{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T21:20:47Z","timestamp":1774473647080,"version":"3.50.1"},"reference-count":59,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000166","name":"Division of Physics","doi-asserted-by":"crossref","award":["PHY-2117997"],"award-info":[{"award-number":["PHY-2117997"]}],"id":[{"id":"10.13039\/100000166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"Abstract<\/jats:title>\n \n We report a gravitational-wave parameter estimation algorithm,\n AMPLFI<\/jats:monospace>\n , based on likelihood-free inference using normalizing flows. The focus of\n AMPLFI<\/jats:monospace>\n is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search,\n Aframe<\/jats:monospace>\n . We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has\n \n \n \n <\/jats:tex-math>\n \n \n \n \u223c<\/mml:mo>\n <\/mml:mrow>\n 6<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n million trainable parameters with training times\n \n \n \n <\/jats:tex-math>\n \n \n \n \u2272<\/mml:mo>\n <\/mml:mrow>\n 24<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n h. Based on online deployment on a mock data stream of LIGO-Virgo data,\n Aframe<\/jats:monospace>\n +\n AMPLFI<\/jats:monospace>\n is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of\n \n \n \n <\/jats:tex-math>\n \n \n \n \u223c<\/mml:mo>\n <\/mml:mrow>\n 6<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n s.\n <\/jats:p>","DOI":"10.1088\/2632-2153\/ad8982","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T19:00:36Z","timestamp":1729537236000},"page":"045030","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Rapid likelihood free inference of compact binary coalescences using accelerated hardware"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0038-5468","authenticated-orcid":true,"given":"D","family":"Chatterjee","sequence":"first","affiliation":[]},{"given":"E","family":"Marx","sequence":"additional","affiliation":[]},{"given":"W","family":"Benoit","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6428-7668","authenticated-orcid":true,"given":"R","family":"Kumar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4448-3681","authenticated-orcid":true,"given":"M","family":"Desai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1920-6618","authenticated-orcid":true,"given":"E","family":"Govorkova","sequence":"additional","affiliation":[]},{"given":"A","family":"Gunny","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5666-3637","authenticated-orcid":true,"given":"E","family":"Moreno","sequence":"additional","affiliation":[]},{"given":"R","family":"Omer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4083-6390","authenticated-orcid":true,"given":"R","family":"Raikman","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3836-7751","authenticated-orcid":true,"given":"M","family":"Saleem","sequence":"additional","affiliation":[]},{"given":"S","family":"Aggarwal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8262-2924","authenticated-orcid":true,"given":"M W","family":"Coughlin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8189-3741","authenticated-orcid":true,"given":"P","family":"Harris","sequence":"additional","affiliation":[]},{"given":"E","family":"Katsavounidis","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"mlstad8982bib1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.116.061102","article-title":"Observation of gravitational waves from a binary black hole merger","volume":"116","author":"Abbott","year":"2016","journal-title":"Phys. Rev. Lett."},{"key":"mlstad8982bib2","article-title":"GW151226: observation of gravitational waves from a 22-solar-mass binary black hole coalescence","volume":"116","author":"Abbott","year":"2016","journal-title":"Phys. Rev. Lett."},{"key":"mlstad8982bib3","article-title":"GWTC-3: compact binary coalescences observed by LIGO and virgo during the second part of the third observing run","author":"Abbott","year":"2021"},{"key":"mlstad8982bib4","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2316474121","article-title":"Low-latency gravitational wave alert products and their performance at the time of the fourth ligo-virgo-kagra observing run","volume":"121","author":"Sharma Chaudhary","year":"2024","journal-title":"Proc. Natl Acad. Sci."},{"key":"mlstad8982bib5","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1088\/0004-637X\/748\/2\/136","article-title":"Toward early-warning detection of gravitational waves from compact binary coalescence","volume":"748","author":"Cannon","year":"2012","journal-title":"Astrophys. J."},{"key":"mlstad8982bib6","doi-asserted-by":"publisher","first-page":"L21","DOI":"10.3847\/2041-8213\/abed54","article-title":"First demonstration of early warning gravitational-wave alerts","volume":"910","author":"Magee","year":"2021","journal-title":"Astrophys. J. Lett."},{"key":"mlstad8982bib7","first-page":"1","article-title":"Introducing new GCN Kafka broker and web site for transient alerts","volume":"32419","author":"Barthelmy","year":"2022","journal-title":"GRB Coordinates Netw."},{"key":"mlstad8982bib8","first-page":"1","article-title":"Announcing the GW treasure map","volume":"26244","author":"Wyatt","year":"2019","journal-title":"GRB Coordinates Netw."},{"key":"mlstad8982bib9","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.21105\/joss.01247","article-title":"SkyPortal: an astronomical data platform","volume":"4","author":"van der Walt","year":"2019","journal-title":"J. Open Source Softw."},{"key":"mlstad8982bib10","doi-asserted-by":"publisher","DOI":"10.1117\/12.2312293","article-title":"General-purpose software for managing astronomical observing programs in the LSST era","volume":"10707","author":"Street","year":"2018","journal-title":"Proc. SPIE"},{"key":"mlstad8982bib11","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.102.062003","article-title":"Sensitivity and performance of the advanced LIGO detectors in the third observing run","volume":"102","author":"Buikema","year":"2020","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib12","doi-asserted-by":"publisher","DOI":"10.1088\/0264-9381\/32\/7\/074001","article-title":"Advanced LIGO","volume":"32","author":"Aasi","year":"2015","journal-title":"Class. Quantum Grav."