10 citations · 42 across the 18 of their papers we have counts for
19 papers · 1 filter
Field-Level Baryon Acoustic Oscillation Reconstruction of the DESI DR1 Luminous Red Galaxies with Linear Field Transformer (LiFT)
Liam Parker, Uroš Seljak, Adrian E. Bayer +2
We present the first application of neural field-level baryon-acoustic oscillation (BAO) reconstruction to real spectroscopic survey data. We develop Linear Field Transformer (LiFT…
MujicΛ: Reconstructing Initial Conditions from Incomplete Redshift Surveys with Projected Optimization
Chenze Dong, Benjamin Horowitz, Adrian E. Bayer +1
In this paper, we introduce MujicΛ (Mapping the Universe with Jax-based Initial Condition ReconstrΛction), an optimization-based framework for reconstructing initial conditions fro…
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…
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…