5 papers
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
Variations in the Milky Way's Stellar Mass Function at [Fe/H] < -1
Jiadong Li, Hans-Walter Rix, Yuan-Sen Ting +14
We present the first determination of the Galactic stellar mass function (MF) for low-mass stars (0.2-0.5 M_sun) at metallicities [Fe/H] < -1. A sample of ~53,000 stars was selecte…
Millions of Main-Sequence Binary Stars from Gaia BP/RP Spectra
Jiadong Li, Hans-Walter Rix, Yuan-Sen Ting +6
We present the main-sequence binary (MSMS) Catalog derived from Gaia Data Release 3 BP/RP (XP) spectra. Leveraging the vast sample of low-resolution Gaia XP spectra, we develop a f…
Differentiable Stellar Atmospheres with Physics-Informed Neural Networks
Jiadong Li, Mingjie Jian, Yuan-Sen Ting +1
We present Kurucz-a1, a physics-informed neural network (PINN) that emulates 1D stellar atmosphere models under Local Thermodynamic Equilibrium (LTE), addressing a critical bottlen…
Identification of 30,000 White Dwarf-Main Sequence binaries candidates from Gaia DR3 BP/RP(XP) low-resolution spectra
Jiadong Li, Yuan-Sen Ting, Hans-Walter Rix +5
White dwarf-main sequence (WDMS) binary systems are essential probes for understanding binary stellar evolution and play a pivotal role in constraining theoretical models of variou…