Exoplanet imaging data challenge: benchmarking the various image processing methods for exoplanet detection

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

A comparison of novel algorithms for unbiased recovery of extended disk signals with ADI - Application to the case of MWC 758 and its protoplanet candidates.

<p class="p1">With its large sub-mm continuum cavity, asymmetric clumps and spiral arms, the disc of MWC 758 is an ideal test laboratory to search for embedded planets at an early stage of formation and study their dynamical interplay with the disc. As such, two protoplanet candidates have been proposed in this disc by independent teams based on thermal IR high-contrast imaging data, which both require confirmation. In this contribution, I will compare three novel algorithms that we designed to alleviate current shortcomings stemming from the application of state-of-the-art Angular Differential Imaging (ADI)-based algorithms - namely geometric biases induced to azimuthally extended signals from the aggressive modeling and subtraction of the stellar halo. The first algorithm relies on an iterative approach, while the other two rely on an inverse-problem approach, with and without regularisation terms, respectively, to recover the unbiased circumstellar intensity distribution. I will show the images obtained from the application of these algorithms to the case of MWC 758, for which we considered the SPHERE (H23) and NIRC2 (L’) datasets obtained in the best observing conditions for this study. I will then compare these images to predictions from hydro-dynamical simulations testing the hypotheses of a single (internal) eccentric companion, and two giant (internal+external) planets on circular orbits, respectively, and will discuss the likelihood of each scenario based on the similarity to our new images.</p&gt

A faint companion around CrA-9: protoplanet or obscured binary?

Understanding how giant planets form requires observational input from directly imaged protoplanets. We used VLT/NACO and VLT/SPHERE to search for companions in the transition disc of 2MASS J19005804-3645048 (hereafter CrA-9), an accreting M0.75 dwarf with an estimated age of 1-2 Myr. We found a faint point source at ∼0.7-arcsec separation from CrA-9 (∼108 au projected separation). Our 3-epoch astrometry rejects a fixed background star with a 5σ significance. The near-IR absolute magnitudes of the object point towards a planetary-mass companion. However, our analysis of the 1.0-3.8$\,\mu$m spectrum extracted for the companion suggests it is a young M5.5 dwarf, based on both the 1.13-μm Na index and comparison with templates of the Montreal Spectral Library. The observed spectrum is best reproduced with high effective temperature ($3057^{+119}_{-36}$K) BT-DUSTY and BT-SETTL models, but the corresponding photometric radius required to match the measured flux is only $0.60^{+0.01}_{-0.04}$ Jovian radius. We discuss possible explanations to reconcile our measurements, including an M-dwarf companion obscured by an edge-on circum-secondary disc or the shock-heated part of the photosphere of an accreting protoplanet. Follow-up observations covering a larger wavelength range and/or at finer spectral resolution are required to discriminate these two scenarios

Exoplanet imaging data challenge: benchmarking the various image processing methods for exoplanet detection

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.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 [email protected]