{"id":"https://openalex.org/W7127079564","doi":"https://doi.org/10.1051/epjconf/202533701299/pdf","title":"Efficient ML-Assisted Particle Track Reconstruction Designs","display_name":"Efficient ML-Assisted Particle Track Reconstruction Designs","publication_year":2025,"publication_date":"2025-10-07","ids":{"openalex":"https://openalex.org/W7127079564","doi":"https://doi.org/10.1051/epjconf/202533701299/pdf"},"language":"en","primary_location":{"id":"pmh:oai:edpsciences.org:dkey/10.1051/epjconf/202533701299","doi":"https://doi.org/10.1051/epjconf/202533701299/pdf","is_oa":false,"landing_page_url":"https://www.epj-conferences.org/10.1051/epjconf/202533701299/pdf","pdf_url":"https://www.epj-conferences.org/10.1051/epjconf/202533701299/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/epjconf/202533701299","raw_type":"Text"},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100600328","display_name":"S. Caron","orcid":"https://orcid.org/0000-0003-2941-2829","observed_orcids":["https://orcid.org/0000-0003-2941-2829"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sascha Caron","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098921299","display_name":"Nadezhda Dobreva","orcid":"https://orcid.org/0009-0006-0923-6054","observed_orcids":["https://orcid.org/0009-0006-0923-6054"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nadezhda Dobreva","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102678179","display_name":"Antonio Ferrer S\u00e1nchez","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antonio Ferrer S\u00e1nchez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136466203","display_name":"Jos\u00e9 D. Mart\u00edn-Guerrero","orcid":"https://orcid.org/0000-0001-9378-0285","observed_orcids":["https://orcid.org/0000-0001-9378-0285"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jos\u00e9 D. Mart\u00edn-Guerrero","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024917187","display_name":"Uraz Odyurt","orcid":"https://orcid.org/0000-0003-1094-0234","observed_orcids":["https://orcid.org/0000-0003-1094-0234"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Uraz Odyurt","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086082507","display_name":"Roberto Ruiz de Austri","orcid":"https://orcid.org/0000-0003-3688-9609","observed_orcids":["https://orcid.org/0000-0003-3688-9609"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roberto Ruiz de Austri Bazan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019575785","display_name":"Z. Wolffs","orcid":"https://orcid.org/0000-0002-0688-3380","observed_orcids":["https://orcid.org/0000-0002-0688-3380","https://orcid.org/0000-0001-5100-2522","https://orcid.org/0000-0001-9606-7688"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zef Wolffs","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124819994","display_name":"Yue Zhao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue Zhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.44675183,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11044","display_name":"Particle Detector Development and Performance","score":0.9487000107765198,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"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/T11044","display_name":"Particle Detector Development and Performance","score":0.9487000107765198,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"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/T10048","display_name":"Particle physics theoretical and experimental studies","score":0.01979999989271164,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"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/T10921","display_name":"Neutrino Physics Research","score":0.00419999985024333,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-track-reconstruction","display_name":"particle track reconstruction","score":0.9829999804496765},{"id":"https://openalex.org/keywords/particle-physics","display_name":"particle physics","score":0.597000002861023},{"id":"https://openalex.org/keywords/transformer","display_name":"transformer models","score":0.7379999756813049},{"id":"https://openalex.org/keywords/u-net","display_name":"U-Net","score":0.9940000176429749},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.5699999928474426},{"id":"https://openalex.org/keywords/high-luminosity-lhc","display_name":"High-Luminosity LHC","score":0.8529999852180481},{"id":"https://openalex.org/keywords/offline-reconstruction","display_name":"offline reconstruction","score":0.8349999785423279},{"id":"https://openalex.org/keywords/detector-occupancy","display_name":"detector occupancy","score":0.5519999861717224},{"id":"https://openalex.org/keywords/kalman-filter-limitations","display_name":"Kalman filter limitations","score":0.5429999828338623}],"concepts":[{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6590999960899353},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6554999947547913},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5105999708175659},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4634999930858612},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4381999969482422},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4088999927043915},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.3903999924659729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33660000562667847},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.3172999918460846},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.2732999920845032},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.27219998836517334},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2671999931335449},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2590000033378601},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.25279998779296875},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:edpsciences.org:dkey/10.1051/epjconf/202533701299","doi":"https://doi.org/10.1051/epjconf/202533701299/pdf","is_oa":false,"landing_page_url":"https://www.epj-conferences.org/10.1051/epjconf/202533701299/pdf","pdf_url":"https://www.epj-conferences.org/10.1051/epjconf/202533701299/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/epjconf/202533701299","raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"display_name":"Affordable and clean energy","score":0.7196987271308899,"id":"https://metadata.un.org/sdg/7"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Track":[0],"reconstruction":[1],"is":[2],"a":[3,78],"crucial":[4],"part":[5],"of":[6,28,49,64,118,135,151],"High":[7,31],"Energy":[8],"Physics":[9],"experiments.":[10],"Traditional":[11],"methods":[12],"for":[13,61,94,132,144],"the":[14,26,29,47,62,85,92,106,124,133],"task,":[15],"relying":[16],"on":[17,35,100,140],"Kalman":[18],"Filters,":[19],"scale":[20],"poorly":[21],"with":[22],"detector":[23,59],"occupancy.":[24],"In":[25],"context":[27],"upcoming":[30],"Luminosity-LHC,":[32],"solutions":[33],"based":[34],"Machine":[36],"Learning":[37],"(ML)":[38],"and":[39,69,90,97],"deep":[40,137],"learning":[41,138],"are":[42,121],"very":[43],"appealing.":[44],"We":[45,67,81],"investigate":[46],"feasibility":[48],"training":[50],"multiple":[51],"ML":[52],"architectures":[53],"to":[54],"infer":[55],"track-defining":[56],"parameters":[57],"from":[58,123],"signals,":[60],"application":[63,134],"offline":[65],"reconstruction.":[66],"study":[68],"compare":[70],"three":[71],"Transformer":[72],"model":[73],"designs,":[74],"as":[75,77,146,148],"well":[76,147],"U-Net":[79],"architecture.":[80],"describe":[82],"in":[83],"detail":[84],"two":[86],"most":[87],"promising":[88],"approaches":[89],"benchmark":[91],"pipelines":[93],"physics":[95],"performance":[96],"inference":[98],"speed":[99],"methodically":[101],"simplified":[102,119],"datasets,":[103],"generated":[104],"by":[105],"recently":[107],"developed":[108],"simulation":[109],"framework,":[110],"REDuced":[111],"VIrtual":[112],"Detector":[113],"(REDVID).":[114],"Our":[115,127],"second":[116],"batch":[117],"datasets":[120],"derived":[122],"TrackML":[125],"dataset.":[126],"preliminary":[128],"results":[129],"show":[130],"promise":[131],"such":[136],"techniques":[139],"more":[141],"realistic":[142],"data":[143],"tracking,":[145],"efficient":[149],"elimination":[150],"solutions.":[152]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2026-02-03T00:00:00"}