Embed Size (px)
Citation preview
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
V.Chiochia 1, M.Swartz 2, D.Fehling 2, G.Giurgiu 2, P.Maksimovic 21 University of Zürich (Switzerland), 2 Johns Hopkins University (USA)
A novel technique for the reconstruction and simulation of hits in the CMS Pixel Detector
2008 Nuclear Science Symposium19-25 October 2008Dresden, Germany
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
OutlineIntroductionSilicon irradiation: effects and physical modeling
Charge collection and trapping, Lorentz deflectionDouble trap effective model
Hit reconstruction for irradiated sensorsTemplate reconstruction
Performances and physics resultsPosition resolutionImpact on b-taggingHit splitting
2
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
Detector layoutThree barrel layers and two end-cap disks at each barrel end
Barrel layers at 4 cm, 7 cm, and 11 cm radius~700 modules made of 16 chips in barrel region (67k channels/module)End-cap disks: 24 blades made of 7 sensors (4 or 3 per side)About 67x106 channels in total, L~1 m, R~30 cm
3
Sensors and front-end electronics:“n-in-n” design with p-spray (barrel) and p-stop (endcaps) isolation100(rf)x150(z) mm2 pixel cell, charge sharing in 4 T magnetic fieldPSI 0.25 mm CMOS chip with column drain full analogue readout
~1m
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
Radiation environment
4
Irradiation fluence integrated per year
at full LHC luminosity
sppinelastic = 80 mbL = 1034 cm-2s-1
⊗ B field
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
Irradiation effects
5
Transverse coordinate
Irradiation modifies the electric field profile
Variation of the Lorentz angle deflection
Track
E
Longitudinal coordinate
Track
E
Trapping suppressescharge collected
from large drift distances
Biased cluster shapes
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
Double trap effective model
6
n-type silicon
p-type silicon
Current density
Charge carrierconcentration
Space chargedensity
Electric field
One acceptor and one donor levelin the silicon mid-gap can effectively parameterize
radiation damage
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
PIXELAV: a sensor simulationElectrostatic simulation based on TCAD plus charge creation, drift and signal induction based on custom program PIXELAV.Incorporates double-trap effective model of radiation damage. Trap parameters (concentration, cross sections) extracted from a fit to test beam data.The model describes measured cluster shapes in a fluence range relevant at LHC Feq=(0.5-6)x1014 n/cm2 and various bias voltages.The simulation is used to extract average cluster shapes, called templates
7
m)µPosition (0 500 1000
Cha
rge
(Arb
itrar
y un
its)
0
0.5
1
1.5
2
2.5
3=200 VbiasV
m)µPosition (0 500 1000
Cha
rge
(Arb
itrar
y un
its)
0
0.5
1
1.5
2
2.5
3=150 VbiasV
(a) (b)
m)µPosition (0 500 1000
Cha
rge
(Arb
itrar
y un
its)
0
0.5
1
1.5
2
2.5
3
3.5
4
=450 VbiasV
m)µPosition (0 500 1000
Cha
rge
(Arb
itrar
y un
its)
0
0.5
1
1.5
2
2.5
3
3.5=300 VbiasV
(c) (d)
where: T is the sensor thickness, and pitchyF/L are the pitches of the first and last pixels in the y-projection.
These expressions are valid when the cluster contains the double-size pixels that are present at the edges of the
readout chips. The use of the average pitch sizes to approximate W ye! makes it insensitive to the track direction
and appropriate for the first pass of a two pass hit reconstruction algorithm without sacrificing much resolution.
Problems do arise, however, when equations 2 and 3 are used to reconstruct hits in a radiation damaged detector.
After an exposure of 6 ! 1014 neq/cm2, the residual distributions develop biases of 30-50 µm and the resolutions
are significantly worsened. To overcome these difficulties, a new technique that uses a priori information to fit
the entire projected cluster shapes was developed. It is based upon a detailed simulation that was developed to
interpret several beam test measurements. The following sections describe the simulation and the new simulation-
based reconstruction technique.
5 Pixelav Simulation
The detailed sensor simulation, Pixelav [4], incorporates the following elements: an accurate model of charge
deposition by primary hadronic tracks (in particular to model delta rays) [8]; a realistic electric field map resulting
from the simultaneous solution of Poisson’s Equation, carrier continuity equations, and various charge transport
models; an established model of charge drift physics including mobilities, Hall Effect, and 3-D diffusion; a simula-
tion of charge trapping and the signal induced from trapped charge; and a simulation of electronic noise, response,
and threshold effects.
