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Public Liquid-Argon Calorimeter Plots on Upgrade

Introduction

The LAr Upgrade simulation plots below are approved to be shown by ATLAS speakers at conferences and similar events.

Please do not add figures on your own. Contact the LAr project leader in case of questions and/or suggestions.

Phase I Upgrade Plots

The plots from the LAr Phase I Upgrade TDR can be found here.

Demonstrator for the ATLAS LAr calorimeter Phase I Trigger Readout Upgrade

Two LAr Phase I trigger upgrade demonstrator boards (2 Demonstrator LTDBs, LAr Trigger Digitizer Boards) were installed in-situ on the LAr detector in July 2014 (coverage: 9π/16 < φ < 11π/16, 0 < η < 1.4). To receive the digital super-cell energies ABBA boards (LDPB pre-prototype) were installed in USA15. One ABBA board receives data from one LTDB 320 super cells. Super-cell data has been recorded for a large number of time slices with this pre-prototype backend electronics.

Data from 2017 runs: Energy comparison, timing resolution, overview plots and shower properties

General Information

  • The plots show data from pp collisions, physics run 334487, recorded on August 30, 2017.
  • Events were triggered by dedicated triggers requiring either a Level 1 electromagnetic cluster (EM) with ET > 20 GeV or a Level 1 Jet with ET > 100 GeV within the demonstrator acceptance (for EM (Jet) 1.6 < φ < 2.2 (2.3), 0 < η < 1.5). Those triggers were prescaled to have a combined event rate of approximately 1 Hz.
  • Events from the LAr demonstrator are matched to events collected in the main read-out using their bunch-crossing ID and brunch-crossing time. An alternative method is the matching according to the L1 ID of the events.
  • Two different prototypes of LAr Trigger Digitizer boards (LTDBs) were installed: The first for the region φ = 1.81 and φ = 1.91; the other for φ = 2.01 and φ = 2.11. This leads to different calibration and also slightly different results for those two regions.

Description of procedure

  • Energy and timing resolution of demonstrator system
    • All events for given supercell that satisfy |Edem - Emain | / Emain < 0.5 and Emain > 2 GeV are used.
    • The events are then further separated into 15 bins with equal statistics. For each of these bins the mean and RMS value is obtained.
    • The chosen functional parametrisation for the energy resolution is given by σ/E = b/E ⊕ c and thus neglects the stochastic (shower) component (∝ 1/E0.5), as the resolution is obtained from a ratio of the two read-outs and thus both are affected in the same way by the shower fluctuations.
    • The functional parametrisation for the timing resolution is chosen to match the public plots from the main read-out.
  • Overview of mean and RMS values for energy and timing distributions
    • Out of all events with |Edem - Emain | / Emain < 0.5 and Emain > 2 GeV are used, only the 20% highest energy deposits are used. &to; Same requirement as for plots in next section.
    • The mean and RMS values are then obtained and plotted for each layer.
  • Comparison of the shower modelling
    • Supercell with highest energy in shower fulfils Emain > 20 GeV and |Edem - Emain | / Emain < 0.1.
    • Other supercells are considered if they fulfil |Edem - Emain | / Emain < 0.8 and Escell > 1% Emax.
    • The two above criteria largely resemble the selection used to extract event displays.
    • Shower cone defined as supercells within |φ - φmax | < 0.15 (three iphi layers) and |η - ηmax | < 0.05 (width of a trigger tower) of maximum energy deposit.
    • The shower shape variables are defined in the LAr Phase I Upgrade TDR as follows (with E(i) as the energy measured in the i-th layer):
      Rη = (E(2)T,Δη×Δφ=0.075×0.2)/(E(2)T,Δη×Δφ=0.175×0.2)
      wη, 2 = ((Σ(E(2)T × η2)Δη×Δφ=0.075×0.2)/(E(2)T,Δη×Δφ=0.075×0.2) - ((Σ(E(2)T × η)Δη×Δφ=0.075×0.2)/(E(2)T,Δη×Δφ=0.075×0.2))2)0.5
      f3 = (E(3)T,Δη×Δφ=0.2×0.2)/(E(1)T,Δη×Δφ=0.075×0.2 + E(2)T,Δη×Δφ=0.075×0.2 + E(3)T,Δη×Δφ=0.2×0.2)

Measured energy comparison for the middle layer: The measured supercell (SC) energies of the LAr Phase I demonstrator are compared to summed LAr cell energies in ATLAS by calculating their ratio (ESC / ΣSC Ecells) for ESC > 2 GeV. The energy spectrum is subdivided into 15 bins and the width of the distribution shown. The supercells in the middle layer consist of 4 LAr cells. The width of the energy ratio is below 1 % in the high-energy tail.

pulseRelEDev_Ebinned_middle_RMS.png
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Measured energy comparison by layer: The measured supercell (SC) energies of the LAr Phase I demonstrator are compared to summed LAr cell energies in ATLAS by calculating their ratio (ESC / ΣSC Ecells) for ESC > 2 GeV. The energy spectrum is subdivided into 15 bins and the width of the distribution shown. The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. The width of the energy ratio is below 1 − 2 % in the high-energy tail, depending on the calorimeter layer.

comparison_energy_log.png
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Measured timing resolution for the middle layer: The measured supercell timing distribution of the LAr Phase I demonstrator is obtained for a selected supercell. It is subdivided into 15 energy-bins and the width of the distribution shown for each bin. The timing resolution is around 0.5 ns in the high-energy tail, such that the identification of the bunch-crossing ID is possible due to the resolution being much smaller than 25 ns. The supercells in the middle layer consist of 4 LAr cells.

pulseTiming_Ebinned_middle_RMS.png
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Measured timing resolution by layer: The measured supercell timing distribution of the LAr Phase I demonstrator is obtained for selected supercells. It is subdivided into 15 energy-bins and the width of the distribution shown for each bin. The timing resolution is below 0.5 − 1.0 ns in the high-energy tail, such that the identification of the bunch-crossing ID is possible due to the resolution being much smaller than 25 ns. The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively.

comparison_timing_log.png
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Energy scale for demonstrator φ-slice: The measured supercell (SC) energies of the LAr Phase I demonstrator are compared to summed LAr cell energies in ATLAS by calculating their ratio (ESC / ΣSC Ecells) and the mean value of the distribution is shown. The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. Good agreement is observed between the two systems, while residual shifts of the mean are due to the preliminary calibration of the supercells.

relEDev_Mean_etaDep_19.png
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Energy comparison for demonstrator φ-slice: The measured supercell (SC) energies of the LAr Phase I demonstrator are compared to summed LAr cell energies in ATLAS by calculating their ratio (ESC / ΣSC Ecells) and the RMS value of the distribution is shown. The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. The typical width of the energy ratio of the front and middle layers is well below 2%, while the presampler and back layer exhibit higher values.

relEDev_RMS_etaDep_19.png
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Mean timing for demonstrator φ-slice: The measured supercell timing distribution of the LAr Phase I demonstrator is obtained and the mean value of the distribution shown. The identification of the bunch-crossing ID is possible due to the low deviation of the mean from 0 ns and a RMS value much smaller than 25 ns. The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. The small shift of the means is due to the preliminary calibration of the supercells.

timing_Mean_etaDep_19.png
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Timing resolution for demonstrator φ-slice: The measured supercell timing distribution of the LAr Phase I demonstrator is obtained and the RMS value of the distribution shown. The identification of the bunch-crossing ID is possible due to the low deviation of the mean from 0 ns and a RMS value much smaller than 25 ns. The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. The typical timing resolution of the front and middle layers is below 1 ns, while the presampler and back layer have slight higher timing resolutions.

timing_RMS_etaDep_19.png
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Shower modelling of the demonstrator read-out: Supercell energy and timing information of the LAr Phase I demonstrator and summed LAr cell energies in ATLAS are compared for triggered showers. Several quantities are given and show a good agreement between the two read-outs. The events were observed in pp physics data, collected on August 30, 2017. Only well-reconstructed energy deposits above 1% of EmaxSC are used. Rη gives an estimate of the energy fraction of the shower cone in the middle layer, f3 is the energy fraction of the shower in the back layer and the width of the showers is further parametrised by wη, 2.

coneOverTotal.png
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cone.png
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energyResol.png
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Reta.png
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wEta.png
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f3.png
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Data from 2017 runs: pulse shapes and timing distributions

General Information
  • The plots show data from pp collisions, physics run 334487, recorded on August 30, 2017.
  • Events were triggered by dedicated triggers requiring either a Level 1 electromagnetic cluster (EM) with ET > 20 GeV or a Level 1 Jet with ET > 100 GeV within the demonstrator acceptance (for EM (Jet) 1.6 < φ < 2.2 (2.3), 0 < η < 1.5). Those triggers were prescaled to have a combined event rate of approximately 1 Hz.
  • Events from the LAr demonstrator are matched to events collected in the main read-out using their bunch-crossing ID and brunch-crossing time. An alternative method is the matching according to the L1 ID of the events.
Description of procedure
  • Measured pulse shapes for each layer and comparison with prediction:
    • Measured pulse shapes are obtained from raw data of the demonstrator system and normalised according to measured energy in main read-out (to obtain a pulse height in ADC/GeV). Different options for averaging several events are applied.
    • The Response Transformation Method is used to predict the pulse shape from calibration data. The normalisation is solely based on the dedicated demonstrator calibration coefficients and thus independent of the normalisation for the measured pulse shapes.
  • Pulse timing distributions for each layer are based on the reconstruction of the demonstrator pulses with OFCs.
  • Out of all events with |Edem − Emain |/Emain < 0.5 and Emain > 2 GeV, only the 20% highest energy deposits are used.
    • cuts were checked to create no bias on pulse shape or timing
    • cuts applied:
      • Presampler: 4.70 GeV (178 events)
      • Front layer: 7.80 GeV (133 events)
      • Middle layer: 11.05 GeV (282 events)
      • Back layer: 5.55 GeV (107 events)

Comparison of extracted pulse shapes for front layer: Measured pulse shapes (red) of a front layer supercell in the LAr Phase I demonstrator are compared to the, independently obtained, predicted pulse shape (black). The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. Very good agreement between the different averaging methods is obtained. Only the five measurements around the peak (dashed line) are used for the energy and timing reconstruction. The shape difference at ≅ 1000 ns is expected to originate from the modeling of the electrode position in the LAr gap.

