{"id":"https://openalex.org/W3047667920","doi":"https://doi.org/10.1088/2632-2153/abfaed","title":"Complete parameter inference for GW150914 using deep learning","display_name":"Complete parameter inference for GW150914 using deep learning","publication_year":2021,"publication_date":"2021-04-22","ids":{"openalex":"https://openalex.org/W3047667920","doi":"https://doi.org/10.1088/2632-2153/abfaed","mag":"3047667920"},"language":"en","primary_location":{"id":"doi:10.1088/2632-2153/abfaed","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/abfaed","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/abfaed/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://iopscience.iop.org/article/10.1088/2632-2153/abfaed/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076536596","display_name":"Stephen Green","orcid":"https://orcid.org/0000-0002-6987-6313"},"institutions":[{"id":"https://openalex.org/I149899117","display_name":"Max Planck Society","ror":"https://ror.org/01hhn8329","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I149899117"]},{"id":"https://openalex.org/I4210127646","display_name":"Max Planck Institute for Gravitational Physics","ror":"https://ror.org/03sry2h30","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210127646"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Stephen R Green","raw_affiliation_strings":["Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am M\u00fchlenberg 1, 14476 Potsdam, Germany","Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society"],"raw_orcid":"https://orcid.org/0000-0002-6987-6313","affiliations":[{"raw_affiliation_string":"Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am M\u00fchlenberg 1, 14476 Potsdam, Germany","institution_ids":["https://openalex.org/I4210127646"]},{"raw_affiliation_string":"Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society","institution_ids":["https://openalex.org/I149899117"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011973729","display_name":"J. R. Gair","orcid":"https://orcid.org/0000-0002-1671-3668"},"institutions":[{"id":"https://openalex.org/I149899117","display_name":"Max Planck Society","ror":"https://ror.org/01hhn8329","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I149899117"]},{"id":"https://openalex.org/I4210127646","display_name":"Max Planck Institute for Gravitational Physics","ror":"https://ror.org/03sry2h30","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210127646"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jonathan Gair","raw_affiliation_strings":["Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am M\u00fchlenberg 1, 14476 Potsdam, Germany","Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society"],"raw_orcid":"https://orcid.org/0000-0002-1671-3668","affiliations":[{"raw_affiliation_string":"Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am M\u00fchlenberg 1, 14476 Potsdam, Germany","institution_ids":["https://openalex.org/I4210127646"]},{"raw_affiliation_string":"Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society","institution_ids":["https://openalex.org/I149899117"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076536596"],"corresponding_institution_ids":["https://openalex.org/I149899117","https://openalex.org/I4210127646"],"apc_list":{"value":1600,"currency":"GBP","value_usd":1962},"apc_paid":{"value":1600,"currency":"GBP","value_usd":1962},"fwci":0.2699,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57890617,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"biblio":{"volume":"2","issue":"3","first_page":"03LT01","last_page":"03LT01"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10463","display_name":"Pulsars and Gravitational Waves Research","score":0.9994000196456909,"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":0.9994000196456909,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear 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":0.9775000214576721,"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/estimator","display_name":"Estimator","score":0.5755642652511597},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5727967619895935},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5641449093818665},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.5269245505332947},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.47125816345214844},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.459150493144989},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4431259334087372},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4373885989189148},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4201987385749817},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.41680407524108887},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3466910123825073},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3419581651687622},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3418940305709839},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33793115615844727},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25166618824005127},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23051100969314575}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5755642652511597},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5727967619895935},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5641449093818665},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5269245505332947},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.47125816345214844},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.459150493144989},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4431259334087372},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4373885989189148},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4201987385749817},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.41680407524108887},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3466910123825073},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3419581651687622},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3418940305709839},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33793115615844727},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25166618824005127},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23051100969314575},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1088/2632-2153/abfaed","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/abfaed","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/abfaed/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2008.03312","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.03312","pdf_url":"https://arxiv.org/pdf/2008.03312","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2008.03312","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2008.03312","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"},{"id":"mag:3047667920","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.1088/2632-2153/abfaed","is_oa":true,"landing_page_url":"https://doi.org/10.1088/2632-2153/abfaed","pdf_url":"https://iopscience.iop.org/article/10.1088/2632-2153/abfaed/pdf","source":{"id":"https://openalex.org/S4210200687","display_name":"Machine