scholarly journals Recurrence quantification analysis as a post-processing technique in adaptive optics high contrast imaging

2018 ◽  
Author(s):  
Marco Stangalini ◽  
Fernando Pedichini ◽  
Gianluca Li Causi ◽  
Simone Antoniucci ◽  
Massimiliano Mattioli ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Processing Technique ◽  
Recurrence Quantification Analysis ◽  
High Contrast ◽  
Post Processing ◽  
Contrast Imaging ◽  
High Contrast Imaging ◽  
Recurrence Quantification ◽  
Quantification Analysis

High Contrast Imaging for Python (HCIPy): an open-source adaptive optics and coronagraph simulator

2018 ◽  
Author(s):  
Emiel H. Por ◽  
Sebastiaan Y. Haffert ◽  
Vikram Mark Radhakrishnan ◽  
David S. Doelman ◽  
Maaike van Kooten ◽  
...  
Keyword(s):  
Open Source ◽  
Adaptive Optics ◽  
High Contrast ◽  
Contrast Imaging ◽  
High Contrast Imaging

scholarly journals Medium-resolution integral-field spectroscopy for high-contrast exoplanet imaging

2018 ◽  
Vol 617 ◽  
pp. A144 ◽  
Author(s):  
H. J. Hoeijmakers ◽  
H. Schwarz ◽  
I. A. G. Snellen ◽  
R. J. de Kok ◽  
M. Bonnefoy ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Cross Correlation ◽  
High Contrast ◽  
Contrast Imaging ◽  
Data Set ◽  
Stellar Spectrum ◽  
High Contrast Imaging ◽  
Medium Resolution ◽  
Differential Imaging ◽  
Integral Field

Context. Angular differential imaging (ADI) and spectral differential imaging (SDI) are well-established high-contrast imaging techniques, but their application is challenging for companions at small angular separations from their host stars. Aims. The aim of this paper is to investigate to what extent adaptive-optics assisted, medium-resolution (R ~ 5000) integral field spectrographs (IFS) can be used to directly detect the absorption of molecular species in the spectra of planets and substellar companions when these are not present in the spectrum of the star. Methods. We analysed archival data of the β Pictoris system taken with the SINFONI integral field spectrograph located at ESO’s Very Large Telescope, originally taken to image β Pictoris b using ADI techniques. At each spatial position in the field, a scaled instance of the stellar spectrum is subtracted from the data after which the residuals are cross-correlated with model spectra. The cross-correlation co-adds the individual absorption lines of the planet emission spectrum constructively, while this is not the case for (residual) telluric and stellar features. Results. Cross-correlation with CO and H2O models results in significant detections of β Pictoris b with signal-to-noise ratios (S/Ns) of 13.7 and 16.4 respectively. Correlation with a T = 1700 K BT-Settl model provides a detection with an S/N of 22.8. This in contrast to application of ADI, which barely reveals the planet. While the adaptive optics system only achieved modest Strehl ratios of 19–27% leading to a raw contrast of 1:240 at the planet position, cross-correlation achieves a 3σ contrast limit of 2.7 × 10−5 in this 2.5 hr data set, a factor ~40 below the raw noise level at an angular distance of 0.36′′ from the star. Conclusions. Adaptive-optics assisted, medium-resolution IFS, such as SINFONI on the VLT and OSIRIS on the Keck Telescope, can be used for high-contrast imaging utilizing cross-correlation techniques for planets that are close to their star and embedded in speckle noise. We refer to this method as molecule mapping, and advocate its application to observations with future medium resolution instruments, in particular ERIS on the VLT, HARMONI on the ELT and NIRSpec, and MIRI on the JWST.

scholarly journals Supervised detection of exoplanets in high-contrast imaging sequences

2018 ◽  
Vol 613 ◽  
pp. A71 ◽  
Author(s):  
C. A. Gomez Gonzalez ◽  
O. Absil ◽  
M. Van Droogenbroeck
Keyword(s):  
State Of The Art ◽  
Low Rank ◽  
Machine Learning Techniques ◽  
High Contrast ◽  
Post Processing ◽  
Learning Context ◽  
Contrast Imaging ◽  
Characteristic Analysis ◽  
High Contrast Imaging ◽  
Learning Techniques

Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on unsupervised learning techniques, such as low-rank approximations, for generating a model point spread function (PSF) and subtracting the residual starlight and speckle noise. Aims. In order to maximize the detection rate of HCI instruments and survey campaigns, advanced algorithms with higher sensitivities to faint companions are needed, especially for the speckle-dominated innermost region of the images. Methods. We propose a reformulation of the exoplanet detection task (for ADI sequences) that builds on well-established machine learning techniques to take HCI post-processing from an unsupervised to a supervised learning context. In this new framework, we present algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). We test these algorithms on real ADI datasets from VLT/NACO and VLT/SPHERE HCI instruments. We then assess their performances by injecting fake companions and using receiver operating characteristic analysis. This is done in comparison with state-of-the-art ADI algorithms, such as ADI principal component analysis (ADI-PCA). Results. This study shows the improved sensitivity versus specificity trade-off of the proposed supervised detection approach. At the diffraction limit, SODINN improves the true positive rate by a factor ranging from ~2 to ~10 (depending on the dataset and angular separation) with respect to ADI-PCA when working at the same false-positive level. Conclusions. The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. In addition, it offers the possibility of re-processing existing HCI databases to maximize their scientific return and potentially improve the demographics of directly imaged exoplanets.

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