{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:53:23Z","timestamp":1772556803597,"version":"3.50.1"},"reference-count":74,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"International Partnership Program of the Chinese Academy of Sciences","award":["Grant No. 025GJHZ2023106GC"],"award-info":[{"award-number":["Grant No. 025GJHZ2023106GC"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFC2201903"],"award-info":[{"award-number":["2021YFC2201903"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as laser interferometer space antenna, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals. However, such analyses usually entail significant computational costs. To address these challenges, inspired by the latest progress in generative models, we explore the application of continuous normalizing flows (CNFs) on the parameter estimation of MBHBs. Specifically, we employ linear interpolation and trig interpolation methods to construct transport paths for training CNFs. Additionally, we creatively introduce a parameter transformation method based on the symmetry in the detector\u2019s response function. This transformation is integrated within CNFs, allowing us to train the model using a simplified dataset, and then perform parameter estimation on more general data, hence also acting as a crucial factor in improving the training speed. In conclusion, for the first time, within a comprehensive and reasonable parameter range, we have achieved a complete and unbiased 11-dimensional rapid inference for MBHBs in the presence of astrophysical confusion noise using CNFs. In the experiments based on simulated data, our model produces posterior distributions comparable to those obtained by nested sampling.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad8da9","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T22:57:30Z","timestamp":1730415450000},"page":"045040","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4350-1852","authenticated-orcid":true,"given":"Bo","family":"Liang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2155-3280","authenticated-orcid":true,"given":"Minghui","family":"Du","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1353-391X","authenticated-orcid":true,"given":"He","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yuxiang","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8800-5626","authenticated-orcid":true,"given":"Xiaotong","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7186-0994","authenticated-orcid":true,"given":"Li-e","family":"Qiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9533-8025","authenticated-orcid":true,"given":"Ziren","family":"Luo","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"mlstad8da9bib1","doi-asserted-by":"publisher","DOI":"10.1088\/0264-9381\/32\/7\/074001","article-title":"Advanced ligo","volume":"32","author":"The LIGO Scientific Collaboration","year":"2015","journal-title":"Class. Quantum Grav."},{"key":"mlstad8da9bib2","doi-asserted-by":"publisher","DOI":"10.1088\/0264-9381\/32\/2\/024001","article-title":"Advanced virgo: a second-generation interferometric gravitational wave detector","volume":"32","author":"Acernese","year":"2014","journal-title":"Class. Quantum Grav."},{"key":"mlstad8da9bib3","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1038\/s41550-018-0658-y","article-title":"Kagra: 2.5 generation interferometric gravitational wave detector","volume":"3","author":"Akutsu","year":"2019","journal-title":"Nat. Astron."},{"key":"mlstad8da9bib4","article-title":"Laser interferometer space antenna","author":"Amaro-Seoane","year":"2017"},{"key":"mlstad8da9bib5","article-title":"The laser interferometer space antenna: unveiling the millihertz gravitational wave sky","author":"Baker","year":"2019"},{"key":"mlstad8da9bib6","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1093\/nsr\/nwx116","article-title":"The Taiji Program in Space for gravitational wave physics and the nature of gravity","volume":"4","author":"Wen-Rui","year":"2017","journal-title":"Nat. Sci. Rev."},{"key":"mlstad8da9bib7","doi-asserted-by":"publisher","first-page":"05A108","DOI":"10.1093\/ptep\/ptaa083","article-title":"The Taiji program: a concise overview","volume":"2021","author":"Luo","year":"2020","journal-title":"Prog. Theor. Exp. Phys."},{"key":"mlstad8da9bib8","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s42005-021-00529-z","article-title":"China\u2019s first step towards probing the expanding Universe and the nature of gravity using a space borne gravitational wave antenna","volume":"4","author":"Taiji Scientific Collaboration","year":"2021","journal-title":"Commun. Phys."},{"key":"mlstad8da9bib9","doi-asserted-by":"publisher","DOI":"10.1088\/0264-9381\/33\/3\/035010","article-title":"Tianqin: a space-borne gravitational wave detector","volume":"33","author":"Luo","year":"2016","journal-title":"Class. Quantum Grav."},{"key":"mlstad8da9bib10","doi-asserted-by":"publisher","first-page":"S153","DOI":"10.1088\/0264-9381\/20\/10\/318","article-title":"Interferometry for the LISA technology package (LTP) aboard SMART-2","volume":"20","author":"Heinzel","year":"2003","journal-title":"Class. Quantum Grav."