Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011

TL;DR

This paper introduces the 4S method, an explainable machine learning approach that improves high-contrast imaging data processing, enabling better detection of faint exoplanets like AF Lep b by reducing signal loss compared to traditional PCA.

Contribution

The paper develops the 4S method, a novel post-processing technique that constrains noise modeling to minimize planet signal loss in high-contrast imaging data.

Findings

4S outperforms PCA in archival datasets, especially at close star separations.

The method enables detection of AF Lep b in 2011 data, 11 years before its official discovery.

Up to 1.5 magnitudes deeper contrast achieved with 4S.

Abstract

The main challenge of exoplanet high-contrast imaging (HCI) is to separate the signal of exoplanets from their host stars, which are many orders of magnitude brighter. HCI for ground-based observations is further exacerbated by speckle noise originating from perturbations in Earth's atmosphere and imperfections in the telescope optics. Various data post-processing techniques are used to remove this speckle noise and reveal the faint planet signal. Often, however, a significant part of the planet signal is accidentally subtracted together with the noise. In the present work, we use explainable machine learning to investigate the reason for the loss of the planet signal for one of the most used post-processing methods: principal component analysis (PCA). We find that PCA learns the shape of the telescope point spread function for high numbers of PCA components. This representation of the noise captures not only the speckle noise but also the characteristic shape of the planet signal. Building on these insights, we develop a new post-processing method (4S) that constrains the noise model to minimize this signal loss. We apply our model to 11 archival HCI datasets from the VLT-NACO instrument in the L'-band and find that our model consistently outperforms PCA. The improvement is largest at close separations to the star ($\leq 4 \lambda /D$) providing up to 1.5 magnitudes deeper contrast. This enhancement enables us to detect the exoplanet AF Lep b in data from 2011, 11 years before its subsequent discovery. We present updated orbital parameters for this object.