A Possible Converter to Denoise the Images of Exoplanet Candidates through Machine Learning Techniques
This is an incremental improvement for astronomers using direct imaging to study exoplanets, potentially saving observational time on over-subscribed telescopes.
The paper tackles the problem of needing many telescope images to detect exoplanets by proposing a machine learning converter to denoise images from fewer frames, achieving improved signal-to-noise ratio with a specific model called MWIN5-RB.
The method of direct imaging has detected many exoplanets and made important contribution to the field of planet formation. The standard method employs angular differential imaging (ADI) technique, and more ADI image frames could lead to the results with larger signal-to-noise-ratio (SNR). However, it would need precious observational time from large telescopes, which are always over-subscribed. We thus explore the possibility to generate a converter which can increase the SNR derived from a smaller number of ADI frames. The machine learning technique with two-dimension convolutional neural network (2D-CNN) is tested here. Several 2D-CNN models are trained and their performances of denoising are presented and compared. It is found that our proposed Modified five-layer Wide Inference Network with the Residual learning technique and Batch normalization (MWIN5-RB) can give the best result. We conclude that this MWIN5-RB can be employed as a converter for future observational data.