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Register a new userThe Future of Exoplanet Direct Detection
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Diffraction fundamentally limits our ability to image and characterize exoplanets. Current and planned coronagraphic searches for exoplanets are making incredible strides but are fundamentally limited by the inner working angle of a few lambda/D. Some crucial topics, such as demographics of exoplanets within the first 50 Myr and the infrared characterization of terrestrial planets, are beyond the reach of the single aperture angular resolution for the foreseeable future. Interferometry offers some advantages in exoplanet detection and characterization and we explore in this white paper some of the potential scientific breakthroughs possible. We demonstrate here that investments in exoplanet interferometry could open up new possibilities for speckle suppression through spatial coherence, a giant boost in astrometric precision for determining exoplanet orbits, ability to take a census of young giant exoplanets (clusters <50 Myr age), and an unrivaled potential for infrared nulling from space to detect terrestrial planets and search for atmospheric biomarkers. All signs point to an exciting future for exoplanets and interferometers, albeit a promise that will take decades to fulfill.
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Instrumentation techniques in the field of direct imaging of exoplanets have greatly advanced over the last two decades. Two of the four NASA-commissioned large concept studies involve a high-contrast instrument for the imaging and spectral characterization of exo-Earths from space: LUVOIR and HabEx. This whitepaper describes the status of 8 optical testbeds in the US and France currently in operation to experimentally validate the necessary technologies to image exo-Earths from space. They explore two complementary axes of research: (i) coronagraph designs and manufacturing and (ii) active wavefront correction methods and technologies. Several instrument architectures are currently being analyzed in parallel to provide more degrees of freedom for designing the future coronagraphic instruments. The necessary level of performance has already been demonstrated in-laboratory for clear off-axis telescopes (HabEx-like) and important efforts are currently in development to reproduce this accomplishment on segmented and/or on-axis telescopes (LUVOIR-like) over the next two years.
The direct detection of exoplanets with high-contrast instruments can be boosted with high spectral resolution. For integral field spectrographs yielding hyperspectral data, this means that the field of view consists of diffracted starlight spectra and a spatially localized planet. Analysis usually relies on cross-correlation with theoretical spectra. In a purely blind-search context, this supervised strategy can be biased with model mismatch and/or be computationally inefficient. Using an approach that is inspired by the remote-sensing community, we aim to propose an alternative to cross-correlation that is fully data-driven, which decomposes the data into a set of individual spectra and their corresponding spatial distributions. This strategy is called spectral unmixing. We used an orthogonal subspace projection to identify the most distinct spectra in the field of view. Their spatial distribution maps were then obtained by inverting the data. These spectra were then used to break the original hyperspectral images into their corresponding spatial distribution maps via non-negative least squares. The performance of our method was evaluated and compared with a cross-correlation using simulated hyperspectral data with medium resolution from the ELT/HARMONI integral field spectrograph. We show that spectral unmixing effectively leads to a planet detection solely based on spectral dissimilarities at significantly reduced computational cost. The extracted spectrum holds significant signatures of the planet while being not perfectly separated from residual starlight. The sensitivity of the supervised cross-correlation is three to four times higher than with unsupervised spectral unmixing, the gap is biased toward the former because the injected and correlated spectrum match perfectly. The algorithm was furthermore vetted on real data obtained with VLT/SINFONI of the beta Pictoris system.
Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on beta Pictoris with VLT/NACO. Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. (abridged).
EarthFinder is a NASA Astrophysics Probe mission concept selected for study as input to the 2020 Astrophysics National Academies Decadal Survey. The EarthFinder concept is based on a dramatic shift in our understanding of how PRV measurements should be made. We propose a new paradigm which brings the high precision, high cadence domain of transit photometry as demonstrated by Kepler and TESS to the challenges of PRV measurements at the cm/s level. This new paradigm takes advantage of: 1) broad wavelength coverage from the UV to NIR which is only possible from space to minimize the effects of stellar activity; 2) extremely compact, highly stable, highly efficient spectrometers (R>150,000) which require the diffraction-limited imaging possible only from space over a broad wavelength range; 3) the revolution in laser-based wavelength standards to ensure cm/s precision over many years; 4) a high cadence observing program which minimizes sampling-induced period aliases; 5) exploiting the absolute flux stability from space for continuum normalization for unprecedented line-by-line analysis not possible from the ground; and 6) focusing on the bright stars which will be the targets of future imaging missions so that EarthFinder can use a ~1.5 m telescope.
We outline the important role that ground-based, Doppler monitoring of exoplanetary systems will play in advancing our theories of planet formation and dynamical evolution. A census of planetary systems requires a well designed survey to be executed over the course of a decade or longer. A coordinated survey to monitor several thousand targets each at ~1000 epochs (~3-5 million new observations) will require roughly 40 dedicated spectrographs. We advocate for improvements in data management, data sharing, analysis techniques, and software testing, as well as possible changes to the funding structures for exoplanet science.
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