This page contains plots related to the HighGranularity Timing Detector project part of the ATLAS PhaseII upgrade, to be used by ATLAS speakers at conferences and similar events.
Please do not add figures on your own. Contact the responsible HGTD project leader in case of questions and/or suggestions.
Module overlap scheme: Schematic drawing showing the overlap between the modules on the front and back of one cooling disk of the HGTD. The sensors overlap 20% at r > 320 mm, and 80% for r < 320 mm. 
pdf, png 
Material distributions: Material distributions for the HGTD as a function of pseudorapidity , expressed in (a) radiation lengths and (b) nuclear interaction lengths . The material is broken down into various components of the HGTD. The moderator is situated completely behind the active detector but included here as it is located within the hermetic vessel of the HGTD. 
pdf, png 
Material distributions: Material distributions for the HGTD as a function of pseudorapidity , expressed in (a) radiation lengths and (b) nuclear interaction lengths . The material is broken down into various components of the HGTD. The moderator is situated completely behind the active detector but included here as it is located within the hermetic vessel of the HGTD. 
pdf, png 
Main HGTD design parameters 
pdf, png 
Event display: Visualization of a simulated QCD dijet event showing HGTD hits and trajectories of charged particles. An angular slice has been removed, and volumes representing some ITk services and all services and supports of the HGTD are also removed to expose the individual modules. 
pdf, png 
Module placement: The layout of individual HGTD modules is shown for the first cooling disk for (a) one quadrant without any rotation, and for (b) the full disk with the 15 degree rotation. The modules are laid out in the same way for the second cooling disk (not shown) which is rotated in the opposite direction to avoid noninstrumented gaps from overlapping for both disks. 
pdf, png 
Module placement: The layout of individual HGTD modules is shown for the first cooling disk for (a) one quadrant without any rotation, and for (b) the full disk with the 15 degree rotation. The modules are laid out in the same way for the second cooling disk (not shown) which is rotated in the opposite direction to avoid noninstrumented gaps from overlapping for both disks. 
pdf, png 
Timing resolution: The expected HGTD timing resolution (a) per hit and (b) per track as function of radius and $\eta$ after different amounts of delivered integrated luminosity at the HLLHC. The different curves show how the sensor timing resolution deteriorates due to radiation exposure. The scenarios shown here include a planned replacement of the modules at $R < 320$~mm after half of the HLLHC program. The intrinsic timing resolution of the sensors and the contribution from the readout electronics are both considered and are added in quadrature. 
pdf, png 
Timing resolution: The expected HGTD timing resolution (a) per hit and (b) per track as function of radius and $\eta$ after different amounts of delivered integrated luminosity at the HLLHC. The different curves show how the sensor timing resolution deteriorates due to radiation exposure. The scenarios shown here include a planned replacement of the modules at $R < 320$~mm after half of the HLLHC program. The intrinsic timing resolution of the sensors and the contribution from the readout electronics are both considered and are added in quadrature. 
pdf, png 
Occupancy: Hit occupancy as a function of the radius for a pixel size of 1.3 x 1.3 mm2 at a pileup of 200. 
pdf, png 
Number of hits per track: The average number of hits as a function of the position in xy plane. The overlap between the active areas of the modules on the front and back of the cooling plates is 80% at r < 320 mm and 20% at larger radii.. 
png 
HGTD hit time distribution, before (red) and after (blue) the reference time, t_{0} , calibration procedure. The calibration constant is calculated every 1 ms from the mean of the smeared hit times of a grid of 15 by 15 sensors corresponding to one ASIC. The nominal hit time distribution is obtained from a Geant 4 simulation of the ATLAS Detector which includes the time resolution of the sensor and the time dispersion of the LHC collision. NonGaussian tails arise from late particles, backscatter, and other effects. Additional hit time smearing is applied to model the effects of clock jitter and time dispersion arising in the ASIC, flex cable, lpGBT, and FELIX. The expected systematic LHC RF variation time is added as an additional effect. Finally, a sinusoidally varying 100 ps offset of 20 ms period is added to model sources of time jitter that might arise from heat cycles or other effects. 
pdf 
Hit time resolution, t_{smear}  t_{reco} after the t_{0} calibration procedure as a function of the variation period, and for several different choices of calibration window time, shown for R=150 mm. t_{reco} is the hit time taken from simulation and includes inherent hit time resolution effects from the sensor and electronics and the collision time spread. The t_{smear} term adds additional sources of time jitter from the ASIC, FELIX, flex cable, lpGBT, and ATLAS collision time drift, with an additional sinusoidally varying 100 ps offset of variable period. If no calibration is applied the time jitter is approximately 70ps and is shown as the dashed line. For a variation period of greater than 10 ms, and with the right choice of calibration window size, the calibration procedure will always improve the t_{0} precision. 
pdf 
Hit time resolution, t_{smear}  t_{reco} after the t_{0} calibration procedure as a function of the variation period, and for several different choices of calibration window time, shown for R=350 mm. t_{reco} is the hit time taken from simulation and includes inherent hit time resolution effects from the sensor and electronics and the collision time spread. The t_{smear} term adds additional sources of time jitter from the ASIC, FELIX, flex cable, lpGBT, and ATLAS collision time drift, with an additional sinusoidally varying 100 ps offset of variable period. If no calibration is applied the time jitter is approximately 70ps and is shown as the dashed line. For a variation period of greater than 10 ms, and with the right choice of calibration window size, the calibration procedure will always improve the t_{0} precision. 
pdf 
LGAD sensors of different vendors, geometries and types have been studied by HGTD institutes, including:
Microscope photo of an HPK3.150 15x15 array (partial view). 
jpg 
IV measurement of 25 pads from an unirradiated HPK3.150 5x5 array without UBM measured with a 5x5 probe card at room temperature (all pads and GR grounded).  pdf 
Breakdown voltage 2D map of 15x15 array: 2D map of breakdown voltage of an HPK3.150 15x15 array (~2x2 cm2 large sensor) measured with an automatic probe station (i.e. scanning of each pad one after another).  pdf 
Time resolution vs. gain for two irradiated HPK LGADs of 50 and 30 um thicknesses with time walk correction applied.  pdf 
Collected charge as a function of bias voltage for different fluences for HPK3.150 sensors. Solid markers indicate n irradiation (n), open markers 70 MeV p irradiation at CYRIC (pCy). Measurements were performed at 30 C. 
pdf 
Collected charge as a function of bias voltage for different fluences for HPK3.250 sensors after n irradiation (n). Measurements were performed at 30 C. 
pdf 
Collected charge as a function of bias voltage for different fluences for FBKUFSD3C60 sensors after n irradiation (n). Measurements were performed at 30 C. 
pdf 
Collected charge as a function of bias voltage for different fluences for HPKProto30 sensors after n irradiation (n). Measurements were performed at 20 C and 27 C. 
pdf 
Time resolution as a function of bias voltage for different fluences for HPK3.150 sensors. Solid markers indicate n irradiation (n), open markers 70 MeV p irradiation at CYRIC (pCy). Measurements were performed at 30 C. 
pdf 
Time resolution as a function of bias voltage for different fluences for HPK3.250 sensors after n irradiation (n). Measurements were performed at 30 C. 
pdf 
Time resolution as a function of bias voltage for different fluences for FBKUFSD3C60 sensors after n irradiation (n). Measurements were performed at 30 C. 
pdf 
Time resolution as a function of bias voltage for different fluences for HPKProto30 sensors after n irradiation (n). Measurements were performed at 20 C and 27 C. 
pdf 
InterPad distances for several HPK3.150 sensors measured with a laser TCT system  pdf 
Hit efficiency as a function of collected charge. The curve includes the data of 16 individual sensors before and after irradiation, which all show a universal behaviour. The threshold to accept events with a hit was chosen at a measured noise occupancy of 0.1% and 0.01%, respectively. A hit efficiency above 99% is obtained for a charge larger than 2 fC.  pdf 
*The charge at Vmax and 95% of Vmax as a function of fluence for the different sensor types.*  pdf 
Leakage current for single pads at 30 C as a function of bias voltage for HPK3.150 irradiated with 1 MeV neutrons (solid lines) and 70 MeV protons (dashed lines). The dasheddotted horizontal line represents the ALTIROC maximum acceptable current of 5 uA.  pdf 
Collected charge vs bias voltage for sensors irradiated to 3E15 Neq cm2 and 6E15 Neq cm2, respectively. In the plots are measured data of the existing prototypes and the simulated prospect of the proposed sensors combining deep implantation of the Boron gain layer with carbon implantation.  pdf 
Collected charge vs bias voltage for sensors irradiated to 3E15 Neq cm2 and 6E15 Neq cm2, respectively. In the plots are measured data of the existing prototypes and the simulated prospect of the proposed sensors combining deep implantation of the Boron gain layer with carbon implantation.  pdf 
Time of arrival in a channel of an unirradiated 2x2 LGAD array bumpbonded on an ALTIROC0 ASIC as a function of the amplitude of the preamplifier probe. The profile of the 2D distribution (black points) and a polynomial fit (red line) are superimposed. The fit is used to correct for the time walk effect.   pdf 
Time resolution of a channel of an unirradiated 2x2 LGAD array bumpbonded on an ALTIROC0 ASIC as a function of the discriminator threshold (in DAC units) before and after time walk correction. A SiPM with a resolution of 40 ps is used as a time reference  it's contribution has been substracted. The amplitude of the preamplifier probe is used to correct for the time walk, resulting in a 30% improvement in the time resolution.   pdf 
*Jitter measured as a function of the injected charge for a Cd = 3.5 pF.. 

