2 citations · 2 across the 3 of their papers we have counts for
15 papers · 1 filter
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…
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…
Flinch: A Differentiable Framework for Field-Level Inference of Cosmological parameters from curved sky data
Andrea Crespi, Marco Bonici, Arthur Loureiro +6
We present Flinch, a fully differentiable and high-performance framework for field-level inference on angular maps, developed to improve the flexibility and scalability of current…
Transfer Learning Beyond the Standard Model
Veena Krishnaraj, Adrian E. Bayer, Christian Kragh Jespersen +1
Machine learning enables powerful cosmological inference but typically requires many high-fidelity simulations covering many cosmological models. Transfer learning offers a way to…