extractionMethods_front_average.png
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extractionMethods_front_hist.png
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extractionMethods_front_maxE.png
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Measured pulse shapes for each layer: Measured pulse shapes (red) of supercells in the LAr Phase I demonstrator are compared to the, independently obtained, predicted pulse shapes (black). The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. Good agreement is observed between measurement and prediction, while remaining small normalisation offsets are due to the preliminary calibration of the supercells. Only the five measurements around the peak (dashed line) are used for the energy and timing reconstruction. The shape difference at ≅ 1000 ns is expected to originate from the modelling of the electrode position in the LAr gap.

pulseShape_presampler.png
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pulseShape_front.png
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pulseShape_middle.png
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pulseShape_back.png
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Pulse timing distributions for each layer: The measured supercell timing distribution of the LAr Phase I demonstrator is given for selected supercells. The identification of the bunch-crossing ID is possible due to the low deviation of the mean from 0 ns and a RMS value much smaller than 25 ns. The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. The average supercell energy of the used events is 6.7 GeV in the presampler, 13.3 GeV in the front layer, 24.2 GeV in the middle layer and 8.9 GeV in the back layer. The small shift of the means is due to the preliminary calibration of the supercells.

pulseTiming_presampler.png
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pulseTiming_front.png
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pulseTiming_middle.png
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pulseTiming_back.png
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Data from 2017 runs and event displays

  • The plots show data from pp collisions, physics run 328099, recorded between June 27 and June 28, 2017.
  • Events were triggered by dedicated triggers requiring either a Level 1 electromagnetic cluster (EM) with ET > 20 GeV or a Level 1 Jet with ET > 100 GeV within the demonstrator acceptance (for EM(Jet) 1.6 < φ < 2.2(2.3), 0 < η < 1.5). Those triggers were prescaled to have a combined event rate of approximately 1 Hz.
  • Events from the LAr demonstrator are matched to events collected in the main read-out using their bunch-crossing ID and brunch-crossing time. An alternative method is the matching according to the L1 ID of the events.

Correlation of energy measurements for each layer: The measured supercell (SC) energies of the LAr Phase I demonstrator are compared to summed LAr cell energies in ATLAS for ESC > 1 GeV. The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. Good agreement is observed between the two read-outs.

scatter_presampler.png
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scatter_front.png
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scatter_middle.png
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scatter_back.png
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Difference of energy measurements for each layer: The measured supercell (SC) energies of the LAr Phase I demonstrator are compared to summed LAr cell energies in ATLAS by calculating the difference (ESC − ΣSC Ecells) for ESC > 2 GeV. The supercells in the presampler, front, middle and back layer consist of 4, 8, 4 and 8 LAr cells, respectively. Good agreement is observed between the two read-outs. The width of the distribution is compatible with the expected noise level. The shift of the means is due to the preliminary calibration of the supercells.

difference_presampler.png
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difference_front.png
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difference_middle.png
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difference_back.png
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Event display of the partial demonstrator region: Supercell energies of the LAr Phase I demonstrator and summed LAr cell energies in ATLAS are given for the same shower. The event with ID 1912797011 was observed in the pp physics run 328099, obtained between June 27 and June 28, 2017. The geometrical coverage of the demonstrator system is partially shown. The volume of the depicted boxes is proportional to the deposited energy. Only energy deposits above 1% of EmaxSC are plotted.

Event_0_Demonstrator_Partial.png
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Event display of the demonstrator region: Supercell energies of the LAr Phase I demonstrator and summed LAr cell energies in ATLAS are given for the same shower. The event with ID 2214598379 was observed in the pp physics run 328099, obtained between June 27 and June 28, 2017. The full geometrical coverage of the demonstrator system is shown. The volume of the depicted boxes is proportional to the deposited energy. Only energy deposits above 1% of EmaxSC are plotted.

Event_0_Demonstrator_Full.png
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Event_1_Demonstrator.png
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Event_2_Demonstrator.png
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Event_3_Demonstrator.png
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Noise and calibration pulse shapes

Noise values and calibration pulses with several amplitudes are shown in the following. Plots show ADC counts (12 bits ADC) as well as their equivalent transverse energies for the different sections of the calorimeter.

Super-cell pulse shapes for each layer: Responses of four super cells (one from each layer) from the LAr Phase I demonstrator installed in ATLAS to injected calibration pulses (DAC = 1000 counts to each LAr cell), the equivalent energy for DAC = 1000 is shown in subsequent plots. The super cell outputs are the sums of 4, 8, 4 and 8 LAr cells for Presampler, Front, Middle and Back layer, respectively. Size and shape of pulses are as expected and vary due to different detector and electronics properties.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/fig1.png
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Pulse maximum in ADC counts for each layer: Rapidity dependence of the pulse maximum in ADC counts (pedestal subtracted) for the super cells from the LAr Phase I demonstrator in ATLAS for injected calibration pulses (DAC = 1000 counts to each LAr cell), the equivalent energy for DAC = 1000 is shown in subsequent plots. The super cell outputs are the sums of 4, 8, 4 and 8 LAr cells for Presampler, Front, Middle and Back layer, respectively. The variations in response, especially in the back layer and at η = 0.8, are due to the change in electrode segmentation, calibration and readout electronics.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/fig2.png
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Equivalent transverse energy for each layer: Rapidity dependence of the equivalent transverse energy for an injected calibration pulse of DAC = 1000 counts into each LAr cell. The super cell outputs are the sums of 4, 8, 4 and 8 LAr cells for Presampler, Front, Middle and Back layer, respectively. The jump seen at η = 0.8 reflects the change of absorber thickness, electrodes and calibration resistors.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/fig3.png
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Noise level of super cells in ADC counts: Rapidity dependence of the noise (RMS) in ADC counts for the super cells from the LAr Phase I demonstrator in ATLAS. The super cell outputs are the sums of 4, 8, 4 and 8 LAr cells for Presampler, Front, Middle and Back layer, respectively. The jump seen at η = 0.8 reflects the change of electrodes’ segmentation at that position. The noise level is well below 1 ADC count and consistent with test bench measurements.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/fig4.png
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Noise level of super cells in transverse energy: Rapidity dependence of the noise (RMS) in transverse energy for the super cells from the LAr Phase I demonstrator in ATLAS. The super cell outputs are the sums of 4, 8, 4 and 8 LAr cells for Presampler, Front, Middle and Back layer, respectively. The jump seen at η = 0.8 reflects the change of absorber thickness, electrodes and calibration resistors. The noise level is as expected between 100 and 250 MeV per super cell.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/fig5.png
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Pulse shapes of a front layer super cell: Pulse shapes of a super cell from the LAr Phase I demonstrator installed in ATLAS for injected calibration pulses with different amplitudes (DAC = 2000, 4000, 6000, 8000, 10000 counts), the equivalent energy for these DAC values is shown in subsequent plots. The super cell outputs are the sums of 8 LAr front layer cells. Size and shape of pulses are as expected and show good linearity up to DAC = 8000, while beyond, analog saturation occurs upstream of the demonstrator board.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/fig6.png
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Pulse maximum versus DAC value: Pulse maximum (in ADC counts) for four different super cells from the LAr Phase I demonstrator installed in ATLAS for injected calibration pulses with different amplitudes (DAC = 2000, 4000, 6000, 8000, 10000 counts), the equivalent energies for these DAC values are shown in subsequent plots. The super-cell outputs are the sums of 8 (4) LAr front (middle) layer cells. Good linearity up to DAC = 8000 (DAC = 6000) for the front (middle) layer is observed, while beyond, analog saturation occurs upstream of the demonstrator board.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/fig7.png
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Pulse maximum versus transverse energy: Pulse maximum (in ADC counts) for four different super cells from the LAr Phase I demonstrator installed in ATLAS for injected calibration pulses with different amplitudes (DAC = 2000, 4000, 6000, 8000, 10000 counts), plotted in units of equivalent transverse energy. The super-cell outputs are the sums of 8 (4) LAr front (middle) layer cells. Analog saturation upstream of the demonstrator board occurs at different transverse energy values depending on the calorimeter layer and rapidity.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/fig8.png
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Total noise on the trigger readout path of the demonstrator test set-up: Here the RMS on the trigger readout path in MeV is shown. It was measured in a setup which is equivalent to a crate in ATLAS, with a half-full Front End Crate (FEC) equipped with Front End Boards (FEBs). Trigger towers 1-14 correspond to an eta-region of 0 to 1.4. Trigger towers 16-29 are the same in eta, but adjacent in phi. The values represented by the full circles were measured by a spectrum analyzer, the values shown in open circles were measured with Flash ADCs. For the computation a pedestal run with 5000 events and 8 samples was used.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/FADC_noise_poster.png
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Fraction of total noise which is coherent for Phase I demonstrator measured in ATLAS: Here the total noise which is coherent is shown as fraction of the total noise per readout channel (Coherent Noise Fraction = CNF) The CNF for feedthroughs (FT) 7-12 on the detector has been computed, of which FT 9 and 10 belong to the demonstrator crate I06, FT 7 and 8 to I05 and FT 11 and 12 to I07. For the computation a pedestal run with 40000 events and 32 samples was used. The board in the first slot reads out the presampler, the boards in the following seven slots read out the front layer, the next two boards the back layer and the last four boards the middle layer of the calorimeter. The last entry is the CNF of the whole halfcrate. The coherent noise fraction rho was calculated using the formula in this link.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/cnf_poster.png
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LAr Trigger Digitizer Board (LTDB) demonstrator noise measured on the demonstrator installed in ATLAS: Here, the RMS of the 12-bit ADC of the 320 channels of the LTDB demonstrator measured in USA15 is shown. For the computation a pedestal run with 16384 events was used. One ADC count corresponds to roughly 125 MeV..

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/LTDB_noise.png
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LAr Trigger Digitizer Board (LTDB) demonstrator pedestal measured on the demonstrator installed in ATLAS: Here, the pedestal values of the 12-bit ADC of the 320 channels of the LTDB demonstrator measured in USA15 are shown. For the computation a pedestal run with 16384 events was used.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/LTDB_pedestal.png
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Total noise on main readout of calorimeter cells of demonstrator crate I06 (ATLAS): In this plot, the RMS of the 128 channels of the Front End Boards (FEBs) of the demonstrator crate installed in ATLAS is shown. The FEBs read out the calorimeter cells. There are 28 such boards in one Front End Crate (FEC). The FEBs read out signals from different layers of the calorimeter. The noise levels of the boards vary because different capacitances and gains are applied to their respective cells. For the computation of the RMS a pedestal run with 3000 events and 32 samples was used. The noise level is not higher compared to the neighboring crates on the detector (e.g. see plots for crate I05).

