A one-armed CNN for exoplanet detection from light curves
This is an incremental improvement for astronomers using CNNs in exoplanet detection, focusing on model simplification and performance estimation.
The paper tackled exoplanet detection from light curves by proposing Genesis, a simplified one-armed CNN, and found it reduces parameters by over 95% with only a 0.5% performance drop compared to Astronet, while Monte Carlo cross-validation shows a 0.7% lower estimate and increased resolution decreases performance by 0.5%.
We propose Genesis, a one-armed simplified Convolutional Neural Network (CNN)for exoplanet detection, and compare it to the more complex, two-armed CNN called Astronet. Furthermore, we examine how Monte Carlo cross-validation affects the estimation of the exoplanet detection performance. Finally, we increase the input resolution twofold to assess its effect on performance. The experiments reveal that (i)the reduced complexity of Genesis, i.e., a more than 95% reduction in the number of free parameters, incurs a small performance cost of about 0.5% compared to Astronet, (ii) Monte Carlo cross-validation provides a more realistic performance estimate that is almost 0.7% below the original estimate, and (iii) the twofold increase in input resolution decreases the average performance by about 0.5%. We conclude by arguing that further exploration of shallower CNN architectures may be beneficial in order to improve the generalizability of CNN-based exoplanet detection across surveys.