1 code implementation • 13 Mar 2024 • Minjong Cheon, Yo-Hwan Choi, Seon-Yu Kang, Yumi Choi, Jeong-Gil Lee, Daehyun Kang
This model achieves forecasting accuracy comparable to higher-resolution counterparts with significantly less computational resources, requiring only 4 NVIDIA A100 GPUs and less than 12 hours of training.
1 code implementation • 20 Jun 2016 • Karl D. Gordon, Morgan Fouesneau, Heddy Arab, Kirill Tchernyshyov, Daniel R. Weisz, Julianne J. Dalcanton, Benjamin F. Williams, Eric F. Bell, Luciana Bianchi, Martha Boyer, Yumi Choi, Andrew Dolphin, Leo Girardi, David W. Hogg, Jason S. Kalirai, Maria Kapala, Alexia R. Lewis, Hans-Walter Rix, Karin Sandstrom, Evan D. Skillman
We present the Bayesian Extinction And Stellar Tool (BEAST), a probabilistic approach to modeling the dust extinguished photometric spectral energy distribution of an individual star while accounting for observational uncertainties common to large resolved star surveys.
Astrophysics of Galaxies