372 citations · 1.7k across the 44 of their papers we have counts for
7 papers · 1 filter
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…
Forecasting the power of Higher Order Weak Lensing Statistics with automatically differentiable simulations
Denise Lanzieri, François Lanusse, Chirag Modi +4
We present the Differentiable Lensing Lightcone (DLL), a fully differentiable physical model designed for being used as a forward model in Bayesian inference algorithms requiring a…
pmwd: A Differentiable Cosmological Particle-Mesh -body Library
Yin Li, Libin Lu, Chirag Modi +7
The formation of the large-scale structure, the evolution and distribution of galaxies, quasars, and dark matter on cosmological scales, requires numerical simulations. Differentia…
Differentiable Cosmological Simulation with Adjoint Method
Yin Li, Chirag Modi, Drew Jamieson +5
Rapid advances in deep learning have brought not only myriad powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic…
Reconstructing the Universe with Variational self-Boosted Sampling
Chirag Modi, Yin Li, David Blei
Forward modeling approaches in cosmology have made it possible to reconstruct the initial conditions at the beginning of the Universe from the observed survey data. However the hig…
nbodykit: an open-source, massively parallel toolkit for large-scale structure
Nick Hand, Yu Feng, Florian Beutler +4
We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface (MPI),…