The Performance plots for Phase 1 Pixel Detector October 2018

Plots and Results

Cluster Properties

  • On-track cluster charge and sizes are the important quantities for the assessment and monitoring of the detector conditions.
  • Clusters are required to be attached to the tracks with pT > 1 GeV.
  • Cluster charges are normalised by the track impact angle to the sensor
    • Normalised on-track cluster charge distributions are fitted by the Landau function convoluted with Gaussian.
  • The cluster size in local x direction is determined mostly by the Lorentz angle sharing (BPix) and the geometry of the detector (FPix).
  • The cluster size in local y direction is determined by the geometry of the detector and in FPix also by the Lorentz angle sharing
    • BPix: The layers at smaller radius see more shallow tracks → longer cluster along y;
    • FPix: Disk 3 sees smaller η range at large η → track impact angle closer to perpendicular → smallest cluster along y.

The Barrel Pixel Detector
ch1D InstLumi barrel 6to8 Run 317626.png
Normalised on-track cluster charge for different layers of the Barrel Pixel detector.
sX1D InstLumi barrel 6to8 Run 317626.png
Cluster size in local x direction for different layers of the Barrel Pixel detector.
sY1D InstLumi barrel 6to8 Run 317626.png
Cluster size in local y direction for different layers of the Barrel Pixel detector.

The Forward Pixel Detector
ch1D InstLumi disk 6to8 Run 317626.png
Normalised on-track cluster charge for different disks of the Forward Pixel detector.
sX1D InstLumi disk 6to8 Run 317626.png
Cluster size in local x direction for different disks of the Forward Pixel detector.
sY1D InstLumi disk 6to8 Run 317626.png
Cluster size in local y direction for different disks of the Forward Pixel detector.
Expected ordering in cluster sizes is observed:
  • In local y: cluster sizes decreases when going from Layer 1 to Layer 4 and from Disk 1 to Disk 3;
  • In local x: cluster sizes are similar, as expected from similar charge-sharing effects
    • In Layer 1 smaller size measured due to the higher thresholds.

Bias Voltage Scans

The Bias Voltage Scans are performed multiple times a year to monitor the evolution of the silicon bulk due to the radiation

  • The applied bias voltage is increased from zero to the operational/higher voltage and the changes in the performances (hit efficiency and cluster charge) are followed
    • Hit efficiency is the probability to find any cluster within 1mm around an expected hit independent of the cluster quality (less affected by charge collection efficiency).
  • The scans can be done
    • On a full layers/disk (performed in the special runs) - full scan,
    • On a few non-overlapping modules during the data taking (negligible effect on the data quality) - mini scan.

Full Bias Voltage Scans - Layer 1

Two full bias voltage scans were performed in 2018 at the integrated luminosity of 0.5 pb-1 and 31.5 fb-1

  • The results for six modules in BPix Layer 1 replaced during 2017 YETS (referred as new modules) are shown separately;
  • The effect of radiation damage is visible in the shift of the plateau between two scans;
  • Hit efficiency is maximised at lower bias voltage, a very slow increase with increasing bias voltage is observed.
  • Operation voltage in Layer 1 at the startup was 400 V. After second scan increased to the highest safe value of 450 V;
  • Cluster size along local y increases until charge collection efficiency is maximised
  • Cluster size along local x eventually decreases due to decrease of the Lorentz Angle.

Layer 1
AvgNormOnTrkCluCharge vs BiasVoltage HVBiasScans L1.png
Average on-track cluster charge as a function of the bias voltage for Layer 1 of the Barrel Pixel detector.
HitEfficiency vs BiasVoltage HVBiasScans L1.png
Hit Efficiency as a function of the bias voltage for Layer 1 of the Barrel Pixel detector

Layer 1
AvgOnTrkCluSize vs BiasVoltage HVBiasScans L1.png
Average on-track cluster size as a function of the bias voltage for Layer 1 of the Barrel Pixel detector.
AvgOnTrkCluSizeX vs BiasVoltage HVBiasScans L1.png
Average on-track cluster size along local x direction as a function of the bias voltage for Layer 1 of the Barrel Pixel detector.
AvgOnTrkCluSizeY vs BiasVoltage HVBiasScans L1.png
Average on-track cluster size in local y direction as a function of the bias voltage for Layer 1 of the Barrel Pixel detector.

Mini Bias Voltage Scans - Layer 2

A series of mini HV scans were carried out at different integrated luminosities

  • Four modules were selected in Layer 2 to study the time evolution of the silicon bulk due to the radiation;
  • The effect of irradiation can be observed in the shift of the plateau to the higher HV values;
  • Operational voltage at the beginning of 2018 was 250 V. It was increased to 300 V.

