{"id":"https://openalex.org/W3085430103","doi":"https://doi.org/10.1088/2632-2153/abb781","title":"Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks","display_name":"Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks","publication_year":2020,"publication_date":"2020-09-11","ids":{"openalex":"https://openalex.org/W3085430103","doi":"https://doi.org/10.1088/2632-2153/abb781","mag":"3085430103"},"language":"en","primary_location":{"id":"doi:10.1088/2632-2153/abb781","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/abb781","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/abb781/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://iopscience.iop.org/article/10.1088/2632-2153/abb781/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086802301","display_name":"Jack Griffiths","orcid":"https://orcid.org/0000-0002-4052-1770"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"J Griffiths","raw_affiliation_strings":["Now at: University of Cambridge, Cavendish Laboratory, Cambridge, CB3 0HE, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Now at: University of Cambridge, Cavendish Laboratory, Cambridge, CB3 0HE, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006753275","display_name":"Steven Kleinegesse","orcid":null},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"S Kleinegesse","raw_affiliation_strings":["Now at: University of Edinburgh, School of Informatics, Edinburgh, EH8 9AB, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Now at: University of Edinburgh, School of Informatics, Edinburgh, EH8 9AB, United Kingdom","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008235699","display_name":"Daniel J. Saunders","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"D Saunders","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109921699","display_name":"Richard A. Taylor","orcid":"https://orcid.org/0000-0002-9082-6644"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"R Taylor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5063689819","display_name":"A. Vacheret","orcid":"https://orcid.org/0000-0001-7792-4349"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"A Vacheret","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-7792-4349","affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5063689819"],"corresponding_institution_ids":[],"apc_list":{"value":1600,"currency":"GBP","value_usd":1962},"apc_paid":{"value":1600,"currency":"GBP","value_usd":1962},"fwci":6.1121,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.97047176,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"1","issue":"4","first_page":"045022","last_page":"045022"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11949","display_name":"Nuclear Physics and Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11949","display_name":"Nuclear Physics and Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11216","display_name":"Radiation Detection and Scintillator Technologies","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10597","display_name":"Nuclear reactor physics and engineering","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scintillator","display_name":"Scintillator","score":0.7881692051887512},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6803597211837769},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6772605776786804},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6232203245162964},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5725489258766174},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5352490544319153},{"id":"https://openalex.org/keywords/photomultiplier","display_name":"Photomultiplier","score":0.5173087120056152},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4869705140590668},{"id":"https://openalex.org/keywords/silicon-photomultiplier","display_name":"Silicon photomultiplier","score":0.4814452528953552},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4153193235397339},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.33332282304763794},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.2595372796058655}],"concepts":[{"id":"https://openalex.org/C161694136","wikidata":"https://www.wikidata.org/wiki/Q910990","display_name":"Scintillator","level":3,"score":0.7881692051887512},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6803597211837769},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6772605776786804},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6232203245162964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5725489258766174},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5352490544319153},{"id":"https://openalex.org/C113879476","wikidata":"https://www.wikidata.org/wiki/Q61897384","display_name":"Photomultiplier","level":3,"score":0.5173087120056152},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4869705140590668},{"id":"https://openalex.org/C104335537","wikidata":"https://www.wikidata.org/wiki/Q1085747","display_name":"Silicon