{"id":"https://openalex.org/W7220736899","doi":"https://doi.org/10.48550/arxiv.2610.05121","title":"Fast Bayesian Updating of the Neutron-Star Equation of State with Neural Posterior and Evidence Estimation","display_name":"Fast Bayesian Updating of the Neutron-Star Equation of State with Neural Posterior and Evidence Estimation","publication_year":2026,"publication_date":"2026-10-04","ids":{"openalex":"https://openalex.org/W7220736899","doi":"https://doi.org/10.48550/arxiv.2610.05121"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.05121","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.05121","pdf_url":"https://arxiv.org/pdf/2610.05121","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","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.05121","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5157696982","display_name":"Prashant Thakur","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Thakur, Prashant","raw_affiliation_strings":["Department of Physics , Yonsei University , Seoul 03722 , South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Physics , Yonsei University , Seoul 03722 , South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5157696982"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"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/T10463","display_name":"Pulsars and Gravitational Waves Research","score":0.9726999998092651,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"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/T10463","display_name":"Pulsars and Gravitational Waves Research","score":0.9726999998092651,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"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/T10093","display_name":"Nuclear physics research studies","score":0.010599999688565731,"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/T11323","display_name":"Gamma-ray bursts and supernovae","score":0.0013000000035390258,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"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/neutron-star-equation-of-state","display_name":"neutron star equation of state","score":0.9879999756813049},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.7850000262260437},{"id":"https://openalex.org/keywords/neural-posterior-estimation","display_name":"neural posterior estimation","score":0.8999999761581421},{"id":"https://openalex.org/keywords/evidence-estimation","display_name":"evidence estimation","score":0.7260000109672546},{"id":"https://openalex.org/keywords/gw170817","display_name":"GW170817","score":0.9309999942779541},{"id":"https://openalex.org/keywords/psr-j1614-2230","display_name":"PSR J1614-2230","score":0.8859999775886536},{"id":"https://openalex.org/keywords/mass-radius-relation","display_name":"mass-radius relation","score":0.7559999823570251},{"id":"https://openalex.org/keywords/tidal-deformability","display_name":"tidal deformability","score":0.9980000257492065},{"id":"https://openalex.org/keywords/hyperonic-matter","display_name":"hyperonic matter","score":0.8529999852180481},{"id":"https://openalex.org/keywords/nucleonic-matter","display_name":"nucleonic matter","score":0.9440000057220459},{"id":"https://openalex.org/keywords/relativistic-mean-field-model","display_name":"relativistic mean field","score":0.9359999895095825},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.3149999976158142}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6777999997138977},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.6355999708175659},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6100999712944031},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6086999773979187},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5400999784469604},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4632999897003174},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46320000290870667},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4499000012874603},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4406000077724457},{"id":"https://openalex.org/C99173435","wikidata":"https://www.wikidata.org/wiki/Q4874469","display_name":"Bayesian experimental design","level":5,"score":0.4404999911785126},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.4065999984741211},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.4011000096797943},{"id":"https://openalex.org/C142291917","wikidata":"https://www.wikidata.org/wiki/Q4165283","display_name":"Bayes factor","level":4,"score":0.3991999924182892},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C53810900","wikidata":"https://www.wikidata.org/wiki/Q214967","display_name":"Equation of state","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3352999985218048},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3165000081062317},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3003999888896942},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.2815999984741211},{"id":"https://openalex.org/C173974348","wikidata":"https://www.wikidata.org/wiki/Q1469893","display_name":"Fiducial