Layer 2
AvgNormOnTrkCluCharge vs BiasVoltage HVBiasScans L2OneHVGrp.png
Average on-track cluster charge as a function of the bias voltage for Layer 2 of the Barrel Pixel detector. Different colours correspond to different integrated luminosity.
AvgOnTrkCluSize vs BiasVoltage HVBiasScans L2OneHVGrp.png
Average on-track cluster size as a function of the bias voltage for Layer 2 of the BarrelPixel detector. Different colours correspond to different integrated luminosity.
Layer 2
AvgOnTrkCluSizeX vs BiasVoltage HVBiasScans L2OneHVGrp.png
Average on-track cluster size along local x direction as a function of the bias voltage for Layer 2 of the Barrel Pixel detector. Different colours correspond to different integrated luminosity.
AvgOnTrkCluSizeY vs BiasVoltage HVBiasScans L2OneHVGrp.png
Average on-track cluster size in local y direction as a function of the bias voltage for Layer 2 of the Barrel Pixel detector. Different colours correspond to different integrated luminosity.

Timing Scan

* The cluster properties and the hit efficiency are studied as a function of the time delay:

    • Efficiency/pixel loss at higher delays affect the center pixels of the clusters, while at lower delays the shoulders;
    • The goal is to set the delay as high as possible to maximise resolution by measuring the shoulders as well as possible, but still safely far from the falling edge when clusters would be split.

Timing scan - Forward Pixel Detector

  • The operational setting is shown by the dashed line.

Forward Pixel Detector
AvgNormOnTrkCluCharge vs Delay Disks 2018Apr17 Scan1and3.png
Average normalised on-track cluster charge as a function of the time delay for different disks of the Forward Pixel detector.
AvgOnTrkCluSize vs Delay Disks 2018Apr17 Scan1and3.png
Average on-track cluster size as a function of the time delay for different disks of the Forward Pixel detector.
HitEfficiency vs Delay Disks 2018Apr17 Scan1and3.png
Hit efficiency as a function of the time delay for different disks of the Forward Pixel detector.

Timing Scan - Barrel Pixel Detector

  • There is ~12 ns time difference between Layer 1 (PROC600 ) and Layer 2 (PSI46dig) chips, but two layers are in the same readout group
  • Only a common timing setting can be chosen;
    • The setting which maximises the efficiency for both layers, indicated by the dashed red line, is chosen
      • Layer 1 is optimised also for the best resolution;
      • For Layer 2 the resolution is suboptimal.
  • The timing of all layers are changed at the same time:
    • If one layer is inefficient, the measurements of the cluster properties are affected by the missing layer and have large systematic uncertainty (intervals indicated by the shaded bands).

Barrel Pixel Detector
AvgNormOnTrkCluCharge vs Delay Layers 2018Apr17 Scan1and3.png
Average normalised on-track cluster charge as a function of the time delay for different layers of the Barrel Pixel detector.
AvgOnTrkCluSize vs Delay Layers 2018Apr17 Scan1and3.png
Average on-track cluster size as a function of the time delay for different layers of the Barrel Pixel detector.
HitEfficiency vs Delay Layers 2018Apr17 Scan1and3.png
Hit efficiency as a function of the time delay for different layers of the Barrel Pixel detector.

Bad Components

The list of the bad components with known problems that are expected to be stable in time is transferred to the DB and used in the reconstruction:

  • Several modules left from 2017 (black line);
  • New bad components in 2018 (red line);
  • The functional ROCs, connected to the broken DCDC converters in 2017, which show the higher level of noise are marked inefficient in reconstruction (purpleline);
  • In one sector in Layer 3 and two sectors in Layer 4 the HV is switched off to protect modules that lost the low voltage, most probably due to the bad connection in supply tube (blue line)

Percentage of the efficient components: 94.3%.

  • Barrel Pixel detector: 93.5 %,
  • Forward Pixel detector: 96.7 %.

RNG1 map.png
Cluster occupancy map for the Ring 1 of the Forward Pixel detector.
RNG2 map.png
Cluster occupancy map for the Ring 2 of the Forward Pixel detector.
LYR1 map.png
Cluster occupancy map for the Layer 1 of the Barrel Pixel detector

LYR2 map.png
Cluster occupancy map for the Layer 2 of the Barrel Pixel detector.
LYR3 map.png
Cluster occupancy map for the Layer 3 of the Barrel Pixel detector.
LYR4 map.png
Cluster occupancy map for the Layer 4 of the Barrel Pixel detector.

