Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSpectral unmixing for exoplanet direct detection in hyperspectral data
391
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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.
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.
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).
We describe the contents and functionality of the NASA Exoplanet Archive, a database and tool set funded by NASA to support astronomers in the exoplanet community. The current content of the database includes interactive tables containing properties of all published exoplanets, Kepler planet candidates, threshold-crossing events, data validation reports and target stellar parameters, light curves from the Kepler and CoRoT missions and from several ground-based surveys, and spectra and radial velocity measurements from the literature. Tools provided to work with these data include a transit ephemeris predictor, both for single planets and for observing locations, light curve viewing and normalization utilities, and a periodogram and phased light curve service. The archive can be accessed at http://exoplanetarchive.ipac.caltech.edu.
The radial-velocity (RV) method is one of the most successful in the detection of exoplanets, but is hindered by the intrinsic RV variations of the star, which can easily mimic or hide true planetary signals. kima is a package for the detection and characterization of exoplanets using RV data. It fits a sum of Keplerian curves to a timeseries of RV measurements and calculates the evidence for models with a fixed number Np of Keplerian signals, or after marginalising over Np. Moreover, kima can use a GP with a quasi-periodic kernel as a noise model, to deal with activity-induced signals. The hyperparameters of the GP are inferred together with the orbital parameters. The code is written in C++, but includes a helper Python package, pykima, which facilitates the analysis of the results.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
