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.
The plots from the LAr Phase I Upgrade TDR can be found here.
Two LAr Phase I trigger upgrade demonstrator boards (2 Demonstrator LTDBs, LAr Trigger Digitizer Boards) were installed insitu on the LAr detector in July 2014 (coverage: 9π/16 < φ < 11π/16, 0 < η < 1.4). To receive the digital supercell energies ABBA boards (LDPB preprototype) were installed in USA15. One ABBA board receives data from one LTDB 320 super cells. Supercell data has been recorded for a large number of time slices with this preprototype backend electronics.
General Information
Description of procedure
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 (E_{SC} / Σ_{SC} E_{cells}) for E_{SC} > 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 highenergy tail.



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 (E_{SC} / Σ_{SC} E_{cells}) for E_{SC} > 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 highenergy tail, depending on the calorimeter layer.



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 energybins and the width of the distribution shown for each bin. The timing resolution is around 0.5 ns in the highenergy tail, such that the identification of the bunchcrossing ID is possible due to the resolution being much smaller than 25 ns. The supercells in the middle layer consist of 4 LAr cells.



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 energybins and the width of the distribution shown for each bin. The timing resolution is below 0.5 − 1.0 ns in the highenergy tail, such that the identification of the bunchcrossing 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.



Energy scale for demonstrator φslice: The measured supercell (SC) energies of the LAr Phase I demonstrator are compared to summed LAr cell energies in ATLAS by calculating their ratio (E_{SC} / Σ_{SC} E_{cells}) 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.



Energy comparison for demonstrator φslice: The measured supercell (SC) energies of the LAr Phase I demonstrator are compared to summed LAr cell energies in ATLAS by calculating their ratio (E_{SC} / Σ_{SC} E_{cells}) 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.



Mean timing for demonstrator φslice: 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 bunchcrossing 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 resolution for demonstrator φslice: 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 bunchcrossing 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.



Shower modelling of the demonstrator readout: 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 readouts. The events were observed in pp physics data, collected on August 30, 2017. Only wellreconstructed energy deposits above 1% of E_{max}^{SC} are used. R_{η} gives an estimate of the energy fraction of the shower cone in the middle layer, f_{3} is the energy fraction of the shower in the back layer and the width of the showers is further parametrised by w_{η, 2}.


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.


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.


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 bunchcrossing 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.


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 E_{SC} > 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 readouts.



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 (E_{SC} − Σ_{SC} E_{cells}) for E_{SC} > 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 readouts. 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.



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 E_{max}^{SC} are plotted.



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 E_{max}^{SC} are plotted.


Supercell 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.


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.


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.


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.


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.


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.


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 supercell 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.


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 supercell 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.


Total noise on the trigger readout path of the demonstrator test setup: 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 halffull Front End Crate (FEC) equipped with Front End Boards (FEBs). Trigger towers 114 correspond to an etaregion of 0 to 1.4. Trigger towers 1629 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. 

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) 712 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. 

LAr Trigger Digitizer Board (LTDB) demonstrator noise measured on the demonstrator installed in ATLAS: Here, the RMS of the 12bit 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.. 

LAr Trigger Digitizer Board (LTDB) demonstrator pedestal measured on the demonstrator installed in ATLAS: Here, the pedestal values of the 12bit 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. 

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). 

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. 

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 PhaseII LAr Calorimeter readout (low gain curve in red, high gain curve in blue). The quantization noise curves assume the use of a 12bit successive approximation register (SAR) with a dynamic range enhancer (DRE) to obtain a 14bit ADC with 12bit precision. Gain switching occurs close to the highest energy digitized in the high gain.


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 PhaseII LAr Calorimeter readout (low gain curve in red, high gain curve in blue). The quantization noise curves assume the use of a 12bit successive approximation register (SAR) with a dynamic range enhancer (DRE) to obtain a 14bit ADC with 12bit precision. Gain switching occurs close to the highest energy digitized in the high gain.


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 PhaseII 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.


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 PhaseII 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.


Si NIEL fluence in ATLAS under HLLHC 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 rz locations of the corresponding readout regions of the HEC. 

Si NIEL fluence in ATLAS under HLLHC 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 rz locations of the corresponding readout regions of the HEC. 

