Don’t Pay Attention to the Noise: Learning Self-supervised Light Curve Representations with a Denoising Time Series Transformer

This is the official repo associated with the above work presented at ICLR 2022 AI for Earth & Space Science and ICML 2022 Machine Learning for Astrophysics workshops.

It contains some utilities to process light curve (through Pytorch Dataset and Transform objects), as well as the Denoising Time Series Transformer implemented as a Pytorch lightning module.

We advise to run the code in a dedicated virtual/conda environment after installing the required dependencies:

pip install -r requirements

TESS data used for experiments can be downloaded by executing the bash script tesscurl_sector_1_lc.sh saved in the data directory (and found on STSCI's archive website)

Finally the DTST can be run with:

main.py --train_path [path_to_training_directory]

See options with python main.py --help