},{"key":"mlstad8da9bib11","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.106.102004","article-title":"Resolving galactic binaries using a network of space-borne gravitational wave detectors","volume":"106","author":"Zhang","year":"2022","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib12","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.93.024003","article-title":"Science with the space-based interferometer eLISA: Supermassive black hole binaries","volume":"93","author":"Klein","year":"2016","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib13","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.125.141101","article-title":"Detecting scalar fields with extreme mass ratio inspirals","volume":"125","author":"Maselli","year":"2020","journal-title":"Phys. Rev. Lett."},{"key":"mlstad8da9bib14","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1086\/498236","article-title":"Finding the electromagnetic counterparts of cosmological standard sirens","volume":"637","author":"Kocsis","year":"2006","journal-title":"Astrophys. J."},{"key":"mlstad8da9bib15","doi-asserted-by":"publisher","DOI":"10.1016\/j.newar.2020.101525","article-title":"The quest for dual and binary supermassive black holes: a multi-messenger view","volume":"86","author":"De Rosa","year":"2019","journal-title":"New Astron. Rev."},{"key":"mlstad8da9bib16","doi-asserted-by":"publisher","first-page":"L93","DOI":"10.1086\/429618","article-title":"The Afterglow of massive black hole coalescence","volume":"622","author":"Milos","year":"2005","journal-title":"Astrophys. J. Lett."},{"key":"mlstad8da9bib17","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1111\/j.1365-2966.2006.10905.x","article-title":"On the search of electromagnetic cosmological counterparts to coalescences of massive black hole binaries","volume":"372","author":"Dotti","year":"2006","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"mlstad8da9bib18","doi-asserted-by":"publisher","first-page":"002","DOI":"10.1103\/JCAP04(2016)002","article-title":"Science with the space-based interferometer eLISA. III: probing the expansion of the Universe using gravitational wave standard sirens","volume":"04","author":"Tamanini","year":"2016","journal-title":"J. Cosmol. Astropart. Phys."},{"key":"mlstad8da9bib19","doi-asserted-by":"publisher","first-page":"235","DOI":"10.22323\/1.436.0235","article-title":"The Hubble constant tension: current status and future perspectives through new cosmological probes","volume":"U2022","author":"Dainotti","year":"2023","journal-title":"PoS"},{"key":"mlstad8da9bib20","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1038\/s41550-022-01847-0","article-title":"Overview and progress on the laser interferometer space antenna mission","volume":"6","author":"Bayle","year":"2022","journal-title":"Nat. Astron."},{"key":"mlstad8da9bib21","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.72.043005","article-title":"LISA data analysis using Markov chain Monte Carlo methods","volume":"72","author":"Cornish","year":"2005","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib22","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.101.123021","article-title":"Global analysis of the gravitational wave signal from galactic binaries","volume":"101","author":"Littenberg","year":"2020","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib23","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.107.063004","article-title":"Prototype global analysis of LISA data with multiple source types","volume":"107","author":"Littenberg","year":"2023","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib24","article-title":"Fast \u03b5-free inference of simulation models with bayesian conditional density estimation","author":"Papamakarios","year":"2016"},{"key":"mlstad8da9bib25","article-title":"Flexible statistical inference for mechanistic models of neural dynamics","author":"Lueckmann","year":"2017"},{"key":"mlstad8da9bib26","article-title":"Automatic posterior transformation for likelihood-free inference","author":"Greenberg","year":"2019"},{"key":"mlstad8da9bib27","article-title":"Inferring atmospheric properties of exoplanets with flow matching and neural importance sampling","author":"Gebhard","year":"2023"},{"key":"mlstad8da9bib28","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.127.241103","article-title":"Real-time gravitational-wave science with neural posterior estimation","volume":"127","author":"Dax","year":"2021","journal-title":"Phys. Rev. Lett."},{"key":"mlstad8da9bib29","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevD.102.104057","article-title":"Gravitational-wave parameter estimation with autoregressive neural network flows","author":"Green","year":"2020"},{"key":"mlstad8da9bib30","article-title":"Simulation-based inference for exoplanet atmospheric retrieval: insights from winning the ariel data challenge 2023 using normalizing flows","author":"Aubin","year":"2023"},{"key":"mlstad8da9bib31","doi-asserted-by":"publisher","DOI":"10.1007\/s11433-023-2270-7","article-title":"Advancing space-based gravitational wave astronomy: rapid parameter estimation via normalizing flows","volume":"67","author":"Minghui","year":"2024","journal-title":"Sci. China Phys. Mech. Astron."