How does neural network help reveal the mass of (nearly) all the stars | Hsiang-Chih Hwang

Speaker: Dr Hsiang-Chih Hwang, Postdoctoral Fellow of the Institute for Advanced Study (USA)

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 talk is part of the Liverpool Virtual Seminar Series on Data Intensive Science; more information can be found at https://indico.ph.liv.ac.uk/e/data_sc...

#bigdata #datascience #dataseminar #science #data #spectroscopy