# Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning

## TL;DR

This paper enhances deep learning models for exoplanet transit classification by integrating scientific domain knowledge, leading to higher accuracy, better recall for low signal-to-noise signals, and smaller, more generalizable models suitable for future missions.

## Contribution

The authors incorporate domain-specific features and data augmentation into deep learning models, significantly improving exoplanet detection accuracy and recall, especially for challenging signals.

## Key findings

- Achieved 97.5% accuracy and 98.0% precision in classification.
- 15-20% improvement in recall for low SNR transits.
- Smaller models with better generalization for future missions.

## Abstract

Space-based missions such as Kepler, and soon TESS, provide large datasets that must be analyzed efficiently and systematically. Recent work by Shallue & Vanderburg (2018) successfully used state-of-the-art deep learning models to automatically classify Kepler transit signals as either exoplanets or false positives; our application of their model yielded 95.8% accuracy and 95.5% average precision. Here we expand upon that work by including additional scientific domain knowledge into the network architecture and input representations to significantly increase overall model performance to 97.5% accuracy and 98.0% average precision. Notably, we achieve 15-20% gains in recall for the lowest signal-to-noise transits that can correspond to rocky planets in the habitable zone. We input into the network centroid time-series information derived from Kepler data plus key stellar parameters taken from the Kepler DR25 catalogue. We also implement data augmentation techniques to alleviate model over-fitting. These improvements allow us to drastically reduce the size of the model, while still maintaining improved performance; smaller models are better for generalization, for example from Kepler to TESS data. This work illustrates the importance of including expert domain knowledge in even state-of-the-art deep learning models when applying them to scientific research problems that seek to identify weak signals in noisy data. This classification tool will be especially useful for upcoming space-based photometry missions focused on finding small planets, such as TESS and PLATO.