The European Space Agency's Planck satellite, launched on 14 May 2009, is the third-generation space experiment in the field of cosmic microwave background (CMB) research. It will image the anisotropies of the CMB over the whole sky, with unprecedented sensitivity ( ΔT T ∼ 2 × 10 −6 ) and angular resolution (∼5 arcmin). Planck will provide a major source of information relevant to many fundamental cosmological problems and will test current theories of the early evolution of the Universe and the origin of structure. It will also address a wide range of areas of astrophysical research related to the Milky Way as well as external galaxies and clusters of galaxies. The ability of Planck to measure polarization across a wide frequency range (30−350 GHz), with high precision and accuracy, and over the whole sky, will provide unique insight, not only into specific cosmological questions, but also into the properties of the interstellar medium. This paper is part of a series which describes the technical capabilities of the Planck scientific payload. It is based on the knowledge gathered during the on-ground calibration campaigns of the major subsystems, principally its telescope and its two scientific instruments, and of tests at fully integrated satellite level. It represents the best estimate before launch of the technical performance that the satellite and its payload will achieve in flight. In this paper, we summarise the main elements of the payload performance, which is described in detail in the accompanying papers. In addition, we describe the satellite performance elements which are most relevant for science, and provide an overview of the plans for scientific operations and data analysis.
MADmap is a software application used to produce maximum-likelihood images of the sky from time-ordered data which include correlated noise, such as those gathered by Cosmic Microwave Background (CMB) experiments. It works efficiently on platforms ranging from small workstations to the most massively parallel supercomputers. Map-making is a critical step in the analysis of all CMB data sets, and the maximum-likelihood approach is the most accurate and widely applicable algorithm; however, it is a computationally challenging task. This challenge will only increase with the next generation of ground-based, balloon-borne and satellite CMB polarization experiments. The faintness of the B-mode signal that these experiments seek to measure requires them to gather enormous data sets. MADmap is already being run on up to O(10 11 ) time samples, O(10 8 ) pixels and O(10 4 ) cores, with ongoing work to scale to the next generation of data sets and supercomputers. We describe MADmap's algorithm based around a preconditioned conjugate gradient solver, fast Fourier transforms and sparse matrix operations. We highlight MADmap's ability to address problems typically encountered in the analysis of realistic CMB data sets and describe its application to simulations of the Planck and EBEX experiments. The massively parallel and distributed implementation is detailed and scaling complexities are given for the resources required. MADmap is capable of analysing the largest data sets now being collected on computing resources currently available, and we argue that, given Moore's Law, MADmap will be capable of reducing the most massive projected data sets.
Aims. We compare the performance of multiple codes written by different groups for making polarized maps from Planck-sized, all-sky cosmic microwave background (CMB) data. Three of the codes are based on a destriping algorithm; the other three are implementations of an optimal maximum-likelihood algorithm. Methods. Time-ordered data (TOD) were simulated using the Planck Level-S simulation pipeline. Several cases of temperature-only data were run to test that the codes could handle large datasets, and to explore effects such as the precision of the pointing data. Based on these preliminary results, TOD were generated for a set of four 217 GHz detectors (the minimum number required to produce I, Q, and U maps) under two different scanning strategies, with and without noise. Results. Following correction of various problems revealed by the early simulation, all codes were able to handle the large data volume that Planck will produce. Differences in maps produced are small but noticeable; differences in computing resources are large.
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