92 citations · 800 across the 37 of their papers we have counts for
4 papers · 1 filter
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
D. Huppenkothen, M. Ntampaka, M. Ho +19
Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…
Painting baryons onto N-body simulations of galaxy clusters with image-to-image deep learning
Urmila Chadayammuri, Michelle Ntampaka, John ZuHone +2
Galaxy cluster mass functions are a function of cosmology, but mass is not a direct observable, and systematic errors abound in all its observable proxies. Mass-free inference can…
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…
Predicting the impact of feedback on matter clustering with machine learning in CAMELS
Ana Maria Delgado, Daniel Angles-Alcazar, Leander Thiele +7
Extracting information from the total matter power spectrum with the precision needed for upcoming cosmological surveys requires unraveling the complex effects of galaxy formation…