NAM2019
  • 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
  • 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
Strong Lensing with Bayesian Neural Networks
AstroML
James
Pearson
Date Submitted
2021-07-05 00:00:00
The University of Nottingham
James Pearson, Jacob Maresca, Nan Li, Simon Dye
Strong galaxy-galaxy gravitational lensing is the distortion of the paths of light rays from a background galaxy into arcs or rings as viewed from Earth, caused by the gravitational field of an intervening foreground lens galaxy. The vast quantity of strong lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For this purpose, we train an approximate Bayesian convolutional neural network (CNN) to predict mass profile parameters and associated uncertainties, and compare its accuracy to that of conventional parametric modelling. In addition, we present a method for combining the CNN with conventional modelling in an automated fashion, where the CNN provides initial priors on the latter's parameters. These methods are tested on a range of increasingly complex lensing systems, from standard smooth parametric mass and light profiles to images containing hydrodynamical EAGLE galaxies, Hubble Ultra Deep Field source galaxies and the inclusion of foreground mass structures. While the CNN is undoubtedly the fastest modelling method, the combination of the two increases the speed of conventional modelling, especially when priors include CNN-predicted uncertainties. This, combined with significantly improved accuracy, highlights the benefits one can obtain through combining neural networks with conventional techniques in order to achieve an efficient automated modelling approach.

NAM 2020 Logo AWRAS Logo

 

Bath University LogoUKRI STFC new

All attendees are expected to show respect and courtesy to other attendees and staff, and to adhere to the NAM Code of Conduct.

© 2023 Royal Astronomical Society

Login