LE algo
- Page still under edit* read at your own risk (and don't believe any of it... yet)*
Results using Gerard's leading edge algo to extract drift-time from CDC prototype cosmics data
- 50/50 mixed Ar, CO2
- Recalibrated MFCs
- Outer plenum added to CDC prototype to prevent leaks
- New modified HVB
- 2050V
- Modified preamp4 (terminating resistors removed)
- Prototype tilted at 45o to enclosing scintillators (scints trigger the daq)
The original algorithm is here LE algo It takes a number of samples from the ADC data, interpolates 4 extra points between each sample, convolutes them with the filter to give an approximation to the original pre-sampled data, and returns the first value above a predefined threshold. There are a variety of implementations below. The "without interpolation" results are from a simple comparison between ADC data and threshold.
Below: excerpt from one event in one straw showing interpolated and sampled fADC data
Below: Events with ADC data going above pedestal mean + 5 pedestal sigma are selected. 50 samples starting 25 samples before the threshold crossing are sent to the LE algo after subtracting mean pedestal. The threshold for the LE algo is 2 pedestal sigma. Pedestal mean is for 100 early samples in the event. Pedestal sigma is for 100 early samples in 100 events.
Left: drift-time with interpolation; Right: no interpolation
- Huge number of events below is the total for the run, above is the total for hits ch23, 5+ total hits**********
Below: 20 samples starting 10 samples before the threshold crossing are sent to the LE algo after pedestal subtraction.
Below: Events with ADC data going above pedestal mean + 5 sigma are selected. Events with 5 or more hits are selected. 20 samples starting 10 samples before the threshold crossing are sent to the LE algo after subtracting mean pedestal. The threshold for the LE algo is 3 sigma. Left: drift-time with interpolation; Right: no interpolation
Below: As above, for the interpolated data, but only events where a track can be fitted are selected. Tzero was obtained by fitting a straight line to the leading edge of the drift time histo (between the overall maximum and the minimum preceding it) and taking the value where it crosses the x-axis (this needs work). First the drift-time histo was put into 4ns bins to unify any double-maxima in the histogram. (Events with ADC data going above pedestal mean + 5 sigma are selected. Events with 5 or more hits are selected. 20 samples starting 10 samples before the threshold crossing are sent to the LE algo after subtracting mean pedestal. The threshold for the LE algo is 3 sigma.)
Negative residuals come from a tzero that is too small (or drift time that is too large). 3 sigma is ~ 30 in ADC value, probably too late. Lowering the LE threshold makes it more likely to accept noise before the signal and so the # tracked events decreases slightly.
Below: as above, for LE threshold of 1 sigma. (Events with ADC data going above pedestal mean + 5 sigma are selected. Events with 5 or more hits are selected. 20 samples starting 10 samples before the threshold crossing are sent to the LE algo after subtracting mean pedestal. The threshold for the LE algo is 1 sigma.)
Pink line: pedestal from samples 0-100 Green line: threshold to find the signal Blue line: LE threshold Grey line: fitted drift time
The above pics look too wrong to be true. Maybe I made an error in the plot. ?? In any case the pedestal is not optimal.
For most of the above pics I had calculated the interpolated threshold crossing time wrong. For the pics below I had accidentally switched interpolation off. I did say don't believe any of this stuff yet, it's early days.
Changed the code to use mean pedestal of 5th to 15th samples before the higher threshold-crossing point. Blue vertical lines mark the region sent to the LE filter algo.