Direct imaging is a major step in the hunt of extrasolar planets. But observing exoplanets using ground-based telescopes is a very challenging task! The main difficulties are the huge difference in brightness between the host star and its potential companions and the small angular separation between them. These difficulties are amplified by the image degradation caused by the Earth’s turbulent atmosphere and the imperfect optics in the instrument. Therefore, ground-based high-contrast imaging (HCI) relies on the use of adaptive optics, which corrects in real time for the atmosphere turbulence by using a deformable mirror, and coronagraphy, which suppresses most of the light coming from the host star. The following video, prepared by the NASA Exoplanet Exploration Program, explains the role of coronagraphy and adaptive optics correction in the specific case of space-based observations:

Even using these cutting-edge technologies, the resulting images suffer from bright starlight residuals hiding the presence of planetary companions. Among them, the quasi-statics speckles are of the same size as the exoplanet signal and often brighter. Therefore, the two remaining components of high-contrast imaging are (1) the data acquisition techniques, which focuses on introducing some diversity in the data to later disentangle between the planetary companions and the speckle field and (2) the post-processing techniques that exploit this diversity to carve out further the starlight residuals and recover the planetary signals (see the sub-challenges section below). Post-processing techniques is what ultimately pushes the performance of the HCI instruments.

Objectives

A multitude of HCI post-processing algorithms and pipelines have been developed in the past thirteen years. Pueyo (2018) offers an ample discussion on the exoplanet detection algorithms proposed in the literature. The goal of this challenge is not only to compare, in a fair and robust way, existing post-processing algorithms, but also to spur the design of new techniques, spark new collaborations and ideas, and share knowledge.

With the aim of creating a manageable competition, we will focus exclusively on the detection of point-like sources (exoplanets). Other tasks, such as the characterization of companions, the detection of extended sources, reference star differential imaging and the usage of metadata/telemetry, will be the subject of future editions of the challenge.

Note: You don’t need to be an astronomer or expert in high-contrast imaging to participate! We welcome the participation of non-domain experts. On this website you will find extensive documentation about the challenge and how to participate. We expect domain experts will use their tools, for outsiders we have prepared a starting kit in the resources section.

Computer science and machine learning fields have a long tradition conducting data challenges and competitions. Repositories of benchmark (curated) datasets are an integral part of the field of machine learning. We want to integrate these practices to the field of high-contrast imaging. In the future, the process of testing new algorithms will be much straightforward and robust, once the community adopts the standard metrics (with their open-source implementations) and the benchmark library resulting from this challenge.