Exoplanet Detection by Machine Learning with Data Augmentation by Koray Aydoğan. on Wednesday 30 November

It has recently been demonstrated that deep learning has significant

potential to automate parts of the exoplanet detection pipeline using light

curve data from satellites such as Kepler \cite{borucki2010kepler}

\cite{koch2010kepler} and NASA's Transiting Exoplanet Survey Satellite (TESS)

\cite{ricker2010transiting}. Unfortunately, the smallness of the available

datasets makes it difficult to realize the level of performance one expects

from powerful network architectures.

In this paper, we investigate the use of data augmentation techniques on

light curve data from to train neural networks to identify exoplanets. The

augmentation techniques used are of two classes: Simple (e.g. additive noise

augmentation) and learning-based (e.g. first training a GAN

\cite{goodfellow2020generative} to generate new examples). We demonstrate that

data augmentation has a potential to improve model performance for the

exoplanet detection problem, and recommend the use of augmentation based on

generative models as more data becomes available.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.15577v1