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/totalnoise_demonstrator.png
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Total noise on main readout of calorimeter cells of crate I05 (ATLAS): In this plot, the RMS of all channels of the FEBs of one of the neighbour crates (I05) of the demonstrator crate I06 in ATLAS is shown. For the computation of the noise a pedestal run with 3000 events and 32 samples was used.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/total_noise_I05.png
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Phase II Upgrade Plots

Estimated quantization noise as function of energy in the front layer of the LAr EM barrel calorimeter, with the two gain system proposed for the Phase-II LAr Calorimeter readout (low gain curve in red, high gain curve in blue). The quantization noise curves assume the use of a 12-bit successive approximation register (SAR) with a dynamic range enhancer (DRE) to obtain a 14-bit ADC with 12-bit precision. Gain switching occurs close to the highest energy digitized in the high gain.

intr_electr_res_fl_prel.png
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Estimated quantization noise as function of energy in the LAr EM middle layer in the endcap outer wheel, with the two gain system proposed for the Phase-II LAr Calorimeter readout (low gain curve in red, high gain curve in blue). The quantization noise curves assume the use of a 12-bit successive approximation register (SAR) with a dynamic range enhancer (DRE) to obtain a 14-bit ADC with 12-bit precision. Gain switching occurs close to the highest energy digitized in the high gain.

intr_electr_res_ml_prel.png
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Bipolar shapers can have different numbers of integration stages, as well as various peaking times, which both affect the total noise (electronics plus pileup) on the analog pulse of the Phase-II LAr Calorimeter readout. The figure shows the total noise as a function of the level of pileup, μ, for a cell from the EM middle layer at η = 0.5, obtained after optimal filtering, for different number of integration stages in the shaper. The optimal filtering coefficients (OFC) are computed for each case separately. No significant differences in the noise level can be seen in the EM case. These results do not include a detailed simulation of the electronics circuit, which could affect the results.

shaper.png
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Bipolar shapers can have different numbers of integration stages, as well as various peaking times, which both affect the total noise (electronics plus pileup) on the analog pulse of the Phase-II LAr Calorimeter readout. The figure shows the total noise as a function of the level of pileup, μ, for a HEC cell in the first layer at η = 2.35, obtained after optimal filtering, for different number of integration stages in the shaper. The optimal filtering coefficients (OFC) are computed for each case separately. For the HEC, a CR-(RC)3 shaper improves the noise by 5% over a CR-(RC)2. These results do not include a detailed simulation of the electronics circuit, which could affect the results.

Total_Noise.png
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NIEL Simulations for HEC Cold Electronics in Phase II

Si NIEL fluence in ATLAS under HL-LHC conditions after 3000 fb-1 and with an applied safety factor of 2 to account for simulation uncertainties. The color coded fluences in the ASICs are shown at the r-z locations of the corresponding readout regions of the HEC.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/HL-LHC-NIEL-PSB-Region-FLUKA-RBTF2013-2x3000ifb.jpg
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Si NIEL fluence in ATLAS under HL-LHC conditions after 3000 fb-1 and with an applied safety factor of 2 to account for simulation uncertainties. The color coded fluences in the ASICs are shown at the r-z locations of the corresponding readout regions of the HEC.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/HL-LHC-NIEL-PSB-Region-FLUKA-RBTF2013-2x3000ifb.jpg
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Simulated Noise

Simulated noise in the Liquid Argon and Tile calorimeters at the electron scale (bunch spacing $\Delta$t=25ns): Quadratic sum of simulated electronics and pile-up noise per calorimeter cell for each calorimeter layer as function of pseudo-rapidity. The pile-up simulation is done by overlaying GEANT4 simulated events from PYTHIA (v6.4) including non diff ractive and di ffractive events. The overlay takes into account the full sensitive time of the detector (~500ns for the LAr) and the bunch train structure. For proton-proton collisions at √s = 14TeV and a bunch spacing of $\Delta$t=25ns. The luminosity and corresponding average overlaying interactions per bunch crossing <µ> (pile-up events) are specified below (and inside) each figure. The standard ATLAS electronics are assumed in the simulation. A comparison of data and MC for <µ> = 0 and  <µ> = 14 can be seen at the corresponding links for the LAr and for the Tile calorimeter.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-30-25.png
Total noise for L=1.09x1034 corresponding to <µ>=30
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-40-25.png
Total noise for L=1.45x1034 corresponding to <µ>=40
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-60-25.png
Total noise for L=2.17x1034 corresponding to <µ>=60
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-80-25.png
Total noise for L=2.90x1034 corresponding to <µ>=80
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-100-25.png
Total noise for L=3.62x1034 corresponding to <µ>=100
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-140-25.png
Total noise for L=5.07x1034 corresponding to <µ>=140
eps version

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-200-25.png
Total noise for L=7.25x1034 corresponding to <µ>=200
eps version

Simulated noise in the Liquid Argon and Tile calorimeters at the electron scale (bunch spacing $\Delta$t=50ns): Quadratic sum of simulated electronics and pile-up noise per calorimeter cell for each calorimeter layer as function of pseudo-rapidity. The pile-up simulation is done by overlaying GEANT4 simulated events from PYTHIA (v6.4) including non diff ractive and di ffractive events. The overlay takes into account the full sensitive time of the detector (~500ns for the LAr) and the bunch train structure. For proton-proton collisions at √s = 14TeV and a bunch spacing of $\Delta$t=50ns. The luminosity and corresponding average overlaying interactions per bunch crossing <µ> (pile-up events) are specified below (and inside) each figure. The standard ATLAS electronics are assumed in the simulation. A comparison of data and MC for <µ> = 0 and  <µ> = 14 can be seen at the corresponding links for the LAr and for the Tile calorimeter.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-30-50.png
Total noise for L=0.54x1034 corresponding to <µ>=30
eps version

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-40-50.png
Total noise for L=0.73x1034 corresponding to <µ>=40
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-60-50.png
Total noise for L=1.09x1034 corresponding to <µ>=60
eps version

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-80-50.png
Total noise for L=1.45x1034 corresponding to <µ>=80
eps version

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-100-50.png
Total noise for L=1.81x1034 corresponding to <µ>=100
eps version

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-140-50.png
Total noise for L=2.54x1034 corresponding to <µ>=140
eps version

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_plot_OFLCOND-MC12-HPS-19-200-50.png
Total noise for L=3.62x1034 corresponding to <µ>=200
eps version

High Granularity FCal Performance Studies

The below results are based on simulated VBF 2.6 TeV Higgs boson production and di-jet production at 14 TeV with μ=190-200 assuming the current ATLAS FCal, a high-granularity sFCal and the FCal with reduced acceptance

Total Noise Ratio: Noise ratio per calorimeter cell as a function of |η| for all layers for a high- granularity sFCal over FCal at a centre-of-mass energy of 14 TeV and for a mean number of pile-up event of μ=200. Small deviations from 1 in the inner-most (s)FCal1 and full (s)FCal2/3 layers are due to the 6% denser sFCal1 compared to FCal1 which causes inelastic pp collisions to deposit more energy in the first module. In the outer region of sFCal1 the noise is 0.4 times the FCal1 noise. The ratio 1.1/0.4 = 2.75 of ratios for inner over outer cells is non-trivial and indicates that an sFCal with finer granularity improves the separation of hard-scatter signal from pile-up. The granularity ratio is 4. Therefore a double ratio of 4 would mean no improvement at all since all small cells would be fully correlated. A double ratio of 2 would be the maximal possible improvement in case all small cells are uncorrelated. 2.75 lies between these extremes, and since it is smaller than 4 means that the increase in granularity helps to suppress pile-up.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/noise_tot_ratio_sFCal_SmallGaps_over_FCal_mu200-new_prelim.png
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Number of Jet Constituents: Distribution of the number of constituents (clusters) for quark jets produced in the vector-boson fusion process 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙−𝑣 𝑙+𝑣 𝑞𝑞 at 14 TeV centre-of-mass energy simulated for the current ATLAS FCal, a high-granularity sFCal, and three scenarios with reduced FCal acceptance. The increase in granularity and better separation of signal from pile-up leads to larger number of constituents in the sFCal compared to FCal. The vector-boson fusion events were simulated with the Powheg and Pythia8 Monte Carlo generators in narrow-width approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pile-up events, μ, between 190 and 210.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/sFCalReview_NConst_VBF2600_prelim.png
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Cell Significance: Cell significance (Ecell/σcell) for the cell with the largest absolute cell energy over total noise simulated for the current ATLAS FCal, a high-granularity sFCal, and three scenarios with reduced FCal acceptance. Clusters are seeded when the absolute ratio is above 4. Cluster splitting can lead to entries with smaller ratios. The sFCal distribution is enhanced on the positve side while it remains close to the FCal distribution on the negative side. The negative entries are due to pile-up only, while on the positve side signal and pile- up contribute. The increase of mainly the positive side indicates that the signal detection ability is improved for the sFCal while the background remains on the same level. The distributions are obtained for vector-boson fusion events 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙−𝑣 𝑙+𝑣 𝑞𝑞 at 14 TeV centre-of-mass energy, simulated with the Powheg and Pythia8 Monte Carlo generators in narrow-width approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pile-up events, μ, between 190 and 210.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/sFCalReview_CellSig_VBF2600_prelim.png
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Average pT Density: Shown is the simulated profile of the average median pT density, ρ, evaluated from positive-energy cell towers in the ATLAS LAr calorimeters. For the forward calorimeter, five different scenarios are studied: the current ATLAS FCal, a high-granularity sFCal, and three scenarios with reduced FCal acceptance. When a ρ-based pile-up suppression will be applied in the jet reconstruction it is expected that a larger amount of pT will be removed for jets in the forward region in case of the sFCal. The distributions are obtained for vector-boson fusion events 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙−𝑣 𝑙+𝑣 𝑞𝑞 at 14 TeV centre-of-mass energy, simulated with the Powheg and Pythia8 Monte Carlo generators in narrow-width approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pile-up events, μ, between 190 and 210.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/MedianDensityProfile_vs_eta.png
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Number of Jets: Simulated integral jet pT distribution for hard scattering and pile-up jets and the relative fraction of hard scattering jets detected in the ATLAS FCal and in a high-granularity sFCal. An area-based pT subtraction is applied. The amount of pT subtracted from a jet is increased by a factor of 10 (thereby effectively killing the jet) in the case that it fails one of the jet shape variable cuts, which are based on jet width, transverse momentum sum of the jet constituents relative to the jet direction and the electromagnetic energy fraction. The distributions are obtained for vector-boson fusion events 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙−𝑣 𝑙+𝑣 𝑞𝑞 at 14 TeV centre-of-mass energy, simulated with the Powheg and Pythia8 Monte Carlo generators in narrow-width approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pile-up events, μ, between 190 and 210.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/PUSuppression_NJetsAndHSFraction_vs_PtCut.png
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Ratio of Pile-Up Jets: Ratio of number of identified pile-up jets and total number of reconstructed jets as a function of efficiency for determining a hard-scattering jet as simulated in di-jet events at 14 TeV for the ATLAS FCal and a high-granularity sFCal. All jets are selected in the pseudo-rapidity range 3.8<|η|<4.2 and in the pT range between 50 GeV and 70 GeV. Also shown is the double- ratio comparing the sFCal and FCal performance. The jet classification was performed using a likelihood ratio constructed from the jet width, the jet mass, the transverse momentum sum of the jet constituents relative to the jet direction, and the number of jet constituents (clusters).

