Exoplanet Imaging Data Challenge, phase II: Characterization of exoplanet signals in high-contrast images
F. Cantalloube, V. Christiaens, C. Cantero, E. Nasedkin, A. Cioppa, O., Absil, J. M. Bonse, P. Delorme, C. Gomez-Gonzalez, S. Juillard, J. Mazoyer,, M. Samland Ruffio J.-B.i, and Van Droogenbroeck M.c

TL;DR
This paper discusses the second phase of the Exoplanet Imaging Data Challenge, focusing on benchmarking and comparing advanced algorithms for characterizing exoplanet signals in high-contrast images, including their position and spectral properties.
Contribution
It introduces a community platform for fair benchmarking of state-of-the-art exoplanet signal characterization algorithms in high-contrast imaging datasets.
Findings
Comparison of various algorithms' characterization capabilities
Identification of strengths and weaknesses in current techniques
Promotion of collaborative development in exoplanet imaging analysis
Abstract
Today, there exists a wide variety of algorithms dedicated to high-contrast imaging, especially for the detection and characterisation of exoplanet signals. These algorithms are tailored to address the very high contrast between the exoplanet signal(s), which can be more than two orders of magnitude fainter than the bright starlight residuals in coronagraphic images. The starlight residuals are inhomogeneously distributed and follow various timescales that depend on the observing conditions and on the target star brightness. Disentangling the exoplanet signals within the starlight residuals is therefore challenging, and new post-processing algorithms are striving to achieve more accurate astrophysical results. The Exoplanet Imaging Data Challenge is a community-wide effort to develop, compare and evaluate algorithms using a set of benchmark high-contrast imaging datasets. After a first…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astronomy and Astrophysical Research · Adaptive optics and wavefront sensing
