Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning
Abstract
Further advances in exoplanet detection and characterisation require sampling a diverse population of extrasolar planets. One technique to detect these distant worlds is through the direct detection of their thermal emission. The so-called direct imaging technique, is suitable for observing young planets far from their star. These are very low signal-to-noise-ratio (SNR) measurements and limited ground truth hinders the use of supervised learning approaches. In this paper, we combine deep generative and discriminative models to bypass the issues arising when directly training on real data. We use a Generative Adversarial Network to obtain a suitable dataset for training Convolutional Neural Network classifiers to detect and locate planets across a wide range of SNRs. Tested on artificial data, our detectors exhibit good predictive performance and robustness across SNRs. To demonstrate the limits of the detectors, we provide maps of the precision and recall of the model per pixel of the input image. On real data, the models can re-confirm bright source detections.
Cite
Text
Yip et al. "Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46133-1_20Markdown
[Yip et al. "Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/yip2019ecmlpkdd-pushing/) doi:10.1007/978-3-030-46133-1_20BibTeX
@inproceedings{yip2019ecmlpkdd-pushing,
title = {{Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning}},
author = {Yip, Kai Hou and Nikolaou, Nikolaos and Coronica, Piero and Tsiaras, Angelos and Edwards, Billy and Changeat, Quentin and Morvan, Mario and Biller, Beth and Hinkley, Sasha and Salmond, Jeffrey and Archer, Matthew and Sumption, Paul and Choquet, Elodie and Soummer, Remi and Pueyo, Laurent and Waldmann, Ingo P.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {322-338},
doi = {10.1007/978-3-030-46133-1_20},
url = {https://mlanthology.org/ecmlpkdd/2019/yip2019ecmlpkdd-pushing/}
}