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

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Data-driven model validation for neutrino-nucleus cross section measurements. / MicroBooNE Collaboration ; Blake, A.; Gu, L. et al.
In: Physical Review D, 30.04.2025.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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MicroBooNE Collaboration, Blake A, Gu L, Mahmud T, Mawby I, Nowak J et al. Data-driven model validation for neutrino-nucleus cross section measurements. Physical Review D. 2025 Apr 30.

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@article{c5b8d003b49d46c0b29a3df57467a2e8,
title = "Data-driven model validation for neutrino-nucleus cross section measurements",
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 mis-modeling, 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. ",
keywords = "hep-ex",
author = "{MicroBooNE Collaboration} and P. Abratenko and O. Alterkait and Aldana, {D. Andrade} and L. Arellano and J. Asaadi and A. Ashkenazi and S. Balasubramanian and B. Baller and A. Barnard and G. Barr and J. Barrow and V. Basque and Rodrigues, {O. Benevides} and S. Berkman and A. Bhanderi and A. Bhat and M. Bhattacharya and M. Bishai and A. Blake and B. Bogart and T. Bolton and Brunetti, {M. B.} and L. Camilleri and D. Caratelli and F. Cavanna and G. Cerati and A. Chappell and Conrad, {J. M.} and M. Convery and L. Cooper-Troendle and Crespo-Anadon, {J. I.} and Tutto, {M. Del} and Dennis, {S. R.} and P. Detje and R. Diurba and Z. Djurcic and K. Duffy and S. Dytman and B. Eberly and P. Englezos and A. Ereditato and C. Fang and Fleming, {B. T.} and L. Gu and T. Mahmud and I. Mawby and J. Nowak and N. Patel and I. Pophale",
year = "2025",
month = apr,
day = "30",
language = "English",
journal = "Physical Review D",
issn = "1550-7998",
publisher = "American Physical Society",

}

RIS

TY - JOUR

T1 - Data-driven model validation for neutrino-nucleus cross section measurements

AU - MicroBooNE Collaboration

AU - Abratenko, P.

AU - Alterkait, O.

AU - Aldana, D. Andrade

AU - Arellano, L.

AU - Asaadi, J.

AU - Ashkenazi, A.

AU - Balasubramanian, S.

AU - Baller, B.

AU - Barnard, A.

AU - Barr, G.

AU - Barrow, J.

AU - Basque, V.

AU - Rodrigues, O. Benevides

AU - Berkman, S.

AU - Bhanderi, A.

AU - Bhat, A.

AU - Bhattacharya, M.

AU - Bishai, M.

AU - Blake, A.

AU - Bogart, B.

AU - Bolton, T.

AU - Brunetti, M. B.

AU - Camilleri, L.

AU - Caratelli, D.

AU - Cavanna, F.

AU - Cerati, G.

AU - Chappell, A.

AU - Conrad, J. M.

AU - Convery, M.

AU - Cooper-Troendle, L.

AU - Crespo-Anadon, J. I.

AU - Tutto, M. Del

AU - Dennis, S. R.

AU - Detje, P.

AU - Diurba, R.

AU - Djurcic, Z.

AU - Duffy, K.

AU - Dytman, S.

AU - Eberly, B.

AU - Englezos, P.

AU - Ereditato, A.

AU - Fang, C.

AU - Fleming, B. T.

AU - Gu, L.

AU - Mahmud, T.

AU - Mawby, I.

AU - Nowak, J.

AU - Patel, N.

AU - Pophale, I.

PY - 2025/4/30

Y1 - 2025/4/30

N2 - 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 mis-modeling, 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.

AB - 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 mis-modeling, 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.

KW - hep-ex

M3 - Journal article

JO - Physical Review D

JF - Physical Review D

SN - 1550-7998

ER -