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.
H., Tsiaras A., Waldmann I
2 Pith papers cite this work, alongside 26 external citations. Polarity classification is still indexing.
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A comprehensive public dataset of simulated Ariel exoplanet transmission spectra is released to benchmark detrending algorithms, with an ML baseline highlighting dataset shift risks.
citing papers explorer
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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.
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A public dataset of Ariel simulated observations for developing exoplanetary atmosphere data reduction pipelines
A comprehensive public dataset of simulated Ariel exoplanet transmission spectra is released to benchmark detrending algorithms, with an ML baseline highlighting dataset shift risks.