Publication
Neural networks to identify particles using topological properties of calorimeters
| Summary: | 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. |
|---|---|
| Main Authors: | Damazio,Denis Oliveira |
| Other Authors: | Seixas,José Manoel de |
| Subject: | Neural networks calorimeters electronic instrumentation |
| Year: | 2004 |
| Country: | Brazil |
| Document type: | article |
| Access type: | open access |
| Associated institution: | IBICT |
| Language: | English |
| Origin: | Oasisbr |
| _version_ | 1871512089220087808 |
|---|---|
| author | Damazio,Denis Oliveira |
| author2 | Seixas,José Manoel de |
| author2_role | author |
| author_facet | Damazio,Denis Oliveira Seixas,José Manoel de |
| author_role | author |
| country_str | BR |
| creators_json_txt | [{\"Person.name\":\"Damazio,Denis Oliveira\"},{\"Person.name\":\"Seixas,José Manoel de\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Damazio,Denis Oliveira Seixas,José Manoel de |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Neural networks calorimeters electronic instrumentation |
| datacite.titles.title.fl_str_mv | Neural networks to identify particles using topological properties of calorimeters |
| dc.creator.none.fl_str_mv | Damazio,Denis Oliveira Seixas,José Manoel de |
| dc.format.none.fl_str_mv | text/html |
| dc.identifier.none.fl_str_mv | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592004000100008 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Sociedade Brasileira de Automática |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| 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 |
| dc.subject.none.fl_str_mv | Neural networks calorimeters electronic instrumentation |
| dc.title.fl_str_mv | Neural networks to identify particles using topological properties of calorimeters |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| id | oasisbr_a6b712613280d530c2e66276f322bd6a |
| identifier.url.fl_str_mv | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592004000100008 |
| inst_facet_str | urn:organizationAcronym:oasisbr{{{_:::_}}}IBICT |
| instacron_str | OASISBR |
| institution | IBICT |
| instname_str | IBICT |
| language | eng |
| network_acronym_str | oasisbr |
| network_name_str | Oasisbr |
| oai_identifier_str | oai:scielo:S0103-17592004000100008 |
| organization_str_mv | urn:organizationAcronym:oasisbr |
| person_str_mv | Damazio,Denis Oliveira Seixas,José Manoel de |
| publishDate | 2004 |
| publisher.none.fl_str_mv | Sociedade Brasileira de Automática |
| repo_facet_str | urn:repositoryAcronym:oasisbr{{{_:::_}}}Oasisbr |
| reponame_str | Oasisbr |
| repository_id_str | urn:repositoryAcronym:oasisbr |
| service_str_mv | urn:repositoryAcronym:oasisbr |
| spelling | Neural networks to identify particles using topological properties of calorimetersDamazio,Denis OliveiraSeixas,José Manoel deNeural networkscalorimeterselectronic instrumentationopen accesshttp://purl.org/coar/access_right/c_abf2http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592004000100008URLhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592004000100008URLHasVersion2004-03-01The 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.engtext/htmlSociedade Brasileira de AutomáticaSba: Controle & Automação Sociedade Brasileira de Automatica v.15 n.1 2004reponame:Sba: Controle & Automação Sociedade Brasileira de Automaticainstname:Sociedade Brasileira de Automática (SBA)instacron:SBAjournal articlehttp://purl.org/coar/resource_type/c_6501literatureVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85 |
| spellingShingle | Neural networks to identify particles using topological properties of calorimeters Damazio,Denis Oliveira Neural networks calorimeters electronic instrumentation |
| status | SINGLETON |
| status_str | VoR |
| subject.fl_str_mv | Neural networks calorimeters electronic instrumentation |
| title | 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_short | Neural networks to identify particles using topological properties of calorimeters |
| title_sort | Neural networks to identify particles using topological properties of calorimeters |
| topic | Neural networks calorimeters electronic instrumentation |
| topic_facet | Neural networks calorimeters electronic instrumentation |
| url | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592004000100008 |
| visible | 1 |
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