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Automated Editing of Radio Interferometer Data with Pyflag Enno Middelberg
Abstract:
Editing of radio interferometer data, a process commonly known as ``flagging'', can be laborious and time-consuming. One quickly tends to flag more data than actually required, sacrificing sensitivity and image fidelity in the process. I describe a program, Pyflag, which can analyse radio interferometer data to filter out measurements which are likely to be affected by interference. Pyflag uses two algorithms to allow for data sets which are either dominated by receiver noise or by source structure. Together, the algorithms detect essentially all affected data whilst the amount of data which is not affected by interference but falsely marked as such is kept to a minimum. The sections marked by Pyflag are very similar to what would be deemed affected by the observer in a visual inspection of the data. Pyflag displays its results concisely and allows the user to add and remove flags interactively. It is written in Python, is easy to install and use, and has a variety of options to adjust its algorithms to a particular observing situation. I describe how Pyflag works and illustrate its effect using data from typical observations.
- Introduction
- How Pyflag works
- Step 1: Amplitude-based flagging
- Step 2: Rms-based flagging
- Step 3: Postprocessing to find small clusters of bad points
- Step 4: Postprocessing to find larger clusters of bad points
- Display of the results
- Application of flags to the data
- Limitations
- Examples
- Bibliography
- Usage
- About this document ...
Next: Introduction
Enno Middelberg 2006-01-12
Enno Middelberg
Abstract:
Editing of radio interferometer data, a process commonly known as ``flagging'', can be laborious and time-consuming. One quickly tends to flag more data than actually required, sacrificing sensitivity and image fidelity in the process. I describe a program, Pyflag, which can analyse radio interferometer data to filter out measurements which are likely to be affected by interference. Pyflag uses two algorithms to allow for data sets which are either dominated by receiver noise or by source structure. Together, the algorithms detect essentially all affected data whilst the amount of data which is not affected by interference but falsely marked as such is kept to a minimum. The sections marked by Pyflag are very similar to what would be deemed affected by the observer in a visual inspection of the data. Pyflag displays its results concisely and allows the user to add and remove flags interactively. It is written in Python, is easy to install and use, and has a variety of options to adjust its algorithms to a particular observing situation. I describe how Pyflag works and illustrate its effect using data from typical observations.
- Introduction
- How Pyflag works
- Step 1: Amplitude-based flagging
- Step 2: Rms-based flagging
- Step 3: Postprocessing to find small clusters of bad points
- Step 4: Postprocessing to find larger clusters of bad points
- Display of the results
- Application of flags to the data
- Limitations
- Examples
- Bibliography
- Usage
- About this document ...
Next: Introduction
Enno Middelberg 2006-01-12