28 citations · 42 across the 5 of their papers we have counts for
5 papers
Information-Ordered Bottlenecks for Adaptive Semantic Compression
Matthew Ho, Xiaosheng Zhao, Benjamin Wandelt
We present the information-ordered bottleneck (IOB), a neural layer designed to adaptively compress data into latent variables ordered by likelihood maximization. Without retrainin…
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…
A Machine Learning Approach to Enhancing eROSITA Observations
John Soltis, Michelle Ntampaka, John Wu +5
The eROSITA X-ray telescope, launched in 2019, is predicted to observe roughly 100,000 galaxy clusters. Follow-up observations of these clusters from Chandra, for example, will be…
The Dynamical Mass of the Coma Cluster from Deep Learning
Matthew Ho, Michelle Ntampaka, Markus Michael Rau +4
In 1933, Fritz Zwicky's famous investigations of the mass of the Coma cluster led him to infer the existence of dark matter \cite{1933AcHPh...6..110Z}. His fundamental discoveries…
CLMM: a LSST-DESC Cluster weak Lensing Mass Modeling library for cosmology
M. Aguena, C. Avestruz, C. Combet +22
We present the v1.0 release of CLMM, an open source Python library for the estimation of the weak lensing masses of clusters of galaxies. CLMM is designed as a standalone toolkit o…