Deep neural networks are trained to recover low-order Fourier elliptical components describing overall shape and orientation from simulated transit light curves of arbitrary 2D objects.
E., Mohanty S., 2024, @doi [ ] 10.1093/mnras/stae191 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.4314C 528
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Beyond Spherical geometry: Unraveling complex features of objects orbiting around stars from its transit light curve using deep learning
Deep neural networks are trained to recover low-order Fourier elliptical components describing overall shape and orientation from simulated transit light curves of arbitrary 2D objects.