AGNet: Weighing Black Holes with Machine Learning

11/30/2020
by   Joshua Yao-Yu Lin, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

08/17/2021

AGNet: Weighing Black Holes with Deep Learning

Supermassive black holes (SMBHs) are ubiquitously found at the centers o...
06/02/2021

Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes

Among the most extreme objects in the Universe, active galactic nuclei (...
10/02/2020

Machine-learning-enhanced time-of-flight mass spectrometry analysis

Mass spectrometry is a widespread approach to work out what are the cons...
08/20/2018

Peptide-Spectra Matching from Weak Supervision

As in many other scientific domains, we face a fundamental problem when ...
10/13/2014

Markov Random Fields and Mass Spectra Discrimination

For mass spectra acquired from cancer patients by MALDI or SELDI techniq...
11/04/2019

Predicting the properties of black holes merger remnants with Deep Neural Networks

We present the first estimation of the mass and spin of Kerr black holes...
09/25/2020

Predicting galaxy spectra from images with hybrid convolutional neural networks

Galaxies can be described by features of their optical spectra such as o...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.