  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
1$\sigma$ 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.
