Exoplanet Imaging Data Challenge: benchmarking the various image processing methods for exoplanet detection

TL;DR

The paper presents the Exoplanet Imaging Data Challenge, a community effort to benchmark and compare various image processing methods for direct exoplanet detection using shared datasets and a competitive platform.

Contribution

It introduces a standardized data challenge platform for benchmarking exoplanet imaging algorithms, including dataset collection, organization, and preliminary results from initial participant submissions.

Findings

Initial participant results demonstrate diverse detection capabilities.

The challenge highlights current limitations in exoplanet imaging methods.

Organizational lessons inform future improvements for benchmarking efforts.

Abstract

The Exoplanet Imaging Data Challenge is a community-wide effort meant to offer a platform for a fair and common comparison of image processing methods designed for exoplanet direct detection. For this purpose, it gathers on a dedicated repository (Zenodo), data from several high-contrast ground-based instruments worldwide in which we injected synthetic planetary signals. The data challenge is hosted on the CodaLab competition platform, where participants can upload their results. The specifications of the data challenge are published on our website. The first phase, launched on the 1st of September 2019 and closed on the 1st of October 2020, consisted in detecting point sources in two types of common data-set in the field of high-contrast imaging: data taken in pupil-tracking mode at one wavelength (subchallenge 1, also referred to as ADI) and multispectral data taken in pupil-tracking mode (subchallenge 2, also referred to as ADI mSDI). In this paper, we describe the approach, organisational lessons-learnt and current limitations of the data challenge, as well as preliminary results of the participants submissions for this first phase. In the future, we plan to provide permanent access to the standard library of data sets and metrics, in order to guide the validation and support the publications of innovative image processing algorithms dedicated to high-contrast imaging of planetary systems.