},{"key":"mlstad8982bib13","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":"2015","journal-title":"Class. Quantum Grav."},{"key":"mlstad8982bib14","doi-asserted-by":"publisher","first-page":"05A101","DOI":"10.1093\/ptep\/ptaa125","article-title":"Overview of KAGRA: Detector design and construction history","volume":"2021","author":"Akutsu","year":"2020","journal-title":"Prog. Theor. Exp. Phys."},{"key":"mlstad8982bib15","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.119.161101","article-title":"GW170817: Observation of gravitational waves from a binary neutron star inspiral","volume":"119","author":"Abbott","year":"2017","journal-title":"Phys. Rev. Lett."},{"key":"mlstad8982bib16","doi-asserted-by":"publisher","first-page":"L12","DOI":"10.3847\/2041-8213\/aa91c9","article-title":"Multi-messenger Observations of a binary neutron star merger","volume":"848","author":"Abbott","year":"2017","journal-title":"Astrophys. J. Lett."},{"key":"mlstad8982bib17","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1038\/s41550-020-1130-3","article-title":"Lessons from counterpart searches in LIGO and virgo\u2019s third observing campaign","volume":"4","author":"Coughlin","year":"2020","journal-title":"Nat. Astron."},{"key":"mlstad8982bib18","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.114.071104","article-title":"Accelerated gravitational-wave parameter estimation with reduced order modeling","volume":"114","author":"Canizares","year":"2015","journal-title":"Phys. Rev. Lett."},{"key":"mlstad8982bib19","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.108.123040","article-title":"Rapid localization and inference on compact binary coalescences with the advanced ligo-virgo-kagra gravitational-wave detector network","volume":"108","author":"Morisaki","year":"2023","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib20","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.102.104020","article-title":"Rapid parameter estimation of gravitational waves from binary neutron star coalescence using focused reduced order quadrature","volume":"102","author":"Morisaki","year":"2020","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib21","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.100.043030","article-title":"Parallelized inference for gravitational-wave astronomy","volume":"100","author":"Talbot","year":"2019","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib22","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3847\/1538-4357\/acf5cd","article-title":"Fast gravitational wave parameter estimation without compromises","volume":"958","author":"Wong","year":"2023","journal-title":"Astrophys. J."},{"key":"mlstad8982bib23","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.108.064055","article-title":"Fast likelihood evaluation using meshfree approximations for reconstructing compact binary sources","volume":"108","author":"Pathak","year":"2023","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib24","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.109.024053","article-title":"Prompt sky localization of compact binary sources using a meshfree approximation","volume":"109","author":"Pathak","year":"2024","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib25","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":"2021","journal-title":"Nat. Phys."},{"key":"mlstad8982bib26","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.108.042004","article-title":"Sequential simulation-based inference for gravitational wave signals","volume":"108","author":"Bhardwaj","year":"2023","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib27","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":"mlstad8982bib28","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.8.021019","article-title":"Optimal search for an astrophysical gravitational-wave background","volume":"8","author":"Smith","year":"2018","journal-title":"Phys. Rev. X"},{"key":"mlstad8982bib29","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.93.024013","article-title":"Rapid bayesian position reconstruction for gravitational-wave transients","volume":"93","author":"Singer","year":"2016","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib30","doi-asserted-by":"publisher","first-page":"54","DOI":"10.3847\/1538-4357\/ab8dbe","article-title":"A machine learning-based source property inference for compact binary mergers","volume":"896","author":"Chatterjee","year":"2020","journal-title":"Astrophys. J."},{"key":"mlstad8982bib31","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-4271631\/v1","article-title":"A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences","author":"Marx","year":"2024"},{"key":"mlstad8982bib32","article-title":"Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy","author":"Gabbard","year":"2020"},{"key":"mlstad8982bib33","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":"mlstad8982bib34","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.80.084043","article-title":"Comparison of post-newtonian templates for compact binary inspiral signals in gravitational-wave detectors","volume":"80","author":"Buonanno","year":"2009","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib35","article-title":"LIGO algorithm library\u2014LALSuite","author":"LIGO Scientific Collaboration","year":"2018"},{"key":"mlstad8982bib36","article-title":"Pytorch lightning","author":"The PyTorch Lightning team","year":"2024"},{"key":"mlstad8982bib37","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-01261-8_1","article-title":"Group normalization","author":"Wu","year":"2018"},{"key":"mlstad8982bib38","article-title":"Deep residual learning for image recognition","author":"Kaiming","year":"2015"},{"key":"mlstad8982bib39","article-title":"Vicreg: