Difference between revisions of "CDC algo tuning"

Revision as of 17:47, 6 December 2013

Using prototype data from CDC_50_50 (run 31942) with offline analysis

50/50 Ar/CO₂ and cosmics, 2100V, prototype horizontal

Original code, many samples upsampled

1. Event pedestal is mean of 100 samples ending at trigger time
2. Find hit threshold crossing
3. Step back <pedlead> points to find new local pedestal
4. Search forward to find high threshold crossing
5. Search backward to find low threshold crossing
6. Project through both thresholds to find pedestal crossing time

All thresholds found using upsampled data, with interpolation

Thresholds 5-4-1 pedlead 16 tz 7 tfix 1000

Thresholds 5-4-2 pedlead 16 tz 7 tfix 1000

Thresholds 5-4-3 pedlead 16 tz 7 tfix 1000

Trying out ideas for simplification

Ideas

1. Check whether interpolating threshold xings makes any difference - no, not if extrapolating through thresholds to pedestal.
2. Use the 3rd sample (or mean of 3rd & 4th samples) before hit threshold as local pedestal
3. Try reverting to one threshold crossing instead of the extrapolation - only slight increase to res width
4. Try something easier for event pedestal - mean of 4 samples immediately before trigger - this might be an improvement, probably because the pedestal is closer in time to the hits

All thresholds found using upsampled data, without interpolation at the high and low threshold xings

Thresholds 5-4-1 pedlead 16 tz 7 tfix 1000

Both thresholds found using upsampled data; for timing, used low threshold xing only, with interpolation

Thresholds 5-1 pedlead 16 tz 7 tfix 1000

1. Event pedestal is mean of 4 samples ending at trigger time

Both thresholds found using upsampled data; for timing, used low threshold xing only, with interpolation; 4 sample event pedestal

Thresholds 5-1 pedlead 16 tz 7 tfix 1000

Fewer samples upsampled

1. Event pedestal is mean of 100 samples ending at trigger time
2. Find hit threshold crossing
3. Upsample region (20 samples) around threshold crossing, with threshold crossing sample in set position (10th sample)
4. Search again for hit threshold in small region of upsampled data
5. Step back <pedlead> points to find new pedestal
6. Search forward to find high threshold crossing
7. Search backward to find low threshold crossing
8. Project through both thresholds to find pedestal crossing time

Thresholds 5-4-1 pedlead 16 tz 7 tfix 1000

This is not exactly the same as the first case (when 100 samples are upsampled) because after the hit threshold is found using regular samples, the search through the upsampled data is restricted to a small region. If the upsampled values (every 5th point) are not exactly the same as the sampled data, the wider search could run on further.

Did not search again for hit threshold, used sample 9, High and low thresholds found using upsampled data, with interpolation

Thresholds 5-4-1 pedlead 16 tz 7 tfix 1000

1. Event pedestal is mean of 4 samples ending at trigger time
2. Find hit threshold crossing
3. Upsample region (20 samples) around threshold crossing, with threshold crossing sample in set position (10th sample)
4. Search again for hit threshold in small region of upsampled data
5. Step back <pedlead> points to find new pedestal
6. Search forward to find high threshold crossing
7. Search backward to find low threshold crossing
8. Project through both thresholds to find pedestal crossing time

Thresholds 5-4-1 pedlead 16 tz 7 tfix 1000

1. Event pedestal is mean of 4 samples ending at trigger time
2. Find hit threshold crossing
3. Upsample region (20 samples) around threshold crossing, with threshold crossing sample in set position (10th sample)
4. Search again for hit threshold in small region of upsampled data
5. Step back <pedlead> points to find new pedestal
6. Search backward from hit threshold to find low threshold crossing, take this to be hit time (no extrapolation)