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/ROC-eff-eta38to42.png
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Number of Pile-Up Jets: Number of identified pile-up jets per event as a function of efficiency for determining a hard-scattering jet in high-mass VBF Higgs events at 14 TeV for the ATLAS FCal and a high- granularity sFCal. All jets are selected in the pseudo-rapidity range 3.2<|η|<3.8 and in the pT range above 20 GeV. The jet reconstruction requires a positive cluster-vertex-fraction and a pile- up correction based on the average median pT density, ρ, evaluated from positive-energy cell towers in the ATLAS LAr calorimeters. The simulation of charged particle tracks is based on the ITk tracking system assuming a tracking coverage of |η|<4 and an ideal ITk detector resolution. The distributions are obtained for vector-boson fusion events 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙−𝑣 𝑙+𝑣 𝑞𝑞 at 14 TeV centre-of-mass energy, simulated with the Powheg and Pythia8 Monte Carlo generators in narrow-width approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pile-up events, μ, between 190 and 210.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/ROC_effHS_pt20_extr_JES_preliminary.png
eps version, pdf version

LAr Phase-II Energy Reconstruction with Artificial Neural Networks and Implementation in FPGAs

The results below are based on simulated energy deposits in the LAr EMB at a mean number of pile-up events of <μ>=140. The simulation of the sample sequences have been prepared using the AREUS software. The neural network development has been performed using the Keras/Tensorflow frameworks. FPGA implementations are for an Intel Stratix-10.

Top: Sample sequence (black) of an EMB Middle cell at (η,φ)=(0.5125,0.0125) as simulated by AREUS, together with the true transverse energy deposits (yellow) shifted by five BC to improve the plot visibility, at <μ>=140 as a function of the BC counter.
Middle: The Convolutional Neural Network (CNN) for pulse tagging provides a hit probability (green) for each BC. Its training is based on a binary input sequence (blue) with values of unity for energy deposits 3 σ above noise threshold.
Bottom: The transverse energy reconstruction CNN makes its predictions (green) based on the probability of the tagging layer and the input samples.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_sequence.png
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Architecture of an Artificial Neural Network (ANN) with four convolutional layers. The dataflow goes from bottom to top. The input sequence is first processed by the tagging part of the network in the bottom part of the figure. After a concatenation layer, the tag output and the input sequence are processed by the transverse energy reconstruction part of the ANN. The total receptive field of this network incorporates 13 bunch crossings.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_architecture.png
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Signal efficiency and background rejection receiver operating characteristic (ROC) curves of the two presented Artificial Neural Networks (yellow, purple) and their tagging part (green), compared to the Optimal Filtering (OF) with MaxFinder (red). Signal refers to deposits with ETtrue above 240 MeV (3σ above noise threshold), background those below. Efficiencies are calculated for an EMB Middle LAr cell (η=0.5125 and φ=0.0125) simulated with AREUS assuming <μ>=140. Approaching the upper right corner of the plot indicates signal efficiencies of 100% and a background rejection of 100% and would therefore be optimal. For better visibility, the results are shown only in the range above 75%. Filled bands represent the statistical uncertainty.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_ROC.png
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Single-cell application of Long Short-Term Memory (LSTM) based recurrent networks. The LSTM cell and its dense decoder are computed at every bunch crossing (BC). They analyse the present signal amplitude and output of the past cell, accumulating long range information through a recurrent application. By design, the network predicts the deposited transverse energy with a delay of six BC.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_LSTM_singlecell_archi.png
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Sliding window application of LSTM based recurrent networks. At each instant, the signal amplitude of the four past and present bunch crossings are input into an LSTM layer. The last cell output is concatenated with a dense operation consisting of a single neuron, and providing the transverse energy prediction.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_LSTM_slidewindow_archi.png
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Transverse energy reconstruction performance for the optimal filtering and the various ANN algorithms. The performance is assessed by comparing the true transverse energy deposited in an EMB Middle LAr cell (η=0.5125 and φ=0.0125) to the ANN prediction after simulating the sampled pulse with AREUS assuming <μ>=140. Only energies 3σ above the noise threshold are considered. The mean, the median, the standard deviation, and the smallest range that contains 98% of the events are shown.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_perf_summary_plot.png
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Resolution of the transverse energy reconstruction as a function of the gap, i.e. the distance in units of bunch crossings (BC), between two consecutive energy deposits for the optimal filtering (OF) algorithm and a subsequent maximum finder. Only deposits with ETtrue above 240 MeV (3σ above noise threshold) are considered. Inputs to the OF are sampled pulses obtained from the simulation of an EMB Middle LAr cell (η=0.5125 and φ=0.0125) with AREUS using <μ>=140.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_ofmax_gap_resolution.png
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Resolution of the transverse energy reconstruction as a function of the gap, i.e. the distance in units of bunch crossings (BC), between two consecutive energy deposits for the Long Short-Term Memory single-cell algorithm. Only deposits with ETtrue above 240 MeV (3σ above noise threshold) are considered. Inputs to the LSTM are sampled pulses obtained from the simulation of an EMB Middle LAr cell (η=0.5125 and φ=0.0125) with AREUS using <μ>=140.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_lstm_singlecell_gap_resolution.png
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Resolution of the transverse energy reconstruction as a function of the gap, i.e. the distance in units of bunch crossings (BC), between two consecutive energy deposits for the Vanilla-RNN sliding-window algorithm. Only deposits with ETtrue above 240 MeV (3σ above noise threshold) are considered. Inputs to the Vanilla-RNN are sampled pulses obtained from the simulation of an EMB Middle LAr cell (η=0.5125 and φ=0.0125) with AREUS using <μ>=140.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_vrnn_gap_resolution.png
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Resolution of the transverse energy reconstruction as a function of the gap, i.e. the distance in units of bunch crossings (BC), between two consecutive energy deposits for the Convolutional Neural Network (CNN) algorithm. Only deposits with ETtrue above 240 MeV (3σ above noise threshold) are considered. Inputs to the CNN are sampled pulses obtained from the simulation of an EMB Middle LAr cell (η=0.5125 and φ=0.0125) with AREUS using <μ>=140.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_gap_resolution.png
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Relative deviation of the firmware implementations from the software results for the different transverse energy reconstruction Artificial Neural Networks (ANN). Only bunch crossings with predictions different from zero and true transverse energies larger than 240 MeV are considered. Inputs to the ANNs are sampled pulses obtained from the simulation of an EMB Middle LAr cell (η=0.5125$ and &ph;i=0.0125) with AREUS assuming <μ>=140.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_hlsVScpp_reso.png
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Results below are based on updated AREUS simulations with energy deposits in the LAr EMB at a mean number of pile-up events of <μ>=140 and also other cells as indicated in the caption. Results refer to new convolutional neural networks using the Keras/Tensorflow frameworks.

Architecture of an Artificial Neural Network (ANN) with four convolutional layers and tagging part. The data flow goes from bottom to top. The input sequence is first processed by the tagging part of the network in the bottom half of the figure. After a concatenation layer, the tag output and the input sequence are processed by the transverse momentum magnitude reconstruction part of the ANN. The total field of view of this network incorporates 20 bunch crossings.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/NN_architektur_T2Conv_k37_Dil1_E2Conv_k58_Dil1_LN4.png
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Architecture of an Artificial Neural Network (ANN) with two convolutional layers. The data flow goes from bottom to top. The input sequence is first processed by a convolutional layer with dilation 1, which densely screens its input samples. The second convolutional layer with dilation 3 sparsely screens the output of the previous layer. This results in a larger total field of view of 22 bunch crossings without increasing the number of parameters.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/NN_architektur_E2Conv_k76_Dil13_LN71.png
eps version, pdf version
Top: Display of a sample sequence (black) of an EMB Middle cell at (η,φ)=(0.5125,0.0125), together with the true transverse momentum magnitude ET (blue) and the Optimal Filtering (OF) with MaxFinder output (red) as a function of the BC counter.
Bottom: Similar display but with the output of the 2-Conv Convolutional Neural Network (CNN) (red).
The sequence was simulated with AREUS, using <μ>=140. True energies include in-time pile-up. The CNN shows an improvement in detecting hits coming in close succession. The two hits with a gap of 2 around bunch crossing 5 are well reconstructed by the CNN. That is not the case for the OF, which detects only a single peak. Around BC 65 a hit follows within an undershoot of a previous hit. While the OF underestimates its energy, the CNN reconstructs it much better.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_vs_of_sequence.png
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Difference of the reconstructed and true transverse momentum magnitude (ET) as a function of the gap, i.e. the distance in units of bunch crossings (BC), between two consecutive energy deposits for the 4-Conv with Tagging Convolutional Neural Network (CNN) algorithm. Inputs to the CNN are sampled pulses obtained from the simulation of an EMB Middle LAr cell (η=0.5125 and φ=0.0125) with AREUS using <μ>=140. The structure at ETpred-ETtrue=-0.5 GeV arises, since only deposits with ETtrue (including in-time pile-up) above 0.5 GeV are considered.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_4conv_gap_resolution_EMBMid05.png
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Difference of the reconstructed and true transverse momentum magnitude (ET) as a function of the gap, i.e. the distance in units of bunch crossings (BC), between two consecutive energy deposits for the 2-Conv Convolutional Neural Network (CNN) algorithm for a set of representative detector cells. Inputs to the CNN are sampled pulses obtained from the AREUS simulation using <μ>=140. The structure at ETpred-ETtrue=-0.5 GeV arises, since only deposits with ETtrue (including in-time pile-up) above 0.5 GeV are considered.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_2conv_gap_resolution_EMBPres05.png
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_2conv_gap_resolution_EMBFront05.png
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_2conv_gap_resolution_EMBFront10.png
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_2conv_gap_resolution_EMBMid05.png
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_2conv_gap_resolution_EMBMid10.png
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_2conv_gap_resolution_OuterEMEC20.png
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Difference of the reconstructed and true transverse momentum magnitude (ET) as a function of the gap, i.e. the distance in units of bunch crossings (BC), between two consecutive energy deposits for the Optimal Filtering (OF) algorithm and a subsequent MaxFinder. Inputs to the OF are sampled pulses obtained from the simulation of an EMB Middle LAr cell (η=0.5125 and φ=0.0125) with AREUS using <μ>=140. The structure at ETpred-ETtrue=-0.5 GeV arises, since only deposits with ETtrue (including in-time pile-up) above 0.5 GeV are considered.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_ofmax_gap_resolution_EMBMid.png
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Distribution of the deviation between predicted and true transverse momentum magnitude (ET) for the Optimal Filtering Algorithm (OF) with MaxFinder (red), a four-layered Convolutional Neural Network with tagging part (orange) and a plain two-layered Convolutional Neural Network (blue). The histogram content is normalized to the number of total entries within the displayed axis range. Inputs to the filters are sampled pulses obtained from the simulation of an EMB Middle LAr cell (η=0.5125 and φ=0.0125) with AREUS using <μ>=140. Artificial signal hits following a flat energy distribution up to 130 GeV with a gaussian distributed distance (μ=30, σ=10 BC) are injected.
Top: Only signals with ETtrue lower than 0.5 GeV contribute.
Middle: Only signals with ETtrue larger than 0.5 GeV contribute.
Bottom: Median with 68 % range and gaussian fitted mean with standard deviation for bins of ETtrue.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_deviation_distribution_lowE.png
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_deviation_distribution_highE.png
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https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_deviation_distribution_statistics.png
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Spectrum of transverse momentum magnitude (ET) predictions for the two presented Artificial Neural Networks (orange, blue), compared to the Optimal Filter (OF) with MaxFinder, for bunch crossings without any energy deposit. ETtrue includes in-time pile-up. The evaluation data stream was simulated for an EMB Middle LAr cell (η=0.5125 and φ=0.0125) with AREUS using <μ>=140.

