Pub Date : 2020-10-05DOI: 10.1103/PHYSREVC.103.044604
S. Gardiner
Background: Large argon-based neutrino detectors, such as those planned for the Deep Underground Neutrino Experiment (DUNE), have the potential to provide unique sensitivity to low-energy ($sim$10 MeV) electron neutrinos produced by core-collapse supernovae. Despite their importance for neutrino energy reconstruction, nuclear de-excitations following charged-current $nu_e$ absorption on $^{40}$Ar have never been studied in detail at supernova energies. Purpose: I develop a model of nuclear de-excitations that occur following the $^{40}mathrm{Ar}(nu_e,e^{-})^{40}mathrm{K}^*$ reaction. This model is applied to the calculation of exclusive cross sections. Methods: A simple expression for the inclusive differential cross section is derived under the allowed approximation. Nuclear de-excitations are described using a combination of measured $gamma$-ray decay schemes and the Hauser-Feshbach statistical model. All calculations are carried out using a novel Monte Carlo event generator called MARLEY (Model of Argon Reaction Low Energy Yields). Results: Various total and differential cross sections are presented. Two de-excitation modes, one involving only $gamma$-rays and the other including single neutron emission, are found to be dominant at few tens-of-MeV energies. Conclusions: Nuclear de-excitations have a strong impact on the achievable energy resolution for supernova $nu_e$ detection in liquid argon. Tagging events involving neutron emission, though difficult, could substantially improve energy reconstruction. Given a suitable calculation of the inclusive cross section, the MARLEY nuclear de-excitation model may readily be applied to other scattering processes.
{"title":"Nuclear de-excitations in low-energy charged-current \u0000νe\u0000 scattering on \u0000Ar40","authors":"S. Gardiner","doi":"10.1103/PHYSREVC.103.044604","DOIUrl":"https://doi.org/10.1103/PHYSREVC.103.044604","url":null,"abstract":"Background: Large argon-based neutrino detectors, such as those planned for the Deep Underground Neutrino Experiment (DUNE), have the potential to provide unique sensitivity to low-energy ($sim$10 MeV) electron neutrinos produced by core-collapse supernovae. Despite their importance for neutrino energy reconstruction, nuclear de-excitations following charged-current $nu_e$ absorption on $^{40}$Ar have never been studied in detail at supernova energies. Purpose: I develop a model of nuclear de-excitations that occur following the $^{40}mathrm{Ar}(nu_e,e^{-})^{40}mathrm{K}^*$ reaction. This model is applied to the calculation of exclusive cross sections. Methods: A simple expression for the inclusive differential cross section is derived under the allowed approximation. Nuclear de-excitations are described using a combination of measured $gamma$-ray decay schemes and the Hauser-Feshbach statistical model. All calculations are carried out using a novel Monte Carlo event generator called MARLEY (Model of Argon Reaction Low Energy Yields). Results: Various total and differential cross sections are presented. Two de-excitation modes, one involving only $gamma$-rays and the other including single neutron emission, are found to be dominant at few tens-of-MeV energies. Conclusions: Nuclear de-excitations have a strong impact on the achievable energy resolution for supernova $nu_e$ detection in liquid argon. Tagging events involving neutron emission, though difficult, could substantially improve energy reconstruction. Given a suitable calculation of the inclusive cross section, the MARLEY nuclear de-excitation model may readily be applied to other scattering processes.","PeriodicalId":8463,"journal":{"name":"arXiv: Nuclear Theory","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78795469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}