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definitions

definitions. Pedestals: Correlation matrix: Correlation coefficients (not used in the test analysis). Noise: all 8 samples; all 15,000 events. excellent: no non-Gaussian tails. Pedestals: sample-by-sample. small wave of cross-talk from pulsing or readout

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definitions

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  1. definitions • Pedestals: • Correlation matrix: • Correlation coefficients (not used in the test analysis)

  2. Noise: all 8 samples; all 15,000 events • excellent: no non-Gaussian tails

  3. Pedestals: sample-by-sample • small wave of cross-talk from pulsing or readout • OK: amplitude ~1 ADC count << noise (2.3 ADC counts RMS)

  4. noise correlations Nearby samples: K~0.20 One sample apart: K~ - 0.07

  5. Numerical Results • First 1000 events: mv0=610.079, rms0=2.329 • Subsequent 14,000 events: removed events (all 8 samples), if at least one of the first 5 samples has |Qi-mv0|>3*rms0; • 180 events from 14,000 removed, or 1.2% )consistent with Gaussian noise) • Andrey’s results are consistent with results produced by current code Results from Code

  6. Results and code difference • Previous (MTCC) matrix elements calculation code is in OnlineDB/CSCCondDB/interface/AutoCorrMat.h • Definition difference • Time samples numbering (ex. C33 – in new code means 3rd time sample, in older code it is actually 4th sample) -> All elements values are shifted by one time sample • Question: Which one is correct and should be used? • Calculation difference • New code • Old Code Ca,b = <Va·Vb> , where Va = Qa – Ped • Question: Which one should be used?

  7. Crosstalk difference • Huge intercept difference reported last meeting was using older results (from September), which actually was invalid (red values) (I need to update Web published results ASAP! Cause they show September values) • After September, the calculation code was fixed and new results are more close to MTCC (green). They are not published on the web, but they were available since October from our usual location at .cms network. • Still there is average up to 100% difference (new values are bigger) in intercept values and about 20% in slopes, which I need to understand…

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