AGNet: Weighing Black Holes with Deep Learning. (arXiv:2108.07749v2 [astro-ph.GA] UPDATED)

Supermassive black holes (SMBHs) are ubiquitously found at the centers of
most massive galaxies. Measuring SMBH mass is important for understanding the
origin and evolution of SMBHs. However, traditional methods require
spectroscopic data which is expensive to gather. 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 light curves for a
sample …

arxiv astro deep learning