Several of the Pixelav details described in [4] have changed since they were published. The commercial semicon-
ductor simulation code now used to generate a full three dimensional electric field map is the ISE TCAD package
[9]. The charge transport simulation originally integrated the position and velocity equations which required very
small step sizes to maintain stability. It was modified to integrate only the position equation by using the fully-
saturated drift velocity,
d!r
dt=
µ!
q !E + µrH!E ! !B + qµ2r2
H( !E · !B) !B"
1 + µ2r2H | !B|2
(6)
where µ( !E) is the mobility, q = ±1 is the sign of the charge carrier, !E is the electric field, !B is the magnetic field,
and rH is the Hall factor of the carrier. The use of the fully-saturated drift velocity permits much larger integration
steps and significantly increases the speed of the code. A final speed enhancement results from the implementation
of adaptive step sizing in the Runge-Kutta integrations using the Cash-Karp embedded 5th-order technique [10].
Pixelav was developed to use the vector (SIMD) processing on the PowerPC G4 and G5 families of processors. A
port to the less capable Intel SSE architecture has recently been performed. Early testing indicates that the speed
of the ported code running on a 2.8 GHz Xeon is approximately 50% of the speed achieved on a 2.5 GHz G5
processor.
The simulation was originally written to interpret beam test data from several unirradiated and irradiated sensors. It
was extremely successful in this task, demonstrating that simple type inversion is unable to describe the measured
charge collection profiles in irradiated sensors and yielding unambiguous observations of doubly-peaked electric
fields in those same sensors [11]. In these studies, charge collection across the sensor bulk was measured using
the “grazing angle technique” [12]. As is shown in Fig. 5, the surface of the test sensor was oriented by a small
angle (15!) with respect to the pion beam. Several samples of data were collected with zero magnetic field and
at temperature of "10!C. The charge measured by each pixel along the y direction sampled a different depth zin the sensor. Precise entry point information from the beam telescope was used to produce finely binned charge
collection profiles.
Readout chip
track
15o
z axis
y axis
p+ sensor backplane
n+ pixel implant
Bump bond
Collected charge
High electric fieldLow electric field
Figure 5: The grazing angle technique for determining charge collection profiles. The charge measured by each
pixel along the y direction samples a different depth z in the sensor.
The charge collection profiles for a sensor irradiated to a fluence of ! = 5.9 ! 1014 neq/cm2 and operated at bias
4
Full line: PIXELAV simulationFull dots: test beam measurements
V.Chiochia, M.Swartz et al.
Nucl.Instrum.Meth.A565, p.212-220,2006Nucl.Instrum.Meth.A568, p.51-55,2006
IEEE Trans.Nucl.Sci.52, p.1067-1075,2005
Sensor irradiation: Feq=6x1014 n/cm2
Grazing angle technique
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
Hit position reconstructionStandard charge interpolation used for track seeding and pattern recognition for best timing performance“Template based” hit reconstruction used for final track fit
Interpolates measured and expected cluster shape at a given angleReturns fit x2 probability, in addition to hit positionOverall timing performance of tracking code is still acceptable
8
r-f coordinate z coordinate
M.Swartz, D.Fehling, G,Giurgiu, P.Maksimovic, V.Chiochia
CERN-CMS-NOTE-2007-033
low charge bin high charge bin
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
Performance after irradiation
After irradiation the position resolution can be recovered with the template technique:Transverse coordinate: between 10% and 70% improvementLongitudinal coordinate: up to 60% improvementLargest improvement at high rapidities
9
(b)(a)
After first four years of operation at the innermost layer
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
Performances in physics events
Template-based hit reconstruction improves pulls of d0 and f0 parameters even for unirradiated sensorsBetter control over distribution tails (30-50% improvement on RMS)
10
pullPtEntries 14612Mean 0.347RMS 1.314
