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Register a new userStatistical inversion of the LOFAR Epoch of Reionization experiment data model
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Added by
Panagiotis Labropoulos
Publication date
2010
fields
Physics
and research's language is
English
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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 achieve this, very long integrations, accurate calibration for stations and ionosphere and reliable foreground removal are essential. One of the prospective observing windows for the LOFAR EoR project will be centered at the North Celestial Pole (NCP). We present results from observations of the NCP window using the LOFAR highband antenna (HBA) array in the frequency range 115 MHz to 163 MHz. The data were obtained in April 2011 during the commissioning phase of LOFAR. We used baselines up to about 30 km. With about 3 nights, of 6 hours each, effective integration we have achieved a noise level of about 100 microJy/PSF in the NCP window. Close to the NCP, the noise level increases to about 180 microJy/PSF, mainly due to additional contamination from unsubtracted nearby sources. We estimate that in our best night, we have reached a noise level only a factor of 1.4 above the thermal limit set by the noise from our Galaxy and the receivers. Our continuum images are several times deeper than have been achieved previously using the WSRT and GMRT arrays. We derive an analytical explanation for the excess noise that we believe to be mainly due to sources at large angular separation from the NCP.
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, management, 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.
Future high redshift 21-cm experiments will suffer from a high degree of contamination, due both to astrophysical foregrounds and to non-astrophysical and instrumental effects. In order to reliably extract the cosmological signal from the observed data, it is essential to understand very well all data components and their influence on the extracted signal. Here we present simulated astrophysical foregrounds datacubes and discuss their possible statistical effects on the data. The foreground maps are produced assuming 5 deg x 5 deg windows that match those expected to be observed by the LOFAR Epoch-of-Reionization (EoR) key science project. We show that with the expected LOFAR-EoR sky and receiver noise levels, which amount to ~52 mK at 150 MHz after 300 hours of total observing time, a simple polynomial fit allows a statistical reconstruction of the signal. We also show that the polynomial fitting will work for maps with realistic yet idealised instrument response, i.e., a response that includes only a uniform uv coverage as a function of frequency and ignores many other uncertainties. Polarized galactic synchrotron maps that include internal polarization and a number of Faraday screens along the line of sight are also simulated. The importance of these stems from the fact that the LOFAR instrument, in common with all current interferometric EoR experiments has an instrumentally polarized response.
We present a conceptual design study of external calibrators in the 21 cm experiment towards detecting the globally averaged radiation of the epoch of reionization (EoR). Employment of external calibrator instead of internal calibrator commonly used in current EoR experiments allows to remove instrumental effects such as beam pattern, receiver gain and instability of the system if the conventional three-position switch measurements are implemented in a short time interval. Furthermore, in the new design the antenna system is placed in an underground anechoic chamber with an open/closing ceiling to maximally reduce the environmental effect such as RFI and ground radiation/reflection. It appears that three of the four external calibrators proposed in this paper, including two indoor artificial transmitters and one outdoor celestial radiation (the Galactic polarization), fail to meet our purpose. Diurnal motion of the Galactic diffuse emission turns to be the most possible source as an external calibrator, for which we have discussed the observational strategy and the algorithm of extracting the EoR signal.
A number of experiments are set to measure the 21-cm signal of neutral hydrogen from the Epoch of Reionization (EoR). The common denominator of these experiments are the large data sets produced, contaminated by various instrumental effects, ionospheric distortions, RFI and strong Galactic and extragalactic foregrounds. In this paper, the first in a series, we present the Data Model that will be the basis of the signal analysis for the LOFAR (Low Frequency Array) EoR Key Science Project (LOFAR EoR KSP). Using this data model we simulate realistic visibility data sets over a wide frequency band, taking properly into account all currently known instrumental corruptions (e.g. direction-dependent gains, complex gains, polarization effects, noise, etc). We then apply primary calibration errors to the data in a statistical sense, assuming that the calibration errors are random Gaussian variates at a level consistent with our current knowledge based on observations with the LOFAR Core Station 1. Our aim is to demonstrate how the systematics of an interferometric measurement affect the quality of the calibrated data, how errors correlate and propagate, and in the long run how this can lead to new calibration strategies. We present results of these simulations and the inversion process and extraction procedure. We also discuss some general properties of the coherency matrix and Jones formalism that might prove useful in solving the calibration problem of aperture synthesis arrays. We conclude that even in the presence of realistic noise and instrumental errors, the statistical signature of the EoR signal can be detected by LOFAR with relatively small errors. A detailed study of the statistical properties of our data model and more complex instrumental models will be considered in the future.
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