69 citations · 194 across the 15 of their papers we have counts for
7 papers · 1 filter
Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering
Chirag Modi, Shivam Pandey, Matthew Ho +3
Simulation-based inference (SBI) is a promising approach to leverage high fidelity cosmological simulations and extract information from the non-Gaussian, non-linear scales that ca…
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…
Benchmarks and Explanations for Deep Learning Estimates of X-ray Galaxy Cluster Masses
Matthew Ho, John Soltis, Arya Farahi +3
We evaluate the effectiveness of deep learning (DL) models for reconstructing the masses of galaxy clusters using X-ray photometry data from next-generation surveys. We establish t…
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…
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
Matthew Ho, Arya Farahi, Markus Michael Rau +1
We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs). We discuss the statistical bac…
A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
Matthew Ho, Markus Michael Rau, Michelle Ntampaka +3
We demonstrate the ability of convolutional neural networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters wi…