/ ndf 2! 213.3 / 71Constant 0.00070! 0.06513 Mean 0.0101! 0.3644 Sigma 0.008! 1.207
-10 -8 -6 -4 -2 0 2 4 6 8 10
-410
-310
-210
-110
pullPtEntries 14612Mean 0.347RMS 1.314
/ ndf 2! 213.3 / 71Constant 0.00070! 0.06513 Mean 0.0101! 0.3644 Sigma 0.008! 1.207
pullPtEntries 14612Mean 0.3473RMS 1.264
/ ndf 2! 160.1 / 68Constant 0.00071! 0.06633 Mean 0.0099! 0.3606 Sigma 0.01! 1.19
pullPtEntries 14612Mean 0.3473RMS 1.264
/ ndf 2! 160.1 / 68Constant 0.00071! 0.06633 Mean 0.0099! 0.3606 Sigma 0.01! 1.19
tpull of p
pullQoverpEntries 14612Mean 0.3694RMS 1.36
/ ndf 2! 230.1 / 81Constant 0.00070! 0.06439 Mean 0.0102! 0.3722 Sigma 0.01! 1.22
-25 -20 -15 -10 -5 0 5 10 15 20 25
-410
-310
-210
-110pullQoverp
Entries 14612Mean 0.3694RMS 1.36
/ ndf 2! 230.1 / 81Constant 0.00070! 0.06439 Mean 0.0102! 0.3722 Sigma 0.01! 1.22
pullQoverpEntries 14612Mean 0.3704RMS 1.314
/ ndf 2! 175.9 / 73Constant 0.00068! 0.06564 Mean 0.0100! 0.3708 Sigma 0.007! 1.201
pullQoverpEntries 14612Mean 0.3704RMS 1.314
/ ndf 2! 175.9 / 73Constant 0.00068! 0.06564 Mean 0.0100! 0.3708 Sigma 0.007! 1.201
pull of qoverp parameter
pullPhi0Entries 14612Mean 0.1835RMS 1.536
/ ndf 2! 407.4 / 128Constant 0.00080! 0.07174 Mean 0.009! 0.175 Sigma 0.008! 1.081
-25 -20 -15 -10 -5 0 5 10 15 20 25
-410
-310
-210
-110pullPhi0
Entries 14612Mean 0.1835RMS 1.536
/ ndf 2! 407.4 / 128Constant 0.00080! 0.07174 Mean 0.009! 0.175 Sigma 0.008! 1.081
pullPhi0Entries 14612Mean 0.1838RMS 1.16
/ ndf 2! 185 / 70Constant 0.00079! 0.07489 Mean 0.0088! 0.1738 Sigma 0.007! 1.052
pullPhi0Entries 14612Mean 0.1838RMS 1.16
/ ndf 2! 185 / 70Constant 0.00079! 0.07489 Mean 0.0088! 0.1738 Sigma 0.007! 1.052
0 parameter"pull of
pullD0Entries 14612Mean 0.1581RMS 1.805
/ ndf 2! 480.3 / 159Constant 0.00077! 0.07101 Mean 0.0091! 0.1331 Sigma 0.007! 1.087
-25 -20 -15 -10 -5 0 5 10 15 20 25
-410
-310
-210
-110
pullD0Entries 14612Mean 0.1581RMS 1.805
/ ndf 2! 480.3 / 159Constant 0.00077! 0.07101 Mean 0.0091! 0.1331 Sigma 0.007! 1.087
pullD0Entries 14612Mean 0.1357RMS 1.162
/ ndf 2! 218 / 74Constant 0.00082! 0.07429 Mean 0.0088! 0.1285 Sigma 0.008! 1.058
pullD0Entries 14612Mean 0.1357RMS 1.162
/ ndf 2! 218 / 74Constant 0.00082! 0.07429 Mean 0.0088! 0.1285 Sigma 0.008! 1.058
pull of d0 parameter
pullDzEntries 14612Mean 0.005677RMS 1.649
/ ndf 2! 496.8 / 135Constant 0.00077! 0.06856 Mean 0.009483! -0.001908 Sigma 0.008! 1.124
-25 -20 -15 -10 -5 0 5 10 15 20 25
-410
-310
-210
-110
pullDzEntries 14612Mean 0.005677RMS 1.649
/ ndf 2! 496.8 / 135Constant 0.00077! 0.06856 Mean 0.009483! -0.001908 Sigma 0.008! 1.124
pullDzEntries 14612Mean 0.006603RMS 1.351
/ ndf 2! 271.5 / 95Constant 0.00080! 0.07338 Mean 0.008889! 0.001615 Sigma 0.008! 1.067
pullDzEntries 14612Mean 0.006603RMS 1.351
/ ndf 2! 271.5 / 95Constant 0.00080! 0.07338 Mean 0.008889! 0.001615 Sigma 0.008! 1.067
pull of dz parameter
pullThetaEntries 14612Mean -0.004284RMS 1.428
/ ndf 2! 424.3 / 100Constant 0.0008! 0.0695 Mean 0.009356! -0.003902 Sigma 0.008! 1.115
-25 -20 -15 -10 -5 0 5 10 15 20 25
-410
-310
-210
-110 pullThetaEntries 14612Mean -0.004284RMS 1.428
/ ndf 2! 424.3 / 100Constant 0.0008! 0.0695 Mean 0.009356! -0.003902 Sigma 0.008! 1.115
pullThetaEntries 14612Mean -0.001831RMS 1.265
/ ndf 2! 255.9 / 89Constant 0.0008! 0.0733 Mean 0.008925! -0.001964 Sigma 0.007! 1.069
pullThetaEntries 14612Mean -0.001831RMS 1.265
/ ndf 2! 255.9 / 89Constant 0.0008! 0.0733 Mean 0.008925! -0.001964 Sigma 0.007! 1.069
parameter"pull of
Standard Reco
Template Reco Only
Template Seeding+Reco
d0
f0
Effect of template reconstruction was studied on b-jetsIn addition to hit reconstruction, templates can be used to reject track seeds incompatible with impact angleObserve improvement in light quark rejection factor of 2-3 w.r.t. standard hit reconstruction!
b-tag efficiency
QCD Pt=80-120 GeV
Prelim
inary
10-2
light
qua
rk e
ffici
ency
10-3
no templates / with templates
Pull
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
Ongoing studies and applicationsQuality flags associated to reconstructed hits:
Secondary clusters from delta rays can be effectively removed with a hit x2 probability cut.The collection of track seeds can be cleaned by requiring cluster shapes compatible with the seed angle. We have proved that track reconstruction timing can be reduced by a factor of two!