https://twiki.cern.ch/twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/phase2_ann_cnn_fakes.png
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Major updates:
-- MartinAleksa - 06-Aug-2013

Responsible: MartinAleksa
Subject: public

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Unknown file formateps Event_0_Demonstrator_Full.eps r1 manage 21.7 K 2017-09-12 - 14:03 SteffenStaerz Demonstrator event display: event 0 (full)
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PNGpng Event_0_Demonstrator_Full.png r2 r1 manage 96.2 K 2017-09-12 - 14:47 SteffenStaerz Demonstrator event display: event 0 (full)
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PNGpng Event_0_Demonstrator_Partial.png r3 r2 r1 manage 102.3 K 2017-09-12 - 14:48 SteffenStaerz Demonstrator event display: event 0 (zoom)
Unknown file formateps Event_0_Main_Full.eps r1 manage 22.8 K 2017-09-12 - 14:03 SteffenStaerz Demonstrator event display: event 0 (full)
PDFpdf Event_0_Main_Full.pdf r1 manage 17.4 K 2017-09-12 - 14:03 SteffenStaerz Demonstrator event display: event 0 (full)
PNGpng Event_0_Main_Full.png r2 r1 manage 98.4 K 2017-09-12 - 14:49 SteffenStaerz Demonstrator event display: event 0 (full)
Unknown file formateps Event_0_Main_Partial.eps r1 manage 23.6 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 0 (zoom)
PDFpdf Event_0_Main_Partial.pdf r1 manage 17.7 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 0 (zoom)
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Unknown file formateps Event_1_Demonstrator.eps r1 manage 22.2 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 1
PDFpdf Event_1_Demonstrator.pdf r1 manage 17.3 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 1
PNGpng Event_1_Demonstrator.png r2 r1 manage 97.0 K 2017-09-12 - 14:51 SteffenStaerz Demonstrator event display: event 1
Unknown file formateps Event_1_Main.eps r1 manage 23.9 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 1
PDFpdf Event_1_Main.pdf r1 manage 22.4 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 1
PNGpng Event_1_Main.png r2 r1 manage 98.8 K 2017-09-12 - 14:51 SteffenStaerz Demonstrator event display: event 1
Unknown file formateps Event_2_Demonstrator.eps r1 manage 22.3 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 2
PDFpdf Event_2_Demonstrator.pdf r1 manage 17.3 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 2
PNGpng Event_2_Demonstrator.png r2 r1 manage 97.4 K 2017-09-12 - 14:52 SteffenStaerz Demonstrator event display: event 2
Unknown file formateps Event_2_Main.eps r1 manage 26.2 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 2
PDFpdf Event_2_Main.pdf r1 manage 23.6 K 2017-09-12 - 14:04 SteffenStaerz Demonstrator event display: event 2
PNGpng Event_2_Main.png r2 r1 manage 104.4 K 2017-09-12 - 14:53 SteffenStaerz Demonstrator event display: event 2
Unknown file formateps Event_3_Demonstrator.eps r1 manage 22.8 K 2017-09-12 - 14:05 SteffenStaerz Demonstrator event display: event 3
PDFpdf Event_3_Demonstrator.pdf r1 manage 17.5 K 2017-09-12 - 14:05 SteffenStaerz Demonstrator event display: event 3
PNGpng Event_3_Demonstrator.png r2 r1 manage 96.0 K 2017-09-12 - 14:53 SteffenStaerz Demonstrator event display: event 3
Unknown file formateps Event_3_Main.eps r1 manage 21.4 K 2017-09-12 - 14:05 SteffenStaerz Demonstrator event display: event 3
PDFpdf Event_3_Main.pdf r1 manage 17.0 K 2017-09-12 - 14:05 SteffenStaerz Demonstrator event display: event 3
PNGpng Event_3_Main.png r2 r1 manage 94.5 K 2017-09-12 - 14:54 SteffenStaerz Demonstrator event display: event 3
Unknown file formateps FADC_noise_poster.eps r1 manage 17.1 K 2015-02-23 - 17:28 MartinAleksa Phase I Upgrade Demonstrator Plots (eps)
PNGpng FADC_noise_poster.png r1 manage 14.0 K 2015-02-23 - 17:08 MartinAleksa Phase I Upgrade Demonstrator Plots
Unknown file formateps HL-LHC-NIEL-PSB-Region-FLUKA-RBTF2013-2x3000ifb.eps r1 manage 20.2 K 2014-03-18 - 17:34 MartinAleksa NIEL Simulations for HEC Cold Electronics in Phase II
JPEGjpg HL-LHC-NIEL-PSB-Region-FLUKA-RBTF2013-2x3000ifb.jpg r1 manage 237.6 K 2014-03-18 - 17:34 MartinAleksa NIEL Simulations for HEC Cold Electronics in Phase II
PDFpdf HL-LHC-NIEL-PSB-Region-FLUKA-RBTF2013-2x3000ifb.pdf r1 manage 11.0 K 2014-03-18 - 17:34 MartinAleksa NIEL Simulations for HEC Cold Electronics in Phase II
Unknown file formateps LTDB_noise.eps r1 manage 136.9 K 2015-02-23 - 17:28 MartinAleksa Phase I Upgrade Demonstrator Plots (eps)
PNGpng LTDB_noise.png r1 manage 14.2 K 2015-02-23 - 17:08 MartinAleksa Phase I Upgrade Demonstrator Plots
Unknown file formateps LTDB_pedestal.eps r1 manage 153.2 K 2015-02-23 - 17:28 MartinAleksa Phase I Upgrade Demonstrator Plots (eps)
PNGpng LTDB_pedestal.png r1 manage 15.9 K 2015-02-23 - 17:08 MartinAleksa Phase I Upgrade Demonstrator Plots
Unknown file formateps MedianDensityProfile_vs_eta.eps r1 manage 41.3 K 2016-10-18 - 14:44 MartinAleksa  
PDFpdf MedianDensityProfile_vs_eta.pdf r1 manage 35.0 K 2016-10-18 - 14:44 MartinAleksa  
PNGpng MedianDensityProfile_vs_eta.png r1 manage 28.0 K 2016-10-18 - 14:44 MartinAleksa  
Unknown file formateps NN_architektur_E2Conv_k76_Dil13_LN71.eps r1 manage 169.9 K 2023-05-02 - 11:44 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf NN_architektur_E2Conv_k76_Dil13_LN71.pdf r1 manage 105.6 K 2023-05-02 - 10:38 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng NN_architektur_E2Conv_k76_Dil13_LN71.png r1 manage 288.8 K 2023-05-02 - 11:44 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps NN_architektur_T2Conv_k37_Dil1_E2Conv_k58_Dil1_LN4.eps r1 manage 196.2 K 2023-05-02 - 11:44 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf NN_architektur_T2Conv_k37_Dil1_E2Conv_k58_Dil1_LN4.pdf r1 manage 118.3 K 2023-05-02 - 10:52 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng NN_architektur_T2Conv_k37_Dil1_E2Conv_k58_Dil1_LN4.png r1 manage 261.0 K 2023-05-02 - 11:44 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps PUSuppression_NJetsAndHSFraction_vs_PtCut.eps r1 manage 23.5 K 2016-10-18 - 14:44 MartinAleksa  
PDFpdf PUSuppression_NJetsAndHSFraction_vs_PtCut.pdf r1 manage 21.2 K 2016-10-18 - 14:44 MartinAleksa  
PNGpng PUSuppression_NJetsAndHSFraction_vs_PtCut.png r1 manage 30.0 K 2016-10-18 - 14:44 MartinAleksa  
JPEGjpg Poster_plots_approval_new.jpg r1 manage 37.7 K 2015-02-23 - 17:19 MartinAleksa CNF Explanation
Unknown file formateps ROC-eff-eta38to42.eps r1 manage 1596.5 K 2016-10-18 - 16:12 MartinAleksa  
PDFpdf ROC-eff-eta38to42.pdf r1 manage 690.8 K 2016-10-18 - 16:12 MartinAleksa  
PNGpng ROC-eff-eta38to42.png r1 manage 655.0 K 2016-10-18 - 16:12 MartinAleksa  
Unknown file formateps ROC_effHS_pt20_extr_JES_preliminary.eps r1 manage 13.7 K 2016-10-18 - 14:44 MartinAleksa  
PDFpdf ROC_effHS_pt20_extr_JES_preliminary.pdf r1 manage 19.1 K 2016-10-18 - 14:44 MartinAleksa  
PNGpng ROC_effHS_pt20_extr_JES_preliminary.png r1 manage 27.9 K 2016-10-18 - 14:44 MartinAleksa  
Unknown file formateps Reta.eps r1 manage 56.4 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable R_eta
PDFpdf Reta.pdf r1 manage 14.7 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable R_eta
PNGpng Reta.png r1 manage 68.6 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable R_eta
Unknown file formateps Reta_log.eps r1 manage 55.8 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable R_eta
PDFpdf Reta_log.pdf r1 manage 14.6 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable R_eta
PNGpng Reta_log.png r1 manage 62.6 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable R_eta
Unknown file formateps Total_Noise.eps r1 manage 10.7 K 2017-09-12 - 19:35 SteffenStaerz Total Noise (HEC)
PDFpdf Total_Noise.pdf r1 manage 14.2 K 2017-09-12 - 19:35 SteffenStaerz Total Noise (HEC)
PNGpng Total_Noise.png r1 manage 114.4 K 2017-09-12 - 19:35 SteffenStaerz Total Noise (HEC)
Unknown file formateps cnf_poster.eps r1 manage 8.8 K 2015-02-23 - 17:28 MartinAleksa Phase I Upgrade Demonstrator Plots (eps)
PNGpng cnf_poster.png r1 manage 12.4 K 2015-02-23 - 17:08 MartinAleksa Phase I Upgrade Demonstrator Plots
Unknown file formateps comparison_energy_log.eps r1 manage 56.8 K 2018-07-04 - 12:08 PeterJohannesFalke Measured energy comparison by layer
PDFpdf comparison_energy_log.pdf r1 manage 16.7 K 2018-07-04 - 12:08 PeterJohannesFalke Measured energy comparison by layer
PNGpng comparison_energy_log.png r1 manage 79.9 K 2018-07-04 - 12:08 PeterJohannesFalke Measured energy comparison by layer
