Digitization for the Forward Endcap Calorimeter Viktor Rodin - - PowerPoint PPT Presentation

digitization for the forward endcap calorimeter
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Digitization for the Forward Endcap Calorimeter Viktor Rodin - - PowerPoint PPT Presentation

kvi - center for advanced kvi - center for advanced radiation technology radiation technology | 1 | 1 Digitization for the Forward Endcap Calorimeter Viktor Rodin Myroslav Kavatsyuk kvi - center for advanced kvi - center for advanced


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Digitization for the Forward Endcap Calorimeter

Viktor Rodin Myroslav Kavatsyuk

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Main goals

  • Implement the digitization procedure,

used in EMC digitizers, into the Pandaroot

  • Cross-checking of the simulation

perfomance with realisitic digitization.

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Introduction

FEE Output

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Introduction

FEE Output

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Digitizer

Sampling ADC board Sampling – 80 MS/s Resolution – 14 bit Input channels – 32 High/low gain splitting 2 Kintex-7 FPGAs Online feature extraction Interface – Optical, SFP, LC-type

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Digitizer

Sampling ADC board Sampling – 80 MS/s Resolution – 14 bit Input channels – 32 High/low gain splitting 2 Kintex-7 FPGAs Online feature extraction Interface – Optical, SFP, LC-type

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Feature extraction

SADC

FE

FE Base- line E

CFT

Time

Tr I/A

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Waveform shape

New shape is not yet in Pandaroot repository.

Samples

The shape of waveform from the FEE Old – shape of waveform which used in Pandaroot now. New – shape of waveform from the final FEE version

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Pile-up case

EMC TDR

The expected hit rate in the Forward Endcap EMC Example of the pile-up case

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Feature extraction with filters

SADC MWD I MWD II

FE

FE Base- line MA E

CFT

Time

Tr

long short

I/A

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Pile-up recovery algorithm

Moving Window Deconvolution filter (MWD) reduces the length of pulse, and therefore minimizes the overlap between pulses. Moving Average filter suppress the noise, hence increases the accuracy

  • f the feature extraction procedure.

Both filters are used in the pile-up recovery algorithm.

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MWD and MA filters

x(i) – value of sample, m – length of window in samples, – inverted index of exponential tail of the pulse.

τ

L – number of samples for averaging. (Usually it is half of MWD length)

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Main issues

  • What is realistic noise?
  • Which values of parameters are optimal

for the MWD and MA filters ?

  • What resolution will be after the feature

extraction considering the previous point?

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Waveforms from Bochum

Waveforms provided by the Bochum group:

  • Measurement with light pulser in the range
  • f 10 MeV to 12 GeV;
  • final version of ADC;
  • production Fw Endcap subunit.

Used to determine noise parameters and resolution.

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Resolution studies

Waveform data, 262 MeV

Full Highgain waveform Full Lowgain waveform

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Noise measurement, experiment

400 first samples from each waveform after baseline substraction. Lowgain Mean – 0.5177 Std Dev – 4.936 Highgain Mean – 0.5209 Std Dev – 14.08

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Noise measurement, simulations

Noise level in Pandaroot has been adjusted to the experimental results. Lowgain Mean – 0.5898 Std Dev – 4.915 Highgain Mean – 0.514 Std Dev – 13.98

Counts Counts Deviation Deviation

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Analysis of waveforms

Integral and amplitude resolution have been evaluated. Two modes were tested – dynamic and fixed window. In the first case, integration continues if the value is above a certain

  • threshold. In the second case, the integration is limited by

sampling range.

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Experimental results

Using of longer MA and MWD filters is better for the energy determination?

best resolution value in case

  • f integration
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Pandaroot simulation

Light pulser simulation in Pandaroot with energy 0.262 GeV Same behaviour as in the case of measurements.

Amplitude resolution

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Pandaroot simulation

Gabler Fit was used to study single crystal and cluster resolutions.

Example of reconstructed 1GeV gamma peak

Energy, GeV

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Pandaroot simulation

Resolution does not improve with increasing MWD length. Without MA and MWD Resolution – 0.06955 Shower effect yields bigger effect than smoothing of noise 10000 gammas with energy 0.262 GeV shot to a single point at the hit rate 1kHz.

Single crystal resolution

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Pandaroot simulation

10000 gammas with energy 0.262 GeV shot to a single point at the hit rate 1kHz.

Cluster resolution

Resolution without MA and MWD - 0.04394 Smoothing makes resolution worse if filters are longer. Why?

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Pandaroot simulation

  • Cluster resolution depends on the number of digis.
  • MA smoothes rising edge of pulse, thus time

determination for low energy hits becomes worse and some of them cannot be detected. Solution: Change thresholds No MA and MWD resolution 0.04394 MA with MWD 5(10) resolution 0.05121

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Pandaroot simulation

Unsatisfactory perfomance. Why?

A resolution in case if there are no filters, Highgain Threshold =40 A.U. 10000 gammas with energy 1 GeV shot to a single point at the hit rate 1kHz.

Reconstructed gamma

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Pandaroot simulation

Lower threshold – more digis are reconstructed. But at some level, one can detect more noise and less the real signals.

Resolution dependence on Highgain Threshold

H Threshold

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Pandaroot simulation

MWD and MA Highgain Threshold =20 A.U. TDR requirement 0.03

Reconstructed gamma

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Pandaroot simulation

Length of MWD(MA) 20(10) Due to the MA, it is possible to apply lower threshold level. Short pulse but resolution is better!

Resolution dependence on Highgain Threshold

H Threshold

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Pandaroot simulation

10000 gammas with energy 1 GeV shot to a single point at the hit rate 500kHz.

Pile-up case

No MA and MWD H Thr =40 Reconstructed gamma

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Pandaroot simulation

Pile-up case

Resolution without MWD and MA 10000 gammas with energy 1 GeV shot to a single point at the hit rate 500kHz.

H Threshold

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Pandaroot simulation

Pile-up case

MA and MWD H Thr =20 TDR requirement 0.03 10000 gammas with energy 1 GeV shot to a single point at the hit rate 500kHz.

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Pandaroot simulation

Pile-up case

Resolution_newAlg Resolution with MWD and MA 10000 gammas with energy 1 GeV shot to a single point at the hit rate 500kHz.

H Threshold

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Summary

  • The noise features are investigated
  • The optimal parameters for the pile-up

recovery algorithm are found

  • Pile-up recovery algorithm is imlemented in

Pandaroot and it improves the resolution

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Thanks for attention!

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Backup slides

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Waveforms from Bochum

The Bochum group produces the final version of FwE EMC subunits. 4 data files with waveform events corresponding different energy (10 MeV – 12 GeV) have been sent to us from their side. Each data file contains more than 700 events. Each event represents the output from 32 APDs with Highgain and Lowgain channels – in total 64 waveforms per event.

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kvi - center for advanced radiation technology PndEmcPSAFPGA PileupAnalyser PndEmcPSAFPGA SampleAnalyser PndEmcHighLowPSA PndEmcFWEndcap Digi GetActiveWaveform GetSignal Put( signal ) GetHit( iHit, energy, time ) Process( theWaveform ) GeHit PndEmcDigi create new Digi

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MWD and MA filters

Shifted differentiation Sum

PANDA Collaboration Meeting 19/2, Darmstadt, 26-06-2019

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Noise measurement

PANDA Collaboration Meeting 19/2, Darmstadt, 26-06-2019

Similar procedure has been done in Pandaroot to study the noise level. Discrepancy was observed. Lowgain Mean -0.5002 Std Dev 0.6928 Highgain Mean -0.537 Std Dev 14.3