# AGNet: Weighing Black Holes with Machine Learning

arXiv: https://arxiv.org/abs/2011.15095 · HTML: https://mlsanity.com/paper/2011.15095/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations](https://mlsanity.com/paper/2402.03325/) (2024)
- [Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes](https://mlsanity.com/paper/2106.01450/) (2021)
- [Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches](https://mlsanity.com/paper/2502.15297/) (2025)

## Abstract (arXiv)

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.