Splitting of merged clusters:High dense or very collimated jets can lead to overlapping clusters in the pixel layers. The template fit can be used to ‘split’ clusters (4 resulting combinations for each cluster). Wrong combinations are removed by pattern recognition.
Simulation of irradiated pixel detector for MC production
PIXELAV simulation is too slow for event generation.Our approach: re-weight clusters from standard (unirradiated) simulation using PIXELAV templates
11
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 16164
Mean 2.485e+04
RMS 8329
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17528
Mean 2.632e+04
RMS 9224
h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 17148
Mean 2.944e+04
RMS 1.017e+04
h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17587
Mean 3.51e+04
RMS 1.224e+04
h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500 h_Qb_eta_mup_4
Entries 17823
Mean 4.271e+04
RMS 1.48e+04
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 18011
Mean 5.306e+04
RMS 1.793e+04
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 17199
Mean 6.647e+04
RMS 2.198e+04
h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200 h_Qb_eta_mup_7
Entries 11719
Mean 8.43e+04
RMS 2.826e+04
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 6828
Mean 1.058e+05
RMS 3.365e+04
h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350h_Qb_eta_mup_9
Entries 5621
Mean 1.335e+05
RMS 4.25e+04
!=0.125!=0.375 !=0.625 !=0.875 !=1.125
!=1.375 !=1.625 !=1.875 !=2.125 !=2.375
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_0
Entries 15955
Mean 2.476e+04
RMS 7339
+"
-"
e+
e-
!
other
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
Q (el)0 100 200 300 400
10!0
1000
2000
3000
4000
5000
h_Qb_eta_mup_1
Entries 17240
Mean 2.611e+04
RMS 7715
h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000
4500h_Qb_eta_mup_2
Entries 16685
Mean 2.925e+04
RMS 8531
h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
3500
4000h_Qb_eta_mup_3
Entries 17068
Mean 3.502e+04
RMS 9863
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
3000
h_Qb_eta_mup_4
Entries 16900
Mean 4.282e+04
RMS 1.191e+04
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
Q (el)0 100 200 300 400
10!0
500
1000
1500
2000
2500
h_Qb_eta_mup_5
Entries 16697
Mean 5.362e+04
RMS 1.378e+04
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
1200
1400
1600
1800
2000
h_Qb_eta_mup_6
Entries 15648
Mean 6.703e+04
RMS 1.545e+04
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
Q (el)0 100 200 300 400
10!0
200
400
600
800
1000
h_Qb_eta_mup_7
Entries 10123
Mean 8.623e+04
RMS 1.802e+04
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
Q (el)0 100 200 300 400
10!0
100
200
300
400
500
h_Qb_eta_mup_8
Entries 5728
Mean 1.097e+05
RMS 2.141e+04
h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
Q (el)0 100 200 300 400
10!0
50
100
150
200
250
300
350 h_Qb_eta_mup_9
Entries 4481
Mean 1.416e+05
RMS 2.489e+04
!=0.125 !=0.375 !=0.625 !=0.875 !=1.125
!=1.375 !=1.625 !=1.875 !=2.125 !=2.375
Eve
nts
Eve
nts
103
103
Secondaryinteractions
With P(x2)>10-3 cut
V. Chiochia (Uni. Zürich) – IEEE-NSS 2008 - Dresden, Germany
Summary
Performance of pixel detectors at LHC will evolve with time and irradiation. Ultimate precision for track seeding, vertexing and b-tagging relies on unbiased hit reconstruction in any condition.A detailed physical modeling of detector response after irradiation was used to develop novel hit reconstruction techniquesAverage cluster shapes (‘templates’) can be used to interpolate measured clusters
Fit provides precise hit position and x2 probability. Large improvements after irradiation but also before irradiation at high rapiditiesThe techniques improves track parameters (e.g. impact parameter) and reduces light quark contamination in b-tagged jetsThe hit probability is used as cleaning cut for delta rays and to reduce the number of track seeds
Ongoing studies:Splitting of merged clusters in dense jetsSimulation of irradiated response for MC event production
12