LGAD (Low Gain Avalanche Diode) sensors have been exposed to 120 GeV charged pions at CERN SPS H6 beam line in September 2017.
border=1 cellpadding=10 cellspacing=10>
Signal efficiency in an unirradiated array of four LGAD sensors of 1.1 x 1.1 mm^{2} each, as a function of the X and Y coordinates (in mm). The voltage threshold to select the signal is 3 times larger than the noise (~5mV). The efficiency in the bulk is larger than 99.8%.  eps  pdf 
Signal efficiency in an irradiated array of four LGAD sensors of 1.1 x 1.1 mm^{2} each, as a function of the X and Y coordinates (in mm). The voltage threshold to select the signal is 3 times larger than the noise (~5mV). The bottom right pad is not displayed due to a broken channel in the readout board. ). The efficiency in the bulk is larger than 99.8%.  eps  pdf 
Signal amplitude in the bulk of LGAD pads of size 1.1 x 1.1 mm^{2} in an array sensor. The dashed line shows the default threshold corresponding to 3 times the noise.  eps  pdf 
Signal efficiency in the bulk of LGAD pads of size 1.1 x 1.1 mm^{2} in an array sensor as a function of the voltage threshold. The dashed line shows the default threshold corresponding to 3 times the noise.  eps  pdf 
Signal efficiency in the interpad region for an unirradiated array of four LGAD sensors of 1.1 x 1.1 mm^{2} each, as a function of X (in mm) for 3 different voltage thresholds.  eps  pdf 
Signal efficiency in the interpad region for an irradiated array of four LGAD sensors of 1.1 x 1.1 mm^{2} each, as a function of X (in mm) for 3 different voltage thresholds.  eps  pdf 
Time resolution in an unirradiated array of four LGAD sensors of 1.1 x 1.1 mm^{2} each, as a function of the X and Y coordinates (in mm). The bottom left pad is not displayed because this channel was not plugged to the same oscilloscope as the quartz+SiPM used as a reference to estimate the time resolution (in this case, more sophisticated analysis technique would be required). The time resolution is larger in the guard rings around the pads where there is no multiplication of the charge. The fluctuations are dominated by statistical fluctuations since very small bins are used in order to show the structure around the pad.  eps  pdf 
Time resolution in an irradiated array of four LGAD sensors of 1.1 x 1.1 mm^{2} each, as a function of the X and Y coordinates (in mm). The bottom right pad is not displayed due to a broken channel in the readout board. The time resolution is larger in the guard rings around the pads where there is no multiplication of the charge.  eps  pdf 
LGAD (Low Gain Avalanche Diode) sensors have been exposed to 120 GeV charged pions at CERN SPS H6B beam line in August 2016 Sensor characteristics: * Two single pad LGAD produced by CNM through RD50 * 1.2 x 1.2 mm^{2} size (C=3.3 pF) * 45 μm thickness Readout : * Board designed and assembled at University of California Santa Cruz: first stage trans impedance preamplifier on printed circuit (R_{f} =470 Ohm) followed by a second stage broadband amplifier (gain 20 dB) * The data were read‐out by a oscilloscope with 40 GSample/s and a 2 GHz bandwidth. The setup was equipped with a 3x3x10 mm^{3} quartz read out by a SiPM to have a reference. Time resolution of reference is about 15‐17 ps
border=1 cellpadding=10 cellspacing=10>
Typical Pulse Shape: Typical pulse shape recorded with a LGAD sensor with 200 V bias voltage with 120 GeV pions. The charge is computed integrating the signal over the yellow area, taking into account the gain of the electronics readout (~95).  
Charge Distribution: Charge distribution for a LGAD biased with 150 V. A Landau convoluted by a Gaussian fit is superimposed.  
Charge as a Function of Bias Voltage: Most probable value of the charge as a function of the bias voltage for two LGAD sensors.  
Gain as a Function of Bias Voltage: Gain as a function of the bias voltage for two LGAD sensors. The gain is computed as the charge divided by 0.46 fC, which is the mean signal deposited by dE/dx in 45 μm of silicon when no amplification mechanism is present.  
Signal to Noise Ratio: Signal to noise ratio as a function of the bias voltage for two LGAD sensors. Signal is measured as the amplitude at the signal peak. The noise is computed as the rms of the baseline and does not take into account the increase of the Landau width due to the multiplication process in the LGAD.  
Time Resolution: Time resolution as a function of the bias voltage for two LGAD sensors. The time of each sensor is extracted at 20 % of the signal amplitude with a linear interpolation between the measurements. The time resolution is extracted from data in which the considered sensor was not used for triggering, by computing the rms of all time differences between the considered sensor, the reference quartz/!SiPM and the trigger sensor. The resulting equation system (under the assumption of uncorrelated resolutions) is solved o obtain the time resolution. of each device.  
Time Resolution: Time resolution as a function of the gain for two LGAD sensors. The time of each sensor is extracted at 20 % of the signal amplitude with a linear interpolation between the measurements. The time resolution is extracted from data in which the considered sensor was not used for triggering, by computing the rms of all time differences between the considered sensor, the reference quartz/!SiPM and the trigger sensor. The resulting equation system (under the assumption of uncorrelated resolutions) is solved o obtain the time resolution. of each device.  
Signal Rise Time: Signal rise time (computed between 20% and 80 % of the signal amplitude) as a function of the bias voltage for two LGAD Sensors of each device. 
Many of these are deprecated as newer versions exist, so please check carefully that they are still valid and relevant before using any of them!
Occupancy: The percentage of readout cells (occupancy) of the HGTD in which the deposited energy is greater than 0.02MeV is shown as function of radius with pileup of mu=200 for readout cell sizes of (1x1)mm^2 and (2x2)mm^2 in full simulation with mu=200. The occupancy for the cell size (1.3x1.3)\mathrm{mm}^2 is an interpolation. 
pdf 
Pileupjet tagging: Average number of pileup tracks in jets associated to the primary vertex as a function of the jet pseudorapidity in VBF Higgs to invisible events with 200 additional interactions before and after a cut on the track time using HGTD. Tracks are required to pass the quality cut requirements described in ATLPHYSPUB2016025, have a transverse momentum larger than 0.9 GeV, and their impact parameter z0 be within 2 sigma of the primary vertex position, where sigma is defined by the ITk z0 impact parameter resolution as a function of eta and pT. In the case of the ITk+HGTD, tracks are required to have a relative time difference with respect to the truth vertex time of 2 sigma of the HGTD time resolution. The time resolution of the tracks is assumed to be 30 ps. Jets, reconstructed from calorimeter topoclusters using the antikt R=0.4 algorithm, are required to have pt> 50 GeV. 
pdf, png 
HGTD Electron Isolation: Efficiency of the leptons isolation as function of the pileup density using the ITk and ITk + HGTD. The efficiency is defined as the probability that no track with p_T> 1GeV other than the signal track is within DeltaR = 0.2 from the electron. In the ITk+HGTD case there is an extra constraint: the time of the tracks must be compatible with the time of the electron track candidate. Only tracks passing a Pt and eta dependent longitudinal impact parameter selection are accepted.


HGTD jets: The pileup jet efficiency versus eta for jets with 30<pT<50GeV for an 88% hardscatter jet efficiency using a pT and eta requirement on the R_pT discriminant, in PowhegPythia ttbar events. The region below eta=2.4 shows the performance gains of a 30ps timing resolution full eta coverage detector, the region above shows the performance the acceptance planned for the HGTD. 

HGTD jets: The pileup jet efficiency versus pT for jets with 2.4<eta<3.8 for an 88% hardscatter jet efficiency using a pT and eta requirement on the R_pT discriminant, in PowhegPythia ttbar events. 

HGTD btagging: Rejection versus efficiency for the MV1 btagger in the HGTD region (eta > 2.4). The rejection for a given efficiency is significantly improved when including the HGTD through the rejection of pileup tracks tagged with timing information as input to the MV1 algorithm. 

HGTD Luminosity : Linearity of the average number of HGTD hits in the regions 2.40 < eta < 3.15 and 2.40 < eta < 2.80, as a function of number of interactions. The light blue stars represent samples where several mu=1 minimumbias events have been overlaid to emulate intermediate numbers of interactions (while treating multiple hits in the same channel as one). A straight line is fitted to these points plus the mu=1 point to model how the number of hits depends on mu. The resulting fit is compared to the centrally generated samples with <mu> ranging between 190 and 210. The pixel size used is 1 mm x 1 mm.  eps  pdf 
HGTD Luminosity : Relative statistical uncertainty as a function of <mu> per BCID when averaging for 1 s, based on the expected number of HGTD in the regions 2.40 < eta < 3.15 and 2.40 < eta < 2.80.  eps  pdf 
ITk: Parameterization of the longitudinal track impact parameter z0 resolution as a function of eta, for different pT values. Tracks are required to pass the quality cut requirements described in ATLPHYSPUB2016025.
 eps  pdf 
HGTD event display: RZ event display showing the reconstructed tracks associated to the reconstructed primary vertex in a VBF Higgs to invisible event with 200 additional interactions. Tracks are required to pass the quality cut requirements described in ATLPHYSPUB2016025, have a transverse momentum larger than 0.9 GeV, and their impact parameter z0 be within 2 sigma of the primary vertex position, where sigma is defined by the ITk z0 impact parameter resolution as a function of eta and pT. The length of the lines is proportional to the track pT. The black rectangles (circles) are the positions of the truth (reconstructed) vertices in the z direction. The red rectangle shows the hardscatter truth vertex position. Red (blue) lines indicate if reconstructed tracks are truthmatched to hardscatter (pileup) vertices. Grey lines correspond to reconstructed tracks associated to other pileup primary vertices in the event.
 eps  pdf 
HGTD event display: Same RZ event display showing the reconstructed tracks associated to the reconstructed primary vertex in a VBF Higgs to invisible event with 200 additional interactions, but only for tracks inside antikt R=0.4 calorimeter jets of pT>20 GeV. Tracks are required to pass the quality cut requirements described in ATLPHYSPUB2016025, have a transverse momentum larger than 0.9 GeV, and their impact parameter z0 be within 2 sigma of the primary vertex position, where sigma is defined by the ITk z0 impact parameter resolution as a function of eta and pT. The length of the lines is proportional to the track pT. The black rectangles (circles) are the positions of the truth (reconstructed) vertices in the z direction. The red rectangle shows the hardscatter truth vertex position. Red (blue) lines indicate if reconstructed tracks are truthmatched to hardscatter (pileup) vertices. Grey lines correspond to reconstructed tracks associated to other pileup primary vertices in the event.
 eps  pdf 
HGTD event display: pTweighted 2dimensional distribution of the time and z position of the reconstructed tracks associated to the hardscatter vertex in a VBF Higgs to invisible event with 200 additional interactions. Tracks are required to pass the quality cut requirements described in ATLPHYSPUB2016025, have a transverse momentum larger than 0.9 GeV, and their impact parameter z0 be within 2 sigma of the primary vertex position, where sigma is defined by the ITk z0 impact parameter resolution as a function of eta and pT. The size of the boxes is proportional to the track pT. The time resolution of the tracks is assumed to be 30 ps.  eps  pdf 
Vertexing: pTweighted distribution of the z0 impact parameter of the reconstructed tracks associated to the hardscatter vertex in a VBF Higgs to invisible event with 200 additional interactions. Tracks are required to pass the quality cut requirements described in ATLPHYSPUB2016025, have a transverse momentum larger than 0.9 GeV, and their impact parameter z0 be within 2 sigma of the primary vertex position, where sigma is defined by the ITk z0 impact parameter resolution as a function of eta and pT.  eps  pdf 
Vertexing: pTweighted distribution of the time of the reconstructed tracks associated to the hardscatter vertex in a VBF Higgs to invisible event with 200 additional interactions. Tracks are required to pass the quality cut requirements described in ATLPHYSPUB2016025, have a transverse momentum larger than 0.9 GeV, and their impact parameter z0 be within 2 sigma of the primary vertex position, where sigma is defined by the ITk z0 impact parameter resolution as a function of eta and pT. The time resolution of the tracks is assumed to be 30 ps. The solid vertical lines shows the truth time of the vertex. The two vertical dotted lines indicate a window of 2 sigma(t) around the vertex time, corresponding to a 95% efficiency for keeping hardscatter tracks.
 eps  pdf 
HGTD jets: Pseudorapidity distribution of tracks in jets associated to the primary vertex in VBF Higgs to invisible events with 200 additional interactions. Tracks are required to pass the quality cut requirements described in ATLPHYSPUB2016025, have a transverse momentum larger than 0.9 GeV, and their impact parameter z0 be within 2 sigma of the primary vertex position, where sigma is defined by the ITk z0 impact parameter resolution as a function of eta and pT. Jets, reconstructed from calorimeter topoclusters using the antikt R=0.4 algorithm, are required to have pt> 50 GeV.  eps  pdf 
HGTD jets: Pseudorapidity distribution of tracks in jets associated to the primary vertex in VBF Higgs to invisible events with 200 additional interactions. Tracks are required to pass the quality cut requirements described in ATLPHYSPUB2016025, have a transverse momentum larger than 0.9 GeV, and their impact parameter z0 be within 2 sigma of the primary vertex position, where sigma is defined by the ITk z0 impact parameter resolution as a function of eta and pT. In addition, tracks are required to have a relative time difference with respect to the truth vertex time of 2 sigma of the HGTD time resolution. The time resolution of the tracks is assumed to be 30 ps. Jets, reconstructed from calorimeter topoclusters using the antikt R=0.4 algorithm, are required to have pt> 50 GeV.  eps  pdf 
border=1 cellpadding=10 cellspacing=10> HGTD track matching resolution: Difference between the extrapolated ITK track position and the nearest hit on the HGTD layer 0 in a sample of single charged pions with transverse momentum of 2 GeV generated from the center of ATLAS. The tracks fulfill the quality requirements in ATLPHYSPUB2016025 and are required to have a transverse momentum larger than 1 GeV. The distribution is fit to the sum of two Gaussian functions with the same mean. The resolution of the narrow component is due to the size of the HGTD cells and to uncertainties in the track reconstruction and extrapolation. Its standard deviation of 0.6 mm is smaller than the cell size of 1 mm. The broad component is due to pions which undergo interactions in the material in front of the HGTD, this fraction varies between 5 and 10% depending on the pion momentum.
HGTD beamspot: The distribution of the beam spot for the true time and z vertex is shown for an ATLAS simulation of the Nominal and CrabKissing scenario. For the CrabKissing scenario a rotation of the bunches with an angle of 50 mrad in the yz plane was used.


