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DarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering

We introduce DarkNews, a lightweight Python-based Monte-Carlo generator for beyond-the-Standard-Model neutrino-nucleus scattering. The generator handles the production and decay of heavy neutral leptons via additional vector or scalar mediators, as well as through transition magnetic moments. DarkNe...

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Detalles Bibliográficos
Autores principales: Abdullahi, Asli M., Hoefken Zink, Jaime, Hostert, Matheus, Massaro, Daniele, Pascoli, Silvia
Lenguaje:eng
Publicado: 2022
Materias:
Acceso en línea:http://cds.cern.ch/record/2820008
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author Abdullahi, Asli M.
Hoefken Zink, Jaime
Hostert, Matheus
Massaro, Daniele
Pascoli, Silvia
author_facet Abdullahi, Asli M.
Hoefken Zink, Jaime
Hostert, Matheus
Massaro, Daniele
Pascoli, Silvia
author_sort Abdullahi, Asli M.
collection CERN
description We introduce DarkNews, a lightweight Python-based Monte-Carlo generator for beyond-the-Standard-Model neutrino-nucleus scattering. The generator handles the production and decay of heavy neutral leptons via additional vector or scalar mediators, as well as through transition magnetic moments. DarkNews samples pre-computed neutrino-nucleus upscattering cross sections and heavy neutrino decay rates to produce dilepton and single-photon events in accelerator neutrino experiments. We present two case studies with differential distributions for models that can explain the MiniBooNE excess. The aim of this code is to aid the neutrino theory and experimental communities in performing searches and sensitivity studies for new particles produced in neutrino upscattering.
id cern-2820008
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28200082023-09-10T02:49:26Zhttp://cds.cern.ch/record/2820008engAbdullahi, Asli M.Hoefken Zink, JaimeHostert, MatheusMassaro, DanielePascoli, SilviaDarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scatteringhep-exParticle Physics - Experimenthep-phParticle Physics - PhenomenologyWe introduce DarkNews, a lightweight Python-based Monte-Carlo generator for beyond-the-Standard-Model neutrino-nucleus scattering. The generator handles the production and decay of heavy neutral leptons via additional vector or scalar mediators, as well as through transition magnetic moments. DarkNews samples pre-computed neutrino-nucleus upscattering cross sections and heavy neutrino decay rates to produce dilepton and single-photon events in accelerator neutrino experiments. We present two case studies with differential distributions for models that can explain the MiniBooNE excess. The aim of this code is to aid the neutrino theory and experimental communities in performing searches and sensitivity studies for new particles produced in neutrino upscattering.arXiv:2207.04137CP3-22-36FERMILAB-PUB-22-594-Toai:cds.cern.ch:28200082022-07-08
spellingShingle hep-ex
Particle Physics - Experiment
hep-ph
Particle Physics - Phenomenology
Abdullahi, Asli M.
Hoefken Zink, Jaime
Hostert, Matheus
Massaro, Daniele
Pascoli, Silvia
DarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering
title DarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering
title_full DarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering
title_fullStr DarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering
title_full_unstemmed DarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering
title_short DarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering
title_sort darknews: a python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering
topic hep-ex
Particle Physics - Experiment
hep-ph
Particle Physics - Phenomenology
url http://cds.cern.ch/record/2820008
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