Spectral unmixing for exoplanet direct detection in hyperspectral data

Abstract

Context. The direct detection of faint exoplanets with high-contrast instruments can be boosted by combining it with high spectral resolution. For integral field spectrographs yielding hyperspectral data, this means that the majority of the field of view consists of diffracted starlight spectra and a spatially localized planet. Observation analysis usually relies on classic cross-correlation with theoretical spectra, maximized at the position and with the properties of the planet. In a purely blind-search context, this supervised strategy can be biased with model mismatch and/or be computationally inefficient. Aims. Using an approach that is inspired by the analysis of hyperspectral data within 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. Methods. 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. A matched filter with the instrument point-spread function (or visual inspection) was then used to detect the planet on one of the maps. 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. Results. 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 β Pictoris system. This led to the detection of β Pictoris b with a signal-to-noise ratio of 28.5. Conclusions. Spectral unmixing is a viable alternative strategy to a cross-correlation to search for and characterize exoplanets in hyperspectral data in a purely data-driven approach. The advent of large data from the forthcoming IFS on board JWST and future ELTs motivates further algorithm development along this path.