87 citations · 358 across the 28 of their papers we have counts for
29 papers
Surveying the Universe in 4D: Beating Cosmic Variance with Wide-Field Slitless Spectroscopy from HST, JWST, Euclid, Roman, and Beyond
Andreea Petric, Gaël Noirot, Stacey Alberts +71
We summarize strategies, lessons learned, and future directions from the Space Telescope Science Institute workshop Surveying the Universe in 4D: Beating Cosmic Variance with Wide-…
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…
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…
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…
Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI
Leander Thiele, Adrian E. Bayer, Naoya Takeishi
The simulation cost for cosmological simulation-based inference can be decreased by combining simulation sets of varying fidelity. We propose an approach to such multi-fidelity inf…
Field-Level Comparison and Robustness Analysis of Cosmological N-body Simulations
Adrian E. Bayer, Francisco Villaescusa-Navarro, Sammy Sharief +6
We present the first field-level comparison of cosmological N-body simulations, considering various widely used codes: Abacus, CUBEPM, Enzo, Gadget, Gizmo, PKDGrav, and Ramses.…
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…
Initial Conditions from Galaxies: Machine-Learning Subgrid Correction to Standard Reconstruction
Liam Parker, Adrian E. Bayer, Uros Seljak
We present a hybrid method for reconstructing the primordial density from late-time halos and galaxies. Our approach involves two steps: (1) apply standard Baryon Acoustic Oscillat…
The Power of the Cosmic Web
James Sunseri, Adrian E. Bayer, Jia Liu
We study the cosmological information contained in the cosmic web, categorized as four structure types: nodes, filaments, walls, and voids, using the Quijote simulations and a modi…
Massive s through the CNN lens: interpreting the field-level neutrino mass information in weak lensing
Malika Golshan, Adrian E. Bayer
Modern cosmological surveys probe the Universe deep into the nonlinear regime, where massive neutrinos suppress cosmic structure. Traditional cosmological analyses, which use the 2…
CHARM: Creating Halos with Auto-Regressive Multi-stage networks
Shivam Pandey, Chirag Modi, Benjamin D. Wandelt +7
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations…
Simulation-Based Inference Benchmark for Weak Lensing Cosmology
Justine Zeghal, Denise Lanzieri, François Lanusse +5
Standard cosmological analysis, which relies on two-point statistics, fails to extract the full information of the data. This limits our ability to constrain with precision cosmolo…
Periodicity significance testing with null-signal templates: reassessment of PTF's SMBH binary candidates
Jakob Robnik, Adrian E. Bayer, Maria Charisi +3
Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic s…
The HalfDome Multi-Survey Cosmological Simulations: N-body Simulations
Adrian E. Bayer, Yici Zhong, Zack Li +3
Upcoming cosmological surveys have the potential to reach groundbreaking discoveries on multiple fronts, including the neutrino mass, dark energy, and inflation. Most of the key sc…
Significance of void shape: Neutrino mass from Voronoi void halos?
Adrian E. Bayer, Jia Liu, Christina D. Kreisch +1
Massive neutrinos suppress the growth of cosmic structure on nonlinear scales, motivating the use of information beyond the power spectrum to tighten constraints on the neutrino ma…
Field-Level Inference with Microcanonical Langevin Monte Carlo
Adrian E. Bayer, Uros Seljak, Chirag Modi
Field-level inference provides a means to optimally extract information from upcoming cosmological surveys, but requires efficient sampling of a high-dimensional parameter space. T…
Joint velocity and density reconstruction of the Universe with nonlinear differentiable forward modeling
Adrian E. Bayer, Chirag Modi, Simone Ferraro
Reconstructing the initial conditions of the Universe from late-time observations has the potential to optimally extract cosmological information. Due to the high dimensionality of…
Super-sample covariance of the power spectrum, bispectrum, halos, voids, and their cross covariances
Adrian E. Bayer, Jia Liu, Ryo Terasawa +3
We study the effect of super-sample covariance (SSC) on the power spectrum and higher-order statistics: bispectrum, halo mass function, and void size function. We also investigate…
The DESI -body Simulation Project -- II. Suppressing sample variance with fast simulations
Zhejie Ding, Chia-Hsun Chuang, Yu Yu +19
Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches $\sim20\Gpchcube$. It is a…