Simulation of noise properties of the Murchison Wide-field Array
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REZAEE, NASTARANDate
2015Affiliation
School of PhysicsMetadata
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Masters Research thesisAccess Status
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© 2015 Nastaran Rezaee
Abstract
This thesis is focused on better understanding the origins of the noise in an image taken with the Murchison Wide-field Array (MWA). To investigate this, we process images of fields being used for Epoch of Reionization experiments, and use simulations to characterize the expected noise. For our simulation, we use the MIT Array Performance Simulator (MAPS), which was developed at the MIT Haystack Observatory in 2001. MAPS provides a flexible tool for a new generation of simulated data from low frequency radio telescopes. Here we use MAPS to model the performance of the prototype 32 Tiles instrument of the MWA (MWA−32T) and 128 Tiles (MWA−128T). To characterize the noise in the simulation, we need a good astronomical catalogue to provide input sources for the MAPS simulation. By matching source counterparts between the sources observed by the MWA, and the sources in different catalogues, we found that the best catalogue for simulating the model of the sky with respect to the frequency and the flux ranges of the observed sources is the PKSCAT90.
In our initial simulation we assume that the sky is dominated by thermal noise. However, we also find noise in the image, which indicates that additional sources of noise are present. We use a counts model to estimate the numbers of unresolved sources in our simulation to better simulate the level of noise in the image. Using number counts of extragalactic radio sources, we find that these are the most dominant noise component. These extragalactic radio sources are usually unresolved at the angular resolution of the observations, and will cause a contamination for detecting the 21-cm line of the neutral hydrogen. In our simulations for the MWA−32T and MWA−128T, we use the EoR1 field, without the presence of resolved sources (FornaxA). Resolved sources can cause errors in the flux densities of the image, and decrease the accuracy of estimated noise in the image.
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