{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:36:16Z","timestamp":1743150976652,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031624940"},{"type":"electronic","value":"9783031624957"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-62495-7_6","type":"book-chapter","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:19:24Z","timestamp":1719001164000},"page":"70-81","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Binary Black Hole Parameter Estimation from\u00a0Gravitational Waves with\u00a0Deep Learning Methods"],"prefix":"10.1007","author":[{"given":"Panagiotis N.","family":"Sakellariou","sequence":"first","affiliation":[]},{"given":"Spiros V.","family":"Georgakopoulos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"issue":"5","key":"6_CR1","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1038\/s42254-021-00303-8","volume":"3","author":"M Bailes","year":"2021","unstructured":"Bailes, M., et al.: Gravitational-wave physics and astronomy in the 2020s and 2030s. Nat. Rev. Phys. 3(5), 344\u2013366 (2021)","journal-title":"Nat. Rev. Phys."},{"key":"6_CR2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.95.044028","volume":"95","author":"A Boh\u00e9","year":"2017","unstructured":"Boh\u00e9, A., et al.: Improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors. Phys. Rev. D 95, 044028 (2017). https:\/\/doi.org\/10.1103\/PhysRevD.95.044028","journal-title":"Phys. Rev. D"},{"key":"6_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113378","volume":"151","author":"R Corizzo","year":"2020","unstructured":"Corizzo, R., Ceci, M., Zdravevski, E., Japkowicz, N.: Scalable auto-encoders for gravitational waves detection from time series data. Expert Syst. Appl. 151, 113378 (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113378","journal-title":"Expert Syst. Appl."},{"key":"6_CR4","doi-asserted-by":"publisher","unstructured":"Fan, X., Li, J., Li, X., Zhong, Y., Cao, J.: Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors. Sci. China Phys. Mech. Astron. 62(6) (2019). https:\/\/doi.org\/10.1007\/s11433-018-9321-7","DOI":"10.1007\/s11433-018-9321-7"},{"key":"6_CR5","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.120.141103","volume":"120","author":"H Gabbard","year":"2018","unstructured":"Gabbard, H., Williams, M., Hayes, F., Messenger, C.: Matching matched filtering with deep networks for gravitational-wave astronomy. Phys. Rev. Lett. 120, 141103 (2018). https:\/\/doi.org\/10.1103\/PhysRevLett.120.141103","journal-title":"Phys. Rev. Lett."},{"key":"6_CR6","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.100.063015","volume":"100","author":"TD Gebhard","year":"2019","unstructured":"Gebhard, T.D., Kilbertus, N., Harry, I., Sch\u00f6lkopf, B.: Convolutional neural networks: a magic bullet for gravitational-wave detection? Phys. Rev. D 100, 063015 (2019). https:\/\/doi.org\/10.1103\/PhysRevD.100.063015","journal-title":"Phys. Rev. D"},{"key":"6_CR7","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.97.044039","volume":"97","author":"D George","year":"2018","unstructured":"George, D., Huerta, E.A.: Deep neural networks to enable real-time multimessenger astrophysics. Phys. Rev. D 97, 044039 (2018). https:\/\/doi.org\/10.1103\/PhysRevD.97.044039","journal-title":"Phys. Rev. D"},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.physletb.2017.12.053","volume":"778","author":"D George","year":"2018","unstructured":"George, D., Huerta, E.: Deep learning for real-time gravitational wave detection and parameter estimation: results with advanced LIGO data. Phys. Lett. B 778, 64\u201370 (2018). https:\/\/doi.org\/10.1016\/j.physletb.2017.12.053","journal-title":"Phys. Lett. B"},{"key":"6_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.physletb.2021.136161","volume":"815","author":"PG Krastev","year":"2021","unstructured":"Krastev, P.G., Gill, K., Villar, V.A., Berger, E.: Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning. Phys. Lett. B 815, 136161 (2021). https:\/\/doi.org\/10.1016\/j.physletb.2021.136161","journal-title":"Phys. Lett. B"},{"key":"6_CR10","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.103.063034","volume":"103","author":"YC