3.1k citations · 28k across the 184 of their papers we have counts for
18 papers · 1 filter
Learning Symbolic Physics with Graph Networks
Miles D. Cranmer, Rui Xu, Peter Battaglia +1
We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization. Our expe…
From Dark Matter to Galaxies with Convolutional Neural Networks
Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang +7
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations. However, to co…
Learning neutrino effects in Cosmology with Convolutional Neural Networks
Elena Giusarma, Mauricio Reyes Hurtado, Francisco Villaescusa-Navarro +3
Measuring the sum of the three active neutrino masses, , is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on se…
The Quijote simulations
Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara +26
The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7,000 cosmological models in the hyperplane.…
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates
Miles D. Cranmer, Richard Galvez, Lauren Anderson +2
We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable…
The Simons Observatory: Astro2020 Decadal Project Whitepaper
The Simons Observatory Collaboration, Maximilian H. Abitbol, Shunsuke Adachi +280
The Simons Observatory (SO) is a ground-based cosmic microwave background (CMB) experiment sited on Cerro Toco in the Atacama Desert in Chile that promises to provide breakthrough…