Radio Frequency Interference Detection using Machine Learning.
Radio Frequency Interference Detection using Machine Learning.
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DOI：
10.1088/1757-899x/198/1/012012
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发表时间：
2016-10
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影响因子：
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通讯作者：
Olorato Mosiane;N. Oozeer;Arun Aniyan;B. Bassett
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文献类型：
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作者：
Olorato Mosiane;N. Oozeer;Arun Aniyan;B. Bassett
Radio frequency interference (RFI) has plagued radio astronomy which potentially might be as bad or worse by the time the Square Kilometre Array (SKA) comes up. RFI can be either internal (generated by instruments) or external that originates from intentional or unintentional radio emission generated by man. With the huge amount of data that will be available with up coming radio telescopes, an automated aproach will be required to detect RFI. In this paper to try automate this process we present the result of applying machine learning techniques to cross match RFI from the Karoo Array Telescope (KAT-7) data. We found that not all the features selected to characterise RFI are always important. We further investigated 3 machine learning techniques and conclude that the Random forest classifier performs with a 98% Area Under Curve and 91% recall in detecting RFI.
