PhD topic on Dark Energy tomography with the Euclid satellite
Details about this position are provided in the following PDF.
Interested candidates should send a CV and exam record to Valeria Pettorino, and arrange for 2 reference letters to be sent separately. The application e-mail should be preferably in English. Knowledge of cosmology, general relativity or previous use of CAMB/CLASS codes are an advantage.
Projects are not funded yet and decision on fundings is expected for Spring 2021. In the meantime, shortlisted candidates will be asked to register to ADUM https://www.adum.fr/.
The deconvolution of large survey images with millions of galaxies requires developing a new generation of methods that can take a space-variant point spread function into account. These methods have also to be accurate and fast. We investigate how deep learning might be used to perform this task. We employed a U-net deep neural network architecture to learn parameters that were adapted for galaxy image processing in a supervised setting and studied two deconvolution strategies. The first approach is a post-processing of a mere Tikhonov deconvolution with closed-form solution, and the second approach is an iterative deconvolution framework based on the alternating direction method of multipliers (ADMM). Our numerical results based on GREAT3 simulations with realistic galaxy images and point spread functions show that our two approaches outperform standard techniques that are based on convex optimization, whether assessed in galaxy image reconstruction or shape recovery. The approach based on a Tikhonov deconvolution leads to the most accurate results, except for ellipticity errors at high signal-to-noise ratio. The ADMM approach performs slightly better in this case. Considering that the Tikhonov approach is also more computation-time efficient in processing a large number of galaxies, we recommend this approach in this scenario.
In the spirit of reproducible research, the codes will be made freely available on the CosmoStat website (http://www.cosmostat.org). The testing datasets will also be provided to repeat the experiments performed in this paper.