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Institut d'Astrophysique et
de Géophysique (Bât. B5c)
Quartier Agora
Allée du 6 août, 19C
B-4000 Liège 1 (Sart-Tilman)
Belgique
Tel.: 04.366.9774
Fax: 04.366.9729
de Géophysique (Bât. B5c)
Quartier Agora
Allée du 6 août, 19C
B-4000 Liège 1 (Sart-Tilman)
Belgique
Tel.: 04.366.9774
Fax: 04.366.9729
Seminars
Seminars are regularly held to enable department's researchers as well as external scientists to present the latest findings in their fields. You are most welcome to join us:
| 08/12/2022 : 16h00 | Enhancing Bayesian analysis and post-processing with machine-learning Virginia D'Emilio |
| 15/12/2022 : 16h00 | Environmental impacts of deep-sea mining (TB edited) Pierre-Antoine Dessandier |
| 12/01/2023 : 16h00 | JWST + TRAPPIST1 (To be edited) René Doyon |
| 23/03/2023 : 16h00 | TBA Paulo Freire |
|
Archives: 2022 - 2021 - 2020 2019 - 2018 - 2017 - 2016 - 2015 - 2014 - 2013 - 2012 - 2011 - 2010 2009 - 2008 - 2007 - 2006 - 2005 - 2004 - 2003 - 2002 - 2001 |
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Thursday 08 december, 16h00 (19th Seminar 2022 - poster)
Enhancing Bayesian analysis and post-processing with machine-learning
Virginia D'Emilio (Cardiff University)
Salle de réunion AGO (local -1/14), Institut d'Astrophysique et de Géophysique
Bâtiment B5c, Quartier Agora, Allée du 6 Août, 19C, B-4000 Liège 1 (Sart-Tilman)
Gravitational-wave (GW) astrophysics is largely based on Bayesian inference results of individual observations. These are what we call parameter estimation products. During the last GW observing run, the LIGO-Virgo-KAGRA collaboration detected around 90 signals from compact binaries coalescences. We expect the number of such detections to increase to O(1000) over the next two observing runs, which is challenging for our current analysis and post-processing pipelines. Several efforts in our community have been devoted to boost parameter estimation, especially with the use of machine-learning. In many cases, neural networks seem to offer a powerful alternative to tackle expensive computation, an example being simulation-based inference. But in other cases, as shown by Williams and Rasmussen (1996), real world data modelling problems are well solved by sensible smoothing methods, such as Gaussian Processes. In this talk, I will outline how both these techniques address important interdisciplinary problems and as such are both being investigated in GW and cosmology. Finally, as a concrete example of how they could improve the accuracy and speed of Bayesian data analysis, I will consider the case of dark sirens cosmology with GW.
Virginia D'Emilio (Cardiff University)
Salle de réunion AGO (local -1/14), Institut d'Astrophysique et de Géophysique
Bâtiment B5c, Quartier Agora, Allée du 6 Août, 19C, B-4000 Liège 1 (Sart-Tilman)
Gravitational-wave (GW) astrophysics is largely based on Bayesian inference results of individual observations. These are what we call parameter estimation products. During the last GW observing run, the LIGO-Virgo-KAGRA collaboration detected around 90 signals from compact binaries coalescences. We expect the number of such detections to increase to O(1000) over the next two observing runs, which is challenging for our current analysis and post-processing pipelines. Several efforts in our community have been devoted to boost parameter estimation, especially with the use of machine-learning. In many cases, neural networks seem to offer a powerful alternative to tackle expensive computation, an example being simulation-based inference. But in other cases, as shown by Williams and Rasmussen (1996), real world data modelling problems are well solved by sensible smoothing methods, such as Gaussian Processes. In this talk, I will outline how both these techniques address important interdisciplinary problems and as such are both being investigated in GW and cosmology. Finally, as a concrete example of how they could improve the accuracy and speed of Bayesian data analysis, I will consider the case of dark sirens cosmology with GW.
Environmental impacts of deep-sea mining (TB edited)
Pierre-Antoine Dessandier (IFREMER)
MS Teams
Pierre-Antoine Dessandier (IFREMER)
MS Teams
JWST + TRAPPIST1 (To be edited)
René Doyon (Université de Montréal)
Salle de réunion AGO (local -1/14), Institut d'Astrophysique et de Géophysique
Bâtiment B5c, Quartier Agora, Allée du 6 Août, 19C, B-4000 Liège 1 (Sart-Tilman)
René Doyon (Université de Montréal)
Salle de réunion AGO (local -1/14), Institut d'Astrophysique et de Géophysique
Bâtiment B5c, Quartier Agora, Allée du 6 Août, 19C, B-4000 Liège 1 (Sart-Tilman)
Thursday 23 march, 16h00 (2nd Seminar 2023 - poster)
TBA
Paulo Freire (Max-Planck-Institut für Radioastronomie, Bonn, Germany)
Salle de réunion AGO (local -1/14), Institut d'Astrophysique et de Géophysique
Bâtiment B5c, Quartier Agora, Allée du 6 Août, 19C, B-4000 Liège 1 (Sart-Tilman)
Paulo Freire (Max-Planck-Institut für Radioastronomie, Bonn, Germany)
Salle de réunion AGO (local -1/14), Institut d'Astrophysique et de Géophysique
Bâtiment B5c, Quartier Agora, Allée du 6 Août, 19C, B-4000 Liège 1 (Sart-Tilman)
University of Liège >
Faculty of Sciences >
Department of Astrophysics, Geophysics and Oceanography :
CoWebAGO, June 2009.
