Exoplanet imaging data challenge: benchmarking the various image processing methods for exoplanet detection
Author
Cantalloube, FaustineGomez-Gonzalez, Carlos
Absil, Olivier
Cantero, Carles
Bacher, Regis
Bonse, Markus
Bottom, Michael

Dahlqvist, Carl-Henrik
Desgrange, Célia
Flasseur, Olivier
Fuhrmann, Thomas
Henning, Thomas H.
Jensen-Clem, Rebecca

Kenworthy, Matthew
Mawet, Dimitri

Mesa, Dino
Meshkat, Tiffany
Mouillet, David
Müller, André
Nasedkin, Evert
Pairet, Benoit
Piérard, Sébastien
Ruffio, Jean-Baptiste

Samland, Matthias
Stone, Jordan
Van Droogenbroeck, Marc
Affiliation
Steward Observatory, University of ArizonaIssue Date
2020-12-13Keywords
Adaptive OpticsCoronagraphy
Data challenge
Exoplanet detection
High-contrast imaging
Post-processing techniques
Metadata
Show full item recordPublisher
SPIECitation
Cantalloube, F., Gomez-Gonzalez, C., Absil, O., Cantero, C., Bacher, R., Bonse, M. J., ... & Van Droogenbroeck, M. (2021). Exoplanet imaging data challenge: benchmarking the various image processing methods for exoplanet detection. Adaptive Optics Systems VII Proceedings, 11448.Rights
© 2020 SPIE.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.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 https://exoplanet-imaging-challenge.github.io/. 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. © 2020 SPIE.ISSN
0277-786XVersion
Final published versionae974a485f413a2113503eed53cd6c53
10.1117/12.2574803