Past Awardee

AGNet: Weighing Black Holes with Deep Learning

Xin Liu
Xin Liu

College: Liberal Arts and Sciences
Award year: 2020-2021
NCSA collaborators: Vlad Kindratenko, Matias Carrasco Kind

Supermassive black holes (SMBHs) are usually found at the centers of most galaxies. Measuring SMBH mass is important for fundamental science such as understanding the origin and evolution of SMBHs and enabling the usage of quasars or active galactic nuclei (AGN), however, traditional methods require spectral data which are highly expensive to gather. A pilot program to develop a new, interdisciplinary approach combining astronomy big data with machine learning tools to build a deep learning algorithm, AGNet, that weighs SMBHs using AGN light curves, circumventing the need for expensive spectra will be developed. By training algorithms that directly learn from the data to map out the nonlinear encoding, the field of SMBHs and cosmology will be transformed.