HGTD Performance Plot: Track isolation efficiency for electrons from Z → ee decays with pT > 20 GeV and 0 < η < 3.6 as a function of the number of collisions per mm. The vertices are normally distributed along the beam axis and in time with σ_{z} = 50 mm, σ_{t} = 180 ps and average number of interactions per bunch crossing <mu> = 200. The track isolation efficiency epsilon(pTiso) is defined as the probability that no track with pT> 1 GeV other than the signal track is within dR = 0.2 from the electron. The tracks must satisfy requirements on the longitudinal impact parameter given by the ITK (yellow points) and the time with respect to the Z → ee hard scattering measured with the HGTD within 60 ps (red points) or 120 ps (green points) or 180 ps. The blue points correspond to a selection of tracks from the hard scattering process. A fully efficient timetotrack association is assumed.


HGTD Performance Plot: The efficiency for pileup jets as a function of the efficiency for hardscatter jets with 20<pT<40 GeV in 2.4<η<3.8 region using the RpT discriminant, defined in ATLASCONF2014018, in PowhegPythia tt events. The vertices are normally distributed along the beam axis and in time with σ_{z}=50 mm, σ_{t}=180 ps and <mu>=200. The tracks used in the RpT calculation for the black curve fulfil the quality requirements in ATLPHYSPUB2016025 and are required to have a transverse momentum larger than 1 GeV. The distance between the hardscatter vertex and the z0 impact parameter of the tracks used in the RpT calculation is required to be within 1 mm and 4 mm, depending on the η of the track. The blue curve is obtained using for the RpT calculation tracks matched to true charged particles from the hard scatter vertex. For the red curve, reconstructed tracks selected as for the black curve and with a timing consistent within 60 ps with the hardscatter vertex are considered. The time resolution of the tracks is assumed to be 30 ps. The "Inclined Barrel" layout is described in ATLPHYSPUB2016025. Jets are clustered using the antikt algorithm with R=0.4.
 
HGTD Performance Plot: The efficiency for hardscatter jets versus η for jets with 20<pT<50 GeV for a 2% pileup jets rejection efficiency using a pT and η requirement on the RpT discriminant, in PowhegPythia tt events. The vertices are normally distributed along the beam axis and in time with σ_{z}=50 mm, σ_{t}=180 ps with <mu>=200. The tracks used in the RpT calculation for the black curve fulfill the quality requirements in ATLPHYSPUB2016025 and are required to have a transverse momentum larger than 1 GeV. The distance between the hardscatter vertex and the z0 impact parameter of the tracks used in the RpT calculation is required to be within 1 mm and 4 mm, depending on the η of the track. For the red curve, reconstructed tracks selected as for the black curve and with a timing consistent within 60 ps with the hardscatter vertex truth time are considered. The time resolution of the tracks is assumed to be 30 ps. The "Inclined Barrel" layout is described in ATLPHYSPUB2016025. Jets are clustered using the antikt algorithm with R=0.4.
 
HGTD Performance Plot: The efficiency for hardscatter jets versus η for jets with pT>50 GeV for a 2% pileup jets rejection efficiency using a pT and η requirement on the RpT discriminant, in PowhegPythia tt events. The vertices are normally distributed along the beam axis and in time with σ_{z}=50 mm, σ_{t}=180 ps with ⟨Μ⟩=200. The tracks used in the RpT calculation for the black curve fulfill the quality requirements in ATLPHYSPUB2016025 and are required to have a transverse momentum larger than 1 GeV. The distance between the hardscatter vertex and the z0 impact parameter of the tracks used in the RpT calculation is required to be within 1 mm and 4 mm, depending on the η of the track. For the red curve, reconstructed tracks selected as for the black curve and with a timing consistent within 60 ps with the hardscatter vertex truth time are considered. The time resolution of the tracks is assumed to be 30 ps. The "Inclined Barrel" layout is described in ATLPHYSPUB2016025. Jets are clustered using the antikt algorithm with R=0.4.
 
HGTD beamspot: The local pileup vertex density is shown in full simulation for the nominal HLLHC beamspot scenario for μ=30 and μ=200. The density is calculated as the number of truth vertices in a range of + 3mm around the signal vertex divided by the window size (6mm). The simulation was performed using the nominal beam spot, the other two distributions have been computed using the sigma z of the beam spot.


HGTD beamspot: The local pileup vertex density is shown three HLLHC beamspot scenarios in full simulation. Nominal, Run 4 and 200MHz correspond to different definitions of the beamspot in time and z direction. The standard deviation of the beamspot in z direction is given in the legend. The density is calculated as the number of truth vertices in a range of + 3mm around the signal vertex divided by the window size (6mm). The simulation was performed using the nominal beam spot, the other two distributions have been computed using the sigma z of the beam spot.


HGTD time of arrival: The distribution of the time of arrival in the first layer of the HGTDSiW at a radius of R=200mm (η = 3.6, z=3506mm) is shown for an ATLAS simulation of muons with a transverse momentum of 1 TeV. The width of the distribution is the convolution of the beamspot in z and t.


HGTD occupancy: The occupancy of the HGTD is shown as function of the radius for a pileup of <mu>=200 events for granularities of (1x1)mm^{2} ,(2x2)mm^{2} and (3x3)mm^{2} for the last layer. Above a radius of 280mm the tungsten absorber leads to a higher occupancy for the HGTDSiW compared to the HGDTSi. A horizontal line indicates the maximal tolerable occupancy of 10%. Layer 3 corresponds to the layer with the highest occupancy.
 
HGTD Nominal Pulse Shape : Nominal Pulse Shape of the signal in the HGTD based on measure taken during the TestBeam runs with charged pions. The red and blue error bar represent the two levels of noise on the amplitude added in the simulation.


HGTD Time resolution in the simulation : The distribution of the time resolution for (1 × 1)mm2 and (3 × 3)mm2 cells are shown for an ATLAS simulation of single muon signal in the first layer of the HGTD after subtraction of the time of flight and the time offset. The distributions are normalized to unit area. The total noise is the convolution of three sources: amplitude, nonuniform energy deposits in the sensor and residual electronics noise after the time walk correction.


HGTD vertices : Event display showing the time and z position of all vertices in a Zee event from the full simulation with mu=200. Only tracks reconstructed with a transverse momentum greater than 1 GeV are used. The red circle is the truth hardscatter vertex, the pink circles are the truth vertices with no reconstructed track in the HGTD acceptance, green circles those without accepted tracks outside the HGTD acceptance and the blue circles are the truth vertices with at least one track in the HGTD. The dotted lines are the positions of the reconstructed vertices. The error bar on the y axis is the expected precision of the vertex timing determination in the HGTD, in most cases smaller than the symbol size.


HGTD vertices : Event display showing the time and z position of all vertices in a Zee event from the full simulation with µ=200. The red circle is the truth hardscatter vertex, the pink circles are the truth vertices with no reconstruted track in the HGTD acceptance and the blue circles are the truth vertices with at least one track in the HGTD. Only tracks reconstructed with a transverse momentum greater than 1 GeV are used. The dotted lines are the positions of the reconstructed vertices. The error bar on the y axis is the expected precision of the vertex timing determination in the HGTD, in most cases smaller than the symbol size (zoom of the previous figure).


HGTD electron events display : The energy deposited in the HGTDSiW sampling 3 is shown for an ATLAS simulation of an electron with a transverse momentum of 45 GeV and 200 events of pileup.


HGTD electron event display : The energy deposited in the HGTDSiW sampling 3 is shown for an ATLAS simulation of an electron with a transverse momentum of 45 GeV and 200 events of pileup for the 10 most energetic clusters in an intermediate step of the 5 dimensional electron reconstruction (time, energy and threedimensional positions of energy deposits). The clusters are reconstructed in sampling 3 by summing the energy deposited in the HGTD cells in a circle of radius 15mm.


HGTD electron event display : The energy deposited in the HGTDSiW sampling 3 is shown for an ATLAS simulation of an electron with a transverse momentum of 45 GeV and 200 events of pileup after the final step of the 5D (time, energy, threedimensional positions of energy deposite) electron reconstruction. The substructure of the cylindrical clusters is analyzed by reconstructing tracklets starting from a common position in sampling 0. The average number of hits on the tracklets, the ratio of the sum of the energy depositions of the tracklets to the all cluster hits as well as the tracklet timing reconstruction is used to reject the background. The precision of the electron timing is about 10ps.


HGTD Number of cells per electron cluster: The distribution of the number of cells of the HGTDSiW hit in the cluster for each sampling is shown for an ATLAS simulation of electrons with a transverse momentum of 45 GeV. The clusters are cylindrical clusters with a radius of 15 mm computed to be the cylinder in which the energy deposited is maximum. The tungsten starts at a radius of 280mm.


HGTD Number of cells per photon cluster : The distribution of the number of cells of the HGTDSiW hit in the cluster for each sampling is shown for an ATLAS simulation of electrons with a transverse momentum of 45 GeV. The clusters are cylindrical clusters with a radius of 15 mm computed to be the cylinder in which the energy deposited is maximum. The tungsten starts at a radius of 280mm.


HGTD track matching resolution: Difference between the extrapolated ITK track position and the nearest hit on the HGTD layer 0 in a sample of single charged pions with transverse momentum of 2 GeV generated from the center of ATLAS. The tracks fulfill the quality requirements in ATLPHYSPUB2016025 and are required to have a transverse momentum larger than 1 GeV. The distribution is fit to the sum of two Gaussian functions with the same mean. The resolution of the narrow component is due to the size of the HGTD cells and to uncertainties in the track reconstruction and extrapolation. Its standard deviation of 1.3 mm is smaller than the cell size of 3 mm. The broad component is due to pions which undergo interactions in the material in front of the HGTD, this fraction varies between 5 and 10% depending on the pion momentum.
 
HGTD track matching efficiency for mu=0: Efficiency for matching reconstructed ITK tracks with at least one HGTD hit cell in the four HGTD layers in a sample of single charged pions with pT between 1 and 20 GeV generated from the center of ATLAS. The tracks fulfill the quality requirements in ATLPHYSPUB2016025 and are required to have a transverse momentum larger than 1 GeV. Tracks are matched to the nearest cell in each HGTD layer within a 5 mm radius of the extrapolated position in the xy plane. The points are fit by the function ε(pT) shown in the plot.
 
HGTD track matching efficiency for mu=200: Efficiency for matching reconstructed ITK tracks to at least one HGTD hit cell with the correct track truth time in a VBF sample with an average of 200 pileup interactions. The tracks fulfill the quality requirements in ATLPHYSPUB2016025 and are required to have a transverse momentum larger than 1 GeV. Tracks are matched to the nearest cell in each HGTD layer within a 5 mm radius of the extrapolated position in the xy plane. The track truth time is determined from the truth particle associated to the track. The points are fit by the function ε(pT) shown in the plot.
 
HGTD Track timing resolution for mu=200: Difference between the reconstructed track time and the track truth time in a VBF sample with an average of 200 pileup interactions. The tracks fulfill the quality requirements in ATLPHYSPUB2016025 and are required to have a transverse momentum larger than 1 GeV. Tracks are matched to the nearest cell in each HGTD layer within a 5 mm radius of the extrapolated position in the xy plane. The track time is determined by averaging up to four matched HGTD hit cells whose measured time includes a 30 ps Gaussian smearing for the 1 mm square cells with respect to the GEANT simulated hit time. The track truth time is determined from the truth particle associated to the track. The distribution is fit to the sum of two Gaussian functions with the same mean. The standard deviation of the core Gaussian is quoted in the plot, slightly larger than half the cell time resolution as the track extrapolation efficiency and uninstrumented zones in the HGTD are taken into account, leading to less than 4 HGTD time measurements available in some cases. The broad component (dashed line) corresponds to tracks which are incorrectly matched to HGTD hits in the presence of pileup and has a sigma corresponding to the beamspot spread of about 200 ps.
 
