NRAO Home  >  Green Bank  |  Wiki Topic:    GB > Data > PlanOfRecordC42004 > StandardObservingModes > HowToReduceTPTrackData

Changes | Index | Contents | Search | Statistics | Jump to Topic

Data Reduction Case #3

Total Power Data

Data reduction can be summarized as follows:

1. Calibration
2. Flagging
3. Averaging
4. Combination with a reference spectrum
5. Baseline subtraction
6. Analysis

A total power observation is often one component of a more sophisticated observing procedure. For example, each of the two scans in a position switched observation are total power data. By itself, a total power spectrum may be of limited use because it includes the shape of the bandpass in its spectrum. Still, it is possible to perform some perfunctory reduction to reveal the bandpass shape, for example.

Usually a calibration signal is switched on and off throughout the integration at some rate, say 1 Hz, and the data from each of the phases (calon and caloff) are stored in separate registers. If no calibration signal is available, calibration to antenna temperature is not possible and the result of the total power observation is simply a number of "counts" as a function of frequency.

So, in the most common case the user will have data in two registers: calon and caloff. Usually, there is a set of these registers for each of two polarizations.

Calibration

The first step in reducing the data is to calibrate it. Calibration is the topic of a detailed document that can be found here: RonsDetailedCalibrationDocument?. Issues that may be of concern to the user include how to apply a variable gain factor, how to handle nonlinearities in the gain, and alternate strategies in using the noise tube calibration signal. In the present discussion, only a simple and common case for data calibration will be described.

The observer can calculate an antenna temperature spectrum as follows:

is the calibration temperature of the noise tube calibration signal, usually available from engineering measurements, and is a function of frequency (f) and polarization (p). Note the total power spectrum is also, then, a function of frequency and polarization.

Flagging

Flagging data is as much art as science. It can be applied at any step in data reduction, and in some cases must necessarily be applied prior to calibration. However, immediately after calibration is also a common time to flag. Data can be flagged for RFI, spectrometer glitches, bandpass edges, or other reasons. The process can be automated (e.g. flag the first 10 channels in all available spectra; flag all data greater than a given intensity; flag all data which exceeds ) or it can be done by hand (tedious, but often necessary).

Averaging

It is usually the case that there are many spectra available after calibration, and they must be averaged to proceed toward the final result. In the simplest case, averaging is simply a matter of taking the arithmetic mean of the flux in each spectral channel over all available integrations. However, often it is necessary to flag data from some spectra and not others, so flagged channels must be accounted. Also, if all integration do not have identical integration times or system temperatures, then the average must be weighted accordingly, with a weighting factor of . Another weighting option is to calculate an rms for each spectrum to be averaged, and weight according to rms.

The final spectrum can also be averaged in frequency. This is often accomplished either by applying a Hanning smoothing function, or a boxcar smoothing function.

Combination with a reference spectrum

One must combine the total power spectrum with a reference spectrum in order to obtain an accurate representation of flux density vs. frequency. See HowToReducePSwitchSingleBeamData or HowToReduceFSTrackData for examples of how to do so.

Baseline subtraction

After calibration, it is usually necessary to subtract a baseline from the spectrum to remove any DC offset plus possible curvature in the baseline shape. Baseline subtraction can be preformed before or after averaging. The user must define a region of interest in the spectrum, to which the baseline can be fit. This region is usually as large as possible, but excludes any spectral lines in the spectrum, and often excludes the edges of the spectrum if there are edge effects. It should also exclude and flagged channels. A polynomial baseline can then be fit to the region of interest and subtracted. Other possibilities besides polynomila baselines can be made available, for example a sinusoidal function may be fit, or low frequency components of a Fourier decomposition may be subtracted.

Analysis

At this stage the user has a fully reduced representation of the spectral energy profile towards the source of interest. Analysis depends heavily on the science goals of the project, and can include:

1. Integration of total line intensity
2. Gaussian fitting to lines detected
3. Calculation of statistics over regions of interest (mean, standard deviation, median, centroid, etc.)
4. Comparison of the polarizations (e.g. subtract one from the other)
5. Fitting a rotation curve
6. Measurement of rotation velocities

-- JimBraatz - 27 Jul 2004