  We present a novel approach for classifying stars as binary or exoplanet
using deep learning techniques. Our method utilizes feature extraction, wavelet
transformation, and a neural network on the light curves of stars to achieve
high-accuracy results. We have also compiled a dataset of binary and exoplanet
stars for training and validation by cross-matching observations from multiple
space-based telescopes with catalogs of known binary and exoplanet stars. The
application of wavelet transformation on the light curves has reduced the
number of data points and improved the training time. Our algorithm has shown
exceptional performance, with a test accuracy of 81.17%. This method can be
applied to large datasets from current and future space-based telescopes,
providing an efficient and accurate way of classifying stars.