HGTD track timing resolution for mu=200: Difference between the reconstructed track time and the track truth time in a VBF sample with an average of 200 pileup interactions. The tracks fulfill the quality requirements in ATLPHYSPUB2016025 and are required to have a transverse momentum larger than 1 GeV. Tracks are matched to the nearest cell in each HGTD layer within a 5 mm radius of the extrapolated position in the xy plane. The track time is determined by averaging up to four matched HGTD hit cells whose measured time includes a 60 ps Gaussian smearing for the 3 mm square cells with respect to the GEANT simulated hit time. The track truth time is determined from the truth particle associated to the track. The distribution is fit to the sum of two Gaussian functions with the same mean. The standard deviation of the core Gaussian is quoted in the plot, slightly larger than half the cell time resolution as the extrapolation efficiency and uninstrumented zones in the HGTD are taken into account, leading to less than 4 HGTD time measurements available in some cases. The broad component (dashed line) corresponds to tracks which are incorrectly matched to HGTD hits in the presence of pileup and has a sigma corresponding to the beamspot spread of about 200 ps.
 
HGTD luminosity measurement : The mean number of HGTD hits in the first layer (both sides) as a function of number of interactions for the full HGTD coverage. The data points in the shaded region have been derived by overlaying hits from multiple minimum bias events. It has been assumed that multiple particles passing through the same HGTD cell can not be resolved. A linear fit has been performed using the data points in the left part of the figure; number of interactions <= 100. The bottom figure shows the ratio between the measured values and the linear prediction, where the uncertainties indicates the relative uncertainties on the measured values.
 
HGTD luminosity measurement : The mean number of HGTD hits in the first layer (both sides) as a function of number of interactions in pseudorapidity range 2.8 < η < 3.0. The data points in the shaded region have been derived by overlaying hits from multiple minimum bias events. It has been assumed that multiple particles passing through the same HGTD cell can not be resolved. A linear fit has been performed using the data points in the left part of the figure; number of interactions <= 100. The bottom figure shows the ratio between the measured values and the linear prediction, where the uncertainties indicates the relative uncertainties on the measured values.

HGTD Performance Plot: The plot shows the performance to reject tracks from pileup interactions within jets using the ITk and the HGTD. The tracks must satisfy requirements on the longitudinal impact parameter given by the ITk with 1 mm (3 mm) resolution for η < 2.4 (η > 2.4). The resolution corresponds to an average of the resolution on the ranges considered. The HGTD provides additional rejection or higher selection efficiency by requiring the time of the tracks to be within 2 x σ_{t} (with σ_{t} = 3060 ps) from the hard scattering vertex. A fully efficient timetotrack association is assumed, as well as a negligible contribution from the determination of the time of the hard scattering vertex. The vertices are normally distributed along the beam axis and in time with σ_{z} = 50 mm, σ_{t} = 180 ps and average number of interactions per bunch crossing <μ> = 200. This plot shows the fraction of pileup tracks associated to forward jets as a function of the pileup vertex density, expressed as the number of collisions per mm, using ITk or ITk+HGTD.  
HGTD Performance Plot: The plot shows the performance to reject tracks from pileup interactions within a lepton isolation cone using the ITk and the HGTD. The tracks must satisfy requirements on the longitudinal impact parameter given by the ITk with 1 mm (3 mm) resolution for η < 2.4 (η > 2.4). The resolution corresponds to an average of the resolution on the ranges considered. The HGTD provides additional rejection or higher selection efficiency by requiring the time of the tracks to be within 2 x σ_{t} (with σ_{t} = 3060 ps) from the hard scattering vertex. A fully efficient timetotrack association is assumed, as well as a negligible contribution from the determination of the time of the hard scattering vertex. The vertices are normally distributed along the beam axis and in time with σ_{z} = 50 mm, σ_{t} = 180 ps and average number of interactions per bunch crossing <μ> = 200. This plot shows the track isolation efficiency for electrons from Z → ee decays with p_{T} > 20 GeV and 2.6 < η < 3.6 as a function of the number of collisions per mm. The track isolation efficiency ε(p_{T}^{iso}) is defined as the probability that no track with p_{T} > 1 GeV other than the signal track is within dR = 0.2 from the electron. The tracks must satisfy requirements on the longitudinal impact parameter given by the ITk (black points). The time of the tracks with respect to the Z → ee hard scattering, measured with the HGTD, must be within 2 x σ_{t}, with σ_{t} = 30 ps (red points) or 60 ps (green points). The blue points correspond to a selection of tracks from the hard scattering process. 
Transverse plane of a HGTD layer: Schematic view of the transverse plane of a HGTD layer. The yellow regions have a granularity of 1mm x 1mm, the blue regions 3mm x 3mm. The green lines mark the border of an ASU.  
HGTD ASU: Schematic view of a HGTD ASU in the transverse plane of a layer/sampling of the HGTD. The ASU is made of four sensors of 96mm x 96mm surrounded by a guard ring of 1mm. The wafers are separated by 1mm and are at least 0.5mm from the edge of the PCB.  
HGTDSi: Schematic view of the HGTDSi in positivez and R. The volume thickness in z and the material used in the simulation are listed in the caption.  
HGTDSiW: Schematic view of the HGTDSiW in positivez and R. The volume thickness in z and the material used in the simulation are listed in the legend. The tungsten absorber starts at a radius of 285mm from the beam axis.  
HGTD Occupancy: The occupancy of the HGTD is shown as function of radius separately for each layer (sampling) with pileup of μ=200 for readout cell sizes of (1x1)mm^{2} and (3x3)mm^{2}. At a radius of 285mm the tungsten absorber leads to a higher occupancy.  
HGTD Occupancy: The occupancy of the HGTD is shown as function of the radius for a pileup of μ=200 events for granularities of (1x1)mm^{2} and (3x3)mm^{2} in the third layer/sampling. At a radius of 285mm the tungsten absorber leads to a higher occupancy for the HGTDSiW compared to the HGDTSi.  
HGTD Occupancy: The occupancy of the HGTDSi and HGTDSiW is shown as function of the radius for a pileup of μ=200 events in the third layer/sampling.  
Muon Energy Deposit: The distribution of the energy deposited in the sensors of the HGTD is shown for an ATLAS simulation of muons with a transverse momentum of 1 TeV.  
Muon Energy Deposit: The energy deposited in the HGTD sensors is shown as function of the radius for an ATLAS simulation of muons with a transverse momentum of 1 TeV. The red line shows the expected increase due to the increasing polar angle.  
Muon Inefficiency: The fraction of muons escaping undetected the HGTD is shown as function of the radius for an ATLAS simulation of muons with a transverse momentum of 1 TeV. The minimal requirement for a hit is an energy deposit of 0.02 MeV. The inefficiency is dominated by the uninstrumented zones in the HGTD. The red line indicates a fraction of 0.01.  
Muon Efficiency: The fraction of muons detected in all four samplings/layers of the HGTD is shown TeV as function of the radius for an ATLAS simulation of muons with a transverse momentum of 1 TeV. The minimal requirement for a hit is an energy deposit of 0.02 MeV. The inefficiency is dominated by the uninstrumented zones in the HGTD.  
Muon Inefficiency: The fraction of muons escaping undetected the HGTD is shown TeV as function of the transverse coordinates x and y for an ATLAS simulation of muons with a transverse momentum of 1 TeV. The efficiency includes the effect of the uninstrumented zones of the HGTD.  
Muon Efficiency: The fraction of muons detected in all four samplings/layers of the HGTD is shown as function of the transverse coordinates x and y for an ATLAS simulation of muons with a transverse momentum of 1 TeV. The minimal requirement for a hit is an energy deposit of 0.02 MeV. The inefficiency is dominated by the uninstrumented zones in the HGTD.  
Electron Energy Deposit: The position of the energy deposited in the sensors of the HGTDSiW by an electron with a transverse momentum of 45 GeV in sampling/layer 3 is shown as function of the position in x and y coordinates. The energy deposit is in units 44 keV (MIP).  
Electron Energy Deposit with PileUp: The position of the energy deposited in the sensors of the HGTDSiW by an electron with a transverse momentum of 45 GeV in sampling/layer 3 pileup with a μ=200 events is shown as function of the position in x and y coordinates. The energy deposit is in units 44 keV (MIP). The change of the granularity is visible, e.g. at (x=0, y=285mm) as well as the noninstrumented zones in and between 16 ASUs as white lines parallel to the x axis.  
Electron Energy Deposit with PileUp: Zoom on the position of the energy deposited in the sensors of the HGTDSiW by an electron with a transverse momentum of 45 GeV in sampling/layer 3 pileup with a μ=200 events is shown as function of the position in x and y coordinates. The energy deposit is in units 44 keV (MIP). The change of the granularity is visible, e.g. at (x=0, y=285mm) as well as the noninstrumented zones in and between ASUs as white lines parallel to the x axis.  
Average Cluster Radius: The average cluster radius of electrons with a transverse momentum of 45 GeV in the HGTDSiW is shown as function of the sampling/layer for the region with the tungsten absorbers using an ATLAS simulation. The electron cluster is reconstructed in a cylinder of radius 30mm (three times the Molière radius). The radius is calculated as energy weighted coordinates and the error bar is the RMS.  
Number of Cells in Cluster: The average number of cells in an electron cluster in the HGTD Preshower is shown as function of the sampling/layer for the region with the tungsten absorbers using an ATLAS simulation of electrons with a transverse momentum of 45 GeV. The electron cluster is reconstructed in a cylinder of radius 30mm (three times the Molière radius). The error bars is the RMS. The cluster time resolution would be 510ps if a MIP can be measured in each cell with a timeresolution of 50 ps or better.  
Electron Energy Deposit: The distribution of the energy deposited in the sensor of the HGTDSi in samplings/layers 0 is shown for an ATLAS simulation of electrons with a transverse momentum of 45 GeV.  
Electron Energy Deposit: The distribution of the energy deposited in the sensor of the HGTDSi in samplings/layers 3 is shown for an ATLAS simulation of electrons with a transverse momentum of 45 GeV. For the HGTDSi the dynamic range for 99% quantiles goes up to 0.9 MeV (22xMIP). For the HGTDSiW the dynamic range goes up to 23 MeV (563xMIP). 
Presented are event displays for one VBF event simulated with and without pileup showing the hits on the first layer of the HGTD simulated in front of the LiquidArgon endcap calorimeter. The time distribution of the hits associated to one jet in the sample is shown assuming a detector time resolution of 30 ps and selected with different distances to the reconstructed jet axis.
border=1 cellpadding=10 cellspacing=10>
Scatter Plot of HGTD Hits: Scatter plot of the HGTD hits associated to calorimeter jets with p_{T} > 30 GeV. HGTD cells have size of 1 mm x 1 mm in the inner region (x<300 mm, y<300mm), and 3 mm x 3 mm outside this region up to a radius of 600 mm with respect to the beam axis. Jets are reconstructed with the antikt algorithm using topological clusters and radius parameter of 0.4, the jet momentum is corrected for pileup and calibrated for the detector response. The event simulated is a VBF Higgs decaying to invisible, without pileup in the top and including pileup with an average of 200 interactions in the bottom; hits from the signal jet are shown in red. Only hits within a radius of 0.4 in ηφ coordinates with respect to the jet direction and in the HGTD first sensitive layer are shown.  
Scatter Plot of HGTD Hits: Scatter plot of the HGTD hits associated to calorimeter jets with p_{T} > 30 GeV. HGTD cells have size of 1 mm x 1 mm in the inner region (x<300 mm, y<300mm), and 3 mm x 3 mm outside this region up to a radius of 600 mm with respect to the beam axis. Jets are reconstructed with the antikt algorithm using topological clusters and radius parameter of 0.4, the jet momentum is corrected for pileup and calibrated for the detector response. The event simulated is a VBF Higgs decaying to invisible, without pileup in the top and including pileup with an average of 200 interactions in the bottom; hits from the signal jet are shown in red. Only hits within a radius of 0.4 in ηφ coordinates with respect to the jet direction and in the HGTD first sensitive layer are shown.  
Scatter Plot of HGTD Hits: Scatter plot of the HGTD hits associated to calorimeter jets with p_{T} > 30 GeV. HGTD cells have size of 1 mm x 1 mm in the inner region (x<300 mm, y<300mm), and 3 mm x 3 mm outside this region up to a radius of 600 mm with respect to the beam axis. Jets are reconstructed with the antikt algorithm using topological clusters and radius parameter of 0.4, the jet momentum is corrected for pileup and calibrated for the detector response. The event simulated is a VBF Higgs decaying to invisible, without pileup in the top and including pileup with an average of 200 interactions in the bottom; hits from the signal jet are shown in red. Only hits within a radius of 0.4 in ηφ coordinates with respect to the jet direction and in the HGTD first sensitive layer are shown.  
Scatter Plot of HGTD Hits: Scatter plot of the HGTD hits associated to calorimeter jets with p_{T} > 30 GeV. HGTD cells have size of 1 mm x 1 mm in the inner region (x<300 mm, y<300mm), and 3 mm x 3 mm outside this region up to a radius of 600 mm with respect to the beam axis. Jets are reconstructed with the antikt algorithm using topological clusters and radius parameter of 0.4, the jet momentum is corrected for pileup and calibrated for the detector response. The event simulated is a VBF Higgs decaying to invisible, without pileup in the top and including pileup with an average of 200 interactions in the bottom; hits from the signal jet are shown in red. Only hits within a radius of 0.4 in ηφ coordinates with respect to the jet direction and in the HGTD first sensitive layer are shown.  
Time Distribution of HGTD Hits: Time distribution of HGTD hits for one reconstructed jet in a sample of VBF Higgs events with an average of 200 pileup interactions (black histograms). The jet corresponds to one of the generated VBF quark jets with p_{T} =72 GeV and η =2.7. The events are simulated with a pp collision time smearing of 175 ps; texp denotes the expected flight time from the center of ATLAS assuming a straight path and speed of light. Hits are smeared by 30 ps to simulate the detector resolution. The distributions correspond to hits within a cone of radius 0.4 (left), 0.2 (middle), and 0.1 (right) in ηφ coordinates with respect to the jet direction from the 4 sensitive silicon layers. The red histogram corresponds to the hit distribution of the same jet simulated without pileup.  
Time Distribution of HGTD Hits: Time distribution of HGTD hits for one reconstructed jet in a sample of VBF Higgs events with an average of 200 pileup interactions (black histograms). The jet corresponds to one of the generated VBF quark jets with p_{T} =72 GeV and η =2.7. The events are simulated with a pp collision time smearing of 175 ps; texp denotes the expected flight time from the center of ATLAS assuming a straight path and speed of light. Hits are smeared by 30 ps to simulate the detector resolution. The distributions correspond to hits within a cone of radius 0.4 (left), 0.2 (middle), and 0.1 (right) in ηφ coordinates with respect to the jet direction from the 4 sensitive silicon layers. The red histogram corresponds to the hit distribution of the same jet simulated without pileup.  
Time Distribution of HGTD Hits: Time distribution of HGTD hits for one reconstructed jet in a sample of VBF Higgs events with an average of 200 pileup interactions (black histograms). The jet corresponds to one of the generated VBF quark jets with p_{T} =72 GeV and η =2.7. The events are simulated with a pp collision time smearing of 175 ps; texp denotes the expected flight time from the center of ATLAS assuming a straight path and speed of light. Hits are smeared by 30 ps to simulate the detector resolution. The distributions correspond to hits within a cone of radius 0.4 (left), 0.2 (middle), and 0.1 (right) in ηφ coordinates with respect to the jet direction from the 4 sensitive silicon layers. The red histogram corresponds to the hit distribution of the same jet simulated without pileup. 
<! For significant updates to the topic, consider adding your 'signature' (beneath this editing box) > Major updates:
 AnaHenriques  20190710
 ChristianOhm  20190713
<! Person responsible for the page:
Either leave as is  the creator's name will be inserted;
Or replace the complete REVINFO tag (including percentages symbols) with a name in the form TwikiUsersName >
Responsible: Main.MartinAleksa
Subject: LAr HGTD Public Plots
<! Once this page has been reviewed, please add the name and the date e.g. StephenHaywood  31 Oct 2006 >
I  Attachment  History  Action  Size  Date  Who  Comment 

