This week, as part of the Bay Area Likelihood-Free Inference Meeting hosted by BIDS, the Machine Learning and Statistics Forum is pleased to host Dr. Simon Birrer, a Kavli Institute for Particle Astrophysics and Cosmology (KIPAC) postdoctoral Fellow at Stanford University. Simon will tell us about his work using Approximate Bayesian Computation techniques to solve otherwise intractable inference problems and learn about the nature of Dark Matter using measurements of the strong gravitational lensing effect. Title and abstract for his talk follow:
Title: Probing Dark Matter with Strong Gravitational Lensing
Abstract: Strong gravitational lensing provides a unique window to probe the small scale dark matter clustering and sub-halo mass function. The population of (dark) low mass halos creates imprints in the distortions of lensed images as well as flux ratio perturbations in multiply lensed quasars. The inference on the underlying population statistics from the lensing observable is intractable and likelihood-free methods based on summary statistics and a fast amount of simulations is required. In my talk, I will lay out the problem and describe current approaches and results in constraining the nature of dark matter (e.g. Birrer et al. 2016, Gilman, Birrer et al. 2019 a,b,c).
Full details about this meeting will be posted here: https://bids.github.io/MLStatsForum/.
The Berkeley Statistics and Machine Learning Forum meets biweekly to discuss current applications across a wide variety of research domains and software methodologies. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. Practical questions about the meetings can be directed to BIDS Fellow Francois Lanusse. All interested members of the UC Berkeley and LBL communities are welcome and encouraged to attend. To receive email notifications about the meetings and upvote papers for discussion, please register here.