3.1k citations · 28k across the 184 of their papers we have counts for
186 papers
Learnable wavelet neural networks for cosmological inference
Christian Pedersen, Michael Eickenberg, Shirley Ho
Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astro…
Neutrino mass constraint from an Implicit Likelihood Analysis of BOSS voids
Leander Thiele, Elena Massara, Alice Pisani +4
Cosmic voids identified in the spatial distribution of galaxies provide complementary information to two-point statistics. In particular, constraints on the neutrino mass sum, $\su…
Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models
Ronan Legin, Matthew Ho, Pablo Lemos +4
Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of the universe have provided a way to infer…
Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties
Joshua Fagin, Ji Won Park, Henry Best +6
Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temp…
Recovering Galaxy Cluster Convergence from Lensed CMB with Generative Adversarial Networks
Liam Parker, Dongwon Han, Pablo Lemos Portela +1
We present a new method which leverages conditional Generative Adversarial Networks (cGAN) to reconstruct galaxy cluster convergence from lensed CMB temperature maps. Our model is…
Emulating cosmological growth functions with B-Splines
Ngai Pok Kwan, Chirag Modi, Yin Li +1
In the light of GPU accelerations, sequential operations such as solving ordinary differential equations can be bottlenecks for gradient evaluations and hinder potential speed gain…