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  • NAM2021
    • Contacts
  • Science
    • Science Programme
    • Plenary Talks
    • Parallel Sessions
    • Special Lunches/Discussion Sessions
    • Poster Session
    • NAM Community Session
  • Social
    • Presidential Address
    • Herschel Concert
    • RAS Awards Ceremony
    • Virtual Stonehenge Tour
  • Media
  • Public Engagement
    • Public engagement opportunities
    • Public talk
    • Writing Skyscapes
  • Venue
    • Code of Conduct
    • Accessing the conference
    • Gather.town
    • NAM2021 Slack
    • About Bath

Poster

id
Nessai: Improved nested sampling with normalising flows for gravitational-wave inference
Gravitational Waves
Michael
Williams
Date Submitted
2021-04-29 00:00:00
University of Glasgow
M. J. Williams (University of Glasgow), J. Veitch (University of Glasgow), C. Messenger (University of Glasgow))
Our understanding of the sources that produce gravitational waves hinges on the ability to perform Bayesian inference on the physical parameters that describe them. However, for certain waveforms and systems, this is computationally expensive. In this talk, I will present our new nested sampling algorithm that incorporates machine learning to improve the efficiency. Our new algorithm called nessai is applicable to general problems. In this work, we focus on the application to compact binary coalescence and achieve a factor of two improvement compared to current methods in use by the LVK Collaboration.

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