Unknown file formateps comparison_timing_log.eps r1 manage 55.8 K 2018-07-04 - 14:36 PeterJohannesFalke Measured timing resolution by layer
PDFpdf comparison_timing_log.pdf r1 manage 16.6 K 2018-07-04 - 14:36 PeterJohannesFalke Measured timing resolution by layer
PNGpng comparison_timing_log.png r1 manage 83.4 K 2018-07-04 - 14:36 PeterJohannesFalke Measured timing resolution by layer
Unknown file formateps cone.eps r1 manage 58.5 K 2018-07-04 - 15:37 PeterJohannesFalke Analysis of shower variables
PDFpdf cone.pdf r1 manage 15.0 K 2018-07-04 - 15:37 PeterJohannesFalke Analysis of shower variables
PNGpng cone.png r1 manage 77.3 K 2018-07-04 - 15:37 PeterJohannesFalke Analysis of shower variables
Unknown file formateps coneOverTotal.eps r1 manage 56.9 K 2018-07-04 - 15:37 PeterJohannesFalke Analysis of shower variables
PDFpdf coneOverTotal.pdf r1 manage 14.8 K 2018-07-04 - 15:37 PeterJohannesFalke Analysis of shower variables
PNGpng coneOverTotal.png r1 manage 68.8 K 2018-07-04 - 15:37 PeterJohannesFalke Analysis of shower variables
Unknown file formateps coneOverTotal_log.eps r1 manage 57.8 K 2018-07-04 - 15:38 PeterJohannesFalke Analysis of shower properties
PDFpdf coneOverTotal_log.pdf r1 manage 15.0 K 2018-07-04 - 15:38 PeterJohannesFalke Analysis of shower properties
PNGpng coneOverTotal_log.png r1 manage 67.2 K 2018-07-04 - 15:38 PeterJohannesFalke Analysis of shower properties
Unknown file formateps cone_log.eps r1 manage 58.0 K 2018-07-04 - 15:37 PeterJohannesFalke Analysis of shower variables
PDFpdf cone_log.pdf r1 manage 14.9 K 2018-07-04 - 15:37 PeterJohannesFalke Analysis of shower variables
PNGpng cone_log.png r1 manage 71.9 K 2018-07-04 - 15:37 PeterJohannesFalke Analysis of shower variables
Unknown file formateps difference_back.eps r1 manage 9.2 K 2017-09-12 - 14:01 SteffenStaerz Demonstrator energy measurement
PDFpdf difference_back.pdf r1 manage 13.8 K 2017-09-12 - 14:01 SteffenStaerz Demonstrator energy measurement
PNGpng difference_back.png r2 r1 manage 25.7 K 2017-09-12 - 14:55 SteffenStaerz Demonstrator energy measurement
Unknown file formateps difference_front.eps r1 manage 9.0 K 2017-09-12 - 14:01 SteffenStaerz Demonstrator energy measurement
PDFpdf difference_front.pdf r1 manage 13.7 K 2017-09-12 - 14:01 SteffenStaerz Demonstrator energy measurement
PNGpng difference_front.png r2 r1 manage 25.4 K 2017-09-12 - 14:55 SteffenStaerz Demonstrator energy measurement
Unknown file formateps difference_middle.eps r1 manage 9.8 K 2017-09-12 - 14:02 SteffenStaerz Demonstrator energy measurement
PDFpdf difference_middle.pdf r1 manage 13.9 K 2017-09-12 - 14:02 SteffenStaerz Demonstrator energy measurement
PNGpng difference_middle.png r2 r1 manage 28.3 K 2017-09-12 - 14:55 SteffenStaerz Demonstrator energy measurement
Unknown file formateps difference_presampler.eps r1 manage 9.5 K 2017-09-12 - 14:02 SteffenStaerz Demonstrator energy measurement
PDFpdf difference_presampler.pdf r1 manage 13.9 K 2017-09-12 - 14:02 SteffenStaerz Demonstrator energy measurement
PNGpng difference_presampler.png r2 r1 manage 26.6 K 2017-09-12 - 14:55 SteffenStaerz Demonstrator energy measurement
Unknown file formateps energyResol.eps r1 manage 56.9 K 2018-07-04 - 15:38 PeterJohannesFalke Analysis of shower properties
PDFpdf energyResol.pdf r1 manage 14.3 K 2018-07-04 - 15:38 PeterJohannesFalke Analysis of shower properties
PNGpng energyResol.png r1 manage 69.8 K 2018-07-04 - 15:38 PeterJohannesFalke Analysis of shower properties
Unknown file formateps energyResol_log.eps r1 manage 57.8 K 2018-07-04 - 15:38 PeterJohannesFalke Analysis of shower properties
PDFpdf energyResol_log.pdf r1 manage 14.5 K 2018-07-04 - 15:38 PeterJohannesFalke Analysis of shower properties
PNGpng energyResol_log.png r1 manage 71.8 K 2018-07-04 - 15:38 PeterJohannesFalke Analysis of shower properties
Unknown file formateps extractionMethods_front_average.eps r1 manage 68.2 K 2018-03-05 - 14:24 SteffenStaerz Demonstrator pulse shape extraction methods
PDFpdf extractionMethods_front_average.pdf r1 manage 25.8 K 2018-03-05 - 14:24 SteffenStaerz Demonstrator pulse shape extraction methods
PNGpng extractionMethods_front_average.png r1 manage 57.6 K 2018-03-05 - 14:24 SteffenStaerz Demonstrator pulse shape extraction methods
Unknown file formateps extractionMethods_front_hist.eps r1 manage 68.9 K 2018-03-05 - 14:24 SteffenStaerz Demonstrator pulse shape extraction methods
PDFpdf extractionMethods_front_hist.pdf r1 manage 25.9 K 2018-03-05 - 14:24 SteffenStaerz Demonstrator pulse shape extraction methods
PNGpng extractionMethods_front_hist.png r1 manage 57.7 K 2018-03-05 - 14:24 SteffenStaerz Demonstrator pulse shape extraction methods
Unknown file formateps extractionMethods_front_maxE.eps r1 manage 68.4 K 2018-03-05 - 14:24 SteffenStaerz Demonstrator pulse shape extraction methods
PDFpdf extractionMethods_front_maxE.pdf r1 manage 25.5 K 2018-03-05 - 14:24 SteffenStaerz Demonstrator pulse shape extraction methods
PNGpng extractionMethods_front_maxE.png r1 manage 58.6 K 2018-03-05 - 14:24 SteffenStaerz Demonstrator pulse shape extraction methods
Unknown file formateps f3.eps r1 manage 55.7 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable f3
PDFpdf f3.pdf r1 manage 14.6 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable f3
PNGpng f3.png r1 manage 62.0 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable f3
Unknown file formateps f3_log.eps r1 manage 56.3 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable f3
PDFpdf f3_log.pdf r1 manage 14.7 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable f3
PNGpng f3_log.png r1 manage 63.3 K 2018-07-04 - 15:42 PeterJohannesFalke Shower shape variable f3
Unknown file formateps fig1.eps r2 r1 manage 980.0 K 2015-11-12 - 16:59 MartinAleksa Demonstrator SC Plots (eps)
PDFpdf fig1.pdf r2 r1 manage 70.2 K 2015-11-12 - 17:00 MartinAleksa Demonstrator SC Plots (pdf)
PNGpng fig1.png r2 r1 manage 84.7 K 2015-11-12 - 16:58 MartinAleksa Demonstrator SC plots (png)
Unknown file formateps fig2.eps r2 r1 manage 20.1 K 2015-11-12 - 16:59 MartinAleksa Demonstrator SC Plots (eps)
PDFpdf fig2.pdf r2 r1 manage 10.0 K 2015-11-12 - 17:00 MartinAleksa Demonstrator SC Plots (pdf)
PNGpng fig2.png r2 r1 manage 63.0 K 2015-11-12 - 16:58 MartinAleksa Demonstrator SC plots (png)
Unknown file formateps fig3.eps r2 r1 manage 20.8 K 2015-11-12 - 16:59 MartinAleksa Demonstrator SC Plots (eps)
PDFpdf fig3.pdf r2 r1 manage 10.5 K 2015-11-12 - 17:00 MartinAleksa Demonstrator SC Plots (pdf)
PNGpng fig3.png r2 r1 manage 64.2 K 2015-11-12 - 16:58 MartinAleksa Demonstrator SC plots (png)
Unknown file formateps fig4.eps r2 r1 manage 29.8 K 2015-11-12 - 16:59 MartinAleksa Demonstrator SC Plots (eps)
PDFpdf fig4.pdf r2 r1 manage 14.3 K 2015-11-12 - 17:00 MartinAleksa Demonstrator SC Plots (pdf)
PNGpng fig4.png r2 r1 manage 66.1 K 2015-11-12 - 16:58 MartinAleksa Demonstrator SC plots (png)
Unknown file formateps fig5.eps r2 r1 manage 26.6 K 2015-11-12 - 16:59 MartinAleksa Demonstrator SC Plots (eps)
PDFpdf fig5.pdf r2 r1 manage 12.9 K 2015-11-12 - 17:00 MartinAleksa Demonstrator SC Plots (pdf)
PNGpng fig5.png r2 r1 manage 69.0 K 2015-11-12 - 16:58 MartinAleksa Demonstrator SC plots (png)
Unknown file formateps fig6.eps r2 r1 manage 115.9 K 2015-11-12 - 16:59 MartinAleksa Demonstrator SC Plots (eps)
PDFpdf fig6.pdf r2 r1 manage 84.1 K 2015-11-12 - 17:00 MartinAleksa Demonstrator SC Plots (pdf)
PNGpng fig6.png r2 r1 manage 104.8 K 2015-11-12 - 16:58 MartinAleksa Demonstrator SC plots (png)
Unknown file formateps fig7.eps r2 r1 manage 9.5 K 2015-11-12 - 16:59 MartinAleksa Demonstrator SC Plots (eps)
PDFpdf fig7.pdf r2 r1 manage 5.0 K 2015-11-12 - 17:00 MartinAleksa Demonstrator SC Plots (pdf)
PNGpng fig7.png r2 r1 manage 27.5 K 2015-11-12 - 16:58 MartinAleksa Demonstrator SC plots (png)
Unknown file formateps fig8.eps r2 r1 manage 10.1 K 2015-11-12 - 16:59 MartinAleksa Demonstrator SC Plots (eps)
PDFpdf fig8.pdf r2 r1 manage 5.3 K 2015-11-12 - 17:00 MartinAleksa Demonstrator SC Plots (pdf)
PNGpng fig8.png r2 r1 manage 29.0 K 2015-11-12 - 16:58 MartinAleksa Demonstrator SC plots (png)
Unknown file formateps intr_electr_res_fl_prel.eps r1 manage 20.2 K 2017-09-14 - 14:31 SteffenStaerz Quantization noise
PDFpdf intr_electr_res_fl_prel.pdf r1 manage 25.1 K 2017-09-14 - 14:31 SteffenStaerz Quantization noise
PNGpng intr_electr_res_fl_prel.png r1 manage 43.5 K 2017-09-14 - 14:31 SteffenStaerz Quantization noise
Unknown file formateps intr_electr_res_ml_prel.eps r1 manage 20.4 K 2017-09-14 - 14:31 SteffenStaerz Quantization noise