png  ATLAS_HPK_5x5_SMPL_W8_P11_GRgnd1.png  r1  manage  85.1 K  20190506  23:24  SimoneMicheleMazza  IV of a 5x5 HPK 3.1 array with probe card 
ATLAS_HPK_5x5_SMPL_W8_P11_GRgnd.pdf  r1  manage  30.2 K  20190506  23:24  SimoneMicheleMazza  IV of a 5x5 HPK 3.1 array with probe card  
B24_ch3_120_reso.pdf  r1  manage  14.8 K  20181130  15:29  ChristinaAgapopoulou  
png  B24_ch3_120_reso.png  r1  manage  47.8 K  20181130  15:29  ChristinaAgapopoulou  
eps  Beamspot.eps  r1  manage  182.1 K  20170705  17:31  DirkZerwas  
Beamspot.pdf  r1  manage  212.3 K  20170705  17:31  DirkZerwas  
png  Beamspot.png  r1  manage  32.7 K  20170705  17:31  DirkZerwas  
png  CC_WF_proposal_3E151.png  r1  manage  73.3 K  20190509  22:46  SimoneMicheleMazza  Simulated collected charge for deep B+C 
CC_WF_proposal_3E15.pdf  r1  manage  16.2 K  20190509  22:46  SimoneMicheleMazza  Simulated collected charge for deep B+C  
png  CC_WF_proposal_6E151.png  r1  manage  62.8 K  20190509  22:46  SimoneMicheleMazza  Simulated collected charge for deep B+C 
CC_WF_proposal_6E15.pdf  r1  manage  15.2 K  20190509  22:46  SimoneMicheleMazza  Simulated collected charge for deep B+C  
eps  Charge_Probe_LandauMpv_vs_biasVoltage.eps  r1  manage  9.9 K  20161014  17:57  MartinAleksa  
Charge_Probe_LandauMpv_vs_biasVoltage.pdf  r1  manage  14.5 K  20161014  17:57  MartinAleksa  
png  Charge_Probe_LandauMpv_vs_biasVoltage.png  r1  manage  16.3 K  20161014  17:57  MartinAleksa  
eps  Charge_Trigger_LandauMpv.eps  r1  manage  12.5 K  20161014  17:52  MartinAleksa  
Charge_Trigger_LandauMpv.pdf  r1  manage  15.9 K  20161014  17:52  MartinAleksa  
png  Charge_Trigger_LandauMpv.png  r1  manage  17.7 K  20161014  17:52  MartinAleksa  
png  Charge_vs_fluence1.png  r1  manage  71.1 K  20190509  22:51  SimoneMicheleMazza  Collected charge vs fluence 
Charge_vs_fluence.pdf  r1  manage  15.1 K  20190509  22:51  SimoneMicheleMazza  Collected charge vs fluence  
eps  EffPUvsPT_fwdJets_fixedHS.eps  r1  manage  11.5 K  20171002  18:03  DirkZerwas  
EffPUvsPT_fwdJets_fixedHS.pdf  r1  manage  14.6 K  20171002  18:03  DirkZerwas  
png  EffPUvsPT_fwdJets_fixedHS.png  r1  manage  41.0 K  20171002  18:03  DirkZerwas  
eps  ElectronIsolationZ0only.eps  r1  manage  10.8 K  20170927  13:52  DirkZerwas  
ElectronIsolationZ0only.pdf  r1  manage  14.9 K  20170927  13:52  DirkZerwas  
png  ElectronIsolationZ0only.png  r1  manage  12.1 K  20170927  13:52  DirkZerwas  
eps  Electrons_nCells.eps  r1  manage  79.7 K  20170705  18:02  DirkZerwas  
Electrons_nCells.pdf  r1  manage  34.5 K  20170705  18:02  DirkZerwas  
png  Electrons_nCells.png  r1  manage  104.4 K  20170705  18:02  DirkZerwas  
EventDisplay_CellsXYN_jet.pdf  r1  manage  97.8 K  20161014  17:20  MartinAleksa  
png  EventDisplay_CellsXYN_jet.png  r1  manage  48.8 K  20161014  17:20  MartinAleksa  
EventDisplay_CellsXYN_jet_mu0.pdf  r1  manage  69.6 K  20161014  17:20  MartinAleksa  
png  EventDisplay_CellsXYN_jet_mu0.png  r1  manage  28.1 K  20161014  17:20  MartinAleksa  
EventDisplay_CellsXYP_jet.pdf  r1  manage  114.9 K  20161014  17:20  MartinAleksa  
png  EventDisplay_CellsXYP_jet.png  r1  manage  63.0 K  20161014  17:20  MartinAleksa  
EventDisplay_CellsXYP_jet_mu0.pdf  r1  manage  70.3 K  20161014  17:20  MartinAleksa  
png  EventDisplay_CellsXYP_jet_mu0.png  r1  manage  28.8 K  20161014  17:20  MartinAleksa  
EventDisplay_CellsdT_core1_Jet1.pdf  r1  manage  15.1 K  20161014  17:23  MartinAleksa  
png  EventDisplay_CellsdT_core1_Jet1.png  r1  manage  15.4 K  20161014  17:23  MartinAleksa  
EventDisplay_CellsdT_core2_Jet1.pdf  r1  manage  15.3 K  20161014  17:23  MartinAleksa  
png  EventDisplay_CellsdT_core2_Jet1.png  r1  manage  15.7 K  20161014  17:23  MartinAleksa  
EventDisplay_CellsdT_jet_Jet1.pdf  r1  manage  15.4 K  20161014  17:23  MartinAleksa  
png  EventDisplay_CellsdT_jet_Jet1.png  r1  manage  16.2 K  20161014  17:23  MartinAleksa  
png  FBK_CC1.png  r1  manage  61.4 K  20190506  22:12  SimoneMicheleMazza  Collected charge for HGTD TDR studied sensors 
FBK_CC.pdf  r1  manage  15.0 K  20190506  22:12  SimoneMicheleMazza  Collected charge for HGTD TDR studied sensors  
png  FBK_TimeRes1.png  r1  manage  61.3 K  20190509  22:39  SimoneMicheleMazza  Time resolution for HGTD LGADs 
FBK_TimeRes.pdf  r1  manage  14.7 K  20190509  22:39  SimoneMicheleMazza  Time resolution for HGTD LGADs  
FBK_TimeRes_lin.pdf  r1  manage  15.1 K  20190514  01:07  SimoneMicheleMazza  Time resolution for HGTD LGADs (linear scale)  
png  FBK_TimeRes_lin.png  r1  manage  1215.0 K  20190514  01:07  SimoneMicheleMazza  Time resolution for HGTD LGADs (linear scale) 
eps  Gain_Probe_LandauMpv_vs_biasVoltage.eps  r1  manage  10.0 K  20161014  17:57  MartinAleksa  
Gain_Probe_LandauMpv_vs_biasVoltage.pdf  r1  manage  14.6 K  20161014  17:57  MartinAleksa  
png  Gain_Probe_LandauMpv_vs_biasVoltage.png  r1  manage  16.1 K  20161014  17:57  MartinAleksa  
eps  HGTD0v0.eps  r1  manage  163.6 K  20161014  17:06  MartinAleksa  
HGTD0v0.pdf  r1  manage  30.3 K  20161014  17:06  MartinAleksa  
png  HGTD0v0.png  r1  manage  51.2 K  20161014  17:06  MartinAleksa  
eps  HGTD3v1.eps  r1  manage  175.6 K  20161014  17:06  MartinAleksa  
HGTD3v1.pdf  r1  manage  30.0 K  20161014  17:06  MartinAleksa  
png  HGTD3v1.png  r1  manage  56.6 K  20161014  17:06  MartinAleksa  
eps  HGTDSiW.eps  r1  manage  15.9 K  20161014  16:27  MartinAleksa  
HGTDSiW.pdf  r1  manage  18.9 K  20161014  16:27  MartinAleksa  
png  HGTDSiW.png  r1  manage  33.5 K  20161014  16:27  MartinAleksa  
HGTDradplots.pdf  r1  manage  146.7 K  20161017  17:12  AnaHenriques  HGTDLGAD_rad_tolerance  
HGTD_radiationlevels.pdf  r1  manage  180.1 K  20161017  17:10  AnaHenriques  HGTD expected radiation levels  
eps  HGTD_trkIso_vs_density_time30_60_final_eta0_3.6.eps  r1  manage  16.0 K  20170703  18:32  DirkZerwas  
HGTD_trkIso_vs_density_time30_60_final_eta0_3.6.pdf  r1  manage  15.4 K  20170703  18:32  DirkZerwas  
png  HGTD_trkIso_vs_density_time30_60_final_eta0_3.6.png  r1  manage  42.0 K  20170703  18:32  DirkZerwas  
eps  HGTD_trkIso_vs_density_time30_60_final_eta2.6_3.6.eps  r2 r1  manage  15.9 K  20170619  08:47  MartinAleksa  
HGTD_trkIso_vs_density_time30_60_final_eta2.6_3.6.pdf  r1  manage  15.8 K  20170619  08:47  MartinAleksa  
HGTD_trkIso_vs_density_time30_60_final_eta2.6_3.6.pdf.pdf  r1  manage  62.3 K  20170613  10:37  MartinAleksa  
png  HGTD_trkIso_vs_density_time30_60_final_eta2.6_3.6.png  r2 r1  manage  64.6 K  20170619  08:47  MartinAleksa  
eps  HGTDasu200x200V1.eps  r1  manage  59.8 K  20161014  17:06  MartinAleksa  
HGTDasu200x200V1.pdf  r1  manage  21.5 K  20161014  17:06  MartinAleksa  
png  HGTDasu200x200V1.png  r1  manage  24.7 K  20161014  17:06  MartinAleksa  
eps  HGTimingV7.eps  r1  manage  100.1 K  20161014  17:06  MartinAleksa  
HGTimingV7.pdf  r1  manage  39.7 K  20161014  17:07  MartinAleksa  
png  HGTimingV7.png  r1  manage  343.7 K  20161014  17:07  MartinAleksa  
jpg  HPK_15x15.jpg  r1  manage  212.5 K  20190430  21:36  SimoneMicheleMazza  Microscope photo of an HPK3.150 15x15 array 
png  HPK_30_CC1.png  r1  manage  69.8 K  20190506  22:12  SimoneMicheleMazza  Collected charge for HGTD TDR studied sensors 
HPK_30_CC.pdf  r1  manage  14.8 K  20190506  22:12  SimoneMicheleMazza  Collected charge for HGTD TDR studied sensors  
png  HPK_30_TimeRes1.png  r1  manage  67.1 K  20190509  22:39  SimoneMicheleMazza  Time resolution for HGTD LGADs 
HPK_30_TimeRes.pdf  r1  manage  14.5 K  20190509  22:39  SimoneMicheleMazza  Time resolution for HGTD LGADs  
HPK_30_TimeRes_lin.pdf  r1  manage  14.9 K  20190514  01:07  SimoneMicheleMazza  Time resolution for HGTD LGADs (linear scale)  
png  HPK_30_TimeRes_lin.png  r1  manage  1215.0 K  20190514  01:07  SimoneMicheleMazza  Time resolution for HGTD LGADs (linear scale) 
png  HPK_31_CC1.png  r1  manage  79.0 K  20190506  22:12  SimoneMicheleMazza  Collected charge for HGTD TDR studied sensors 
