{"id":"https://openalex.org/W7164939380","doi":"https://doi.org/10.48550/arxiv.2606.16035","title":"GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation","display_name":"GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation","publication_year":2026,"publication_date":"2026-06-14","ids":{"openalex":"https://openalex.org/W7164939380","doi":"https://doi.org/10.48550/arxiv.2606.16035"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16035","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16035","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.16035","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134823760","display_name":"Cole Granger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Granger, Cole","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138720263","display_name":"James Giroux","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Giroux, James","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015445222","display_name":"R. Tyson","orcid":"https://orcid.org/0000-0002-0635-4198"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tyson, Richard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112003570","display_name":"M. Ungaro","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ungaro, Maurizio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134831940","display_name":"Cristiano Fanelli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fanelli, Cristiano","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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/T10048","display_name":"Particle physics theoretical and experimental studies","score":0.5159000158309937,"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/T10048","display_name":"Particle physics theoretical and experimental studies","score":0.5159000158309937,"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/T13650","display_name":"Computational Physics and Python Applications","score":0.0925000011920929,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11044","display_name":"Particle Detector Development and Performance","score":0.058400001376867294,"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/detector","display_name":"Detector","score":0.8027999997138977},{"id":"https://openalex.org/keywords/calorimeter","display_name":"Calorimeter (particle physics)","score":0.5460000038146973},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4620000123977661},{"id":"https://openalex.org/keywords/superconducting-super-collider","display_name":"Superconducting Super Collider","score":0.41519999504089355},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.41190001368522644},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.3968999981880188},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.39649999141693115},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.3937999904155731}],"concepts":[{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.8027999997138977},{"id":"https://openalex.org/C18073261","wikidata":"https://www.wikidata.org/wiki/Q1722586","display_name":"Calorimeter (particle physics)","level":3,"score":0.5460000038146973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5037000179290771},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4823000133037567},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4620000123977661},{"id":"https://openalex.org/C2780117330","wikidata":"https://www.wikidata.org/wiki/Q1192186","display_name":"Superconducting Super Collider","level":4,"score":0.41519999504089355},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.41190001368522644},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3937999904155731},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3695000112056732},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C60718061","wikidata":"https://www.wikidata.org/wiki/Q1414747","display_name":"Momentum (technical analysis)","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30570000410079956},{"id":"https://openalex.org/C49304495","wikidata":"https://www.wikidata.org/wiki/Q49393","display_name":"Projectile","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.2773999869823456},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.27149999141693115},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2646999955177307},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C109214941","wikidata":"https://www.wikidata.org/wiki/Q18334","display_name":"Particle physics","level":1,"score":0.2605000138282776},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C169590947","wikidata":"https://www.wikidata.org/wiki/Q47506","display_name":"Compiler","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C183680338","wikidata":"https://www.wikidata.org/wiki/Q736634","display_name":"Particle detector","level":3,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16035","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16035","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.16035","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16035","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.7651458978652954}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Modern":[0],"particles":[1],"physics":[2,160],"experiments":[3],"have":[4,17,33],"demonstrated":[5],"an":[6,63],"increasing":[7],"need":[8],"for":[9,75,163],"fast,":[10],"high-fidelity":[11],"detector":[12,15,99],"simulation":[13],"as":[14,35,70,107],"components":[16],"improved":[18],"and":[19,53,96,112,128],"subsequent":[20],"computational":[21],"requirements":[22],"approach":[23],"the":[24,76,79,83,118,129,133],"limits":[25],"of":[26,65,109,132],"available":[27],"resources.":[28],"Recently,":[29],"deep":[30],"generative":[31],"models":[32,51],"emerged":[34],"a":[36,66,71,147,151],"promising":[37],"alternative":[38],"to":[39],"traditional":[40,155],"Monte-Carlo":[41],"methods,":[42],"with":[43],"recent":[44],"works":[45],"drawing":[46],"inspiration":[47],"from":[48],"large":[49],"language":[50],"(LLMs)":[52],"self-supervised":[54],"next-token":[55],"prediction":[56],"methods.":[57],"In":[58],"this":[59],"work,":[60],"we":[61],"present":[62],"application":[64],"GPT-style":[67],"autoregressive":[68],"transformer":[69],"fast":[72],"surrogate":[73],"model":[74,90,119],"calorimeter":[77,105],"inside":[78],"CLAS12":[80],"experiment":[81],"at":[82],"Thomas":[84],"Jefferson":[85],"National":[86],"Accelerator":[87],"Facility.":[88],"The":[89,136],"is":[91],"conditioned":[92],"on":[93,146],"incident":[94],"momentum":[95],"generates":[97],"realistic":[98],"hits":[100],"autoregressively":[101],"across":[102],"all":[103],"nine":[104],"layers":[106],"sequences":[108],"strip,":[110],"ADC,":[111],"TDC":[113],"tokens.":[114],"We":[115],"demonstrate":[116],"that":[117],"faithfully":[120],"reproduces":[121],"hit":[122],"multiplicity,":[123],"spatial":[124],"distributions,":[125],"energy":[126],"deposits,":[127],"energy-momentum":[130],"response":[131],"electromagnetic":[134],"calorimeter.":[135],"generator":[137],"achieves":[138],"inference":[139],"rates":[140],"exceeding":[141],"700":[142],"events":[143],"per":[144],"second":[145],"single":[148],"GPU,":[149],"providing":[150],"substantial":[152],"speedup":[153],"over":[154],"Geant4-based":[156],"simulations":[157],"while":[158],"maintaining":[159],"fidelity":[161],"essential":[162],"high-luminosity":[164],"experimental":[165],"programs.":[166]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-17T00:00:00"}