},{"key":"mlstad8da9bib32","article-title":"Flow matching for generative modeling","author":"Lipman","year":"2022"},{"key":"mlstad8da9bib33","article-title":"Flow matching for scalable simulation-based inference","author":"Dax","year":"2023"},{"key":"mlstad8da9bib34","article-title":"Stochastic interpolants: a unifying framework for flows and diffusions","author":"Albergo","year":"2023"},{"key":"mlstad8da9bib35","article-title":"Improving and generalizing flow-based generative models with minibatch optimal transport","author":"Tong","year":"2024"},{"key":"mlstad8da9bib36","doi-asserted-by":"publisher","DOI":"10.1016\/j.physletb.2023.137904","article-title":"Rapid search for massive black hole binary coalescences using deep learning","volume":"841","author":"Ruan","year":"2023","journal-title":"Phys. Lett. B"},{"key":"mlstad8da9bib37","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.93.044006","article-title":"Frequency-domain gravitational waves from nonprecessing black-hole binaries. I. New numerical waveforms and anatomy of the signal","volume":"93","author":"Husa","year":"2016","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib38","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.93.044007","article-title":"Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era","volume":"93","author":"Khan","year":"2016","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib39","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.59.102003","article-title":"Cancellation of laser noise in an unequal-arm interferometer detector of gravitational radiation","volume":"59","author":"Massimo","year":"1999","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib40","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.65.082003","article-title":"Time-delay interferometry for lisa","volume":"65","author":"Massimo Tinto","year":"2002","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib41","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.66.122002","article-title":"The LISA optimal sensitivity","volume":"66","author":"Prince","year":"2002","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib42","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1806.10734","article-title":"Fourier-domain modulations and delays of gravitational-wave signals","volume":"vol 6","author":"Marsat","year":"2018"},{"key":"mlstad8da9bib43","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.102.023033","article-title":"GPU-accelerated massive black hole binary parameter estimation with LISA","volume":"102","author":"Katz","year":"2020","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib44","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.105.044055","article-title":"Fully automated end-to-end pipeline for massive black hole binary signal extraction from LISA data","volume":"105","author":"Michael","year":"2022","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib45","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.107.123026","article-title":"LISA science performance in observations of short-lived signals from massive black hole binary coalescences","volume":"107","author":"Pratten","year":"2023","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib46","doi-asserted-by":"publisher","DOI":"10.1016\/j.rinp.2019.102918","article-title":"A brief analysis to Taiji: science and technology","volume":"16","author":"Luo","year":"2020","journal-title":"Res. Phys."},{"key":"mlstad8da9bib47","doi-asserted-by":"publisher","DOI":"10.1088\/0264-9381\/25\/6\/065005","article-title":"Sensitivity and parameter-estimation precision for alternate LISA configurations","volume":"25","author":"Vallisneri","year":"2008","journal-title":"Class. Quantum Grav."},{"key":"mlstad8da9bib48","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.102.024089","article-title":"Numerical simulation of sky localization for LISA-TAIJI joint observation","volume":"102","author":"Wang","year":"2020","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib49","doi-asserted-by":"publisher","DOI":"10.1088\/1402-4896\/acd882","article-title":"Revisiting time delay interferometry for unequal-arm LISA and TAIJI","volume":"98","author":"Wang","year":"2023","journal-title":"Phys. Scripta"},{"key":"mlstad8da9bib50","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.76.083006","article-title":"Tests of Bayesian model selection techniques for gravitational wave astronomy","volume":"76","author":"Cornish","year":"2007","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib51","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.107.124022","article-title":"Investigating galactic double white dwarfs for the sub-mHz gravitational wave mission ASTROD-GW","volume":"107","author":"Wang","year":"2023","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib52","doi-asserted-by":"publisher","first-page":"5518","DOI":"10.1093\/mnras\/sty3440","article-title":"A multimessenger study of the Milky Way\u2019s stellar disc and bulge with LISA, Gaia and LSST","volume":"483","author":"Korol","year":"2018","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"mlstad8da9bib53","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.107.064021","article-title":"Confusion noise from galactic binaries for TAIJI","volume":"107","author":"Liu","year":"2023","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib54","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: trends, perspectives and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"mlstad8da9bib55","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abb93a","article-title":"Enhancing gravitational-wave science with machine learning.","volume":"2","author":"Cuoco","year":"2020","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"mlstad8da9bib56","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1038\/s41567-021-01425-7","article-title":"Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy","volume":"18","author":"Gabbard","year":"2022","journal-title":"Nat. Phys."