Date: Tuesday 5 December 2024 – 15:00 (Europe/London)
Speaker: Dr Hsiang-Chih Hwang, Postdoctoral Fellow of the Institute for Advanced Study (USA)
Abstract
Mass is the most fundamental stellar parameter of stars. The mass of a star largely determines its stellar structure, surface temperature, luminosity, chemical evolution, lifetime, and its ultimate fate. However, robust stellar mass measurements are challenging since we cannot place a star on a weight scale. In this talk, I will demonstrate how neural networks with a simple architecture can help reveal the mass of (nearly) all the stars (https://arxiv.org/abs/2308.08584). In particular, I will discuss why neural networks play a critical role in extracting mass information from the orbital motions of wide binaries, which is difficult for traditional statistical tools. Using the combination of neural network and statistical inference, we measure the dynamical masses of stars across the famous "Hertzsprung–Russell diagram", where all the stars are populated in narrow regions in the surface temperature-luminosity space.
This seminar is now available on YouTube: https://youtu.be/iq9Mn_FQc8Y
Biography
Dr Hsiang-Chih Hwang is a postdoctoral fellow of the Institute for Advanced Study (USA). He received his Ph.D. degree in astronomy from Johns Hopkins University in 2021. Before coming to Johns Hopkins, he earned his Bachelor's in Physics and Electrical Engineering at National Taiwan University. He is interested in various topics, including binary stars, three-body systems, white dwarf, Milky Way dynamics, and binary quasars. He is also a coffee and milk tea lover.
