{"id":"https://openalex.org/W7161048710","doi":"https://doi.org/10.48550/arxiv.2605.12165","title":"Machine Learning for neutron source distributions","display_name":"Machine Learning for neutron source distributions","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161048710","doi":"https://doi.org/10.48550/arxiv.2605.12165"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.12165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12165","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.12165","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136050930","display_name":"Jose Ignacio Robledo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Robledo, Jose Ignacio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136073905","display_name":"Norberto Schmidt","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schmidt, Norberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007181665","display_name":"Klaus Lieutenant","orcid":"https://orcid.org/0000-0001-8278-9401"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lieutenant, Klaus","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136072148","display_name":"Jingjing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jingjing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136047913","display_name":"Stefan Kesselheim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kesselheim, Stefan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5014635162","display_name":"Paul Zakalek","orcid":"https://orcid.org/0000-0002-6497-9604"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zakalek, Paul","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/T11949","display_name":"Nuclear Physics and Applications","score":0.34310001134872437,"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":0.34310001134872437,"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":0.09719999879598618,"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":0.08789999783039093,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.5885999798774719},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.47620001435279846},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.46000000834465027},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.4537999927997589},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4343999922275543},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.41990000009536743},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.39399999380111694},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.3815000057220459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6033999919891357},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5885999798774719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5824999809265137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5109000205993652},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.47620001435279846},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.46000000834465027},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4537999927997589},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4343999922275543},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.39399999380111694},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3815000057220459},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37560001015663147},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.33809998631477356},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3174999952316284},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.28610000014305115},{"id":"https://openalex.org/C167723999","wikidata":"https://www.wikidata.org/wiki/Q3773214","display_name":"Sampling distribution","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26739999651908875},{"id":"https://openalex.org/C181833780","wikidata":"https://www.wikidata.org/wiki/Q926734","display_name":"Neutron source","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.12165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12165","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.12165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12165","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"light":[1],"of":[2,22,44,60,78,115,132],"the":[3,20,41,45,50,56,61,111,130,138],"recent":[4],"advancements":[5,142],"in":[6,69,143],"machine":[7,46],"learning,":[8],"we":[9],"propose":[10],"a":[11,31,85,88,91,96],"novel":[12],"approach":[13,117],"to":[14,105],"neutron":[15],"source":[16,51,107,124],"distribution":[17,52,108],"estimation":[18,27],"through":[19,129],"utilisation":[21],"probabilistic":[23,133],"generative":[24,80,92,134],"models.":[25],"The":[26,76,120],"is":[28,37,58,82],"based":[29],"on":[30],"Monte":[32],"Carlo":[33],"particle":[34,63],"list,":[35,64],"which":[36,136],"only":[38],"required":[39],"during":[40],"training":[42],"stage":[43],"learning":[47],"model.":[48,99],"Once":[49],"has":[53],"been":[54],"learned,":[55],"model":[57],"independent":[59],"original":[62],"allowing":[65],"for":[66,140],"further":[67,141],"sampling":[68],"an":[70],"efficient,":[71],"rapid,":[72],"and":[73,95,110,113],"memory-costless":[74],"manner.":[75],"performance":[77],"various":[79],"models":[81],"evaluated,":[83],"including":[84],"variational":[86],"autoencoder,":[87],"normalizing":[89],"flow,":[90],"adversarial":[93],"network,":[94],"denoising":[97],"diffusion":[98],"These":[100],"approaches":[101],"are":[102,118],"then":[103],"compared":[104],"existing":[106],"estimations,":[109],"advantages":[112],"disadvantages":[114],"each":[116],"discussed.":[119],"results":[121],"demonstrate":[122],"that":[123],"distributions":[125],"can":[126],"be":[127],"modeled":[128],"use":[131],"models,":[135],"paves":[137],"way":[139],"this":[144],"field.":[145]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}
