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Register a new userAnalysis Issues for Large CMB Data Sets
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Added by
Krzysztof M. Gorski
Publication date
1998
fields
Physics
and research's language is
English
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Multi-frequency, high resolution, full sky measurements of the anisotropy in both temperature and polarisation of the cosmic microwave background radiation are the goals of the satellite missions MAP (NASA) and Planck (ESA). The ultimate data products of these missions - multiple microwave sky maps, each of which will have to comprise more than 10^6 pixels in order to render the angular resolution of the instruments - will present serious challenges to those involved in the analysis and scientific exploitation of the results of both surveys. Some considerations of the relevant aspects of the mathematical structure of future CMB data sets are presented in this contribution. >>> for better on-screen rendition of the figures see <<< http://www.tac.dk/~healpix or http://www.mpa-garching.mpg.de/~cosmo/contributions.html
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We discuss Spherical Needlets and their properties. Needlets are a form of spherical wavelets which do not rely on any kind of tangent plane approximation and enjoy good localization properties in both pixel and harmonic space; moreover needlets coefficients are asymptotically uncorrelated at any fixed angular distance, which makes their use in statistical procedures very promising. In view of these properties, we believe needlets may turn out to be especially useful in the analysis of Cosmic Microwave Background (CMB) data on the incomplete sky, as well as of other cosmological observations. As a final advantage, we stress that the implementation of needlets is computationally very convenient and may rely completely on standard data analysis packages such as HEALPix.
We present ROMA, a parallel code to produce joint optimal temperature and polarisation maps out of multidetector CMB observations. ROMA is a fast, accurate and robust implementation of the iterative generalised least squares approach to map-making. We benchmark ROMA on realistic simulated data from the last, polarisation sensitive, flight of BOOMERanG.
Over the last few years, needlets have a emerged as a useful tool for the analysis of Cosmic Microwave Background (CMB) data. Our aim in this paper is first to introduce in the CMB literature a different form of needlets, known as Mexican needlets, first discussed in the mathematical literature by Geller and Mayeli (2009a,b). We then proceed with an extensive study of the properties of both standard and Mexican needlets; these properties depend on some parameters which can be tuned in order to optimize the performance for a given application. Our second aim in this paper is then to give practical advice on how to adjust these parameters in order to achieve the best properties for a given problem in CMB data analysis. In particular we investigate localization properties in real and harmonic spaces and propose a recipe on how to quantify the influence of galactic and point source masks on the needlet coefficients. We also show that for certain parameter values, the Mexican needlets provide a close approximation to the Spherical Mexican Hat Wavelets (whence their name), with some advantages concerning their numerical implementation and the derivation of their statistical properties.
Companies and individuals produce numerous tabular data. The objective of this position paper is to draw up the challenges posed by the automatic integration of data in the form of tables so that they can be cross-analyzed. We provide a first automatic solution for the integration of such tabular data to allow On-Line Analysis Processing. To fulfil this task, features of tabular data should be analyzed and the challenge of automatic multidimensional schema generation should be addressed. Hence, we propose a typology of tabular data and discuss our idea of an automatic solution.
Modern machine learning systems such as image classifiers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number of freely available, open source data sets are widely used. We suggest that examining the geo-diversity of open data sets is critical before adopting a data set for use cases in the developing world. We analyze two large, publicly available image data sets to assess geo-diversity and find that these data sets appear to exhibit an observable amerocentric and eurocentric representation bias. Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales. These results emphasize the need to ensure geo-representation when constructing data sets for use in the developing world.
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