Skip to Main content Skip to Navigation
Journal articles

Supervised detection of exoplanets in high-contrast imaging sequences

Abstract : 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.
Complete list of metadata

https://hal-insu.archives-ouvertes.fr/insu-03693565
Contributor : Nathalie POTHIER Connect in order to contact the contributor
Submitted on : Monday, June 13, 2022 - 2:42:38 PM
Last modification on : Friday, August 5, 2022 - 3:37:49 PM

File

aa31961-17.pdf
Publisher files allowed on an open archive

Identifiers

Collections

Citation

C. A. Gomez Gonzalez, O. Absil, M. van Droogenbroeck. Supervised detection of exoplanets in high-contrast imaging sequences. Astronomy and Astrophysics - A&A, EDP Sciences, 2018, 613, ⟨10.1051/0004-6361/201731961⟩. ⟨insu-03693565⟩

Share

Metrics

Record views

4

Files downloads

4