magic-microlensing

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magic-microlensing

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MAGIC: Microlensing Analysis Guided by Intelligent Computation. A PyTorch framework for automatic analysis of realistic microlensing light curves.

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MAGIC: Microlensing Analysis Guided by Intelligent Computation
</h1>

This repo contains the code for the paper

*[MAGIC: Microlensing Analysis Guided by Intelligent Computation](https://iopscience.iop.org/article/10.3847/1538-3881/ac9230)* [(arXiv)](https://arxiv.org/abs/2206.08199)

and the short paper

*[Parameter Estimation in Realistic Binary Microlensing Light Curves with Neural Controlled Differential Equation](https://ml4astro.github.io/icml2022/assets/6.pdf)*

at [ICML 2022 Workshop on Machine Learning for Astrophysics](https://ml4astro.github.io/icml2022/).

# Abstract

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 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 the degenerate solutions even when large data gaps are introduced. As irregular samplings are common in astronomical surveys, our method also has implications to other studies that involve time series.

# Checkpoints
Checkpoints of pretrained models can be downloaded at [Google Drive](https://drive.google.com/drive/folders/1SyvoSLd5g4Q0EjPvkjGpu6NAJsIjKfOi?usp=sharing) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/c81144404fbe4f0f89cd/).

This includes locator with $k=1/3, 0.75, 1, 1.25, 1.5, 1.75, 2$ and estimator with latent dim $32$, $n_G=12$ and diagonal covariance used in the paper.

# Experiments
Experiments and tests in the paper can be reproduced with the Jupyter notebooks in [the `test` folder](./test/) (see [this readme file](./test/README.md)). This folder, along with [the `simulate` folder](./simulate/) also contains additional tests not shown in the paper, which might be useful references for other uses.

As an example, the ground truth v.s. prediction plots of binary microlensing parameters are as follows (taken from the paper):

# Training
Run the scripts [`train_locator.py`](./train_locator.py) and [`train_cde_mdn.py`](./train_cde_mdn.py) to train locator and estimator from scratch. More settings can be specified with additional flags (see the script contents).

Before training the models, you might want to simulate some light curves if you haven't got any. Use the script [`simulate/simulate.py`](./simulate/simulate.py) for the simulation. Note that this step is better done on a CPU cluster.

# Model
As described in the paper, MAGIC consists of a locator and an estimator.

The locator is a one-dimensional U-Net, which can be found in [`model/locator.py`](./model/locator.py).

The estimator is composed of [a neural controlled differential equation](https://github.com/patrick-kidger/torchcde) for feature extraction and [a mixture density network](https://github.com/JasonZHM/full-cov-mdn) for posterior modelling. See [`model/cde_mdn.py`](./model/cde_mdn.py).

In case you'd like to customize the models for your own works, you should also have a look at the other files in [the `model` folder](./model/).

# Citation
If you find our work useful, please give us credit by citing our paper:

```bibtex
@article{zhao2022magic,
  title={MAGIC: Microlensing Analysis Guided by Intelligent Computation},
  author={Zhao, Haimeng and Zhu, Wei},
  journal={The Astronomical Journal},
  volume={164},
  number={5},
  pages={192},
  year={2022},
  publisher={IOP Publishing}
}
```

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