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Author: Fadi Nammour
Machine Learning for Multi-Object Galaxy Deconvolution
This internship aims at developing new reconstruction methods of multi-object galaxy images.
Contact: fadi.nammour@cea.fr
Deadline for application 28 Feb. 2020
More details can be found here.
Radio Astronomical Images Restoration with Shape Constraint
| Authors: | F. NAMMOUR, M. A. SCHMITZ, F. M. NGOLĂ MBOULA, J.-L. STARCK, J. N. GIRARD |
| Journal: | Proceedings of SPIE |
| Year: | 2019 |
| Download: | DOI |
Abstract
Weak gravitational lensing is a very promising probe for cosmology that relies on highly precise shape measurements. Several new instruments are being deployed and will allow for weak lensing studies on unprecedented scales, and at new frequencies. In particular, some of these new instruments should allow for the blooming of radio-weak lensing, specially the SKA with many Petabits per second of raw data. Hence, great challenges will be waiting at the turn. In addition, processing methods should be able to extract the highest precision possible and ideally, be applicable to radio-astronomy. For the moment, the two methods that already exist do not satisfy both conditions. In this paper, we present a new plug-and-play solution where we add a shape constraint to deconvolution algorithms and results show measurements improvement of at least 20%.
