BEGIN:VCALENDAR VERSION:2.0 PRODID:Data::ICal 0.22 BEGIN:VEVENT DESCRIPTION:Scott Field\, University of Massachusetts\n\n
Recently there has been significant interest i n building data-driven gravitational-wave models directly from numerically generated data. These surrogate (or reduced-order) models can faithfully reproduce a parameterized gravitational wave model specified through compu tationally expensive ordinary or partial differential equations with signi ficant speedups. Surrogates can be used\, for example\, to accelerate the generation of effective one-body or numerical relativity (NR) waveform mod els\, thereby reducing the overall runtime of a multi-query data analysis study. In this talk\, I will summarize the key algorithms and approaches t oward building surrogate models as well as survey recent models that cover more of the binary black hole parameter space\, including precession\, ec centricity\, and large- to extreme-mass ratio systems. For surrogates to b e useful\, it is necessary that they be publicly available\, easy-to-use\, and decoupled from the building codes which produce them. In this talk\, I will also describe a lightweight open-source code\, GWSurrogate\, which addresses this issue and enables surrogate models to be used in gravitatio nal-wave data analysis studies. Some preliminary results for re-analyzing GWTC-3 will also be shown.
\n DTSTART:20221107T183000Z LOCATION:Physics\, Room 313 SUMMARY:Surrogate models\, methods\, and applications: Learning high-fideli ty gravitational-wave models from numerical relativity data END:VEVENT END:VCALENDAR