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Compatibility of Neutrino DIS Data and Its Impact on Nuclear Parton Distribution Functions

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arxiv 2204.13157 v1 pith:RIPE363O submitted 2022-04-27 hep-ph hep-exnucl-th

classification hep-phhep-exnucl-th
keywords dataneutrinotensionsanalysisglobalnuclearanalysescharged-lepton
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abstract

In global analyses of nuclear parton distribution functions (nPDFs), neutrino deep-inelastic scattering (DIS) data have been argued to exhibit tensions with the data from charged-lepton DIS. Using the nCTEQ framework, we investigate these possible tensions both internally and with the data sets used in our recent nPDF analysis nCTEQ15WZSIH. We take into account nuclear effects in the calculation of the deuteron structure function $F_2^D$ using the CJ15 analysis. The resulting nPDF fit, nCTEQ15WZSIHdeut, serves as the basis for our comparison with inclusive neutrino DIS and charm dimuon production data. Using $\chi^2$ hypothesis testing, we confirm evidence of tensions with these data and study the impact of the proton PDF baseline as well as the treatment of data correlation and normalization uncertainties. We identify the experimental data and kinematic regions that generate the tensions and present several possible approaches how a consistent global analysis with neutrino data can be performed. We show that the tension can be relieved using a kinematic cut at low $x$ ($x>0.1$) and also investigate a possibility of managing the tensions by using uncorrelated systematic errors. Finally, we present a different approach identifying a subset of neutrino data which leads to a consistent global analysis without any additional cuts. Understanding these tensions between the neutrino and charged-lepton DIS data is important not only for a better flavor separation in global analyses of nuclear and proton PDFs, but also for neutrino physics and for searches for physics beyond the Standard Model.

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  1. Anomalous electroweak physics unraveled via evidential deep learning

    hep-ph 2024-12 conditional novelty 5.0 of 10

    A proof-of-principle demonstration that evidential deep learning can classify and quantify uncertainty for three toy anomalous electroweak scenarios in neutrino deep-inelastic scattering.

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