marker","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2565000057220459},{"id":"https://openalex.org/C170797541","wikidata":"https://www.wikidata.org/wiki/Q452399","display_name":"Error bar","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C3020318244","wikidata":"https://www.wikidata.org/wiki/Q4812187","display_name":"Large sample","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C2984391234","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential sampling","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.05121","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.05121","pdf_url":"https://arxiv.org/pdf/2610.05121","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.2610.05121","doi":"https://doi.org/10.48550/arxiv.2610.05121","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.05121","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":"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.05121","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.05121","pdf_url":"https://arxiv.org/pdf/2610.05121","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/G6205291843","display_name":null,"funder_award_id":"RS-2024-00457037","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G91925318","display_name":null,"funder_award_id":"RS-2024-00457037","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320321314","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7220736899.pdf","grobid_xml":"https://content.openalex.org/works/W7220736899.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4415306444","https://openalex.org/W7157660885","https://openalex.org/W4406120631","https://openalex.org/W7163044653","https://openalex.org/W4388830416","https://openalex.org/W7165924097","https://openalex.org/W4404743377","https://openalex.org/W7212050318","https://openalex.org/W4319592221","https://openalex.org/W7203947971"],"abstract_inverted_index":{"We":[0,61,170],"present":[1],"two":[2],"complementary":[3],"neural":[4,13,27],"routes":[5],"for":[6,161,167,211,218],"microscopic":[7],"neutron-star":[8,194],"EOS":[9,195],"inference:":[10],"truncated":[11],"sequential":[12],"posterior":[14,28],"estimation":[15,29],"with":[16,30,53,69,82,156],"mixture":[17],"importance":[18,31],"sampling":[19,32],"(TSNPE+MIS),":[20],"which":[21,34],"targets":[22],"one":[23],"observation,":[24],"and":[25,40,50,64,74,88,91,101,109,164,215,231],"amortized":[26],"(A-NET+IS),":[33],"is":[35,123],"reused":[36],"across":[37],"nuclear":[38,70,213],"inputs":[39],"NICER":[41,135,221],"sources.":[42],"Both":[43],"are":[44,97,106,228],"corrected":[45],"against":[46],"the":[47,54,77,85,113,141,152,188,237,242],"original":[48],"prior":[49],"full":[51],"likelihood,":[52,244],"effective":[55],"sample":[56],"size":[57],"as":[58],"a":[59,173,178,198,219],"diagnostic.":[60],"test":[62],"nucleonic":[63,162,249],"hyperonic":[65,168],"relativistic":[66],"mean-field":[67],"models":[68],"data,":[71],"NICER,":[72],"GW170817,":[73],"pQCD.":[75],"For":[76,149],"fiducial":[78],"analyses,":[79],"both":[80,245],"agree":[81,235],"UltraNest":[83,157,227],"in":[84,207],"parameter":[86],"posteriors":[87],"90%":[89],"mass-radius":[90],"tidal-deformability":[92],"bands.":[93],"TSNPE+MIS":[94,111],"band-edge":[95],"differences":[96,105,225],"only":[98],"0.024-0.033":[99],"km":[100,108,160,166],"1.02-1.03%,":[102],"while":[103],"A-NET+IS":[104,122],"0.019-0.050":[107],"0.86-1.13%.":[110],"reduces":[112],"computing":[114],"time":[115],"by":[116,251],"factors":[117,253],"of":[118,181,254],"1.6-2.3;":[119],"after":[120],"training,":[121,139],"15-240":[124],"times":[125],"faster":[126],"per":[127],"configuration.":[128],"The":[129],"frozen":[130],"A-NET":[131],"also":[132,171],"analyzes":[133],"three":[134],"sources":[136],"absent":[137],"from":[138,226],"including":[140],"newly":[142],"reported":[143],"high-mass,":[144],"compact":[145],"PSR":[146],"J1614-2230":[147],"posterior.":[148],"this":[150],"source,":[151],"inferred":[153],"$R_{1.4}$":[154],"agrees":[155],"within":[158,236],"0.010":[159],"matter":[163,250],"0.009":[165],"matter.":[169],"introduce":[172],"Green-function":[174],"Evidence":[175,182,190],"Network":[176,191],"(EN),":[177],"regression-based":[179],"variant":[180],"Networks":[183],"and,":[184],"to":[185,193],"our":[186],"knowledge,":[187],"first":[189],"applied":[192],"inference.":[196],"After":[197],"one-time":[199],"cost,":[200],"its":[201],"network":[202],"queries":[203],"return":[204],"absolute":[205],"evidences":[206,234],"about":[208,255],"0.03":[209],"s":[210,217],"new":[212,220],"data":[214],"3.5":[216],"source;":[222],"all":[223],"16":[224],"below":[229],"$1\u03c3$,":[230],"independent":[232],"importance-sampling":[233],"quoted":[238],"EN":[239],"uncertainties.":[240],"Under":[241],"adopted":[243],"evidence":[246],"calculations":[247],"favor":[248],"Bayes":[252],"22:1-27:1.":[256]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-10-07T00:00:00"}