8 citations · 10 across the 4 of their papers we have counts for
5 papers
The Deep Learning Halo Definer: A Multimodal Framework for Halo Mass and Gas Fraction Inference on Galaxy Groups and Clusters
Caleb Ogle, Kalvyn N. Poncelet Adams, Benjamin D. Oppenheimer +5
Accurately inferring dark matter halo properties like the total halo mass (M_{halo}) and gas fractions (f_{gas}) remains particularly challenging at group scales, where low member…
CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations
Kalvyn N. Poncelet Adams, Benjamin D. Oppenheimer, Naomi Gluck +3
Quantifying the information content in multi-wavelength observations is critical for setting exposure times for upcoming X-ray and 21-cm HI radio surveys. We train convolutional ne…
Benchmarking Machine Learning Emulators of Stellar Evolution for Precision Asteroseismology
Naomi Gluck, Earl P. Bellinger, Yan Liang +5
Fast and accurate stellar evolution emulators---surrogate models that approximate expensive simulation outputs with machine learning (ML)---are powerful tools for modern stellar ch…
An Observationally Driven Multifield Approach for Probing the Circum-Galactic Medium with Convolutional Neural Networks
Naomi Gluck, Benjamin D. Oppenheimer, Daisuke Nagai +2
The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets becoming available in the near fut…
Enhanced mass-loss rate evolution of stars with and missing optically-observed type II core-collapse supernovae
Roni Anna Gofman, Naomi Gluck, Noam Soker
We evolve stellar models with zero-age main sequence (ZAMS) mass of under the assumption that they experience an enhanced mass-loss rate when cros…