Robust radio interferometric calibration using the t-distribution
نویسندگان
چکیده
منابع مشابه
Radio Interferometric Calibration Using The SAGE Algorithm
Radio Interferometry is an essential method for astronomical observations. Self-calibration techniques have increased the quality of the radio astronomical observations (and hence the science) by orders of magnitude. Recently, there is a drive towards sensor arrays built using inexpensive hardware and distributed over a wide area acting as radio interferometers. Calibration of such arrays poses...
متن کاملDistributed Radio Interferometric Calibration
Increasing data volumes delivered by a new generation of radio interferometers require computationally efficient and robust calibration algorithms. In this paper, we propose distributed calibration as a way of improving both computational cost as well as robustness in calibration. We exploit the data parallelism across frequency that is inherent in radio astronomical observations that are recor...
متن کاملData Multiplexing in Radio Interferometric Calibration
New and upcoming radio interferometers will produce unprecedented amounts of data that demand extremely powerful computers for processing. This is a limiting factor due to the large computational power and energy costs involved. Such limitations restrict several key data processing steps in radio interferometry. One such step is calibration where systematic errors in the data are determined and...
متن کاملDistributed Model Construction in Radio Interferometric Calibration
Calibration of a typical radio interferometric array yields thousands of parameters as solutions. These solutions contain valuable information about the systematic errors in the data (ionosphere and beam shape). This information could be reused in calibration to improve the accuracy and also can be fed into imaging to improve the fidelity. We propose a distributed optimization strategy to const...
متن کاملRobust mixture regression using the t-distribution
The traditional estimation of mixture regression models is based on the normal assumption of component errors and thus is sensitive to outliers or heavy-tailed errors. A robust mixture regression model based on the t−distribution by extending the mixture of t−distributions to the regression setting is proposed. However, this proposed new mixture regression model is still not robust to high leve...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Monthly Notices of the Royal Astronomical Society
سال: 2013
ISSN: 1365-2966,0035-8711
DOI: 10.1093/mnras/stt1347