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We present a novel approach for classifying stars as binary or exoplanetusing deep learning techniques. Our method utilizes feature extraction, wavelettransformation, and a neural network on the light curves of stars to achievehigh-accuracy results. We have also compiled a dataset of binary and exoplanetstars for training and validation by cross-matching observations from multiplespace-based telescopes with catalogs of known binary and exoplanet stars. Theapplication of wavelet transformation on the light curves has reduced thenumber of data points and improved the training time. Our algorithm has shownexceptional performance, with a test accuracy of 81.17%. This method can beapplied to large datasets from current and future space-based telescopes,providing an efficient and accurate way of classifying stars.

Star Classification: A Deep Learning Approach for Identifying Binary and Exoplanet Stars