2022 NeurIPS Ariel Data Challenge 1st Place

In this ML challenge, we created a retrieval model using a Neural Network in PyTorch to obtain planet parameters from transit data and both star and planet parameters. We demonstrated data analysis, data cleaning, feature engineering and enhancement, dimensionality reduction, and interpretability. The challenge consisted of two tracks - the light track (LT) to predict three quantiles (16%,50%,84%) and the regular track (RT) to predict the full distribution of the planet parameters.