},{"key":"mlstad8da9bib57","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.100.103025","article-title":"Using deep learning to localize gravitational wave sources","volume":"100","author":"Chatterjee","year":"2019","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib58","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.102.104057","article-title":"Gravitational-wave parameter estimation with autoregressive neural network flows","volume":"102","author":"Green","year":"2020","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib59","doi-asserted-by":"publisher","first-page":"03LT01","DOI":"10.1088\/2632-2153\/abfaed","article-title":"Complete parameter inference for GW150914 using deep learning","volume":"2","author":"Green","year":"2021","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"mlstad8da9bib60","article-title":"Lightning-fast gravitational wave parameter inference through neural amortization","author":"Delaunoy","year":"2020"},{"key":"mlstad8da9bib61","doi-asserted-by":"publisher","DOI":"10.1016\/j.physletb.2021.136161","article-title":"Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real ligo data using deep learning","volume":"815","author":"Krastev","year":"2021","journal-title":"Phys. Lett. B"},{"key":"mlstad8da9bib62","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac3843","article-title":"Statistically-informed deep learning for gravitational wave parameter estimation","volume":"3","author":"Hongyu Shen","year":"2021","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"mlstad8da9bib63","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.130.171403","article-title":"Neural importance sampling for rapid and reliable gravitational-wave inference","volume":"130","author":"Dax","year":"2023","journal-title":"Phys. Rev. Lett."},{"key":"mlstad8da9bib64","article-title":"Language modeling with gated convolutional networks","author":"Dauphin","year":"2016"},{"key":"mlstad8da9bib65","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.70.022003","article-title":"Optimal filtering of the LISA data","volume":"70","author":"Krolak","year":"2004","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib66","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.76.069901","article-title":"Optimal filtering of the LISA data","volume":"76","author":"Krolak","year":"2007","journal-title":"Phys.Rev. D"},{"key":"mlstad8da9bib67","doi-asserted-by":"publisher","DOI":"10.1088\/0264-9381\/29\/12\/124015","article-title":"Non-sky-averaged sensitivity curves for space-based gravitational-wave observatories","volume":"29","author":"Vallisneri","year":"2012","journal-title":"Class. Quantum Grav."},{"key":"mlstad8da9bib68","article-title":"xGroup equivariant neural posterior estimation","author":"Dax","year":"2021"},{"key":"mlstad8da9bib69","article-title":"Sgdr: stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2017"},{"key":"mlstad8da9bib70","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2017"},{"key":"mlstad8da9bib71","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.81.062003","article-title":"Bayesian coherent analysis of in-spiral gravitational wave signals with a detector network","volume":"81","author":"Veitch","year":"2010","journal-title":"Phys. Rev. D"},{"key":"mlstad8da9bib72","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1214\/06-BA127","article-title":"Nested sampling for general Bayesian computation","volume":"1","author":"Skilling","year":"2006","journal-title":"Bayesian Anal."},{"key":"mlstad8da9bib73","doi-asserted-by":"publisher","first-page":"27","DOI":"10.3847\/1538-4365\/ab06fc","article-title":"Bilby: a user-friendly Bayesian inference library for gravitational-wave astronomy","volume":"241","author":"Ashton","year":"2019","journal-title":"Astrophys. J."},{"key":"mlstad8da9bib74","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.109.124048","article-title":"Accuracy requirements: assessing the importance of first post-adiabatic terms for small-mass-ratio binaries","author":"Burke","year":"2024"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8da9","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8da9\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8da9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8da9\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8da9\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8da9\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8da9\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8da9\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T11:48:24Z","timestamp":1731498504000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8da9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,13]]},"references-count":74,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,11,13]]},"published-print":{"date-parts":[[2024,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad8da9","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,13]]},"assertion":[{"value":"Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-08-16","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-10-31","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-11-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}