36 citations · 46 across the 3 of their papers we have counts for
4 papers
ROBIN-PIP: Robust Bayesian Field-Level Inference with Physics-Informed Priors
Ludvig Doeser, Simon Ding, Guilhem Lavaux +1
Accurately extracting information from galaxy surveys, particularly at non-linear scales, requires increasingly complex and flexible forward models of the observed galaxy distribut…
Preparing for Rubin-LSST -- Detecting Brightest Cluster Galaxies with Machine Learning in the LSST DP0.2 simulation
Aline Chu, Ludvig Doeser, Simon Ding +1
The future Rubin Legacy Survey of Space and Time (LSST) is expected to deliver its first data release in the current of 2025. The upcoming survey will provide us with images of gal…
: A generative, fast, and differentiable halo model for wide-field galaxy surveys
Simon Ding, Guilhem Lavaux, Jens Jasche
Mock halo catalogues are indispensable data products for developing and validating cosmological inference pipelines. A major challenge in generating mock catalogues is modelling th…
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier +12
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inferenc…