The paper proposes a statistical test for asteroid surface color heterogeneity from sparse multiband photometry and evaluates its performance and sensitivity to model errors through Monte Carlo simulations of synthetic asteroids.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
astro-ph.EP 2representative citing papers
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.
citing papers explorer
-
Prospects for detecting surface color heterogeneity on asteroid surfaces from sparse multiband photometric survey data
The paper proposes a statistical test for asteroid surface color heterogeneity from sparse multiband photometry and evaluates its performance and sensitivity to model errors through Monte Carlo simulations of synthetic asteroids.
-
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.