Hello!  Back to ARA analysis.

Whenever I attempt to decide if a signal was an incoming plane wave, I compute the planarity of the event by summing cyclically the time-differences in adjacent channels that form a polygon.  For square polygons, this looks is like summing the time-difference between channels A and B, B and C, C and D, with D and A.  This sum should be zero for a plane wave, and a normal distribution for thermal noise.  I identify 12 faces within the cubical ARA detectors.  Using the Miller cubic crystal notation, the planes I use are the following: (001) (010) (100) plus opposites, (110) (101) (011) plus opposites.  For the first set, opposite means the other side of the cube, and for the second set, opposite means (T10) (T01) (0T1).

When a calibration pulser hits these surfaces, the wave should create a pulse waveform in each channel.  Computing the cyclic sum (planarity) for each of the twelve polygons, I usually get a number close to zero.  This must be a precision measurement, however.  We know where the calibration pulsers are, and we know where the channels are.  Thus, we can make a prediction for the timing corrections to each channel pair.  Each offset to the time-difference in a channel pair may be introduced by my analysis techniques, or some unknown systematic error in the detector.

My analysis code has a mode in which I can run over just tagged calibration pulses, in runs where there are a minimum number of tagged calibration pulse events.  I first check that there are at least 100 events in a run, and then I compute the mean and rms of 100 timing offsets for every channel pair.  The graphs below show the timing offsets versus time.  By applying these corrections to the data, calibration pulse events have planarities centered on zero, and this match improves with increasing amplitude.

Notice two things about these graphs: 1) Sometimes the data goes haywire, and that is because there are either thermal events tagged as calibration pulses, or a channel died. 2) For good data, that has small errors and small values (<10 ns), there seem to be linear trends that show drift in the station timing.  This drift cannot be introduced by my analysis code.

The graphs are for ARA2 data, and ARA3 plots are coming.