HPK_31_CC.pdf  r1  manage  16.3 K  20190506  22:12  SimoneMicheleMazza  Collected charge for HGTD TDR studied sensors  
png  HPK_31_TimeRes1.png  r1  manage  79.3 K  20190509  22:39  SimoneMicheleMazza  Time resolution for HGTD LGADs 
HPK_31_TimeRes.pdf  r1  manage  16.1 K  20190509  22:39  SimoneMicheleMazza  Time resolution for HGTD LGADs  
HPK_31_TimeRes_lin.pdf  r1  manage  16.4 K  20190514  01:07  SimoneMicheleMazza  Time resolution for HGTD LGADs (linear scale)  
png  HPK_31_TimeRes_lin.png  r1  manage  1215.0 K  20190514  01:07  SimoneMicheleMazza  Time resolution for HGTD LGADs (linear scale) 
png  HPK_32_CC1.png  r1  manage  62.8 K  20190506  22:12  SimoneMicheleMazza  Collected charge for HGTD TDR studied sensors 
HPK_32_CC.pdf  r1  manage  15.4 K  20190506  22:12  SimoneMicheleMazza  Collected charge for HGTD TDR studied sensors  
png  HPK_32_TimeRes1.png  r1  manage  66.3 K  20190509  22:39  SimoneMicheleMazza  Time resolution for HGTD LGADs 
HPK_32_TimeRes.pdf  r1  manage  15.2 K  20190509  22:39  SimoneMicheleMazza  Time resolution for HGTD LGADs  
HPK_32_TimeRes_lin.pdf  r1  manage  15.6 K  20190514  01:07  SimoneMicheleMazza  Time resolution for HGTD LGADs (linear scale)  
png  HPK_32_TimeRes_lin.png  r1  manage  1215.0 K  20190514  01:07  SimoneMicheleMazza  Time resolution for HGTD LGADs (linear scale) 
eps  HSeffPU2_highpt_18.eps  r2 r1  manage  13.2 K  20170705  15:01  DirkZerwas  
HSeffPU2_highpt_18.pdf  r2 r1  manage  15.5 K  20170705  15:01  DirkZerwas  
png  HSeffPU2_highpt_18.png  r2 r1  manage  49.0 K  20170705  15:01  DirkZerwas  
eps  HSeffPU2_lowpt_17.eps  r2 r1  manage  13.3 K  20170705  15:01  DirkZerwas  
HSeffPU2_lowpt_17.pdf  r2 r1  manage  15.5 K  20170705  15:01  DirkZerwas  
png  HSeffPU2_lowpt_17.png  r2 r1  manage  50.3 K  20170705  15:01  DirkZerwas  
eps  IPZ.eps  r1  manage  13.0 K  20170929  19:24  ArielSchwartzman  
IPZ.pdf  r1  manage  19.1 K  20170929  19:23  ArielSchwartzman  
png  IPZ.png  r1  manage  35.0 K  20170929  19:24  ArielSchwartzman  
LumiRelativeStatErrorVsMu.pdf  r1  manage  14.7 K  20180220  11:46  ChristianOhm  
png  LumiRelativeStatErrorVsMu.png  r1  manage  306.7 K  20180220  11:46  ChristianOhm  
eps  METFraction.eps  r1  manage  19.3 K  20171002  18:03  DirkZerwas  
METFraction.pdf  r1  manage  17.7 K  20171002  18:03  DirkZerwas  
png  METFraction.png  r1  manage  45.6 K  20171002  18:03  DirkZerwas  
eps  Muons_ESpectrum.eps  r1  manage  9.1 K  20161014  16:33  MartinAleksa  
gif  Muons_ESpectrum.gif  r1  manage  8.4 K  20161014  16:33  MartinAleksa  
Muons_ESpectrum.pdf  r1  manage  14.3 K  20161014  16:33  MartinAleksa  
eps  Muons_EfctR.eps  r1  manage  10.2 K  20161014  16:38  MartinAleksa  
gif  Muons_EfctR.gif  r1  manage  8.0 K  20161014  16:38  MartinAleksa  
Muons_EfctR.pdf  r1  manage  14.8 K  20161014  16:38  MartinAleksa  
eps  Muons_effR0.eps  r1  manage  10.5 K  20161014  16:39  MartinAleksa  
gif  Muons_effR0.gif  r1  manage  9.9 K  20161014  16:39  MartinAleksa  
Muons_effR0.pdf  r1  manage  15.3 K  20161014  16:39  MartinAleksa  
eps  Muons_effR4.eps  r1  manage  10.6 K  20161014  16:39  MartinAleksa  
gif  Muons_effR4.gif  r1  manage  10.0 K  20161014  16:39  MartinAleksa  
Muons_effR4.pdf  r1  manage  14.7 K  20161014  16:39  MartinAleksa  
eps  Muons_effXY0.eps  r1  manage  14.9 K  20161014  16:45  MartinAleksa  
gif  Muons_effXY0.gif  r1  manage  14.4 K  20161014  16:45  MartinAleksa  
Muons_effXY0.pdf  r1  manage  15.0 K  20161014  16:45  MartinAleksa  
eps  Muons_effXY4.eps  r1  manage  68.4 K  20161014  16:45  MartinAleksa  
gif  Muons_effXY4.gif  r1  manage  21.8 K  20161014  16:45  MartinAleksa  
Muons_effXY4.pdf  r1  manage  25.6 K  20161014  16:45  MartinAleksa  
eps  Occupancy_MB_Si_all.eps  r1  manage  102.6 K  20170929  11:21  DirkZerwas  
Occupancy_MB_Si_all.pdf  r1  manage  23.4 K  20170929  11:21  DirkZerwas  
png  Occupancy_MB_Si_all.png  r1  manage  20.5 K  20170929  11:21  DirkZerwas  
png  PD_voltage1.png  r1  manage  73.4 K  20190509  22:51  SimoneMicheleMazza  Power dissipation 
PD_voltage.pdf  r1  manage  15.2 K  20190509  22:51  SimoneMicheleMazza  Power dissipation  
eps  PUeffpt3050.eps  r1  manage  11.4 K  20171002  13:15  DirkZerwas  
PUeffpt3050.pdf  r1  manage  14.6 K  20171002  13:15  DirkZerwas  
eps  PUeffpt3050.pdf.eps  r1  manage  11.4 K  20171002  13:13  DirkZerwas  
PUeffpt3050.pdf.pdf  r1  manage  14.6 K  20171002  13:13  DirkZerwas  
png  PUeffpt3050.pdf.png  r1  manage  41.9 K  20171002  13:13  DirkZerwas  
png  PUeffpt3050.png  r1  manage  41.9 K  20171002  13:15  DirkZerwas  
eps  Photons_nCellsC0.eps  r1  manage  71.6 K  20170705  18:03  DirkZerwas  
Photons_nCellsC0.pdf  r1  manage  31.9 K  20170705  18:03  DirkZerwas  
png  Photons_nCellsC0.png  r1  manage  107.3 K  20170705  18:03  DirkZerwas  
eps  Pulse_33_48_3.eps  r1  manage  10.9 K  20161014  17:52  MartinAleksa  
Pulse_33_48_3.pdf  r1  manage  16.5 K  20161014  17:52  MartinAleksa  
png  Pulse_33_48_3.png  r1  manage  14.9 K  20161014  17:52  MartinAleksa  
eps  Pulse_Nominal.eps  r1  manage  88.8 K  20170705  17:47  DirkZerwas  
Pulse_Nominal.pdf  r1  manage  52.8 K  20170705  17:47  DirkZerwas  
png  Pulse_Nominal.png  r1  manage  15.8 K  20170705  17:47  DirkZerwas  
eps  RMSMETPU2HGTD.eps  r1  manage  10.9 K  20171002  18:03  DirkZerwas  
RMSMETPU2HGTD.pdf  r1  manage  14.1 K  20171002  18:03  DirkZerwas  
png  RMSMETPU2HGTD.png  r1  manage  40.9 K  20171002  18:03  DirkZerwas  
eps  ROC2438_16.eps  r1  manage  12.2 K  20170705  16:16  DirkZerwas  
ROC2438_16.pdf  r1  manage  15.1 K  20170705  16:17  DirkZerwas  
png  ROC2438_16.png  r2 r1  manage  69.0 K  20170705  16:16  DirkZerwas  
Radiation_IDR_last.pdf  r2 r1  manage  215.6 K  20170223  10:30  AnaHenriques  
eps  RiseTime_Probe_GausMean_vs_biasVoltage.eps  r1  manage  9.2 K  20161014  18:09  MartinAleksa  
RiseTime_Probe_GausMean_vs_biasVoltage.pdf  r1  manage  14.4 K  20161014  18:09  MartinAleksa  
png  RiseTime_Probe_GausMean_vs_biasVoltage.png  r1  manage  16.5 K  20161014  18:09  MartinAleksa  
eps  SOverN_Probe_GausMean_vs_biasVoltage.eps  r1  manage  9.2 K  20161014  18:01  MartinAleksa  
SOverN_Probe_GausMean_vs_biasVoltage.pdf  r1  manage  14.3 K  20161014  18:01  MartinAleksa  
png  SOverN_Probe_GausMean_vs_biasVoltage.png  r1  manage  15.6 K  20161014  18:01  MartinAleksa  
eps  SiItkInclNomminbiasLowPtnHitsVsMuEta2p8to3p0.eps  r1  manage  18.9 K  20170705  16:12  DirkZerwas  
SiItkInclNomminbiasLowPtnHitsVsMuEta2p8to3p0.pdf  r1  manage  21.2 K  20170705  16:12  DirkZerwas  
png  SiItkInclNomminbiasLowPtnHitsVsMuEta2p8to3p0.png  r1  manage  42.8 K  20170705  16:12  DirkZerwas  
eps  SiItkInclNomminbiasLowPtnHitsVsMuEtafull.eps  r1  manage  16.5 K  20170705  16:12  DirkZerwas  
SiItkInclNomminbiasLowPtnHitsVsMuEtafull.pdf  r1  manage  20.1 K  20170705  16:12  DirkZerwas  
png  SiItkInclNomminbiasLowPtnHitsVsMuEtafull.png  r1  manage  40.9 K  20170705  16:12  DirkZerwas  
Sim_July2019_1_ModuleOverlaps.pdf  r1  manage  30.3 K  20190713  10:32  ChristianOhm  Simulation performance plots July 2019, batch 1  
png  Sim_July2019_1_ModuleOverlaps.png  r1  manage  35.5 K  20190713  10:32  ChristianOhm  Simulation performance plots July 2019, batch 1 
Sim_July2019_2a_x0.pdf  r1  manage  99.9 K  20190713  10:32  ChristianOhm  Simulation performance plots July 2019, batch 1  
png  Sim_July2019_2a_x0.png  r1  manage  186.1 K  20190713  10:32  ChristianOhm  Simulation performance plots July 2019, batch 1 
Sim_July2019_2b_lambda.pdf  r1  manage  144.7 K  20190713  10:32  ChristianOhm  Simulation performance plots July 2019, batch 1  
png  Sim_July2019_2b_lambda.png  r1  manage  166.3 K  20190713  10:32  ChristianOhm  Simulation performance plots July 2019, batch 1 
Sim_July2019_3_MainParameters.pdf  r1  manage  91.4 K  20190713  10:32  ChristianOhm  Simulation performance plots July 2019, batch 1  
