Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data
Descripción del Articulo
MINERvA is supported by the Fermi National Accelerator Laboratory under US Department of Energy contract No. DE-AC02-07CH11359 which included the MINERvA construction project. MINERvA construction support was also granted by the United States National Science Foundation under Award PHY-0619727 and b...
| Autores: | , , , , , |
|---|---|
| Formato: | objeto de conferencia |
| Fecha de Publicación: | 2019 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/1189 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/1189 https://doi.org/10.1109/ICASSP.2019.8683736 |
| Nivel de acceso: | acceso abierto |
| Materia: | Física de partículas https://purl.org/pe-repo/ocde/ford#1.03.03 |
| id |
CONC_fda2fd2bfe7794e628aa97ac9bf864e6 |
|---|---|
| oai_identifier_str |
oai:repositorio.concytec.gob.pe:20.500.12390/1189 |
| network_acronym_str |
CONC |
| network_name_str |
CONCYTEC-Institucional |
| repository_id_str |
4689 |
| dc.title.none.fl_str_mv |
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data |
| title |
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data |
| spellingShingle |
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data Song, LH Física de partículas https://purl.org/pe-repo/ocde/ford#1.03.03 |
| title_short |
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data |
| title_full |
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data |
| title_fullStr |
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data |
| title_full_unstemmed |
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data |
| title_sort |
Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data |
| author |
Song, LH |
| author_facet |
Song, LH Chen, F Young, SR Schuman, CD Perdue, G Potok, TE |
| author_role |
author |
| author2 |
Chen, F Young, SR Schuman, CD Perdue, G Potok, TE |
| author2_role |
author author author author author |
| dc.contributor.author.fl_str_mv |
Song, LH Chen, F Young, SR Schuman, CD Perdue, G Potok, TE |
| dc.subject.none.fl_str_mv |
Física de partículas |
| topic |
Física de partículas https://purl.org/pe-repo/ocde/ford#1.03.03 |
| dc.subject.ocde.none.fl_str_mv |
https://purl.org/pe-repo/ocde/ford#1.03.03 |
| description |
MINERvA is supported by the Fermi National Accelerator Laboratory under US Department of Energy contract No. DE-AC02-07CH11359 which included the MINERvA construction project. MINERvA construction support was also granted by the United States National Science Foundation under Award PHY-0619727 and by the University of Rochester. Support for participating MINERvA physicists was provided by NSF and DOE (USA), by CAPES and CNPq (Brazil), by CoNaCyT (Mexico), by CONICYT (Chile), by CONCYTEC, DGI-PUCP and IDI/IGIUNI (Peru), and by Latin American Center for Physics (CLAF). This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education. |
| publishDate |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2019 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
| format |
conferenceObject |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12390/1189 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1109/ICASSP.2019.8683736 |
| dc.identifier.isi.none.fl_str_mv |
482554004024 |
| url |
https://hdl.handle.net/20.500.12390/1189 https://doi.org/10.1109/ICASSP.2019.8683736 |
| identifier_str_mv |
482554004024 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers |
| publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers |
| dc.source.none.fl_str_mv |
reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
| instname_str |
Consejo Nacional de Ciencia Tecnología e Innovación |
| instacron_str |
CONCYTEC |
| institution |
CONCYTEC |
| reponame_str |
CONCYTEC-Institucional |
| collection |
CONCYTEC-Institucional |
| repository.name.fl_str_mv |
Repositorio Institucional CONCYTEC |
| repository.mail.fl_str_mv |
repositorio@concytec.gob.pe |
| _version_ |
1844883093306998784 |
| spelling |
Publicationrp03402600rp03401600rp02098500rp01262500rp00806500rp01263500Song, LHChen, FYoung, SRSchuman, CDPerdue, GPotok, TE2024-05-30T23:13:38Z2024-05-30T23:13:38Z2019https://hdl.handle.net/20.500.12390/1189https://doi.org/10.1109/ICASSP.2019.8683736482554004024MINERvA is supported by the Fermi National Accelerator Laboratory under US Department of Energy contract No. DE-AC02-07CH11359 which included the MINERvA construction project. MINERvA construction support was also granted by the United States National Science Foundation under Award PHY-0619727 and by the University of Rochester. Support for participating MINERvA physicists was provided by NSF and DOE (USA), by CAPES and CNPq (Brazil), by CoNaCyT (Mexico), by CONICYT (Chile), by CONCYTEC, DGI-PUCP and IDI/IGIUNI (Peru), and by Latin American Center for Physics (CLAF). This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education.Se presentó un enfoque de aprendizaje profundo para la reconstrucción de vértices de eventos de interacción neutrino-núcleo, un problema en el dominio de la física de alta energía. En este enfoque, combina datos de energía y tiempo que se recopilan en el detector MIN-ERvA para realizar tareas de clasificación y regresión. Demostramos que la red resultante logra una mayor precisión que los resultados anteriores, mientras que requiere un tamaño de modelo más pequeño y menos tiempo de entrenamiento. En particular, el modelo propuesto supera el estado de la técnica en un 4,00% en precisión de clasificación.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengInstitute of Electrical and Electronics Engineersinfo:eu-repo/semantics/openAccessFísica de partículashttps://purl.org/pe-repo/ocde/ford#1.03.03-1Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time datainfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/1189oai:repositorio.concytec.gob.pe:20.500.12390/11892024-05-30 16:01:40.622http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="4c7853d7-66d5-4c54-8276-87bce6d4d54c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <DOI>https://doi.org/10.1109/ICASSP.2019.8683736</DOI> <ISI-Number>482554004024</ISI-Number> <Authors> <Author> <DisplayName>Song, LH</DisplayName> <Person id="rp03402" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Chen, F</DisplayName> <Person id="rp03401" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Young, SR</DisplayName> <Person id="rp02098" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Schuman, CD</DisplayName> <Person id="rp01262" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Perdue, G</DisplayName> <Person id="rp00806" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Potok, TE</DisplayName> <Person id="rp01263" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Institute of Electrical and Electronics Engineers</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Física de partículas</Keyword> <Abstract>Se presentó un enfoque de aprendizaje profundo para la reconstrucción de vértices de eventos de interacción neutrino-núcleo, un problema en el dominio de la física de alta energía. En este enfoque, combina datos de energía y tiempo que se recopilan en el detector MIN-ERvA para realizar tareas de clasificación y regresión. Demostramos que la red resultante logra una mayor precisión que los resultados anteriores, mientras que requiere un tamaño de modelo más pequeño y menos tiempo de entrenamiento. En particular, el modelo propuesto supera el estado de la técnica en un 4,00% en precisión de clasificación.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
| score |
13.302918 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
