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On Noise Characterization

On Noise Characterization. 14 August 2003. Michel Lefebvre University of Victoria Physics and Astronomy. Noise Characterization. A good characterization of the digital filtering noise is crucial to most analysis The digital filtering noise reduction factor depends on the layer and on 

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On Noise Characterization

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  1. On Noise Characterization 14 August 2003 Michel Lefebvre University of Victoria Physics and Astronomy

  2. Noise Characterization • A good characterization of the digital filtering noise is crucial to most analysis • The digital filtering noise reduction factor depends on the layer and on  • Strategy to estimate the digital filtering noise for each channel using only a given data run: • The pedrms are obtained from the first time sample the usual way; • For the channels with no signal in them (following some criteria), compute the noise/pedrms, where the noise is the result of a gaussian fit; • Obtain the average noise/pedrms ratio for a given layer and ; • Interpolate to other channels (those considered to have signal in them) using the corresponding average noise/pedrms ratio; • This is implemented in TBRootAna as NoiseAlg. On Noise Characterization

  3. Noise Characterization: HEC • The digital filtering noise reduction factor depends on the layer and on  and ranges from 0.54 to 0.70 noise vs  noise/pedrms vs  13188 is a muon run On Noise Characterization

  4. Noise Characterization: EMEC • The digital filtering noise reduction factor depends on the layer and on  and ranges from 0.46 to 0.77 noise/pedrms vs  noise vs  13188 is a muon run On Noise Characterization

  5. Occupancy • Consider the occupancy of all cells for a given run, obtained using pedrms as a noise characterization • occupancy computed using |signal| > 1pedrms • expect channels with no signal to be at 31.7%. They are not! • the occupancy distribution is broad because the noise/pedrms ratio varies over the calorimeters • the number of entries in each plot is the number of connected readout channels for each detector 13188 is a muon run HEC EMEC a few dead channels On Noise Characterization

  6. Noise Quality • The quality of the digital filtering noise can be assessed by studying the occupancy of all cells for a given run • occupancy computed using |signal| > 2 • expect channels with no signal to be at 4.55%. They are! Their distribution is narrow • channels with signal in them clearly have large occupancy (here only a few since this is a muon run) • There are a few very noisy channels with non-gaussian noise (next slide) • the number of entries in each plot is the number of connected readout channels for each detector 13188 is a muon run HEC EMEC a few dead channels On Noise Characterization

  7. Pathological Channels • Some very noisy channels have non-gaussian digital filtering noise distribution • Their pedestal distribution is gaussian; • The non-gaussian nature of the noise distribution seems to be generated by the digital filtering reconstruction • A limited range gaussian fit underestimates the noise, and hence overestimates the occupancy • They obviously need special treatment... • Seems to be limited to some EMEC channels in layers 1 and 2 Ch1024 pedrms Ch1024 noise run 12191 Kanaya On Noise Characterization

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