Simulated noise in the Liquid Argon and Tile calorimeters at the electron scale (bunch spacing t=25ns): Quadratic sum of simulated electronics and pileup noise per calorimeter cell for each calorimeter layer as function of pseudorapidity. The pileup simulation is done by overlaying GEANT4 simulated events from PYTHIA (v6.4) including non diffractive and diffractive events. The overlay takes into account the full sensitive time of the detector (~500ns for the LAr) and the bunch train structure. For protonproton collisions at √s = 14TeV and a bunch spacing of t=25ns. The luminosity and corresponding average overlaying interactions per bunch crossing <µ> (pileup 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. 

Simulated noise in the Liquid Argon and Tile calorimeters at the electron scale (bunch spacing t=50ns): Quadratic sum of simulated electronics and pileup noise per calorimeter cell for each calorimeter layer as function of pseudorapidity. The pileup simulation is done by overlaying GEANT4 simulated events from PYTHIA (v6.4) including non diffractive and diffractive events. The overlay takes into account the full sensitive time of the detector (~500ns for the LAr) and the bunch train structure. For protonproton collisions at √s = 14TeV and a bunch spacing of t=50ns. The luminosity and corresponding average overlaying interactions per bunch crossing <µ> (pileup 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. 

The below results are based on simulated VBF 2.6 TeV Higgs boson production and dijet production at 14 TeV with μ=190200 assuming the current ATLAS FCal, a highgranularity 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 centreofmass energy of 14 TeV and for a mean number of pileup event of μ=200. Small deviations from 1 in the innermost (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 nontrivial and indicates that an sFCal with finer granularity improves the separation of hardscatter signal from pileup. 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 pileup. 

Number of Jet Constituents: Distribution of the number of constituents (clusters) for quark jets produced in the vectorboson fusion process 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙^{−}𝑣 𝑙^{+}𝑣 𝑞𝑞 at 14 TeV centreofmass energy simulated for the current ATLAS FCal, a highgranularity sFCal, and three scenarios with reduced FCal acceptance. The increase in granularity and better separation of signal from pileup leads to larger number of constituents in the sFCal compared to FCal. The vectorboson fusion events were simulated with the Powheg and Pythia8 Monte Carlo generators in narrowwidth approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pileup events, μ, between 190 and 210. 

Cell Significance: Cell significance (E_{cell}/σ_{cell}) for the cell with the largest absolute cell energy over total noise simulated for the current ATLAS FCal, a highgranularity 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 pileup 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 vectorboson fusion events 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙^{−}𝑣 𝑙^{+}𝑣 𝑞𝑞 at 14 TeV centreofmass energy, simulated with the Powheg and Pythia8 Monte Carlo generators in narrowwidth approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pileup events, μ, between 190 and 210. 

Average p_{T} Density: Shown is the simulated profile of the average median p_{T} density, ρ, evaluated from positiveenergy cell towers in the ATLAS LAr calorimeters. For the forward calorimeter, five different scenarios are studied: the current ATLAS FCal, a highgranularity sFCal, and three scenarios with reduced FCal acceptance. When a ρbased pileup suppression will be applied in the jet reconstruction it is expected that a larger amount of p_{T} will be removed for jets in the forward region in case of the sFCal. The distributions are obtained for vectorboson fusion events 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙^{−}𝑣 𝑙^{+}𝑣 𝑞𝑞 at 14 TeV centreofmass energy, simulated with the Powheg and Pythia8 Monte Carlo generators in narrowwidth approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pileup events, μ, between 190 and 210. 

Number of Jets: Simulated integral jet p_{T} distribution for hard scattering and pileup jets and the relative fraction of hard scattering jets detected in the ATLAS FCal and in a highgranularity sFCal. An areabased p_{T} subtraction is applied. The amount of p_{T} 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 vectorboson fusion events 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙^{−}𝑣 𝑙^{+}𝑣 𝑞𝑞 at 14 TeV centreofmass energy, simulated with the Powheg and Pythia8 Monte Carlo generators in narrowwidth approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pileup events, μ, between 190 and 210. 

