Universal Gravitational Wave Parameter Estimation by Deep Learning Universal Gravitational Wave Parameter Estimation by Deep Learning

dc.contributor 林豐利 zh_TW
dc.contributor Lin, Feng-Li en_US
dc.contributor.author 郭瀚翔 zh_TW
dc.contributor.author Kuo, Han-Shiang en_US
dc.date.accessioned 2022-06-08T02:50:41Z
dc.date.available 2021-08-20
dc.date.available 2022-06-08T02:50:41Z
dc.date.issued 2021
dc.description.abstract none zh_TW
dc.description.abstract As the improvement of gravitational wave detectors, gravitationalwave events become more and more popular which opens a new win-dow of astronomy. In 2017, a binary neutron star event, GW170817,has been detected through the gravitational wave and also the electro-magnetic signal. After that, people start to consider an efficient wayto detect the GW and extract its dynamics parameters. In this thesis,we construct a Bayesian inference based on deep learning machine,CVAE, for the parameter estimation of binary black hole coalescence.This machine can obtain the inference of 5-dimensional parameters ofthe GW event within one second, where the parameters are two com-ponent mass m1 , m2 , luminosity distance dL , and time and phase ofcoalescence (tc , φ0 ). Since the noise of real detectors varies from timeto time, in contract to previous CVAE envelopments, we train ourmachine not only by strain data but also the corresponding amplitudespectrum density, which is used to characterize the noise background.We find our machine can obtain the compatible result in comparisonto traditional PE algorithm even with the noise drift, which meansthe noise background varies event by event. Finally, we apply ourmachine to the LIGO/Virgo third observing run (O3) events to testthe performance of our machine against real data. en_US
dc.description.sponsorship 物理學系 zh_TW
dc.identifier 60641018S-39973
dc.identifier.uri https://etds.lib.ntnu.edu.tw/thesis/detail/7ca5070fcf1af8f573716d418032265d/
dc.identifier.uri http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/117676
dc.language 英文
dc.subject none zh_TW
dc.subject Gravitational wave en_US
dc.subject General relativity en_US
dc.subject Data analysis en_US
dc.subject Matched filter en_US
dc.subject Parameter estimation en_US
dc.subject Deep learning en_US
dc.subject Conditional variational autoencoder en_US
dc.subject Normalizing flow en_US
dc.title Universal Gravitational Wave Parameter Estimation by Deep Learning zh_TW
dc.title Universal Gravitational Wave Parameter Estimation by Deep Learning en_US
dc.type 學術論文
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