variance-invariance-covariance regularization for self-supervised learning","author":"Bardes","year":"2022"},{"key":"mlstad8982bib40","article-title":"Group equivariant neural posterior estimation","author":"Dax","year":"2023"},{"key":"mlstad8982bib41","article-title":"Optimizing likelihood-free inference using self-supervised neural symmetry embeddings","author":"Chatterjee","year":"2023"},{"key":"mlstad8982bib42","article-title":"Normalizing flows for probabilistic modeling and inference","author":"Papamakarios","year":"2021"},{"key":"mlstad8982bib43","article-title":"Improving variational inference with inverse autoregressive flow","author":"Kingma","year":"2017"},{"key":"mlstad8982bib44","article-title":"Masked autoregressive flow for density estimation","author":"Papamakarios","year":"2018"},{"key":"mlstad8982bib45","first-page":"pp 881","article-title":"Made: masked autoencoder for distribution estimation","author":"Germain","year":"2015"},{"key":"mlstad8982bib46","first-page":"1","article-title":"Pyro: deep universal probabilistic programming","volume":"20","author":"Bingham","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"mlstad8982bib47","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2019"},{"key":"mlstad8982bib48","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1086\/427976","article-title":"HEALPix: a framework for high-resolution discretization and fast analysis of data distributed on the sphere","volume":"622","author":"G\u00f3rski","year":"2005","journal-title":"Astrophys. J."},{"key":"mlstad8982bib49","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. Suppl."},{"key":"mlstad8982bib50","doi-asserted-by":"publisher","first-page":"3295","DOI":"10.1093\/mnras\/staa2850","article-title":"Bayesian inference for compact binary coalescences with bilby: validation and application to the first ligo-virgo gravitational-wave transient catalogue","volume":"499","author":"Romero-Shaw","year":"2020","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"mlstad8982bib51","doi-asserted-by":"publisher","first-page":"3132","DOI":"10.1093\/mnras\/staa278","article-title":"dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences","volume":"493","author":"Joshua","year":"2020","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"mlstad8982bib52","doi-asserted-by":"publisher","first-page":"2658","DOI":"10.1103\/PhysRevD.49.2658","article-title":"Gravitational waves from merging compact binaries: How accurately can one extract the binary\u2019s parameters from the inspiral waveform?","volume":"49","author":"Cutler","year":"1994","journal-title":"Phys. Rev. D"},{"key":"mlstad8982bib53","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1088\/0004-637X\/725\/1\/496","article-title":"Exploring short gamma-ray bursts as gravitational-wave standard sirens","volume":"725","author":"Nissanke","year":"2010","journal-title":"Astrophys. J."},{"key":"mlstad8982bib54","article-title":"Demonstration of machine learning-assisted real-time noise regression in gravitational wave detectors","author":"Saleem","year":"2023"},{"key":"mlstad8982bib55","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ad3a31","article-title":"GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders","volume":"5","author":"Raikman","year":"2024","journal-title":"Mach. Learn. Sci. Tech."},{"key":"mlstad8982bib56","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3847\/1538-4365\/acdc9f","article-title":"Open Data from the third observing run of LIGO, Virgo, KAGRA and GEO","volume":"267","author":"Abbott","year":"2023","journal-title":"Astrophys. J. Suppl."},{"key":"mlstad8982bib57","article-title":"Open data from the first and second observing runs of advanced LIGO and advanced virgo","volume":"13","author":"Abbott","year":"2021","journal-title":"SoftwareX"},{"key":"mlstad8982bib58","article-title":"Massively parallel hyperparameter tuning","author":"Lisha","year":"2018"},{"key":"mlstad8982bib59","article-title":"Tune: a research platform for distributed model selection and training","author":"Liaw","year":"2018"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8982","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8982\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8982","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8982\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8982\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8982\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8982\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8982\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T09:39:49Z","timestamp":1730281189000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad8982"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"references-count":59,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,10,30]]},"published-print":{"date-parts":[[2024,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad8982","relation":{"has-review":[{"id-type":"doi","id":"10.1088\/2632-2153\/AD8982\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD8982\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD8982\/v2\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD8982\/v2\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD8982\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD8982\/v1\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD8982\/v3\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD8982\/v3\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1088\/2632-2153\/AD8982\/v2\/decision1","asserted-by":"object"}]},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"Rapid likelihood free inference of compact binary coalescences using accelerated hardware","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":"2024-07-26","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-10-21","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-10-30","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}