# Quasar microlensing light curve analysis using deep machine learning

## TL;DR

This paper presents a deep learning method for analyzing quasar microlensing light curves, enabling efficient classification and measurement of accretion disc properties from large datasets.

## Contribution

It introduces a novel deep machine learning approach to analyze simulated microlensing light curves, demonstrating its effectiveness in classifying data and measuring accretion disc structures.

## Key findings

- Successful classification of diverse light curves
- Accretion disc structure can be measured accurately
- Shape of brightness profile has negligible impact

## Abstract

We introduce a deep machine learning approach to studying quasar microlensing light curves for the first time by analyzing hundreds of thousands of simulated light curves with respect to the accretion disc size and temperature profile. Our results indicate that it is possible to successfully classify very large numbers of diverse light curve data and measure the accretion disc structure. The detailed shape of the accretion disc brightness profile is found to play a negligible role, in agreement with Mortonson et al. (2005). The speed and efficiency of our deep machine learning approach is ideal for quantifying physical properties in a `big-data' problem setup. This proposed approach looks promising for analyzing decade-long light curves for thousands of microlensed quasars, expected to be provided by the Large Synoptic Survey Telescope.