{"id":"https://openalex.org/W7214979091","doi":"https://doi.org/10.48550/arxiv.2609.35827","title":"Meta-learning accelerates detector design optimization","display_name":"Meta-learning accelerates detector design optimization","publication_year":2026,"publication_date":"2026-09-22","ids":{"openalex":"https://openalex.org/W7214979091","doi":"https://doi.org/10.48550/arxiv.2609.35827"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.35827","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.35827","pdf_url":"https://arxiv.org/pdf/2609.35827","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.35827","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090626270","display_name":"M. Borisyak","orcid":"https://orcid.org/0000-0002-1493-0319","observed_orcids":["https://orcid.org/0000-0002-1493-0319"]},"institutions":[{"id":"https://openalex.org/I193619901","display_name":"Constructor University","ror":"https://ror.org/02yrs2n53","country_code":"DE","type":"education","lineage":["https://openalex.org/I193619901"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Borisyak, Maxim","raw_affiliation_strings":["Constructor University Bremen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Constructor University Bremen","institution_ids":["https://openalex.org/I193619901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5153603673","display_name":"Nikita Gladin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I193619901","display_name":"Constructor University","ror":"https://ror.org/02yrs2n53","country_code":"DE","type":"education","lineage":["https://openalex.org/I193619901"]},{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]}],"countries":["DE","IT"],"is_corresponding":false,"raw_author_name":"Gladin, Nikita","raw_affiliation_strings":["Constructor University Bremen","University of Bologna"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Constructor University Bremen","institution_ids":["https://openalex.org/I193619901"]},{"raw_affiliation_string":"University of Bologna","institution_ids":["https://openalex.org/I9360294"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5153636662","display_name":"Andrey Ustyuzhanin","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ustyuzhanin, Andrey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T11044","display_name":"Particle Detector Development and Performance","score":0.2904999852180481,"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":0.2904999852180481,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.23989999294281006,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.13379999995231628,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/meta-learning","display_name":"meta-learning","score":0.968999981880188},{"id":"https://openalex.org/keywords/detector-optimization","display_name":"detector optimization","score":0.9710000157356262},{"id":"https://openalex.org/keywords/inference-performance","display_name":"inference performance","score":0.5379999876022339},{"id":"https://openalex.org/keywords/simulation-cost-reduction","display_name":"simulation cost reduction","score":0.47699999809265137},{"id":"https://openalex.org/keywords/straw-tracker","display_name":"straw tracker","score":0.5870000123977661},{"id":"https://openalex.org/keywords/ship-experiment","display_name":"SHiP experiment","score":0.9890000224113464},{"id":"https://openalex.org/keywords/particle-detector-design","display_name":"particle detector design","score":0.5070000290870667},{"id":"https://openalex.org/keywords/continuous-training","display_name":"continuous training","score":0.4740000069141388}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8208000063896179},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6330999732017517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5148000121116638},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.41440001130104065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4081000089645386},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39879998564720154},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.39469999074935913},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.37689998745918274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36629998683929443},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C186394612","wikidata":"https://www.wikidata.org/wiki/Q7098942","display_name":"Optimal design","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27649998664855957},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2624000012874603},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.35827","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.35827","pdf_url":"https://arxiv.org/pdf/2609.35827","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.35827","doi":"https://doi.org/10.48550/arxiv.2609.35827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.35827","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.35827","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.35827","pdf_url":"https://arxiv.org/pdf/2609.35827","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1088949837","display_name":null,"funder_award_id":"EDUNC-33-18- 279-V12","funder_id":"https://openalex.org/F4320320751","funder_display_name":"Ministry of Education - Singapore"},{"id":"https://openalex.org/G2048937990","display_name":null,"funder_award_id":"EDUN C-33-18-279-V12 I-FIM","funder_id":"https://openalex.org/F4320320751","funder_display_name":"Ministry of Education - Singapore"}],"funders":[{"id":"https://openalex.org/F4320320751","display_name":"Ministry of Education - Singapore","ror":"https://ror.org/01kcva023"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214979091.pdf","grobid_xml":"https://content.openalex.org/works/W7214979091.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W1569788011","https://openalex.org/W2113839990","https://openalex.org/W2178031510","https://openalex.org/W2553303224","https://openalex.org/W2795900505","https://openalex.org/W3170380823","https://openalex.org/W4293387938","https://openalex.org/W4394662461","https://openalex.org/W7143532662"],"related_works":["https://openalex.org/W4415827918","https://openalex.org/W7128712370","https://openalex.org/W2755509244","https://openalex.org/W7092185335","https://openalex.org/W7117648621","https://openalex.org/W4378837359","https://openalex.org/W2982641032","https://openalex.org/W3012526487","https://openalex.org/W7100628919","https://openalex.org/W2996766582"],"abstract_inverted_index":{"The":[0,78],"quality":[1,11],"of":[2,12,26,68,102,118,147,155,160,166,171,182],"a":[3,128,189],"detector":[4,33,76],"design":[5,51,135,164,191],"is":[6,40,143],"ultimately":[7],"determined":[8],"by":[9,19,42],"the":[10,13,20,24,31,38,47,50,53,66,69,75,83,92,99,103,112,120,134,140,158,167,172,185,197,202,206],"inference":[14,39,55,70,84,105,121],"it":[15],"enables,":[16],"that":[17],"is,":[18,57],"accuracy":[21],"with":[22,72],"which":[23,161],"quantities":[25],"interest":[27],"are":[28],"reconstructed":[29],"from":[30],"raw":[32],"response.":[34],"For":[35],"complex":[36],"detectors,":[37],"performed":[41],"machine":[43],"learning":[44],"models,":[45],"and":[46,52,97,136,200],"relation":[48],"between":[49],"attainable":[54],"performance":[56,71],"in":[58,209],"general,":[59],"non-trivial.":[60],"In":[61],"this":[62],"work,":[63],"we":[64],"consider":[65],"optimization":[67,141,156],"respect":[73],"to":[74],"design.":[77],"conventional":[79],"approach":[80],"prescribes":[81],"retraining":[82],"model":[85,187],"at":[86,107,124],"every":[87,125],"candidate":[88,126,190],"design,":[89,127],"thus,":[90],"treating":[91],"evaluations":[93],"as":[94],"independent":[95],"tasks":[96],"discarding":[98],"shared":[100,144],"structure":[101],"optimal":[104],"algorithms":[106],"different":[108],"designs.":[109],"We":[110,149],"propose":[111],"meta-learned":[113],"objective":[114],"estimate":[115],"(MLOE):":[116],"instead":[117],"solving":[119],"problem":[122],"anew":[123],"single":[129],"meta-inference":[130,186],"model,":[131],"conditioned":[132],"on":[133,152],"trained":[137],"continually":[138],"along":[139],"path,":[142],"across":[145],"all":[146,210],"them.":[148],"test":[150],"MLOE":[151],"three":[153],"families":[154],"problems,":[157],"last":[159],"comprises":[162],"two":[163],"spaces":[165],"Spectrometer":[168],"Straw":[169],"Tracker":[170],"Search":[173],"for":[174],"Hidden":[175],"Particles":[176],"(SHiP)":[177],"experiment;":[178],"under":[179],"matched":[180],"budgets":[181],"simulation":[183,194],"calls,":[184],"evaluates":[188],"using":[192],"fewer":[193],"calls":[195],"than":[196],"baseline":[198],"strategies":[199],"holds":[201],"better":[203],"rank":[204],"over":[205],"convergence":[207],"curve":[208],"examined":[211],"cases.":[212]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-01T00:00:00"}
