118 citations · 175 across the 9 of their papers we have counts for
6 papers · 1 filter
Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine learning
Ana Maria Delgado, Michelle Ntampaka, Sownak Bose +7
Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for cosmological studies. How…
A Multi-Wavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates
John Soltis, Michelle Ntampaka, Benedikt Diemer +7
The mass accretion rate of galaxy clusters is a key factor in determining their structure, but a reliable observational tracer has yet to be established. We present a state-of-the-…
Self-Similar Mass Accretion History in Scale-Free Simulations
John Soltis, Lehman Garrison
Using a scale-free -body simulation generated with the ABACUS -body code, we test the robustness of halo mass accretion histories via their convergence to self-similarity. We…
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…
Percent-Level Test of Isotropic Expansion Using Type Ia Supernovae
John Soltis, Arya Farahi, Dragan Huterer +1
We propose and implement a novel, robust, and non-parametric test of statistical isotropy of the expansion of the universe, and apply it to around one thousand type Ia supernovae f…