PDFpdf intr_electr_res_ml_prel.pdf r1 manage 25.0 K 2017-09-14 - 14:31 SteffenStaerz Quantization noise
PNGpng intr_electr_res_ml_prel.png r1 manage 45.2 K 2017-09-14 - 14:31 SteffenStaerz Quantization noise
Unknown file formateps noise_tot_ratio_sFCal_SmallGaps_over_FCal_mu200-new_prelim.eps r1 manage 22.1 K 2016-10-18 - 14:43 MartinAleksa  
PDFpdf noise_tot_ratio_sFCal_SmallGaps_over_FCal_mu200-new_prelim.pdf r1 manage 21.8 K 2016-10-18 - 14:43 MartinAleksa  
PNGpng noise_tot_ratio_sFCal_SmallGaps_over_FCal_mu200-new_prelim.png r1 manage 23.0 K 2016-10-18 - 14:43 MartinAleksa  
Unknown file formateps phase2_ann_LSTM_singlecell_archi.eps r1 manage 123.5 K 2021-06-21 - 18:13 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_LSTM_singlecell_archi.pdf r1 manage 43.0 K 2021-06-21 - 18:13 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_LSTM_singlecell_archi.png r1 manage 31.5 K 2021-06-21 - 18:13 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_LSTM_slidewindow_archi.eps r1 manage 335.6 K 2021-06-21 - 18:14 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_LSTM_slidewindow_archi.pdf r1 manage 98.1 K 2021-06-21 - 18:14 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_LSTM_slidewindow_archi.png r1 manage 159.9 K 2021-06-21 - 18:14 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_ROC.eps r1 manage 169.7 K 2021-06-21 - 15:40 ArnoStraessner Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_ROC.pdf r1 manage 70.5 K 2021-06-21 - 15:40 ArnoStraessner Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_ROC.png r1 manage 103.4 K 2021-06-21 - 15:40 ArnoStraessner Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_2conv_gap_resolution_EMBFront05.eps r1 manage 75.9 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_2conv_gap_resolution_EMBFront05.pdf r1 manage 49.1 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_2conv_gap_resolution_EMBFront05.png r1 manage 56.3 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_2conv_gap_resolution_EMBFront10.eps r1 manage 69.7 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_2conv_gap_resolution_EMBFront10.pdf r1 manage 46.9 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_2conv_gap_resolution_EMBFront10.png r1 manage 54.9 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_2conv_gap_resolution_EMBMid05.eps r1 manage 80.4 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_2conv_gap_resolution_EMBMid05.pdf r1 manage 50.3 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_2conv_gap_resolution_EMBMid05.png r1 manage 57.9 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_2conv_gap_resolution_EMBMid10.eps r1 manage 79.1 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_2conv_gap_resolution_EMBMid10.pdf r1 manage 49.7 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_2conv_gap_resolution_EMBMid10.png r1 manage 57.8 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_2conv_gap_resolution_EMBPres05.eps r1 manage 80.1 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_2conv_gap_resolution_EMBPres05.pdf r1 manage 52.1 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_2conv_gap_resolution_EMBPres05.png r1 manage 59.5 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_2conv_gap_resolution_OuterEMEC20.eps r1 manage 74.0 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_2conv_gap_resolution_OuterEMEC20.pdf r1 manage 48.9 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_2conv_gap_resolution_OuterEMEC20.png r1 manage 55.2 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_4conv_gap_resolution_EMBMid05.eps r1 manage 84.8 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_4conv_gap_resolution_EMBMid05.pdf r1 manage 51.9 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_4conv_gap_resolution_EMBMid05.png r1 manage 60.5 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_architecture.eps r1 manage 253.6 K 2021-06-21 - 15:40 ArnoStraessner Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_architecture.pdf r1 manage 80.2 K 2021-06-21 - 15:40 ArnoStraessner Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_architecture.png r1 manage 171.5 K 2021-06-21 - 15:40 ArnoStraessner Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_deviation_distribution_highE.eps r1 manage 57.7 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_deviation_distribution_highE.pdf r1 manage 33.2 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_deviation_distribution_highE.png r1 manage 65.2 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_deviation_distribution_lowE.eps r1 manage 59.6 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_deviation_distribution_lowE.pdf r1 manage 33.6 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_deviation_distribution_lowE.png r1 manage 64.8 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_deviation_distribution_statistics.eps r1 manage 104.2 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_deviation_distribution_statistics.pdf r1 manage 38.5 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_deviation_distribution_statistics.png r1 manage 92.0 K 2023-05-02 - 11:48 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_fakes.eps r1 manage 59.0 K 2023-05-02 - 11:46 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_fakes.pdf r1 manage 34.2 K 2023-05-02 - 11:46 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_fakes.png r1 manage 58.9 K 2023-05-02 - 11:46 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_gap_resolution.eps r1 manage 127.1 K 2021-06-21 - 18:13 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_gap_resolution.pdf r1 manage 34.3 K 2021-06-21 - 18:13 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_gap_resolution.png r1 manage 25.9 K 2021-06-21 - 18:13 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_sequence.eps r1 manage 136.0 K 2021-06-21 - 15:40 ArnoStraessner Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_sequence.pdf r1 manage 55.2 K 2021-06-21 - 15:40 ArnoStraessner Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_sequence.png r1 manage 100.4 K 2021-06-21 - 15:40 ArnoStraessner Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_cnn_vs_of_sequence.eps r1 manage 62.4 K 2023-05-02 - 11:46 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_cnn_vs_of_sequence.pdf r1 manage 33.1 K 2023-05-02 - 11:46 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_cnn_vs_of_sequence.png r1 manage 72.9 K 2023-05-02 - 11:46 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_hlsVScpp_reso.eps r1 manage 14.0 K 2021-06-21 - 18:13 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_hlsVScpp_reso.pdf r1 manage 14.9 K 2021-06-21 - 18:13 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_hlsVScpp_reso.png r1 manage 20.4 K 2021-06-21 - 18:13 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_lstm_singlecell_gap_resolution.eps r1 manage 131.6 K 2021-06-21 - 18:14 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_lstm_singlecell_gap_resolution.pdf r1 manage 33.5 K 2021-06-21 - 18:14 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_lstm_singlecell_gap_resolution.png r1 manage 26.9 K 2021-06-21 - 18:14 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_ofmax_gap_resolution.eps r1 manage 139.5 K 2021-06-21 - 18:15 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_ofmax_gap_resolution.pdf r1 manage 34.7 K 2021-06-21 - 18:15 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_ofmax_gap_resolution.png r1 manage 27.6 K 2021-06-21 - 18:15 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_ofmax_gap_resolution_EMBMid.eps r1 manage 78.8 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_ofmax_gap_resolution_EMBMid.pdf r1 manage 50.5 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_ofmax_gap_resolution_EMBMid.png r1 manage 57.7 K 2023-05-02 - 11:47 AnneSophieBerthold Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_perf_summary_plot.eps r1 manage 13.5 K 2021-06-21 - 18:15 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_perf_summary_plot.pdf r1 manage 14.3 K 2021-06-21 - 18:15 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_perf_summary_plot.png r1 manage 16.4 K 2021-06-21 - 18:15 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
Unknown file formateps phase2_ann_vrnn_gap_resolution.eps r1 manage 130.8 K 2021-06-21 - 18:15 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PDFpdf phase2_ann_vrnn_gap_resolution.pdf r1 manage 33.2 K 2021-06-21 - 18:15 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