Learning Science and Technology","issn_l":"2632-2153","issn":["2632-2153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning: Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W3047667920.pdf"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W1134494461","https://openalex.org/W1583912456","https://openalex.org/W1836465849","https://openalex.org/W1959608418","https://openalex.org/W1966727695","https://openalex.org/W1973724272","https://openalex.org/W1996004845","https://openalex.org/W2011301426","https://openalex.org/W2037939815","https://openalex.org/W2176412452","https://openalex.org/W2194775991","https://openalex.org/W2252795400","https://openalex.org/W2302255633","https://openalex.org/W2343709646","https://openalex.org/W2518108298","https://openalex.org/W2587284713","https://openalex.org/W2754075545","https://openalex.org/W2767526854","https://openalex.org/W2805726701","https://openalex.org/W2899782607","https://openalex.org/W2931388211","https://openalex.org/W2948659923","https://openalex.org/W2953046278","https://openalex.org/W2962897886","https://openalex.org/W2963047245","https://openalex.org/W2963090522","https://openalex.org/W2964121744","https://openalex.org/W2970971581","https://openalex.org/W2971836485","https://openalex.org/W2973034339","https://openalex.org/W2975828310","https://openalex.org/W2999365684","https://openalex.org/W3003966848","https://openalex.org/W3006452559","https://openalex.org/W3016394976","https://openalex.org/W3017872349","https://openalex.org/W3022596706","https://openalex.org/W3031064176","https://openalex.org/W3036761016","https://openalex.org/W3091954596","https://openalex.org/W3100928534","https://openalex.org/W3105850678","https://openalex.org/W3115110045","https://openalex.org/W4248890683","https://openalex.org/W6610566761","https://openalex.org/W6631190155","https://openalex.org/W6635084905","https://openalex.org/W6638667902","https://openalex.org/W6639732818","https://openalex.org/W6640963894","https://openalex.org/W6685562342","https://openalex.org/W6726497184","https://openalex.org/W6730998768","https://openalex.org/W6733471323","https://openalex.org/W6738536549","https://openalex.org/W6758826472","https://openalex.org/W6763486065","https://openalex.org/W6768006734","https://openalex.org/W6796196383"],"related_works":["https://openalex.org/W4226024614","https://openalex.org/W3094172341","https://openalex.org/W1518859682","https://openalex.org/W2895866691","https://openalex.org/W3084010957","https://openalex.org/W1611025066","https://openalex.org/W2052460714","https://openalex.org/W2080979422","https://openalex.org/W3103867616","https://openalex.org/W2798612008","https://openalex.org/W2082330212","https://openalex.org/W3197224138","https://openalex.org/W2912451899","https://openalex.org/W3092547042","https://openalex.org/W151375913","https://openalex.org/W3105104625","https://openalex.org/W3126544483","https://openalex.org/W2111942322","https://openalex.org/W3138534629","https://openalex.org/W17499355"],"abstract_inverted_index":{"Abstract":[0],"The":[1],"LIGO":[2],"and":[3,41,136,193],"Virgo":[4],"gravitational-wave":[5],"observatories":[6],"have":[7],"detected":[8],"many":[9],"exciting":[10],"events":[11],"over":[12,103],"the":[13,19,49,88,104,123,140,147,157,181],"past":[14],"5":[15],"years.":[16],"To":[17,63],"infer":[18],"system":[20,112],"parameters,":[21,113],"iterative":[22],"sampling":[23,135],"algorithms":[24],"such":[25],"as":[26,48,67,69],"MCMC":[27],"are":[28],"typically":[29],"used":[30],"with":[31,54,180,199],"Bayes\u2019":[32],"theorem":[33],"to":[34,43,82,98],"obtain":[35,194],"posterior":[36,100,171],"samples\u2014by":[37],"repeatedly":[38],"generating":[39],"waveforms":[40],"comparing":[42],"measured":[44],"strain":[45,116,177],"data.":[46],"However,":[47],"rate":[50],"of":[51,71,108,125,163,169],"detections":[52],"grows":[53],"detector":[55,115],"sensitivity,":[56],"this":[57,65,76],"poses":[58],"a":[59,93,128,160],"growing":[60],"computational":[61],"challenge.":[62],"confront":[64],"challenge,":[66],"well":[68],"that":[70],"fast":[72],"multimessenger":[73],"alerts,":[74],"in":[75,196],"study":[77],"we":[78],"apply":[79],"deep":[80],"learning":[81],"learn":[83],"non-iterative":[84],"surrogate":[85],"models":[86],"for":[87,133,175],"Bayesian":[89],"posterior.":[90],"We":[91,121,184],"train":[92],"neural-network":[94],"conditional":[95],"density":[96,137],"estimator":[97],"model":[99],"probability":[101],"distributions":[102],"full":[105],"15-dimensional":[106],"space":[107],"binary":[109],"black":[110],"hole":[111],"given":[114],"data":[117,148,178],"from":[118,146],"multiple":[119],"detectors.":[120],"use":[122],"method":[124,187],"normalizing":[126],"flows\u2014specifically,":[127],"neural":[129],"spline":[130],"flow\u2014which":[131],"allows":[132],"rapid":[134],"estimation.":[138],"Training":[139],"network":[141,158],"is":[142],"likelihood-free,":[143],"requiring":[144],"samples":[145,172],"generative":[149],"process,":[150],"but":[151],"no":[152],"likelihood":[153],"evaluations.":[154],"Through":[155],"training,":[156],"learns":[159],"global":[161],"set":[162],"posteriors:":[164],"it":[165],"can":[166],"generate":[167],"thousands":[168],"independent":[170],"per":[173],"second":[174],"any":[176],"consistent":[179],"training":[182],"distribution.":[183],"demonstrate":[185],"our":[186],"by":[188],"performing":[189],"inference":[190],"on":[191],"GW150914,":[192],"results":[195],"close":[197],"agreement":[198],"standard":[200],"techniques.":[201]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}