Residual measurement

Forward Pixel Detector

Triplet metod:

  • Tracks with pT > 4 GeV and hits in three disks are selected and refitted using hits in disks 1 and 3.
  • Trajectory extrapolated to disk 2, residuals with the actual hit are calculated.
  • Residual distribution fitted with the Student-t function.
  • Positions are reconstructed with two algorithms
    • Generic: a simple algorithm based on track position and angle
      • We use it in our High Level Triggers (HLT) and early track iterations offline;
    • Template: an algorithm based on detailed cluster shape simulations predicted by PixelAv
      • We use it in the final fit of each track in the offline reconstruction;
  • Extraction of the intrinsic resolution
    • The data measurement shows only residuals; in simulation the intrinsic resolution can be measured (difference between simulated and reconstructed hit positions)
      • If simulation reproduces the data, the intrinsic resolution in data can be inferred

  • Good agreement between data and simulation.
  • Similar performance with Template and Generic algorithm

h420f3 234 319450  Template.png
Residuals distribution along local-x direction for Disk2 with Data and template reconstruction.
h420f3 234 1  Template.png
Residuals distribution along local-x direction for Disk2 with simulation and template reconstruction.
h420f3 234 319450  Generic.png
Residuals distribution along local-x direction for Disk2 with Data and generic reconstruction..

h421f3 234 319450  Template.png
Residuals distribution along local-y direction for Disk2 with Data and template reconstruction.
h421f3 234 1  Template.png
Residuals distribution along local-y direction for Disk2 with simulation and template reconstruction.
h421f3 234 319450  Generic.png
Residuals distribution along local-y direction for Disk2 with Data and generic reconstruction..

Barrel Pixel Detector

  • Tracks with pT > 12 GeV, and hits in 3 layers are selected and refitted using hits in two of three layers.
  • Trajectory extrapolated to remaining layer, residuals with the actual hit are calculated.
  • Residual distribution fitted with the Student-t function.
  • Triplets considered:
    • Layer 1: propagate from hits on Layer 2 and 3 (large smearing);
    • Layer 2: propagate from hits on Layer 1and 3;
    • Layer 3: propagate from hits on Layer 2 and 4;
    • Layer 4: propagate from hits on Layer 2 and 3 (large smearing).

  • Positions are reconstructed with two algorithms:
    • Generic: a simple algorithm based on track position and angle
      • We use it in our High Level Triggers (HLT) and early track iterations offline.
    • Template: an algorithm based on detailed cluster shape simulations predicted by PixelAv
      • We use it in the final fit of each track in the offline reconstruction.

  • Extraction of the intrinsic resolution:
    • The data measurement shows only residuals; in simulation the intrinsic resolution can be measured (difference between simulated and reconstructed hit positions)
      • If simulation reproduces the data, the intrinsic resolution in data can be inferred.

  • Good agreement between data and simulation.
  • The Template algorithm has better performance

Layer 1
h520 319450 Template CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer1 with Data and template reconstruction.
h520 1 Template CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer1 with simulation and template reconstruction.
h520 319450 Generic CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer1 with Data and generic reconstruction..
h521 319450 Template CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer1 with Data and template reconstruction.
h521 1 Template CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer1 with simulation and template reconstruction.
h521 319450 Generic CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer1 with Data and generic reconstruction..

Layer 2
h420 319450 Template CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer2 with Data and template reconstruction.
h420 1 Template CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer2 with simulation and template reconstruction.
h420 319450 Generic CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer2 with Data and generic reconstruction..
h421 319450 Template CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer2 with Data and template reconstruction.
h421 1 Template CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer2 with simulation and template reconstruction.
h421 319450 Generic CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer2 with Data and generic reconstruction..

Layer 3
hg420 319450 Template CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer3 with Data and template reconstruction.
hg420 1 Template CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer3 with simulation and template reconstruction.
hg420 319450 Generic CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer3 with Data and generic reconstruction..
hg421 319450 Template CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer3 with Data and template reconstruction.
hg421 1 Template CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer3 with simulation and template reconstruction.
hg421 319450 Generic CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer3 with Data and generic reconstruction..

Layer 4
g520 319450 Template CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer4 with Data and template reconstruction.
g520 1 Template CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer4 with simulation and template reconstruction.
g520 319450 Generic CMSSW 10 2 0.png
Residuals distribution along local-x direction for Layer4 with Data and generic reconstruction..
g521 319450 Template CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer4 with Data and template reconstruction.
g521 1 Template CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer4 with simulation and template reconstruction.
g521 319450 Generic CMSSW 10 2 0.png
Residuals distribution along local-y direction for Layer4 with Data and generic reconstruction..

