Justine Zeghal: Simulation-Based Inference: estimating posterior distributions without analytic likelihoods

Galaxy surveys have already made it possible to probe our universe and improve our understanding of it. Upcoming stage IV surveys (such as LSST or Euclid) are expected to be larger and deeper offering the opportunity to refine our estimations even further. To analyze the vast amount of data these surveys will produce and enable new discoveries, we need to update our inference techniques for constraining cosmological parameters. To date, inference techniques typically rely on suboptimal compression of data (most of the time, into the power spectrum) and the use of a Gaussian likelihood function.

Recently, there has been a promising shift from analytical likelihood-based approaches to simulations-based inference. This new paradigm allows for precise inference, enabling to extract all the information embedded in the data, even when the likelihood of the data given the parameters is unknown.

In my presentation, I will explain the two ways of performing inference using a simulation model, whether it is explicit (when you can evaluate the likelihood of your simulation model) or implicit (when you only have simulations). I will discuss the pros and cons of these techniques specifically regarding the number of simulations they need. I will use the example of weak gravitational lensing, but the inference method remains the same in any case!

Seminar by Justine Zeghal (Laboratoire Astroparticule & Cosmologie, Paris)

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Open to all IP2I & LMA members, as well as UCBL and CNRS students and collaborators.

More information on seminars: https://intranet.ip2i.in2p3.fr/vie-scientifique/seminaires-ip2i

IP2I agenda: https://www.ip2i.in2p3.fr/agendav2/

https://indico.in2p3.fr/event/33158/