Ratio of PileUp Jets: Ratio of number of identified pileup jets and total number of reconstructed jets as a function of efficiency for determining a hardscattering jet as simulated in dijet events at 14 TeV for the ATLAS FCal and a highgranularity sFCal. All jets are selected in the pseudorapidity range 3.8<η<4.2 and in the p_{T} 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). 

Number of PileUp Jets: Number of identified pileup jets per event as a function of efficiency for determining a hardscattering jet in highmass VBF Higgs events at 14 TeV for the ATLAS FCal and a high granularity sFCal. All jets are selected in the pseudorapidity range 3.2<η<3.8 and in the p_{T} range above 20 GeV. The jet reconstruction requires a positive clustervertexfraction and a pile up correction based on the average median p_{T} density, ρ, evaluated from positiveenergy 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 vectorboson fusion events 𝑝𝑝 → 𝐻 𝑞𝑞 → 𝑙^{−}𝑣 𝑙^{+}𝑣 𝑞𝑞 at 14 TeV centreofmass energy, simulated with the Powheg and Pythia8 Monte Carlo generators in narrowwidth approximation for a hypothetic Higgs boson mass of 2.6 TeV and an average number of pileup events, μ, between 190 and 210. 

The results below are based on simulated energy deposits in the LAr EMB at a mean number of pileup 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 Stratix10.
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. 

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. 

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 E_{T}^{true} 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. 

Singlecell application of Long ShortTerm 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. 

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. 

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. 

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 E_{T}^{true} 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. 

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 ShortTerm Memory singlecell algorithm. Only deposits with E_{T}^{true} 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. 

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 VanillaRNN slidingwindow algorithm. Only deposits with E_{T}^{true} above 240 MeV (3σ above noise threshold) are considered. Inputs to the VanillaRNN 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. 

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 E_{T}^{true} 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. 

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. 

Responsible: MartinAleksa
Subject: public
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I  Attachment  History  Action  Size  Date  Who  Comment 

