{"id":1528,"date":"2024-10-10T15:22:40","date_gmt":"2024-10-10T13:22:40","guid":{"rendered":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/?p=1528"},"modified":"2024-10-25T12:53:16","modified_gmt":"2024-10-25T10:53:16","slug":"compact-representations-to-detect-gravitational-waves-from-extreme-mass-ratio-inspirals","status":"publish","type":"post","link":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/compact-representations-to-detect-gravitational-waves-from-extreme-mass-ratio-inspirals\/","title":{"rendered":"Compact representations to detect gravitational waves from extreme-mass ratio inspirals"},"content":{"rendered":"

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2024 <\/em><\/p>\n

Master’s Projects<\/p>\n

@Physics\/Astronomy<\/span><\/p>\n

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#Gravitational waves<\/p>\n

#extreme-mass ratio inspirals<\/p>\n

#data representations<\/p>\n

#neural networks<\/p>\n

\u00a0<\/p>\n

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Project Summary<\/strong><\/h3>\n

Extreme Mass Ratio Inspirals (EMRIs), detectable only by the future space-based detector LISA, provide a unique opportunity to probe the environment in galactic centers and test general relativity (GR). We develop a Bayesian framework for model selection to distinguish between vacuum GR and potential deviations caused by environmental effects or modifications to GR. The challenge lies in the complexity of EMRI waveforms, so we explore novel techniques to accelerate the likelihood calculation. Starting with simple linear methods like Principal Component Analysis (PCA), we extend to machine-learning approaches such as autoencoders to compress waveforms and enhance the analysis.<\/p>\n

 <\/p>\n

Quentin Baghi
<\/strong>baghi@apc.in2p3.fr<\/h3>\n