Enhancing gravitational waveform models through dynamic calibration and regression

Seminar
AstroPhysics Seminar
QUEST Center event
No
Speaker
Yoshinta Setyawati, Max-Planck-Institut für Gravitationsphysik (Utrecht University & Albert-Einstein-Institut)
Date
22/04/2021 - 21:30 - 20:00Add to Calendar 2021-04-22 20:00:00 2021-04-22 21:30:00 Enhancing gravitational waveform models through dynamic calibration and regression Fast and accurate binary-black-hole (BBH) merger waveform models that span wide parameter ranges are crucial for future searches and parameter estimation of gravitational-wave data. To date, analytical waveforms that incorporate numerical-relativity information, such as effective-one-body and phenomenological models, play an important role in analysing LIGO and Virgo data. However, these models are not automatically updated every time new numerical waveforms become available. Here we present a new perspective on dynamically tuning waveform models by incorporating sparse information from a more accurate model. We also show the first attempts to use our method to include additional physical effects that were not present in the original model and investigate various techniques that include interpolation and regression implemented in the development of waveform modeling.   The talk will be given over Zoom, at https://zoom.us/j/9290951953   Zoom: https://zoom.us/j/9290951953 Department of Physics physics.dept@mail.biu.ac.il Asia/Jerusalem public
Place
Zoom: https://zoom.us/j/9290951953
Abstract
Fast and accurate binary-black-hole (BBH) merger waveform models that span wide parameter ranges are crucial for future searches and parameter estimation of gravitational-wave data. To date, analytical waveforms that incorporate numerical-relativity information, such as effective-one-body and phenomenological models, play an important role in analysing LIGO and Virgo data. However, these models are not automatically updated every time new numerical waveforms become available. Here we present a new perspective on dynamically tuning waveform models by incorporating sparse information from a more accurate model. We also show the first attempts to use our method to include additional physical effects that were not present in the original model and investigate various techniques that include interpolation and regression implemented in the development of waveform modeling.
 
The talk will be given over Zoom, at https://zoom.us/j/9290951953

 

Last Updated Date : 07/01/2021