Selected publications and their corresponding open source code:
Supervised detection of exoplanets in high-contrast imaging sequences
We reformulate the exoplanet detection task (for angular differential imaging sequences) building on well-established machine learning techniques to take high-contrast imaging post-processing from an unsupervised to a supervised learning context. In this new framework, we presented algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. For instance, SODINN improves the true positive rate by a factor ranging from ∼2 to ∼10 wrt. low-rank based approaches, working at the same false positive rate. The code has been publicly released and can be found on GitHub.
VIP: Vortex Image Processing Package for High-contrast Direct Imaging
The Vortex Image Processing (VIP) library is a python package dedicated to astronomical high-contrast imaging. It relies on the extensive python stack of scientific libraries and aims to provide a flexible framework for high-contrast data and image processing.
Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm
Inspired by recent advances in machine learning algorithms such as robust PCA, we proposed the Low-rank plus Sparse plus Gaussian noise (LLSG) decomposition of angular differential imaging sequences.
Advanced data processing for high-contrast imaging-Pushing exoplanet direct detection limits with machine learning, 2017, Université de Liège. Supervisors: Prof. Jean Surdej, Prof. Marc Van Droogenbroeck and Dr. Olivier Absil. ORBi link.
“Essentially, all models are wrong, but some are useful”, George Box.
“…if the model is going to be wrong anyway, why not see if you can get the computer to ‘quickly’ learn a model from the data, rather than have a human laboriously derive a model from a lot of thought”, Peter Norvig.