15 papers
MujicÎ: Reconstructing Initial Conditions from Incomplete Redshift Surveys with Projected Optimization
Chenze Dong, Benjamin Horowitz, Adrian E. Bayer +1
In this paper, we introduce MujicÎ (Mapping the Universe with Jax-based Initial Condition ReconstrÎction), an optimization-based framework for reconstructing initial conditions f…
Field-Level Inference from Galaxies: BAO Reconstruction
Adrian E. Bayer, Liam Parker, David Valcin +3
Baryon acoustic oscillations (BAO) underpin the key cosmological results from modern spectroscopic galaxy surveys, but nonlinear gravitational evolution limits the precision achiev…
Interpreting Cosmological Information from Neural Networks in the Hydrodynamic Universe
Arnab Lahiry, Adrian E. Bayer, Francisco Villaescusa-Navarro
What happens when a black box (neural network) meets a black box (simulation of the Universe)? Recent work has shown that convolutional neural networks (CNNs) can infer cosmologica…
jFoF: GPU Cluster Finding with Gradient Propagation
Benjamin Horowitz, Adrian E. Bayer
We present jFoF, a fully GPU-native Friends-of-Friends (FoF) halo finder designed for both high-performance simulation analysis and differentiable modeling. Implemented in JAX, jFo…
Impact of Simulation Box Size for Weak Lensing: Replication and Super-Sample Effects
Akira Tokiwa, Adrian E. Bayer, Joaquin Armijo +6
We quantify the bias caused by small simulation box size on weak lensing observables and covariances, considering both replication and super-sample effects for a range of higher-or…
CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
Ningyuan Huang, Richard Stiskalek, Jun-Young Lee +6
Cosmological simulations provide a wealth of data in the form of point clouds and directed trees. A crucial goal is to extract insights from this data that shed light on the nature…