In this communication, we report on the results of the second phase of the Exoplanet Imaging Data Challenge started in 2019. This second phase focuses on the characterization of point sources (exoplanet signals) within multispectral high-contrast images from ground-based telescopes. We collected eight data sets from two high-contrast integral field spectrographs (namely Gemini-S/GPI and VLT/SPHERE-IFS) that we calibrated homogeneously and in which we injected a handful of synthetic planetary signals (ground truth) to be characterized by the data challenge participants. The tasks of the participants consist of (1) extracting the precise astrometry of each injected planetary signals, and (2) extracting the precise spectro-photometry of each injected planetary signal. Additionally, the participants may provide the 1-sigma uncertainties on their estimation for further analyses. When available, the participants can also provide the posterior distribution used to estimate the position/spectrum and uncertainties. The data are permanently available on a Zenodo repository and the participants can submit their results through the EvalAI platform. The EvalAI submission platform opened on April 2022 and closed on the 31st of May 2024. In total, we received 4 valid submissions for the astrometry estimation and 4 valid submissions for the spectrophotometry (each submission, corresponding to one pipeline, has been submitted by a unique participant). In this communication, we present an analysis and interpretation of the results.
KEYWORDS: Planets, Stars, Exoplanets, Signal to noise ratio, Signal detection, Coronagraphy, Point spread functions, Detection and tracking algorithms, Atmospheric modeling, Surface conduction electron emitter displays
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 phase ran in 2020 and focused on the detection capabilities of existing algorithms, the focus of this ongoing second phase is to compare the characterisation capabilities of state-of-the-art techniques. The characterisation of planetary companions is two-fold: the astrometry (estimated position with respect to the host star) and spectrophotometry (estimated contrast with respect to the host star, as a function of wavelength). The goal of this second phase is to offer a platform for the community to benchmark techniques in a fair, homogeneous and robust way, and to foster collaborations.
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