Neural networks to identify particles using topological properties of calorimeters
Autor(a) principal: | |
---|---|
Data de Publicação: | 2004 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Sba: Controle & Automação Sociedade Brasileira de Automatica |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592004000100008 |
Resumo: | The present work describes a neural particle classifier system based on topological mapping of the segmented information provided by a high-energy calorimeter, a detector that measures the energy of incoming particles. The achieved classification efficiencies are above 97.50% for the higher energy particle beams, even when experimental data exhibit unavoidable contamination due to the particle beam generation process, what could jeopardize the classifier performance. Some deterioration in the performance for the lower energy range is also discussed. The reduction on the dimensionality of the data input space caused by the topological mapping may be very helpful when online implementation of the classifier is required. |
id |
SBA-2_a6b712613280d530c2e66276f322bd6a |
---|---|
oai_identifier_str |
oai:scielo:S0103-17592004000100008 |
network_acronym_str |
SBA-2 |
network_name_str |
Sba: Controle & Automação Sociedade Brasileira de Automatica |
repository_id_str |
|
spelling |
Neural networks to identify particles using topological properties of calorimetersNeural networkscalorimeterselectronic instrumentationThe present work describes a neural particle classifier system based on topological mapping of the segmented information provided by a high-energy calorimeter, a detector that measures the energy of incoming particles. The achieved classification efficiencies are above 97.50% for the higher energy particle beams, even when experimental data exhibit unavoidable contamination due to the particle beam generation process, what could jeopardize the classifier performance. Some deterioration in the performance for the lower energy range is also discussed. The reduction on the dimensionality of the data input space caused by the topological mapping may be very helpful when online implementation of the classifier is required.Sociedade Brasileira de Automática2004-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592004000100008Sba: Controle & Automação Sociedade Brasileira de Automatica v.15 n.1 2004reponame:Sba: Controle & Automação Sociedade Brasileira de Automaticainstname:Sociedade Brasileira de Automática (SBA)instacron:SBA10.1590/S0103-17592004000100008info:eu-repo/semantics/openAccessDamazio,Denis OliveiraSeixas,José Manoel deeng2004-06-14T00:00:00Zoai:scielo:S0103-17592004000100008Revistahttps://www.sba.org.br/revista/PUBhttps://old.scielo.br/oai/scielo-oai.php||revista_sba@fee.unicamp.br1807-03450103-1759opendoar:2004-06-14T00:00Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA)false |
dc.title.none.fl_str_mv |
Neural networks to identify particles using topological properties of calorimeters |
title |
Neural networks to identify particles using topological properties of calorimeters |
spellingShingle |
Neural networks to identify particles using topological properties of calorimeters Damazio,Denis Oliveira Neural networks calorimeters electronic instrumentation |
title_short |
Neural networks to identify particles using topological properties of calorimeters |
title_full |
Neural networks to identify particles using topological properties of calorimeters |
title_fullStr |
Neural networks to identify particles using topological properties of calorimeters |
title_full_unstemmed |
Neural networks to identify particles using topological properties of calorimeters |
title_sort |
Neural networks to identify particles using topological properties of calorimeters |
author |
Damazio,Denis Oliveira |
author_facet |
Damazio,Denis Oliveira Seixas,José Manoel de |
author_role |
author |
author2 |
Seixas,José Manoel de |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Damazio,Denis Oliveira Seixas,José Manoel de |
dc.subject.por.fl_str_mv |
Neural networks calorimeters electronic instrumentation |
topic |
Neural networks calorimeters electronic instrumentation |
description |
The present work describes a neural particle classifier system based on topological mapping of the segmented information provided by a high-energy calorimeter, a detector that measures the energy of incoming particles. The achieved classification efficiencies are above 97.50% for the higher energy particle beams, even when experimental data exhibit unavoidable contamination due to the particle beam generation process, what could jeopardize the classifier performance. Some deterioration in the performance for the lower energy range is also discussed. The reduction on the dimensionality of the data input space caused by the topological mapping may be very helpful when online implementation of the classifier is required. |
publishDate |
2004 |
dc.date.none.fl_str_mv |
2004-03-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592004000100008 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592004000100008 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-17592004000100008 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Automática |
publisher.none.fl_str_mv |
Sociedade Brasileira de Automática |
dc.source.none.fl_str_mv |
Sba: Controle & Automação Sociedade Brasileira de Automatica v.15 n.1 2004 reponame:Sba: Controle & Automação Sociedade Brasileira de Automatica instname:Sociedade Brasileira de Automática (SBA) instacron:SBA |
instname_str |
Sociedade Brasileira de Automática (SBA) |
instacron_str |
SBA |
institution |
SBA |
reponame_str |
Sba: Controle & Automação Sociedade Brasileira de Automatica |
collection |
Sba: Controle & Automação Sociedade Brasileira de Automatica |
repository.name.fl_str_mv |
Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA) |
repository.mail.fl_str_mv |
||revista_sba@fee.unicamp.br |
_version_ |
1754824563998326784 |