2020/11/30 by Joshua Yao-Yu Lin, Sneh Pandya, Lin, Joshua Yao-Yu +7
Physics and Astronomy · #Astronomy and Astrophysical Research #Astrophysical Phenomena and Observations #Astrophysics of Galaxies (astro-ph.GA) #FOS: Computer and information sciences #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #High Energy Astrophysical Phenomena (astro-ph.HE) #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2011.15095
openalex publication_date 2020/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Supermassive black holes (SMBHs) are ubiquitously found at the centers of most galaxies. Measuring SMBH mass is important for understanding the origin and evolution of SMBHs. However, traditional methods require spectral data which is expensive to gather. To solve this problem, we present an algorithm that weighs SMBHs using quasar light time series, circumventing the need for expensive spectra. We train, validate, and test neural networks that directly learn from the Sloan Digital Sky Survey (SDSS) Stripe 82 data for a sample of 9,038 spectroscopically confirmed quasars to map out the nonlinear encoding between black hole mass and multi-color optical light curves. We find a 1σ scatter of 0.35 dex between the predicted mass and the fiducial virial mass based on SDSS single-epoch spectra. Our results have direct implications for efficient applications with future observations from the Vera Rubin Observatory.