png  Sim_July2019_3_MainParameters.png  r1  manage  43.7 K  20190713  10:32  ChristianOhm  Simulation performance plots July 2019, batch 1 
png  Sim_July2019_4_EventDisplay.png  r1  manage  289.8 K  20190713  10:32  ChristianOhm  Simulation performance plots July 2019, batch 1 
Sim_July2019_5a_ModulePlacementQuadrant.pdf  r2 r1  manage  239.3 K  20190713  11:22  ChristianOhm  Simulation performance plots July 2019, batch 2  
png  Sim_July2019_5a_ModulePlacementQuadrant.png  r2 r1  manage  743.0 K  20190713  11:22  ChristianOhm  Simulation performance plots July 2019, batch 2 
Sim_July2019_5b_ModulePlacementFullDisk.pdf  r1  manage  239.3 K  20190713  10:34  ChristianOhm  Simulation performance plots July 2019, batch 2  
png  Sim_July2019_5b_ModulePlacementFullDisk.png  r2 r1  manage  1124.8 K  20190713  11:21  ChristianOhm  Simulation performance plots July 2019, batch 2 
Sim_July2019_6a_HitTimingResolution.pdf  r1  manage  23.1 K  20190713  10:35  ChristianOhm  Simulation performance plots July 2019, batch 2  
png  Sim_July2019_6a_HitTimingResolution.png  r1  manage  189.9 K  20190713  10:35  ChristianOhm  Simulation performance plots July 2019, batch 2 
Sim_July2019_6b_TrackTimingResolution.pdf  r1  manage  23.3 K  20190713  10:35  ChristianOhm  Simulation performance plots July 2019, batch 2  
png  Sim_July2019_6b_TrackTimingResolution.png  r1  manage  201.3 K  20190713  10:35  ChristianOhm  Simulation performance plots July 2019, batch 2 
Sim_July2019_7_OccupancyITkStep3p0.pdf  r1  manage  127.2 K  20190713  10:35  ChristianOhm  Simulation performance plots July 2019, batch 2  
png  Sim_July2019_7_OccupancyITkStep3p0.png  r1  manage  49.8 K  20190713  10:35  ChristianOhm  Simulation performance plots July 2019, batch 2 
png  Sim_July2019_8_nHits_xy.png  r1  manage  361.4 K  20190713  10:36  ChristianOhm  Simulation performance plots July 2019, batch 3 
TB_Oct18_toaDistr_tw.pdf  r1  manage  16.2 K  20181130  13:58  ChristinaAgapopoulou  
png  TB_Oct18_toaDistr_tw.png  r1  manage  39.3 K  20181130  14:14  ChristinaAgapopoulou  
png  TCT_distances1.png  r1  manage  83.6 K  20190509  22:52  SimoneMicheleMazza  IP distances with TCT 
TCT_distances.pdf  r1  manage  18.5 K  20190509  22:52  SimoneMicheleMazza  IP distances with TCT  
eps  TOA.eps  r1  manage  10.5 K  20170705  17:43  DirkZerwas  
TOA.pdf  r1  manage  15.2 K  20170705  17:43  DirkZerwas  
png  TOA.png  r1  manage  15.2 K  20170705  17:43  DirkZerwas  
eps  TrackMatchEff_pt.eps  r1  manage  10.9 K  20170704  19:36  DirkZerwas  
TrackMatchEff_pt.pdf  r1  manage  14.9 K  20170704  19:37  DirkZerwas  
png  TrackMatchEff_pt.png  r1  manage  19.3 K  20170704  19:37  DirkZerwas  
eps  TrackTimeMatchEff_EffvsPt.eps  r1  manage  10.9 K  20170704  19:36  DirkZerwas  
TrackTimeMatchEff_EffvsPt.pdf  r1  manage  15.0 K  20170704  19:36  DirkZerwas  
png  TrackTimeMatchEff_EffvsPt.png  r1  manage  20.0 K  20170704  19:36  DirkZerwas  
eps  TrackdTPublicPlot_1mm.eps  r1  manage  14.8 K  20170704  19:36  DirkZerwas  
TrackdTPublicPlot_1mm.pdf  r1  manage  19.0 K  20170704  19:36  DirkZerwas  
png  TrackdTPublicPlot_1mm.png  r1  manage  23.9 K  20170705  07:49  DirkZerwas  
eps  TrackdTPublicPlot_3mm.eps  r1  manage  15.0 K  20170705  07:49  DirkZerwas  
TrackdTPublicPlot_3mm.pdf  r1  manage  19.3 K  20170705  07:48  DirkZerwas  
png  TrackdTPublicPlot_3mm.png  r1  manage  25.8 K  20170705  07:48  DirkZerwas  
eps  TrackdXPublicPlot_1mm.eps  r2 r1  manage  13.2 K  20170705  07:58  DirkZerwas  
TrackdXPublicPlot_1mm.pdf  r2 r1  manage  20.0 K  20170705  07:58  DirkZerwas  
png  TrackdXPublicPlot_1mm.png  r2 r1  manage  19.4 K  20170705  07:58  DirkZerwas  
eps  TrackdXPublicPlot_3mm.eps  r2 r1  manage  14.2 K  20170705  07:57  DirkZerwas  
TrackdXPublicPlot_3mm.pdf  r2 r1  manage  20.3 K  20170705  07:57  DirkZerwas  
png  TrackdXPublicPlot_3mm.png  r2 r1  manage  21.1 K  20170705  07:57  DirkZerwas  
eps  Treco.eps  r1  manage  11.9 K  20170705  17:47  DirkZerwas  
Treco.pdf  r1  manage  16.6 K  20170705  17:47  DirkZerwas  
png  Treco.png  r1  manage  14.3 K  20170705  17:47  DirkZerwas  
eps  Truth_Vertex.eps  r2 r1  manage  15.9 K  20170921  09:30  DirkZerwas  
Truth_Vertex.pdf  r2 r1  manage  20.7 K  20170921  09:31  DirkZerwas  
png  Truth_Vertex.png  r2 r1  manage  28.1 K  20170921  10:09  DirkZerwas  
eps  Truth_Vertex_zoom.eps  r2 r1  manage  12.1 K  20170921  09:32  DirkZerwas  
Truth_Vertex_zoom.pdf  r2 r1  manage  15.4 K  20170921  09:33  DirkZerwas  
png  Truth_Vertex_zoom.png  r2 r1  manage  19.1 K  20170921  09:34  DirkZerwas  
VBD2Dmap15x15ArrayType3p1.pdf  r1  manage  15.1 K  20190502  11:15  JoernLange  VBD map of HPK3.150 15x15 array  
png  VBD2Dmap15x15ArrayType3p1.png  r1  manage  15.4 K  20190502  11:34  JoernLange  VBD map of HPK3.150 15x15 array 
eps  W11_HG11_effR_vs_y.eps  r1  manage  21.4 K  20170124  18:06  MartinAleksa  
W11_HG11_effR_vs_y.pdf  r1  manage  17.8 K  20170124  18:06  MartinAleksa  
png  W11_HG11_effR_vs_y.png  r1  manage  32.6 K  20170124  18:06  MartinAleksa  
eps  bVSlight__MV1.eps  r1  manage  32.8 K  20170929  13:06  DirkZerwas  
bVSlight__MV1.pdf  r1  manage  43.2 K  20170929  13:06  DirkZerwas  
png  bVSlight__MV1.png  r1  manage  26.4 K  20170929  13:06  DirkZerwas  
eps  batch207_deltaT.eps  r1  manage  183.9 K  20180517  15:30  MakovecNikola  
gif  batch207_deltaT.gif  r1  manage  31.8 K  20180517  15:30  MakovecNikola  
batch207_deltaT.pdf  r1  manage  38.2 K  20180517  15:30  MakovecNikola  
eps  batch207_eff.eps  r1  manage  176.6 K  20180517  15:30  MakovecNikola  
gif  batch207_eff.gif  r1  manage  23.9 K  20180517  15:30  MakovecNikola  
batch207_eff.pdf  r1  manage  42.1 K  20180517  15:30  MakovecNikola  
eps  batch207_effX.eps  r1  manage  23.9 K  20180517  15:30  MakovecNikola  
gif  batch207_effX.gif  r1  manage  20.3 K  20180517  15:30  MakovecNikola  
batch207_effX.pdf  r1  manage  25.4 K  20180517  15:30  MakovecNikola  
eps  batch507_deltaT.eps  r1  manage  217.2 K  20180517  15:30  MakovecNikola  
gif  batch507_deltaT.gif  r1  manage  38.0 K  20180517  15:30  MakovecNikola  
batch507_deltaT.pdf  r1  manage  41.3 K  20180517  15:30  MakovecNikola  
eps  batch507_eff.eps  r1  manage  132.9 K  20180517  15:30  MakovecNikola  
gif  batch507_eff.gif  r1  manage  21.0 K  20180517  15:30  MakovecNikola  
batch507_eff.pdf  r1  manage  35.3 K  20180517  15:30  MakovecNikola  
eps  batch507_effX.eps  r1  manage  26.3 K  20180517  15:30  MakovecNikola  
gif  batch507_effX.gif  r1  manage  22.8 K  20180517  15:30  MakovecNikola  
batch507_effX.pdf  r1  manage  28.1 K  20180517  15:30  MakovecNikola  
sh  convert.sh  r1  manage  0.2 K  20190713  13:06  ChristianOhm  Script for converting PDF files to PNG with appropriate quality and size 
eff_vs_Q.pdf  r1  manage  19.2 K  20190509  13:42  JoernLange  
png  eff_vs_Q.png  r1  manage  85.0 K  20190509  13:42  JoernLange  
eps  elec_Display_0.eps  r1  manage  49.8 K  20161014  16:48  MartinAleksa  
gif  elec_Display_0.gif  r1  manage  11.1 K  20161014  16:48  MartinAleksa  
elec_Display_0.pdf  r1  manage  24.2 K  20161014  16:48  MartinAleksa  
eps  elec_Display_200.eps  r1  manage  5681.9 K  20161014  16:48  MartinAleksa  
gif  elec_Display_200.gif  r1  manage  33.5 K  20161014  16:48  MartinAleksa  
elec_Display_200.pdf  r1  manage  1287.7 K  20161014  16:48  MartinAleksa  
eps  elec_Display_lego200.eps  r1  manage  226.8 K  20161014  16:48  MartinAleksa  
gif  elec_Display_lego200.gif  r1  manage  21.7 K  20161014  16:48  MartinAleksa  
elec_Display_lego200.pdf  r1  manage  59.2 K  20161014  16:48  MartinAleksa  
eps  elec_Display_lego200_all.eps  r1  manage  49160.3 K  20170706  11:46  DirkZerwas  
gif  elec_Display_lego200_all.gif  r1  manage  542.3 K  20170705  18:01  DirkZerwas  
elec_Display_lego200_all.pdf  r1  manage  568.4 K  20170706  11:46  DirkZerwas  
