50 citations · 284 across the 21 of their papers we have counts for
12 papers · 1 filter
FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology
Biwei Dai, Po-Wen Chang, Wahid Bhimji +15
Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allow…
Cosmic Shear constraints from HSC Year 3 with clustering calibration of the tomographic redshift distributions from DESI
J. Choppin de Janvry, B. Dai, S. Gontcho A Gontcho +2
We reanalyze cosmological constraints from Hyper Suprime-Cam (HSC) Y3 shear-shear correlation function using new calibration of the tomographic redshift distribution via the cluste…
One latent to fit them all: a unified representation of baryonic feedback on matter distribution
Shurui Lin, Yin Li, Shy Genel +4
Accurate and parsimonious quantification of baryonic feedback on matter distribution is of crucial importance for understanding both cosmology and galaxy formation from observation…
Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps
Kangning Diao, Biwei Dai, Uros Seljak
Simulation-based inference provides a powerful framework for extracting rich information from nonlinear scales in current and upcoming cosmological surveys, and ensuring its robust…
A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks
Divij Sharma, Biwei Dai, Uros Seljak
Weak Lensing (WL) surveys are reaching unprecedented depths, enabling the investigation of very small angular scales. At these scales, nonlinear gravitational effects lead to highe…
A field-level emulator for modeling baryonic effects across hydrodynamic simulations
Divij Sharma, Biwei Dai, Francisco Villaescusa-Navarro +1
We develop a new and simple method to model baryonic effects at the field level relevant for weak lensing analyses. We analyze thousands of state-of-the-art hydrodynamic simulation…
Multiscale Flow for Robust and Optimal Cosmological Analysis
Biwei Dai, Uros Seljak
We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing. Mult…
Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis
Biwei Dai, Uros Seljak
Our universe is homogeneous and isotropic, and its perturbations obey translation and rotation symmetry. In this work we develop Translation and Rotation Equivariant Normalizing Fl…
MADLens, a python package for fast and differentiable non-Gaussian lensing simulations
Vanessa Böhm, Yu Feng, Max E. Lee +1
We present MADLens a python package for producing non-Gaussian lensing convergence maps at arbitrary source redshifts with unprecedented precision. MADLens is designed to achieve h…
Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning
Biwei Dai, Uros Seljak
The goal of generative models is to learn the intricate relations between the data to create new simulated data, but current approaches fail in very high dimensions. When the true…
High mass and halo resolution from fast low resolution simulations
Biwei Dai, Yu Feng, Uros Seljak +1
Generating mocks for future sky surveys requires large volumes and high resolutions, which is computationally expensive even for fast simulations. In this work we try to develop nu…
A gradient based method for modeling baryons and matter in halos of fast simulations
Biwei Dai, Yu Feng, Uros Seljak
Fast N-body PM simulations with a small number of time steps such as FastPM or COLA have been remarkably successful in modeling the galaxy statistics, but their lack of small scale…