Feasibility and performances of compressed sensing and sparse map-making with

Abstract

The Herschel Space Observatory of ESA was launched in May 2009 and has been in operation ever since. From its distant orbit around L2, it needs to transmit a huge quantity of information through a very limited bandwidth. This is especially true for the PACS imaging camera, which needs to compress its data far more than what can be achieved with lossless compression. This is currently solved by including lossy averaging and rounding steps onboard. Recently, a new theory called compressed sensing has emerged from the statistics community. This theory makes use of the sparsity of natural (or astrophysical) images to optimize the acquisition scheme of the data needed to estimate those images. Thus, it can lead to high compression factors. A previous article by Bobin et al. (2008, IEEE J. Selected Topics Signal Process., 2, 718) has shown how the new theory could be applied to simulated Herschel/PACS data to solve the compression requirement of the instrument. In this article, we show that compressed sensing theory can indeed be successfully applied to actual Herschel/PACS data and significantly improves over the standard pipeline. To fully use the redundancy present in the data, we perform a full sky-map estimation and decompression at the same time, which cannot be done in most other compression methods. We also demonstrate that the various artifacts affecting the data (pink noise and glitches, whose behavior is a priori not very compatible with compressed sensing) can also be handled in this new framework. Finally, we compare the methods from the compressed sensing scheme and data acquired with the standard compression scheme. We discuss improvements that can be made on Earth for the creation of sky maps from the data