eps  elec_Display_lego200_cluster.eps  r1  manage  49160.3 K  20170706  11:46  DirkZerwas  
gif  elec_Display_lego200_cluster.gif  r1  manage  235.5 K  20170705  18:01  DirkZerwas  
elec_Display_lego200_cluster.pdf  r1  manage  250.5 K  20170706  11:46  DirkZerwas  
eps  elec_Display_lego200_elec.eps  r1  manage  49160.3 K  20170706  11:46  DirkZerwas  
gif  elec_Display_lego200_elec.gif  r1  manage  211.6 K  20170705  18:01  DirkZerwas  
elec_Display_lego200_elec.pdf  r1  manage  224.6 K  20170706  11:46  DirkZerwas  
eps  elec_Es0.eps  r1  manage  8.4 K  20161014  17:02  MartinAleksa  
gif  elec_Es0.gif  r1  manage  8.7 K  20161014  17:02  MartinAleksa  
elec_Es0.pdf  r1  manage  13.6 K  20161014  17:02  MartinAleksa  
eps  elec_Es3.eps  r1  manage  9.4 K  20161014  17:02  MartinAleksa  
gif  elec_Es3.gif  r1  manage  9.5 K  20161014  17:02  MartinAleksa  
elec_Es3.pdf  r1  manage  13.9 K  20161014  17:02  MartinAleksa  
eps  elec_nCells.eps  r1  manage  7.4 K  20161014  16:54  MartinAleksa  
gif  elec_nCells.gif  r1  manage  7.0 K  20161014  16:54  MartinAleksa  
elec_nCells.pdf  r1  manage  13.4 K  20161014  16:54  MartinAleksa  
eps  elec_showerRadius.eps  r1  manage  7.3 K  20161014  16:54  MartinAleksa  
gif  elec_showerRadius.gif  r1  manage  7.2 K  20161014  16:54  MartinAleksa  
elec_showerRadius.pdf  r1  manage  13.4 K  20161014  16:54  MartinAleksa  
eps  histogram2D_event15_vtxid0_eta3.8.eps  r1  manage  10.7 K  20170929  19:27  ArielSchwartzman  
histogram2D_event15_vtxid0_eta3.8.pdf  r1  manage  15.0 K  20170929  19:27  ArielSchwartzman  
png  histogram2D_event15_vtxid0_eta3.8.png  r1  manage  33.8 K  20170929  19:27  ArielSchwartzman  
eps  histogramT_event15_vtxid0_eta3.8.eps  r1  manage  10.6 K  20170929  19:28  ArielSchwartzman  
histogramT_event15_vtxid0_eta3.8.pdf  r1  manage  15.0 K  20170929  19:28  ArielSchwartzman  
png  histogramT_event15_vtxid0_eta3.8.png  r1  manage  34.6 K  20170929  19:28  ArielSchwartzman  
eps  histogramZ_event15_vtxid0_eta3.8.eps  r1  manage  11.6 K  20170929  19:28  ArielSchwartzman  
histogramZ_event15_vtxid0_eta3.8.pdf  r1  manage  15.3 K  20170929  19:28  ArielSchwartzman  
png  histogramZ_event15_vtxid0_eta3.8.png  r1  manage  32.4 K  20170929  19:28  ArielSchwartzman  
eps  jetfractioneta2_time30.eps  r1  manage  12.6 K  20170929  19:27  ArielSchwartzman  
jetfractioneta2_time30.pdf  r1  manage  14.5 K  20170929  19:27  ArielSchwartzman  
png  jetfractioneta2_time30.png  r1  manage  52.0 K  20170929  19:27  ArielSchwartzman  
eps  jetfractioneta3_time30.eps  r1  manage  12.7 K  20170929  19:27  ArielSchwartzman  
jetfractioneta3_time30.pdf  r1  manage  14.5 K  20170929  19:27  ArielSchwartzman  
png  jetfractioneta3_time30.png  r1  manage  50.5 K  20170929  19:27  ArielSchwartzman  
eps  jetfractioneta5.eps  r1  manage  13.1 K  20170929  19:26  ArielSchwartzman  
jetfractioneta5.pdf  r1  manage  14.6 K  20170929  19:26  ArielSchwartzman  
png  jetfractioneta5.png  r1  manage  41.4 K  20170929  19:26  ArielSchwartzman  
eps  jetfractioneta5_time30.eps  r1  manage  13.3 K  20170929  19:25  ArielSchwartzman  
jetfractioneta5_time30.pdf  r1  manage  14.7 K  20170929  19:25  ArielSchwartzman  
png  jetfractioneta5_time30.png  r1  manage  43.7 K  20170929  19:25  ArielSchwartzman  
jitter_vs_Qinj.pdf  r1  manage  14.1 K  20190527  17:34  SabrinaSacerdoti  ALTIROC1 jitter vs Qinj  
png  jitter_vs_Qinj.png  r1  manage  129.9 K  20190527  17:42  SabrinaSacerdoti  Altiroc1 Jitter vs Qinj 
jitter_vs_Qinj_zoom.pdf  r1  manage  14.2 K  20190527  17:39  SabrinaSacerdoti  ALTIROC1 jitter vs Qinj  
nHitsVsMuMultiple.pdf  r2 r1  manage  28.8 K  20180220  11:37  ChristianOhm  
png  nHitsVsMuMultiple.png  r2 r1  manage  378.3 K  20180220  11:39  ChristianOhm  
eps  occupancy_vs_r_41.eps  r2 r1  manage  16.5 K  20170704  19:19  DirkZerwas  
occupancy_vs_r_41.pdf  r2 r1  manage  18.0 K  20170704  19:19  DirkZerwas  
png  occupancy_vs_r_41.png  r2 r1  manage  39.8 K  20170704  19:18  DirkZerwas  
eps  occupancy_vs_r_42.eps  r1  manage  12.8 K  20161014  16:27  MartinAleksa  
occupancy_vs_r_42.pdf  r1  manage  15.4 K  20161014  16:27  MartinAleksa  
png  occupancy_vs_r_42.png  r1  manage  25.1 K  20161014  16:27  MartinAleksa  
eps  pufraction_2.4_3.8.eps  r2 r1  manage  12.4 K  20170619  08:47  MartinAleksa  
pufraction_2.4_3.8.pdf  r2 r1  manage  52.3 K  20170619  08:47  MartinAleksa  
png  pufraction_2.4_3.8.png  r2 r1  manage  113.0 K  20170619  08:47  MartinAleksa  
eps  pulseHeightDen7.eps  r1  manage  17.0 K  20180517  15:29  MakovecNikola  
gif  pulseHeightDen7.gif  r1  manage  13.7 K  20180517  15:29  MakovecNikola  
pulseHeightDen7.pdf  r1  manage  18.9 K  20180517  15:29  MakovecNikola  
eps  pulseHeightEff7.eps  r1  manage  15.6 K  20180517  15:29  MakovecNikola  
gif  pulseHeightEff7.gif  r1  manage  12.2 K  20180517  15:29  MakovecNikola  
pulseHeightEff7.pdf  r1  manage  19.0 K  20180517  15:29  MakovecNikola  
t0calib_fig01.pdf  r1  manage  15.8 K  20190711  13:59  EmmaElizabethTolley  HGTD T0 calibration performance from TDR draft April 2019  
png  t0calib_fig01.png  r1  manage  208.1 K  20190711  13:59  EmmaElizabethTolley  HGTD T0 calibration performance from TDR draft April 2019 
t0calib_fig02.pdf  r1  manage  15.3 K  20190711  13:59  EmmaElizabethTolley  HGTD T0 calibration performance from TDR draft April 2019  
png  t0calib_fig02.png  r1  manage  233.7 K  20190711  13:59  EmmaElizabethTolley  HGTD T0 calibration performance from TDR draft April 2019 
t0calib_fig03.pdf  r1  manage  15.4 K  20190711  13:59  EmmaElizabethTolley  HGTD T0 calibration performance from TDR draft April 2019  
png  t0calib_fig03.png  r1  manage  232.2 K  20190711  13:59  EmmaElizabethTolley  HGTD T0 calibration performance from TDR draft April 2019 
png  tdr_timing_50_30um_CFD501.png  r1  manage  83.3 K  20190509  22:52  SimoneMicheleMazza  
tdr_timing_50_30um_CFD50.pdf  r1  manage  19.5 K  20190509  22:52  SimoneMicheleMazza  
eps  timeResoProbe_vs_Gain_Probe_LandauMpv.eps  r1  manage  9.1 K  20161014  18:01  MartinAleksa  
timeResoProbe_vs_Gain_Probe_LandauMpv.pdf  r1  manage  14.5 K  20161014  18:01  MartinAleksa  
png  timeResoProbe_vs_Gain_Probe_LandauMpv.png  r1  manage  15.3 K  20161014  18:01  MartinAleksa  
eps  timeResoProbe_vs_biasVoltage.eps  r1  manage  9.6 K  20161014  18:01  MartinAleksa  
timeResoProbe_vs_biasVoltage.pdf  r1  manage  14.5 K  20161014  18:01  MartinAleksa  
png  timeResoProbe_vs_biasVoltage.png  r1  manage  16.2 K  20161014  18:01  MartinAleksa  
toa_vs_amp_TB_Oct18.pdf  r1  manage  23.0 K  20181130  13:58  ChristinaAgapopoulou  
png  toa_vs_amp_TB_Oct18.png  r1  manage  60.8 K  20181130  14:14  ChristinaAgapopoulou  
eps  vertex_density_hl.eps  r1  manage  11.9 K  20170705  17:43  DirkZerwas  
vertex_density_hl.pdf  r1  manage  15.0 K  20170705  17:43  DirkZerwas  
png  vertex_density_hl.png  r1  manage  13.4 K  20170705  17:43  DirkZerwas  
eps  vertex_density_run2.eps  r2 r1  manage  11.4 K  20171025  18:13  DirkZerwas  
vertex_density_run2.pdf  r2 r1  manage  15.6 K  20171025  18:14  DirkZerwas  
png  vertex_density_run2.png  r2 r1  manage  12.0 K  20171025  18:15  DirkZerwas  
eps  xyEffR_W11_HG11_mm.eps  r1  manage  176.2 K  20170124  18:06  MartinAleksa  
xyEffR_W11_HG11_mm.pdf  r1  manage  50.7 K  20170124  18:06  MartinAleksa  
png  xyEffR_W11_HG11_mm.png  r1  manage  41.7 K  20170124  18:06  MartinAleksa  
eps  zrho_event15_vtxid0.eps  r1  manage  15.9 K  20170929  19:28  ArielSchwartzman  
zrho_event15_vtxid0.pdf  r1  manage  16.4 K  20170929  19:28  ArielSchwartzman  
png  zrho_event15_vtxid0.png  r1  manage  93.4 K  20170929  19:28  ArielSchwartzman  
eps  zrho_jets_event15_sel0.eps  r1  manage  14.1 K  20170929  20:13  ArielSchwartzman  
zrho_jets_event15_sel0.pdf  r1  manage  15.3 K  20170929  20:13  ArielSchwartzman  
png  zrho_jets_event15_sel0.png  r1  manage  67.6 K  20170929  20:13  ArielSchwartzman 