118 citations · 175 across the 9 of their papers we have counts for
10 papers
Learning Gaia: A Generative Model Trained on the Gaia Data Release 3 Source Catalog
John Soltis
I train a conditional flow matching model on the positions, photometry, and astrometry of 579 million sources from the Gaia Data Release 3 Source Catalog. Using a probabilis…
How to Craft the Right Language AI Policy For Your Research Group (Some Assembly Required)
Michelle Ntampaka, S. Burke-Spolaor, Ioana Ciucă +14
Language AI is rapidly becoming part of the astronomy research ecosystem, prompting research teams to develop policies governing its use. But resources and advice for AI adoption a…
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…
The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters
Michelle Ntampaka, A. Ciprijanovic, Ana Maria Delgado +4
The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearl…
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…
The Parallax of Omega Centauri Measured from Gaia EDR3 and a Direct, Geometric Calibration of the Tip of the Red Giant Branch and the Hubble Constant
John Soltis, Stefano Casertano, Adam G. Riess
We use data from the ESA Gaia mission Early Data Release 3 (EDR3) to measure the trigonometric parallax of Cen, the first high precision parallax measurement for the most massi…
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…