413 citations · 4.3k across the 109 of their papers we have counts for
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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…
Learning to Predict the Cosmological Structure Formation
Siyu He, Yin Li, Yu Feng +4
Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features…
Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau +4
A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is…
Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning
M. Ntampaka, H. Trac, D. J. Sutherland +3
We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a facto…
A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters
Michelle Ntampaka, Hy Trac, Danica J. Sutherland +3
We present a modern machine learning approach for cluster dynamical mass measurements that is a factor of two improvement over using a conventional scaling relation. Different meth…
A First Look at creating mock catalogs with machine learning techniques
Xiaoying Xu, Shirley Ho, Hy Trac +3
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial f…