photomultiplier","level":4,"score":0.4814452528953552},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4153193235397339},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.33332282304763794},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2595372796058655},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1088/2632-2153/abb781","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/abb781","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/abb781/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1088/2632-2153/abb781","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/abb781","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/abb781/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G6881561830","display_name":"Search for a new form of matter: the sterile neutrino","funder_award_id":"682474","funder_id":"https://openalex.org/F4320338335","funder_display_name":"H2020 European Research Council"}],"funders":[{"id":"https://openalex.org/F4320338335","display_name":"H2020 European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2012867436","https://openalex.org/W2023868441","https://openalex.org/W2166010413","https://openalex.org/W2187089797","https://openalex.org/W2339008828","https://openalex.org/W2412135549","https://openalex.org/W2521612052","https://openalex.org/W2542432605","https://openalex.org/W2549491455","https://openalex.org/W2563484019","https://openalex.org/W2593541179","https://openalex.org/W2622826443","https://openalex.org/W2756289491","https://openalex.org/W2786943156","https://openalex.org/W2808120371","https://openalex.org/W2918502103","https://openalex.org/W2919115771","https://openalex.org/W2966712007","https://openalex.org/W2990832548","https://openalex.org/W3099851408","https://openalex.org/W3100677456","https://openalex.org/W3103298935","https://openalex.org/W3104503472","https://openalex.org/W3104561293","https://openalex.org/W3106166086","https://openalex.org/W6728765006","https://openalex.org/W6785898710"],"related_works":["https://openalex.org/W2083171769","https://openalex.org/W1987141372","https://openalex.org/W3100393446","https://openalex.org/W2588369396","https://openalex.org/W2039054661","https://openalex.org/W3191680357","https://openalex.org/W4309547031","https://openalex.org/W2354435766","https://openalex.org/W2008747842","https://openalex.org/W4301876341"],"abstract_inverted_index":{"Abstract":[0],"We":[1,119],"demonstrate":[2],"the":[3,17,21,24,53,57,78,86,113,122,133,140,144,147,163,177,182],"use":[4],"of":[5,37,107,115,127,143,162],"a":[6,31,64,68,95,199],"convolutional":[7],"neural":[8,98,123],"network":[9,22,54,124,145],"to":[10,20,51,59,74,77,85,103,159,171,190],"perform":[11],"neutron-gamma":[12],"pulse":[13],"shape":[14],"discrimination,":[15,108],"where":[16],"only":[18,154],"inputs":[19],"are":[23],"raw":[25,134,195],"digitised":[26],"silicon":[27],"photomultiplier":[28,70],"signals":[29,178],"from":[30,132],"dual":[32],"scintillator":[33,41],"detector":[34,58,196],"element":[35],"made":[36],"6":[38],"Li":[39],"F:ZnS(Ag)":[40],"and":[42,63,89,94,192],"PVT":[43],"plastic.":[44],"A":[45],"realistic":[46],"labelled":[47],"dataset":[48,165],"was":[49,72],"created":[50],"train":[52],"by":[55,138],"exposing":[56],"an":[60,110,173],"AmBe":[61],"source,":[62],"data-driven":[65],"method":[66,157],"utilising":[67],"separate":[69],"tube":[71],"used":[73],"assign":[75],"labels":[76],"recorded":[79],"signals.":[80],"This":[81,185],"approach":[82],"is":[83,101,125,155,169],"compared":[84],"charge":[87],"integration":[88],"continuous":[90],"wavelet":[91],"transform":[92],"methods":[93],"simpler":[96],"artificial":[97],"net.":[99],"It":[100],"found":[102],"provide":[104],"superior":[105],"levels":[106],"achieving":[109],"area":[111],"under":[112],"curve":[114],"0.996":[116],"\u00b1":[117],"0.003.":[118],"find":[120],"that":[121,152,167,179,202],"capable":[126],"extracting":[128],"interpretable":[129],"features":[130],"directly":[131],"data.":[135],"In":[136],"addition,":[137],"visualising":[139],"high-dimensional":[141],"representations":[142],"with":[146],"t-SNE":[148],"algorithm,":[149],"we":[150],"discover":[151],"not":[153],"this":[156],"robust":[158],"minor":[160],"mislabeling":[161],"training":[164],"but":[166],"it":[168],"possible":[170],"identify":[172],"underlying":[174],"substructure":[175],"within":[176],"goes":[180],"beyond":[181],"original":[183],"labelling.":[184],"technique":[186],"could":[187],"be":[188],"utilised":[189],"explore":[191],"cluster":[193],"complex,":[194],"data":[197],"in":[198],"novel":[200],"way":[201],"may":[203],"reveal":[204],"more":[205],"insights":[206],"than":[207],"standard":[208],"analysis":[209],"methods.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10},{"year":2019,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}