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Starshade in formation flight with a space telescope is a rapidly maturing technology that would enable imaging and spectral characterization of small planets orbiting nearby stars in the not-too-distant future. While performance models of the starshade-assisted exoplanet imaging have been developed and used to design future missions, their results have not been verified from the analyses of synthetic images. Following a rich history of using community data challenges to evaluate image-processing capabilities in astronomy and exoplanet fields, the Starshade Technology Development to TRL5 (S5), a focused technology development activity managed by the NASA Exoplanet Exploration Program, is organizing and implementing a starshade exoplanet data challenge. The purpose of the data challenge is to validate the flow down of requirements from science to key instrument performance parameters and to quantify the required accuracy of noisy background calibration with synthetic images. This data challenge distinguishes itself from past efforts in the exoplanet field in that (1) it focuses on the detection and spectral characterization of small planets in the habitable zones of nearby stars, and (2) it develops synthetic images that simultaneously include multiple background noise terms -- some specific to starshade observations -- including residual starlight, solar glint, exozodiacal light, detector noise, as well as variability resulting from starshades motion and telescope jitter. In this paper, we provide an overview of the design and rationale of the data challenge. Working with data challenge participants, we expect to achieve improved understanding of the noise budget and background calibration in starshade-assisted exoplanet observations in the context of both Starshade Rendezvous with Roman and HabEx.
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Operating in an unprecedented contrast regime ($10^{-7}$ to $10^{-9}$), the Roman Coronagraph Instrument (CGI) will serve as a pathfinder for key technologies needed for future Earth-finding missions. The Roman Exoplanet Imaging Data Challenge (Roman EIDC) was a community engagement effort that tasked participants with extracting exoplanets and their orbits for a 47 UMa-like target star, given: (1) 15 years of simulated precursor radial velocity (RV) data, and (2) six epochs of simulated imaging taken over the course of the Roman mission. The Roman EIDC simulated images include 4 epochs with CGIs Hybrid Lyot Coronagraph (HLC) plus 2 epochs with a starshade (SS) assumed to arrive as part of a Starshade Rendezvous later in the mission. Here, we focus on our in-house analysis of the outermost planet, for which the starshades higher throughput and lower noise floor present a factor of ~4 improvement in signal-to-noise ratio over the narrow-field HLC. We find that, although the RV detection was marginal, the precursor RV data enable the mass and orbit to be constrained with only 2 epochs of starshade imaging. Including the HLC images in the analysis results in improved measurements over RV + SS alone, with the greatest gains resulting from images taken at epochs near maximum elongation. Combining the two epochs of SS imaging with the RV + HLC data resulted in a factor of ~2 better orbit and mass determinations over RV + HLC alone. The Roman CGI, combined with precursor RV data and later mission SS imaging, form a powerful trifecta in detecting exoplanets and determining their masses, albedos, and system configurations. While the Roman CGI will break new scientific and technological ground with direct imaging of giant exoplanets within ~5 AU of V~5 and brighter stars, a Roman Starshade Rendezvous mission would additionally enable the detection of planets out to ~8 AU in those systems.
A starshade suppresses starlight by a factor of 1E11 in the image plane of a telescope, which is crucial for directly imaging Earth-like exoplanets. The state of the art in high contrast post-processing and signal detection methods were developed specifically for images taken with an internal coronagraph system and focus on the removal of quasi-static speckles. These methods are less useful for starshade images where such speckles are not present. This paper is dedicated to investigating signal processing methods tailored to work efficiently on starshade images. We describe a signal detection method, the generalized likelihood ratio test (GLRT), for starshade missions and look into three important problems. First, even with the light suppression provided by the starshade, rocky exoplanets are still difficult to detect in reflected light due to their absolute faintness. GLRT can successfully flag these dim planets. Moreover, GLRT provides estimates of the planets positions and intensities and the theoretical false alarm rate of the detection. Second, small starshade shape errors, such as a truncated petal tip, can cause artifacts that are hard to distinguish from real planet signals; the detection method can help distinguish planet signals from such artifacts. The third direct imaging problem is that exozodiacal dust degrades detection performance. We develop an iterative generalized likelihood ratio test to mitigate the effect of dust on the image. In addition, we provide guidance on how to choose the number of photon counting images to combine into one co-added image before doing detection, which will help utilize the observation time efficiently. All the methods are demonstrated on realistic simulated images.
Launching a starshade to rendezvous with the Nancy Grace Roman Space Telescope would provide the first opportunity to directly image the habitable zones of nearby sunlike stars in the coming decade. A report on the science and feasibility of such a m
ission was recently submitted to NASA as a probe study concept. The driving objective of the concept is to determine whether Earth-like exoplanets exist in the habitable zones of the nearest sunlike stars and have biosignature gases in their atmospheres. With the sensitivity provided by this telescope, it is possible to measure the brightness of zodiacal dust disks around the nearest sunlike stars and establish how their population compares to our own. In addition, known gas-giant exoplanets can be targeted to measure their atmospheric metallicity and thereby determine if the correlation with planet mass follows the trend observed in the Solar System and hinted at by exoplanet transit spectroscopy data. In this paper we provide the details of the calculations used to estimate the sensitivity of Roman with a starshade and describe the publicly available Python-based source code used to make these calculations. Given the fixed capability of Roman and the constrained observing windows inherent for the starshade, we calculate the sensitivity of the combined observatory to detect these three types of targets and we present an overall observing strategy that enables us to achieve these objectives.
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. 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.
All water-covered rocky planets in the inner habitable zones of solar-type stars will inevitably experience a catastrophic runaway climate due to increasing stellar luminosity and limits to outgoing infrared radiation from wet greenhouse atmospheres. Reflectors or scatterers placed near Earths inner Lagrange point (L1) have been proposed as a geo-engineering solution to anthropogenic climate change and an advanced version of this could modulate incident irradiation over many Gyr or rescue a planet from the interior of the habitable zone. The distance of the starshade from the planet that minimizes its mass is 1.6 times the Earth-L1 distance. Such a starshade would have to be similar in size to the planet and the mutual occultations during planetary transits could produce a characteristic maximum at mid-transit in the light-curve. Because of a fortuitous ratio of densities, Earth-size planets around G dwarf stars present the best opportunity to detect such an artifact. The signal would be persistent and is potentially detectable by a future space photometry mission to characterize transiting planets. The signal could be distinguished from natural phenomenon, i.e. starspots or cometary dust clouds, by its shape, persistence, and transmission spectrum.
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