-- TanjaSusa - 2018-10-08

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PNGpng h521_1_Template_CMSSW_10_2_0.png r1 manage 30.4 K 2018-10-19 - 15:01 DanyylBrzhechko Residual distributions for Layer1
PDFpdf h521_319450_Generic_CMSSW_10_2_0.pdf r1 manage 138.9 K 2018-10-19 - 15:01 DanyylBrzhechko Residual distributions for Layer1
PNGpng h521_319450_Generic_CMSSW_10_2_0.png r1 manage 31.7 K 2018-10-19 - 15:01 DanyylBrzhechko Residual distributions for Layer1
PDFpdf h521_319450_Template_CMSSW_10_2_0.pdf r1 manage 139.3 K 2018-10-19 - 15:01 DanyylBrzhechko Residual distributions for Layer1
PNGpng h521_319450_Template_CMSSW_10_2_0.png r1 manage 31.4 K 2018-10-19 - 15:01 DanyylBrzhechko Residual distributions for Layer1
PDFpdf hg420_1_Template_CMSSW_10_2_0.pdf r1 manage 129.7 K 2018-10-19 - 15:04 DanyylBrzhechko Residual distributions for Layer3
PNGpng hg420_1_Template_CMSSW_10_2_0.png r1 manage 31.9 K 2018-10-19 - 15:04 DanyylBrzhechko Residual distributions for Layer3
PDFpdf hg420_319450_Generic_CMSSW_10_2_0.pdf r1 manage 133.4 K 2018-10-19 - 15:04 DanyylBrzhechko Residual distributions for Layer3
PNGpng hg420_319450_Generic_CMSSW_10_2_0.png r1 manage 32.2 K 2018-10-19 - 15:04 DanyylBrzhechko Residual distributions for Layer3
PDFpdf hg420_319450_Template_CMSSW_10_2_0.pdf r1 manage 131.0 K 2018-10-19 - 15:04 DanyylBrzhechko Residual distributions for Layer3
PNGpng hg420_319450_Template_CMSSW_10_2_0.png r1 manage 30.5 K 2018-10-19 - 15:04 DanyylBrzhechko Residual distributions for Layer3
PDFpdf hg421_1_Template_CMSSW_10_2_0.pdf r1 manage 131.0 K 2018-10-19 - 15:05 DanyylBrzhechko Residual distributions for Layer3
PNGpng hg421_1_Template_CMSSW_10_2_0.png r1 manage 30.3 K 2018-10-19 - 15:05 DanyylBrzhechko Residual distributions for Layer3
PDFpdf hg421_319450_Generic_CMSSW_10_2_0.pdf r1 manage 132.8 K 2018-10-19 - 15:05 DanyylBrzhechko Residual distributions for Layer3
PNGpng hg421_319450_Generic_CMSSW_10_2_0.png r1 manage 31.8 K 2018-10-19 - 15:05 DanyylBrzhechko Residual distributions for Layer3
PDFpdf hg421_319450_Template_CMSSW_10_2_0.pdf r1 manage 130.3 K 2018-10-19 - 15:05 DanyylBrzhechko Residual distributions for Layer3
PNGpng hg421_319450_Template_CMSSW_10_2_0.png r1 manage 31.9 K 2018-10-19 - 15:05 DanyylBrzhechko Residual distributions for Layer3
PDFpdf sX1D_InstLumi_barrel_6to8_Run_317626.pdf r1 manage 14.3 K 2018-10-18 - 21:35 SadiaKhalil Cluster sizeX in barrel for Run 317626
PNGpng sX1D_InstLumi_barrel_6to8_Run_317626.png r1 manage 31.9 K 2018-10-18 - 21:35 SadiaKhalil Cluster sizeX in barrel for Run 317626
PDFpdf sX1D_InstLumi_disk_6to8_Run_317626.pdf r1 manage 14.3 K 2018-10-18 - 21:35 SadiaKhalil Cluster sizeX in endcap for Run 317626
PNGpng sX1D_InstLumi_disk_6to8_Run_317626.png r1 manage 30.8 K 2018-10-18 - 21:35 SadiaKhalil Cluster sizeX in endcap for Run 317626
PDFpdf sY1D_InstLumi_barrel_6to8_Run_317626.pdf r1 manage 14.7 K 2018-10-18 - 21:35 SadiaKhalil Cluster sizeY in barrel for Run 317626
PNGpng sY1D_InstLumi_barrel_6to8_Run_317626.png r1 manage 33.5 K 2018-10-18 - 21:35 SadiaKhalil Cluster sizeY in barrel for Run 317626
PDFpdf sY1D_InstLumi_disk_6to8_Run_317626.pdf r1 manage 14.3 K 2018-10-18 - 21:59 SadiaKhalil Cluster sizeY in endcap for Run 317626
PNGpng sY1D_InstLumi_disk_6to8_Run_317626.png r1 manage 30.4 K 2018-10-18 - 21:59 SadiaKhalil Cluster sizeY in endcap for Run 317626
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Topic revision: r23 - 2018-11-08 - TanjaSusa
 
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