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Found 4 papers, 0 papers with code
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.
A deep learning view of the census of galaxy clusters in IllustrisTNG
From this analysis, we observe that the network has utilized regions from cluster centers out to r~300 kpc and r~500 kpc to identify CC and NCC clusters, respectively.
Cosmology and Nongalactic Astrophysics
A Deep Learning Approach to Galaxy Cluster X-ray Masses
Despite our simplifying assumption to neglect spectral information, the resulting mass values estimated by the CNN exhibit small bias in comparison to the true masses of the simulated clusters (-0. 02 dex) and reproduce the cluster masses with low intrinsic scatter, 8% in our best fold and 12% averaging over all.
Cosmology and Nongalactic Astrophysics
Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning
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 factor of two compared to a standard scaling relation approach.
Cosmology and Nongalactic Astrophysics
