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تسجيل مستخدم جديدStatistical inversion of the LOFAR Epoch of Reionization experiment data model
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LOFAR is a new and innovative effort to build a radio-telescope operating at the multi-meter wavelength spectral window. One of the most exciting applications of LOFAR will be the search for redshifted 21-cm line emission from the Epoch of Reionization (EoR). It is currently believed that the Dark Ages, the period after recombination when the Universe turned neutral, lasted until around the Universe was 400,000 years old. During the EoR, objects started to form in the early universe and they were energetic enough to ionize neutral hydrogen. The precision and accuracy required to achieve this scientific goal, can be essentially translated into accumulating large amounts of data. The data model describing the response of the LOFAR telescope to the intensity distribution of the sky is characterized by the non-linearity of the parameters and the large level of noise compared to the desired cosmological signal. In this poster, we present the implementation of a statistically optimal map-making process and its properties. The basic assumptions of this method are that the noise is Gaussian and independent between the stations and frequency channels and that the dynamic range of the data can been enhanced significantly during the off-line LOFAR processing. These assumptions match our expectations for the LOFAR Epoch of Reionization Experiment.
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The aim of the LOFAR Epoch of Reionization (EoR) project is to detect the spectral fluctuations of the redshifted HI 21cm signal. This signal is weaker by several orders of magnitude than the astrophysical foreground signals and hence, in order to ac
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One of the key science projects of the Low-Frequency Array (LOFAR) is the detection of the cosmological signal coming from the Epoch of Reionization (EoR). Here we present the LOFAR EoR Diagnostic Database (LEDDB) that is used in the storage, managem
ent, processing and analysis of the LOFAR EoR observations. It stores referencing information of the observations and diagnostic parameters extracted from their calibration. This stored data is used to ease the pipeline processing, monitor the performance of the telescope and visualize the diagnostic parameters which facilitates the analysis of the several contamination effects on the signals. It is implemented with PostgreSQL and accessed through the psycopg2 python module. We have developed a very flexible query engine, which is used by a web user interface to access the database, and a very extensive set of tools for the visualization of the diagnostic parameters through all their multiple dimensions.
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