K. Pletcher, F. Gao et al.
2 citations
Physical review. D/Physical review. D.
Neutrino Physics Research
3.74
article

Data-driven model validation for neutrino-nucleus cross section measurements

E. Gramellini, O. Alterkait, K. Pletcher, F. Gao, G. A. Horton-Smith, T. Mohayai, A. P. Furmanski, N. Taniuchi, T. Wongjirad, J. Spitz, D. Barrow, J. Rodriguez Rondon, G. P. Zeller, Y. Cao, T. Kobilarcik, G. B. Cerati, X. Ji, M. Bhattacharya, L. Hagaman, S. Balasubramanian, S. R. Dennis, J. St. John, E. L. Snider, J.-Y. Li, D. Schmitz, J. L. Raaf, C. James, K. Duffy, M. H. Shaevitz, Kunio Terao, J.E. Bateman, E. Yandel, I. Pophale, F. Cavanna, B. Baller, A. M. Szelc, N. Nayak, J. M. Conrad, Y. Li, Ryan Cross, S. Mulleriababu, E. Piasetzky, T. Yang, T. Strauss, N. Oza, C. Nguyen, L. Cooper-Troendle, Z. Pavlovic, Y. Chen, V. Meddage, Nicholas D. Lane, K. Miller, J. H. Jo, Z. Imani, M. Toups, Jincheng Wang, B. Bogart, J. I. Crespo-Anadón, J. Barrow, L. Rochester, R. Guenette, M. Reggiani-Guzzo, D. Kalra, D. Garcia-Gamez, P. Detje, A. Blake, H. B. Parkinson, Alexander Trettin, I. Safa, P. Englezos, S. Gardiner, A. Mastbaum, M. Rosenberg, J. Shi, W. Gu, K. Lin, J. Marshall, Tariq Mahmud, N. Pallat, A. J. White, M. Söderberg, J. J. Evans, A. Bhanderi, O. Hen, L. Li, A. Rafique, L. Ren, P. Guzowski, G. Karagiorgi, G. Ge, M. Convery, A. Navrer-Agasson, C. Hilgenberg, S. I. Martynenko, S. F. Pate, Jessie Micallef, J. Zennamo, B. T. Fleming, O. Benevides Rodrigues, B. Irwin, A. Mogan, J. Nowak, S. Berkman, S. Wolbers, M. Wospakrik, D. Marsden, C. Thorpe, V. Paolone, B. Viren, Chaogang Fang, A. Schukraft, A. Ashkenazi, M. Mooney, D. Caratelli, V. Papavassiliou, M. Nebot-Guinot, W. Ketchum, W. C. Louis, V. Radeka, R. Diurba, B. R. Littlejohn, M. Bishai, L. E. Yates, A. Ereditato, L. Camilleri, B. Eberly, A. F. Moor, M. A. Uchida, P. Abratenko, O. Palamara, N. McConkey, I. Mawby, A. Papadopoulou, C. Mariani, D. Torbunov, D. Franco, M. M. Moudgalya, M. Weber, H. Wei, Rob Johnson, L. Mellet, X. Qian, A. Bhat, P. Green, W. Foreman, C. D. Moore, Z. Djurčić, T. Usher, S. Söldner‐Rembold, T. Bolton, M. Ross-Lonergan, S. Dytman, Y.-T. Tsai, N. Patel, N. Lorenzo Martinez, M. B. Brunetti, X. Luo, M. Wospakrik, M. Kirby, Dante Totani, J. Asaadi, W. Wu, H. Greenlee, D. Naples, V. Basque, D. A. Martínez Caicedo, S. Gollapinni, G. Barr, J. Tyler, L. Gu, Y.-J. Jwa, A. Chappell, D. Andrade Aldana, L. Mora Lepin, L. Arellano, M. Stancari, M. S. Ismail, A. Barnard, Jesus Eduardo Munoz Mendez, H. W. Yu, R. Sharankova, M. D. Handley, C. Zhang, W. Seligman
article en
2 citations

Abstract

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mismodeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

Physical review. D/Physical review. D.Vol. 111(9)
New Mexico State University (US), University of Bern (CH), Louisiana State University (US), Tufts University (US), Argonne National Laboratory (US), Fermi National Accelerator Laboratory (US), Los Alamos National Laboratory (US), Tel Aviv University (IL), Queen Mary University of London (GB), University of Pittsburgh (US), Universidad de Granada (ES), Illinois Institute of Technology (US), South Dakota School of Mines and Technology (US), Kansas State University (US), Brookhaven National Laboratory (US), Nankai University (CN), University of Cambridge (GB), University of Michigan (US), University of Minnesota System (US), SLAC National Accelerator Laboratory (US), University of California System (US), University of Manchester (GB), University of Warwick (GB), University of Oxford (GB), University of Chicago (US), Rutgers Sexual and Reproductive Health and Rights (NL), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), Imperial College London (GB), Indiana University (US), University of Cincinnati (US), Massachusetts Institute of Technology (US), Lancaster University (GB), Syracuse University (US), University of Southern Maine (US), Columbia University (US), Michigan State University (US), Colorado State University (US), University of Edinburgh (GB)
National Science Foundation, U.S. Department of Energy, UK Research and Innovation, Royal Society, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Office of Science, Science and Technology Facilities Council, High Energy Physics, Nuclear Physics, Fermilab
Openalex Percentile: Top 8%
Neutrino Physics Research
3.74
DOIOpen Access PDFOpenAlex
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