Identification of long lived charginos in the CMS pixel tracker with a Deep Neural Network

In many models of physics beyond the Standard Model (BSM), Dark Matter (DM) particles are part of some multiplet and could be produced from the decay of other states in the multiplet. An example of this is the production of SUSY neutralinos from chargino decays. The mass split between the two states...

Descripción completa

Detalles Bibliográficos
Autor principal: Bury, Florian Joel J
Lenguaje:eng
Publicado: 2017
Materias:
Particle Physics - Experiment
Acceso en línea:http://cds.cern.ch/record/2281821
_version_ 1780955594254974976
author Bury, Florian Joel J
author_facet Bury, Florian Joel J
author_sort Bury, Florian Joel J
collection CERN
description In many models of physics beyond the Standard Model (BSM), Dark Matter (DM) particles are part of some multiplet and could be produced from the decay of other states in the multiplet. An example of this is the production of SUSY neutralinos from chargino decays. The mass split between the two states could be very small, such that the DM partner could become long-living and decay far from the interaction region. In this report is investigated a scenario where the decay occurs before the strip tracker resulting on a short track hard to distinguish from the background and pile-up. The analysis used here focused on the energy deposit in the pixel tracker by using a deep neural network.
id cern-2281821
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
record_format invenio
spelling cern-22818212019-09-30T06:29:59Zhttp://cds.cern.ch/record/2281821engBury, Florian Joel JIdentification of long lived charginos in the CMS pixel tracker with a Deep Neural NetworkParticle Physics - ExperimentIn many models of physics beyond the Standard Model (BSM), Dark Matter (DM) particles are part of some multiplet and could be produced from the decay of other states in the multiplet. An example of this is the production of SUSY neutralinos from chargino decays. The mass split between the two states could be very small, such that the DM partner could become long-living and decay far from the interaction region. In this report is investigated a scenario where the decay occurs before the strip tracker resulting on a short track hard to distinguish from the background and pile-up. The analysis used here focused on the energy deposit in the pixel tracker by using a deep neural network.CERN-STUDENTS-Note-2017-155oai:cds.cern.ch:22818212017-09-01
spellingShingle Particle Physics - Experiment
Bury, Florian Joel J
Identification of long lived charginos in the CMS pixel tracker with a Deep Neural Network
title Identification of long lived charginos in the CMS pixel tracker with a Deep Neural Network
title_full Identification of long lived charginos in the CMS pixel tracker with a Deep Neural Network
title_fullStr Identification of long lived charginos in the CMS pixel tracker with a Deep Neural Network
title_full_unstemmed Identification of long lived charginos in the CMS pixel tracker with a Deep Neural Network
title_short Identification of long lived charginos in the CMS pixel tracker with a Deep Neural Network
title_sort identification of long lived charginos in the cms pixel tracker with a deep neural network
topic Particle Physics - Experiment
url http://cds.cern.ch/record/2281821
work_keys_str_mv AT buryflorianjoelj identificationoflonglivedcharginosinthecmspixeltrackerwithadeepneuralnetwork

Ejemplares similares