1. Roberto Chierici - CERN
Preliminary results from test beam data
Roberto Chierici - CERN
On behalf of the CMS tracker collaboration
Aim and experimental setup
Event reconstruction
Pedestal, common mode and noise evaluation
Cluster finding
Module performance
S/N, stability, cluster characteristics
Latency scans
Peak and deconvolution modes: features and observations
Efficiencies and delay curves
Conclusions (still preliminary)

2. Experimental setup 25-Oct-2001/3-Nov-2001 25 ns bunch spacing
120 GeV , 
Pitch=183 m
w/p~0.25
Sensor width=500 m
V=300 V
100 mrad
Non irradiated TOB modules
Almost final DAQ setup 1 2 3 4 5 6
Roberto Chierici

3. Event Reconstruction Rough pedestal ped0 (n0 events)
<ADCi> in time
Refined pedestals and first common mode noise (n1 events)
<ADCi>, ADC in time removing strips with pol×(ADCi-ped0)>threshold
CMN0=<ADCi-pedi> over strips
Noise determination and better pedestals/CMN (n2 events)
Exclude those strips for which pol×(ADCi-pedi)>KADC
CMN=<ADCi-pedi> over strips; pedi= <ADCi-CMN> in time
ni2= <ADCi-pedi-CMN>2 in time
Loop over events
Remove bad events, determine noisy/dead strips
Recalculate CMN; update pedestals and noise after n0+n1+n2 events
Cluster finding
si=ADCi-pedi-CMNi consider only those for which si/ni>2
Good clusters if nstrip>0, Scl1/Ncl1>5
Roberto Chierici

4. Pedestals  deconvolution Plots from G. Pásztor Module 1 Module 2
APV 1
APV 2
APV 3
APV 4
Plots from G. Pásztor
Roberto Chierici

5. different updating windows
Noise
Noise < 3 ADC counts
Very stable in
space and time
Module #2: noise for two
different updating windows
N depends upon
the updating
Roberto Chierici

6. Corrected data si=ADCi-pedi-CMNi (CMN~0.3noise)
pedestal runs tell us we correctly estimate our noise
deviation of a factor 2 only outside 4 region
Pedestal run  
=1.04
Roberto Chierici

7. Cluster characteristics
Module #2
S/Ncluster~20
APV 1
APV 2
APV 3
APV 4
Unexpectedly large number of strips per cluster !
Confirmed by different analyses (Pisa)
Effect not present in July 2000 beam test
(but very different settings)
From Pisa group
Roberto Chierici

8. Latency scans Runs in deconvolution mode
Detectors kept at optimal latency value
Latency scans for detectors 3-4
Excellent way for studying delay curves and efficiency
Preliminary 1D tracking
Use 15k muons for alignment of modules (fits to residuals)
Modules 3-5 (4-6) aligned with respect to 1 (2)
Look for coincident clusters in modules 1-5 (2-6) and build a “track” :)
Look what happens around the intercept (10 strips) in modules 3 and 4
Averaging over events
Scans of all APVs.
Alignment procedure as above
Tracking gives worse performance: use the highest strip in module
Runs in peak mode
Roberto Chierici

9. Delay curves q in  10 strips from the intercepts in modules 3,4
no cluster finding
dependence
deconvolution
Non-ideal APV parameters
(VFS=70, Isha=90)
asymmetry
too efficient 25 ns off peak
undershoots
undershoot...
Roberto Chierici

10. Amplitudes in time
Deconvolution
Peak
Roberto Chierici

11. Strips in cluster Peak Mode Symmetric charge sharing (Y)
CAC Cb q L
Cint Cb Q C
Cint q R
preampl.
shaper Cb
Asymmetric diffusion (X) q1 q2
latency
Diffusive component ~ 20% of the total
Roberto Chierici

12. Delay curves per strip (peak)
|L-R|/(L+R+T)<0.2
Test beam data
‘faster’ curve for adjacent strips
signal propagating on 2 closest strips
X-checked by lab calibration
very similar peak ratios
(L. Mirabito)
Cal. channel 1 2
Roberto Chierici

13. Delay curve per strip (deco)
Reasonable shape of the hit strip
deconvolution nicely tuned
|L-R|/(L+R+T)<0.2
Different output for the neighboring:
result of the different pulse shape
(input to the deco not anymore a CRRC 50 ns)
deconvolution enhances q1/q0
Possible explanation given by the
behaviour of the amplifier as R at
high frequency:
+Cint=h.p. filter to adjacent strips
=delay curve
The shape is expected to be APV
parameter dependent !
Consequences for the tracker:
position resolution
cluster reconstruction
two track separation
data volume
studies going on...
Roberto Chierici
Delay (ns)

14. Cluster finding efficiency
~99.5%
Still too efficient at 25 ns
better tuning of APV parameters
Excellent efficiency at 75 ns (mod. 3)
not too sensitive to the position of the maximum
cluster finding much more efficient than charge
integral
Cluster finding cuts to be optimized…
efficiency curve can be adjusted
the efficiency ‘plateau’ can be considerably
smaller
Roberto Chierici

15. Response function
Charge sharing
Diffusive regions
The response function can be determined by assuming a uniform beam intensity
over the strip:
diffusion region
position resolution
(work is going on…)
Roberto Chierici

16. Conclusions The 25 Oct - 3 Nov test beam on 25 ns beam was a success
6 TOB modules tested on a 25 ns beam with the next-to-final DAQ setup
excellent quality of the collected data
Several configurations tried
latency scans in peak and deconvolution
latency scans with different APV parameters
special triggers
Preliminary results very interesting
S/N of clusters ~ 20 (deco, non irradiated detectors), noise as expected
work going on for optimizing delay curves. Excellent track efficiency
CintAPV at high frequency may cause undesired features (not dramatic)
A lot of things to do...
Huge amount of data (340 GB on castor) to analyze
optimize/distribute common tools for data analysis
further investigations + lab tests to be continued
Everyone very welcome to join !
Roberto Chierici

17. Further info
Module #2
Roberto Chierici

18. Module tilting visible
Two events
Zero suppressed info  
Module tilting visible
Roberto Chierici

19. Deconvolution... 2=100 ns 2=50 ns 2=25 ns Roberto Chierici RC(2)
CR-RC(1)
deconvolution
2=100 ns
2=50 ns
2=25 ns
Roberto Chierici

20. DAQ setup
Roberto Chierici

21. Shape vs amplitude
Pulse height ratios are stable for different input amplitudes.
Amplitude
Roberto Chierici

22. Diffusion and sharing
Deconvolution
Peak
Roberto Chierici

23. Some (nice) picture...
Roberto Chierici

24. Charge asymmetry in time
Deconvolution
Peak
Roberto Chierici

25. Charge sharing in time
Deconvolution
Peak
Roberto Chierici

26. Experimental setup (hardware and software) Event reconstruction
Aim
Experimental setup (hardware and software)
Event reconstruction
Pedestal and common mode
Noise evaluation
Cluster finding
Module performance
S/N and stability
Cluster characteristics
Latency scans
Peak and deconvolution mode: features
Efficiency and delay curves per strip
New observations
Resolution curve
Conclusions (preliminary!)
Roberto Chierici

27. From Pisa group
Roberto Chierici

28. 7 entries per track
From Pisa group
Roberto Chierici

29. Special runs Trigger changed from 1001 to 0011
another way to study efficiency off peak
2nd cluster
<n>~1.86
<n>~1.55
Roberto Chierici

30. Further crosschecks (Pisa)
July 2000
November 2001
Roberto Chierici

31. S/N in cluster
Pisa results
Roberto Chierici