Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate

Introduction

This repository contains the source code and demonstration of Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate (project page), which is accepted to NeurISP2020 Workshop on Machine Learning on Physical Science.

Requirements

We recommended the following dependencies.

• Python 3.7
• PyTorch 1.4

Code

• Coming Soon

Reference

If you find this paper or code helpful, please cite this paper:

@inproceedings{liang2020optical,
  title={Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate},
  author={Liang, Gongbo and Su, Yuanyuan and Lin, Sheng-Chieh and Zhang, Yu and Zhang, Yuanyuan and Jacobs, Nathan},
  booktitle={Neural Information Processing Systems (NeurIPS) Workshop on Machine Learning and the Physical Sciences},
  year={2020}
}