Measuring the Vertical Structure of Active Galactic Nuclei Disks with Transformer Models and the Vera C. Rubin Observatory

TL;DR

This paper introduces a transformer-based machine learning model that accurately detects long and short reverberation lags in simulated AGN light curves, promising to enhance vertical structure mapping of AGN disks with upcoming Rubin Observatory data.

Contribution

We develop and validate a novel transformer model that significantly outperforms traditional methods in identifying reverberation lags in AGN light curves.

Findings

Transformer model achieves 96% recall in detecting long negative lags.

Model accuracy in predicting true long lag is 98%.

Outperforms baseline methods by large margins in accuracy.

Abstract

Reverberation mapping is one of the main techniques used to study active galactic nuclei (AGN) accretion disks. Traditional continuum reverberation mapping uses short lags between variability in different wavelength AGN light curves on the light crossing timescale of the disk to measure the radial structure of the disk. The harder-to-detect long negative lag measures lags on the longer inflow timescale, opening up a new window to mapping out the vertical structure of AGN disks. The Vera Rubin Observatory, with its 6 wavebands, long baseline, and high cadence, will revolutionize our ability to detect short and long lags. However, many challenges remain to detect these long lags, such as seasonal gaps in Rubin light curves, the weak signal strength of the long lag relative to the short lag, and the enormous influx of data for millions of AGN from Rubin. Machine learning techniques have the potential to solve many of these issues, but have yet to be applied to the long negative lag problem. We develop and train a transformer-based machine learning model to detect long and short lags in mock Rubin AGN light curves. Our model identifies whether a light curve in our test set has a long negative lag with 96% recall and 0.04% contamination, and is 98% accurate at predicting the true long lag. This accuracy is an enormous improvement over two baseline methods we test on the same mock light curves, the interpolated cross correlation function and javelin, which are only 54% and 21% accurate, respectively.