Shallow Transits -- Deep Learning II: Identify Individual Exoplanetary Transits in Red Noise using Deep Learning

TL;DR

This paper introduces a deep learning neural network that accurately segments and identifies individual shallow exoplanetary transits in noisy light curves, enhancing detection capabilities for future space surveys.

Contribution

It presents a novel deep learning approach using U-Nets and GANs for transit segmentation, advancing beyond previous detection-only models.

Findings

Effective segmentation of shallow transits in red noise

Utilizes advanced deep learning architectures like U-Nets and GANs

Prepares for improved detection in future space missions like PLATO

Abstract

In a previous paper, we have introduced a deep learning neural network that should be able to detect the existence of very shallow periodic planetary transits in the presence of red noise. The network in that feasibility study would not provide any further details about the detected transits. The current paper completes this missing part. We present a neural network that tags samples that were obtained during transits. This is essentially similar to the task of identifying the semantic context of each pixel in an image -- an important task in computer vision, called `semantic segmentation', which is often performed by deep neural networks. The neural network we present makes use of novel deep learning concepts such as U-Nets, Generative Adversarial Networks (GAN), and adversarial loss. The resulting segmentation should allow further studies of the light curves which are tagged as containing transits. This approach towards the detection and study of very shallow transits is bound to play a significant role in future space-based transit surveys such as PLATO, which are specifically aimed to detect those extremely difficult cases of long-period shallow transits. Our segmentation network also adds to the growing toolbox of deep learning approaches which are being increasingly used in the study of exoplanets, but so far mainly for vetting transits, rather than their initial detection.