Cécile Chenot, who is pursuing a PhD at the LCS in signal processing, has been selected to participate to the prestigious 66th Lindau Nobel laureate meeting this summer.
In this article, we describe a new estimate of the Cosmic Microwave Background (CMB) intensity map reconstructed by a joint analysis of the full Planck 2015 data (PR2) and WMAP nine-years. It provides more than a mere update of the CMB map introduced in (Bobin et al. 2014b) since it benefits from an improvement of the component separation method L-GMCA (Local-Generalized Morphological Component Analysis) that allows the efficient separation of correlated components (Bobin et al. 2015). Based on the most recent CMB data, we further confirm previous results (Bobin et al. 2014b) showing that the proposed CMB map estimate exhibits appealing characteristics for astrophysical and cosmological applications: i) it is a full sky map that did not require any inpainting or interpolation post-processing, ii) foreground contamination is showed to be very low even on the galactic center, iii) it does not exhibit any detectable trace of thermal SZ contamination. We show that its power spectrum is in good agreement with the Planck PR2 official theoretical best-fit power spectrum. Finally, following the principle of reproducible research, we provide the codes to reproduce the L-GMCA, which makes it the only reproducible CMB map.
We just released a brand new CMB map, which we obtained from the joint processing of the WMAP 9-year and Planck PR2 data. Check this out !
Check out the new post-doc and PhD positions : jobs
New internships are now available for spring 2016. Two new PhD positions are offered for autumn 2016. Check out the CosmoStat jobs page.
We are glad to announce the first release of the python-based GMCALab toolbox: pyGMCALab. It intends to provide a swiss knife for efficiently solving problems related to sparse BSS using the GMCA framework. This includes sparse BSS, sparse NMF or more recently the blind separation of partially correlated sources.
A new postdoc position in signal and image processing is now open. This job offer is part of the exciting Dedale project funded by the H2020 framework. The postdoc will explore new ways to develop sparse representation learning techniques to analyze multispectral data, with a specific focus on large-scale data !
Find more information here !
We have 3 fully funded open PhD positions. Applications are expected for mid april 2015. Don't wait much longer for applying !