Lin","year":"2021","unstructured":"Lin, Y.C., Wu, J.H.P.: Detection of gravitational waves using Bayesian neural networks. Phys. Rev. D 103, 063034 (2021). https:\/\/doi.org\/10.1103\/PhysRevD.103.063034","journal-title":"Phys. Rev. D"},{"key":"6_CR11","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.105.083013","volume":"105","author":"C Ma","year":"2022","unstructured":"Ma, C., Wang, W., Wang, H., Cao, Z.: Ensemble of deep convolutional neural networks for real-time gravitational wave signal recognition. Phys. Rev. D 105, 083013 (2022). https:\/\/doi.org\/10.1103\/PhysRevD.105.083013","journal-title":"Phys. Rev. D"},{"key":"6_CR12","unstructured":"McLeod, A., Jacobs, D., Chatterjee, C., Wen, L., Panther, F.: Rapid mass parameter estimation of binary black hole coalescences using deep learning (2022)"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Miller, A.L., Singh, N., Palomba, C.: Enabling multi-messenger astronomy with continuous gravitational waves: early warning and sky localization of binary neutron stars in Einstein Telescope (2023)","DOI":"10.1103\/PhysRevD.109.043021"},{"key":"6_CR14","unstructured":"Moreno, A.B.A., Moreno, C.: Convolutional neural network regression to estimate the mass parameter of astrophysical binary black hole systems. In: LatinX in AI Workshop at ICML 2023 (Regular Deadline) (2023)"},{"key":"6_CR15","doi-asserted-by":"publisher","unstructured":"Nitz, A., et al.: GWastro\/PyCBC: v2.3.2 release of PyCBC (2023). https:\/\/doi.org\/10.5281\/zenodo.10137381","DOI":"10.5281\/zenodo.10137381"},{"key":"6_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.physletb.2023.137850","volume":"840","author":"R Qiu","year":"2023","unstructured":"Qiu, R., Krastev, P.G., Gill, K., Berger, E.: Deep learning detection and classification of gravitational waves from neutron star-black hole mergers. Phys. Lett. B 840, 137850 (2023). https:\/\/doi.org\/10.1016\/j.physletb.2023.137850","journal-title":"Phys. Lett. B"},{"key":"6_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.physletb.2023.137904","volume":"841","author":"WH Ruan","year":"2023","unstructured":"Ruan, W.H., Wang, H., Liu, C., Guo, Z.K.: Rapid search for massive black hole binary coalescences using deep learning. Phys. Lett. B 841, 137904 (2023). https:\/\/doi.org\/10.1016\/j.physletb.2023.137904","journal-title":"Phys. Lett. B"},{"issue":"1","key":"6_CR18","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac3843","volume":"3","author":"H Shen","year":"2021","unstructured":"Shen, H., Huerta, E.A., O\u2019Shea, E., Kumar, P., Zhao, Z.: Statistically-informed deep learning for gravitational wave parameter estimation. Mach. Learn. Sci. Technol. 3(1), 015007 (2021). https:\/\/doi.org\/10.1088\/2632-2153\/ac3843","journal-title":"Mach. Learn. Sci. Technol."},{"key":"6_CR19","unstructured":"Shen, H., Huerta, E.A., Zhao, Z.: Deep learning at scale for gravitational wave parameter estimation of binary black hole mergers. arXiv abs\/1903.01998 (2019)"},{"key":"6_CR20","doi-asserted-by":"publisher","unstructured":"Trozzo, L., Badaracco, F.: Seismic and Newtonian noise in the GW detectors. Galaxies 10(1) (2022). https:\/\/doi.org\/10.3390\/galaxies10010020","DOI":"10.3390\/galaxies10010020"},{"key":"6_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.physletb.2020.136029","volume":"812","author":"W Wei","year":"2021","unstructured":"Wei, W., Khan, A., Huerta, E., Huang, X., Tian, M.: Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers. Phys. Lett. B 812, 136029 (2021). https:\/\/doi.org\/10.1016\/j.physletb.2020.136029","journal-title":"Phys. Lett. B"}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62495-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T20:20:18Z","timestamp":1719001218000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62495-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031624940","9783031624957"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62495-7_6","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eannconf.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}