PNGpng phase2_ann_vrnn_gap_resolution.png r1 manage 26.8 K 2021-06-21 - 18:15 ThomasPhilippeCalvet Phase-II ANN performance and FPGA implementation
Unknown file formateps pulseRelEDev_Ebinned_middle_RMS.eps r1 manage 55.7 K 2018-07-04 - 12:08 PeterJohannesFalke Measured energy comparison by layer
PDFpdf pulseRelEDev_Ebinned_middle_RMS.pdf r1 manage 15.3 K 2018-07-04 - 12:08 PeterJohannesFalke Measured energy comparison by layer
PNGpng pulseRelEDev_Ebinned_middle_RMS.png r1 manage 76.6 K 2018-07-04 - 12:08 PeterJohannesFalke Measured energy comparison by layer
Unknown file formateps pulseShape_back.eps r1 manage 69.4 K 2018-03-05 - 14:25 SteffenStaerz Demonstrator pulse shapes
PDFpdf pulseShape_back.pdf r1 manage 26.0 K 2018-03-05 - 14:25 SteffenStaerz Demonstrator pulse shapes
PNGpng pulseShape_back.png r1 manage 59.6 K 2018-03-05 - 14:25 SteffenStaerz Demonstrator pulse shapes
Unknown file formateps pulseShape_front.eps r1 manage 68.9 K 2018-03-05 - 14:27 SteffenStaerz Demonstrator pulse shapes
PDFpdf pulseShape_front.pdf r1 manage 25.9 K 2018-03-05 - 14:25 SteffenStaerz Demonstrator pulse shapes
PNGpng pulseShape_front.png r1 manage 57.7 K 2018-03-05 - 14:25 SteffenStaerz Demonstrator pulse shapes
Unknown file formateps pulseShape_middle.eps r1 manage 68.9 K 2018-03-05 - 14:27 SteffenStaerz Demonstrator pulse shapes
PDFpdf pulseShape_middle.pdf r1 manage 25.9 K 2018-03-05 - 14:27 SteffenStaerz Demonstrator pulse shapes
PNGpng pulseShape_middle.png r1 manage 58.2 K 2018-03-05 - 14:27 SteffenStaerz Demonstrator pulse shapes
Unknown file formateps pulseShape_presampler.eps r1 manage 69.3 K 2018-03-05 - 14:27 SteffenStaerz Demonstrator pulse shapes
PDFpdf pulseShape_presampler.pdf r1 manage 26.1 K 2018-03-05 - 14:27 SteffenStaerz Demonstrator pulse shapes
PNGpng pulseShape_presampler.png r1 manage 60.5 K 2018-03-05 - 14:27 SteffenStaerz Demonstrator pulse shapes
Unknown file formateps pulseTiming_Ebinned_middle_RMS.eps r1 manage 53.1 K 2018-07-04 - 14:36 PeterJohannesFalke Measured timing resolution by layer
PDFpdf pulseTiming_Ebinned_middle_RMS.pdf r1 manage 37.0 K 2018-07-04 - 14:36 PeterJohannesFalke Measured timing resolution by layer
PNGpng pulseTiming_Ebinned_middle_RMS.png r1 manage 71.7 K 2018-07-04 - 14:36 PeterJohannesFalke Measured timing resolution by layer
Unknown file formateps pulseTiming_back.eps r1 manage 31.5 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
PDFpdf pulseTiming_back.pdf r1 manage 13.5 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
PNGpng pulseTiming_back.png r1 manage 36.7 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
Unknown file formateps pulseTiming_front.eps r1 manage 30.5 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
PDFpdf pulseTiming_front.pdf r1 manage 13.5 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
PNGpng pulseTiming_front.png r1 manage 34.6 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
Unknown file formateps pulseTiming_middle.eps r1 manage 31.1 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
PDFpdf pulseTiming_middle.pdf r1 manage 13.4 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
PNGpng pulseTiming_middle.png r1 manage 34.3 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
Unknown file formateps pulseTiming_presampler.eps r1 manage 31.2 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
PDFpdf pulseTiming_presampler.pdf r1 manage 13.5 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
PNGpng pulseTiming_presampler.png r1 manage 35.9 K 2018-03-05 - 14:29 SteffenStaerz Demonstrator timing distribution
Unknown file formateps relEDev_Mean_etaDep_19.eps r1 manage 84.6 K 2018-07-04 - 15:36 PeterJohannesFalke Energy overview plots
PDFpdf relEDev_Mean_etaDep_19.pdf r1 manage 21.5 K 2018-07-04 - 15:36 PeterJohannesFalke Energy overview plots
PNGpng relEDev_Mean_etaDep_19.png r1 manage 143.1 K 2018-07-04 - 15:36 PeterJohannesFalke Energy overview plots
Unknown file formateps relEDev_RMS_etaDep_19.eps r1 manage 86.3 K 2018-07-04 - 15:36 PeterJohannesFalke Energy overview plots
PDFpdf relEDev_RMS_etaDep_19.pdf r1 manage 22.5 K 2018-07-04 - 15:36 PeterJohannesFalke Energy overview plots
PNGpng relEDev_RMS_etaDep_19.png r1 manage 142.7 K 2018-07-04 - 15:36 PeterJohannesFalke Energy overview plots
Unknown file formateps sFCalReview_CellSig_VBF2600_prelim.eps r1 manage 19.3 K 2016-10-18 - 14:43 MartinAleksa  
PDFpdf sFCalReview_CellSig_VBF2600_prelim.pdf r1 manage 16.2 K 2016-10-18 - 14:43 MartinAleksa  
PNGpng sFCalReview_CellSig_VBF2600_prelim.png r1 manage 26.8 K 2016-10-18 - 14:43 MartinAleksa  
Unknown file formateps sFCalReview_NConst_VBF2600_prelim.eps r1 manage 28.8 K 2016-10-18 - 14:43 MartinAleksa  
PDFpdf sFCalReview_NConst_VBF2600_prelim.pdf r1 manage 23.8 K 2016-10-18 - 14:43 MartinAleksa  
PNGpng sFCalReview_NConst_VBF2600_prelim.png r1 manage 28.1 K 2016-10-18 - 14:43 MartinAleksa  
Unknown file formateps scatter_back.eps r1 manage 13.2 K 2017-09-12 - 14:06 SteffenStaerz Demonstrator energy measurement
PDFpdf scatter_back.pdf r1 manage 15.2 K 2017-09-12 - 14:06 SteffenStaerz Demonstrator energy measurement
PNGpng scatter_back.png r2 r1 manage 83.9 K 2017-09-12 - 14:55 SteffenStaerz Demonstrator energy measurement
Unknown file formateps scatter_front.eps r1 manage 14.0 K 2017-09-12 - 14:06 SteffenStaerz Demonstrator energy measurement
PDFpdf scatter_front.pdf r1 manage 15.4 K 2017-09-12 - 14:06 SteffenStaerz Demonstrator energy measurement
PNGpng scatter_front.png r3 r2 r1 manage 87.0 K 2017-09-12 - 14:55 SteffenStaerz Demonstrator energy measurement
Unknown file formateps scatter_middle.eps r1 manage 18.2 K 2017-09-12 - 14:07 SteffenStaerz Demonstrator energy measurement
PDFpdf scatter_middle.pdf r1 manage 16.6 K 2017-09-12 - 14:07 SteffenStaerz Demonstrator energy measurement
PNGpng scatter_middle.png r2 r1 manage 95.1 K 2017-09-12 - 14:55 SteffenStaerz Demonstrator energy measurement
Unknown file formateps scatter_presampler.eps r1 manage 14.6 K 2017-09-12 - 14:07 SteffenStaerz Demonstrator energy measurement
PDFpdf scatter_presampler.pdf r1 manage 15.4 K 2017-09-12 - 14:07 SteffenStaerz Demonstrator energy measurement
PNGpng scatter_presampler.png r3 r2 r1 manage 85.0 K 2017-09-12 - 14:55 SteffenStaerz Demonstrator energy measurement
Unknown file formateps shaper.eps r1 manage 13.8 K 2017-09-12 - 15:29 SteffenStaerz Total Noise, bipolar shaper
PDFpdf shaper.pdf r1 manage 14.4 K 2017-09-12 - 15:29 SteffenStaerz Total Noise, bipolar shaper
PNGpng shaper.png r1 manage 14.5 K 2017-09-12 - 15:29 SteffenStaerz Total Noise, bipolar shaper
Unknown file formateps timing_Mean_etaDep_19.eps r1 manage 85.1 K 2018-07-04 - 15:35 PeterJohannesFalke Timing overview plots
PDFpdf timing_Mean_etaDep_19.pdf r1 manage 21.7 K 2018-07-04 - 15:35 PeterJohannesFalke Timing overview plots
PNGpng timing_Mean_etaDep_19.png r1 manage 147.0 K 2018-07-04 - 15:35 PeterJohannesFalke Timing overview plots
Unknown file formateps timing_RMS_etaDep_19.eps r1 manage 83.9 K 2018-07-04 - 15:35 PeterJohannesFalke Timing overview plots
PDFpdf timing_RMS_etaDep_19.pdf r1 manage 21.9 K 2018-07-04 - 15:35 PeterJohannesFalke Timing overview plots
PNGpng timing_RMS_etaDep_19.png r1 manage 140.8 K 2018-07-04 - 15:35 PeterJohannesFalke Timing overview plots
Unknown file formateps total_noise_I05.eps r2 r1 manage 142.9 K 2015-02-23 - 23:27 MartinAleksa Phase I Upgrade Demonstrator Plots (eps)
PNGpng total_noise_I05.png r2 r1 manage 29.2 K 2015-02-23 - 23:28 MartinAleksa Phase I Upgrade Demonstrator Plots
Unknown file formateps totalnoise_demonstrator.eps r2 r1 manage 142.9 K 2015-02-23 - 23:28 MartinAleksa Phase I Upgrade Demonstrator Plots (eps)
PNGpng totalnoise_demonstrator.png r2 r1 manage 28.5 K 2015-02-23 - 23:29 MartinAleksa Phase I Upgrade Demonstrator Plots
Unknown file formateps wEta.eps r1 manage 57.1 K 2018-07-04 - 15:43 PeterJohannesFalke Shower shape variable w_{eta,2}
PDFpdf wEta.pdf r1 manage 14.8 K 2018-07-04 - 15:43 PeterJohannesFalke Shower shape variable w_{eta,2}
PNGpng wEta.png r1 manage 69.6 K 2018-07-04 - 15:43 PeterJohannesFalke Shower shape variable w_{eta,2}
Unknown file formateps wEta_log.eps r1 manage 56.5 K 2018-07-04 - 15:43 PeterJohannesFalke Shower shape variable w_{eta,2}
PDFpdf wEta_log.pdf r1 manage 14.7 K 2018-07-04 - 15:43 PeterJohannesFalke Shower shape variable w_{eta,2}
PNGpng wEta_log.png r1 manage 64.9 K 2018-07-04 - 15:43 PeterJohannesFalke Shower shape variable w_{eta,2}
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