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Data Reduction Case #6

Point Map Data

Reducing Point Map data proceeds as follows:

1. Reduce the spectrum at each pointing, as you would for a point source observation
2. Grid the data, interpolate if necessary
3. Convolve if necessary
4. Display maps

Point Map data reduction involves a layer of processing on top of point source spectroscopy. A Point Map is nothing more than a series of point source spectra observed at a number of positions spaced on a regular grid. So, the first step in reducing a Point Map is to calibrate and baseline subtract data for each unique pointing. Follow the data reduction case appropriate for the switching scheme used, e.g. if the data were taken in frequency switched mode, then the calibration and reduction should follow HowToReduceFSTrackData.

If the data are taken in total power mode, then the Point Map can be reduced as described in HowToReducePSwitchSingleBeamData, but the user must be careful to define which spectrum represents the "reference" for each signal. Namely, each total power observation on the grid acts as a "signal" spectrum. The reference position, then, may be a point located near the edge of the map, or it may be a position located off the map and observed after every Nth grid position, or it may be determined by a customized process defined by the observer. Regardless of how it is determined, the reference position can then be applied to multiple signal positions. For example, positions 1-10, each a unique pointing on the grid, may each use position 11 (which may be on or off the grid) as the reference in a total power position switched calibration.

Once the observer has a calibrated, baseline-subtracted, averaged, and flagged spectrum for each point on the grid, he can grid the data and convolve with a smoothing function if necessary. Channel averaging may then be applied, and the user may wish to produce countour maps or grayscale maps for each channel, or for a range of channels which have been averaged.

-- JimBraatz - 27 Jul 2004