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ISPSeminar

Title: (invited talk) "Applications of PCA and low-rank plus sparse decompositions in high-contrast Exoplanet imaging"

Speaker: Carlos Gomez, VORTEX Project, Department of Astrophysics, Geophysics & Oceanography, ULg

Location: "Shannon" Seminar Room, Place du Levant 3, Maxwell Building, 1st floor

Date / Time (duration): Thursday 04/02/2016, 11h00 (~ 45')

Slides: pdf

Abstract: Only a small fraction of the confirmed exoplanet candidates known to date have been discovered through direct imaging. Indeed the task of observing planets is very challenging due to the huge difference in contrast between the host star and its potential companions, the small angular separation and image degradation caused by Earthís turbulent atmosphere. Post-processing algorithms play a critical role in direct imaging of exoplanets by boosting the detectability of real companions in a noisy background. Among these data processing techniques, the most recently proposed is the Principal Component Analysis (PCA), a ubiquitous statistical technique already used in background subtraction problems. Inspired by recent advances in machine learning algorithms such as robust PCA, we propose a local three-term decomposition (LLSG) that surpasses current PCA-based post-processing algorithms in terms of detectability of companions at near real-time speed. We test the performance of our new algorithm on a training dataset and show how LLSG decomposition reaches higher signal-to-noise ratio and has an overall better performance in the Receiver Operating Characteristic (ROC) space.

Biography: Carlos Gomez Gonzalez obtained his masterís degree in Astrophysics at Universidad Autonoma de Madrid/Universidad Complutense in 2013. He is carrying out a PhD thesis under the supervision of Jean Surdej (Department of Astrophysics, Geophysics & Oceanography) and Marc Van Droogenbroeck (Montefiore Institute) focused on advanced image processing methods for exoplanet/disk detection in high-contrast imaging
. His research interests include image processing, computer vision and background subtraction, machine learning and classification problems, observational strategies and optimized reference-PSF/background subtraction algorithms.




Last updated February 09, 2016, at 09:54 AM