92 citations · 800 across the 37 of their papers we have counts for
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astro-ph.IM2021★ 11 cited
The Importance of Being Interpretable: Toward An Understandable Machine Learning Encoder for Galaxy Cluster Cosmology
Michelle Ntampaka, Alexey Vikhlinin
We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components:…
astro-ph.IM2021
Building Trustworthy Machine Learning Models for Astronomy
Michelle Ntampaka, Matthew Ho, Brian Nord
Astronomy is entering an era of data-driven discovery, due in part to modern machine learning (ML) techniques enabling powerful new ways to interpret observations. This shift in ou…
astro-ph.CO2021★ 11 cited
Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders
Tibor Rothschild, Daisuke Nagai, Han Aung +3
We develop a machine learning algorithm that generates high-resolution thermal Sunyaev-Zeldovich (SZ) maps of novel galaxy clusters given only halo mass and mass accretion rate. Th…