# MAGIC: Microlensing Analysis Guided by Intelligent Computation

arXiv: https://arxiv.org/abs/2206.08199 · HTML: https://mlsanity.com/paper/2206.08199/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 54 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Domain-adversarial neural networks to address the appearance variability of histopathology images](https://mlsanity.com/paper/1707.06183/) (2017)
- [Deep Recurrent Neural Networks for Supernovae Classification](https://mlsanity.com/paper/1606.07442/) (2016)
- [Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation](https://mlsanity.com/paper/2102.05673/) (2021)
- [deep21: a Deep Learning Method for 21cm Foreground Removal](https://mlsanity.com/paper/2010.15843/) (2020)
- [A Ubiquitous Unifying Degeneracy in Two-Body Microlensing Systems](https://mlsanity.com/paper/2111.13696/) (2021)

## Built on by (most cited first, 1 of 1)

- [Entanglement-induced provable and robust quantum learning advantages](https://mlsanity.com/paper/2410.03094/) (2024)

## Abstract (arXiv)

The modeling of binary microlensing light curves via the standard sampling-based method can be challenging, because of the time-consuming light-curve computation and the pathological likelihood landscape in the high-dimensional parameter space. In this work, we present MAGIC, which is a machine-learning framework to efficiently and accurately infer the microlensing parameters of binary events with realistic data quality. In MAGIC, binary microlensing parameters are divided into two groups and inferred separately with different neural networks. The key feature of MAGIC is the introduction of a neural controlled differential equation, which provides the capability to handle light curves with irregular sampling and large data gaps. Based on simulated light curves, we show that MAGIC can achieve fractional uncertainties of a few percent on the binary mass ratio and separation. We also test MAGIC on a real microlensing event. MAGIC is able to locate degenerate solutions even when large data gaps are introduced. As irregular samplings are common in astronomical surveys, our method also has implications for other studies that involve time series.