eps  Event_0_Demonstrator_Full.eps  r1  manage  21.7 K  20170912  14:03  SteffenStaerz  Demonstrator event display: event 0 (full) 
Event_0_Demonstrator_Full.pdf  r1  manage  17.1 K  20170912  14:03  SteffenStaerz  Demonstrator event display: event 0 (full)  
png  Event_0_Demonstrator_Full.png  r2 r1  manage  96.2 K  20170912  14:47  SteffenStaerz  Demonstrator event display: event 0 (full) 
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Event_0_Demonstrator_Partial.pdf  r1  manage  16.6 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 0 (zoom)  
png  Event_0_Demonstrator_Partial.png  r3 r2 r1  manage  102.3 K  20170912  14:48  SteffenStaerz  Demonstrator event display: event 0 (zoom) 
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Event_0_Main_Full.pdf  r1  manage  17.4 K  20170912  14:03  SteffenStaerz  Demonstrator event display: event 0 (full)  
png  Event_0_Main_Full.png  r2 r1  manage  98.4 K  20170912  14:49  SteffenStaerz  Demonstrator event display: event 0 (full) 
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Event_0_Main_Partial.pdf  r1  manage  17.7 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 0 (zoom)  
png  Event_0_Main_Partial.png  r3 r2 r1  manage  110.3 K  20170912  14:50  SteffenStaerz  Demonstrator event display: event 0 (zoom) 
eps  Event_1_Demonstrator.eps  r1  manage  22.2 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 1 
Event_1_Demonstrator.pdf  r1  manage  17.3 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 1  
png  Event_1_Demonstrator.png  r2 r1  manage  97.0 K  20170912  14:51  SteffenStaerz  Demonstrator event display: event 1 
eps  Event_1_Main.eps  r1  manage  23.9 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 1 
Event_1_Main.pdf  r1  manage  22.4 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 1  
png  Event_1_Main.png  r2 r1  manage  98.8 K  20170912  14:51  SteffenStaerz  Demonstrator event display: event 1 
eps  Event_2_Demonstrator.eps  r1  manage  22.3 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 2 
Event_2_Demonstrator.pdf  r1  manage  17.3 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 2  
png  Event_2_Demonstrator.png  r2 r1  manage  97.4 K  20170912  14:52  SteffenStaerz  Demonstrator event display: event 2 
eps  Event_2_Main.eps  r1  manage  26.2 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 2 
Event_2_Main.pdf  r1  manage  23.6 K  20170912  14:04  SteffenStaerz  Demonstrator event display: event 2  
png  Event_2_Main.png  r2 r1  manage  104.4 K  20170912  14:53  SteffenStaerz  Demonstrator event display: event 2 
eps  Event_3_Demonstrator.eps  r1  manage  22.8 K  20170912  14:05  SteffenStaerz  Demonstrator event display: event 3 
Event_3_Demonstrator.pdf  r1  manage  17.5 K  20170912  14:05  SteffenStaerz  Demonstrator event display: event 3  
png  Event_3_Demonstrator.png  r2 r1  manage  96.0 K  20170912  14:53  SteffenStaerz  Demonstrator event display: event 3 
eps  Event_3_Main.eps  r1  manage  21.4 K  20170912  14:05  SteffenStaerz  Demonstrator event display: event 3 
Event_3_Main.pdf  r1  manage  17.0 K  20170912  14:05  SteffenStaerz  Demonstrator event display: event 3  
png  Event_3_Main.png  r2 r1  manage  94.5 K  20170912  14:54  SteffenStaerz  Demonstrator event display: event 3 
eps  FADC_noise_poster.eps  r1  manage  17.1 K  20150223  17:28  MartinAleksa  Phase I Upgrade Demonstrator Plots (eps) 
png  FADC_noise_poster.png  r1  manage  14.0 K  20150223  17:08  MartinAleksa  Phase I Upgrade Demonstrator Plots 
eps  HLLHCNIELPSBRegionFLUKARBTF20132x3000ifb.eps  r1  manage  20.2 K  20140318  17:34  MartinAleksa  NIEL Simulations for HEC Cold Electronics in Phase II 
jpg  HLLHCNIELPSBRegionFLUKARBTF20132x3000ifb.jpg  r1  manage  237.6 K  20140318  17:34  MartinAleksa  NIEL Simulations for HEC Cold Electronics in Phase II 
HLLHCNIELPSBRegionFLUKARBTF20132x3000ifb.pdf  r1  manage  11.0 K  20140318  17:34  MartinAleksa  NIEL Simulations for HEC Cold Electronics in Phase II  
eps  LTDB_noise.eps  r1  manage  136.9 K  20150223  17:28  MartinAleksa  Phase I Upgrade Demonstrator Plots (eps) 
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eps  LTDB_pedestal.eps  r1  manage  153.2 K  20150223  17:28  MartinAleksa  Phase I Upgrade Demonstrator Plots (eps) 
png  LTDB_pedestal.png  r1  manage  15.9 K  20150223  17:08  MartinAleksa  Phase I Upgrade Demonstrator Plots 
eps  MedianDensityProfile_vs_eta.eps  r1  manage  41.3 K  20161018  14:44  MartinAleksa  
MedianDensityProfile_vs_eta.pdf  r1  manage  35.0 K  20161018  14:44  MartinAleksa  
png  MedianDensityProfile_vs_eta.png  r1  manage  28.0 K  20161018  14:44  MartinAleksa  
eps  PUSuppression_NJetsAndHSFraction_vs_PtCut.eps  r1  manage  23.5 K  20161018  14:44  MartinAleksa  
PUSuppression_NJetsAndHSFraction_vs_PtCut.pdf  r1  manage  21.2 K  20161018  14:44  MartinAleksa  
png  PUSuppression_NJetsAndHSFraction_vs_PtCut.png  r1  manage  30.0 K  20161018  14:44  MartinAleksa  
jpg  Poster_plots_approval_new.jpg  r1  manage  37.7 K  20150223  17:19  MartinAleksa  CNF Explanation 
eps  ROCeffeta38to42.eps  r1  manage  1596.5 K  20161018  16:12  MartinAleksa  
ROCeffeta38to42.pdf  r1  manage  690.8 K  20161018  16:12  MartinAleksa  
png  ROCeffeta38to42.png  r1  manage  655.0 K  20161018  16:12  MartinAleksa  
eps  ROC_effHS_pt20_extr_JES_preliminary.eps  r1  manage  13.7 K  20161018  14:44  MartinAleksa  
ROC_effHS_pt20_extr_JES_preliminary.pdf  r1  manage  19.1 K  20161018  14:44  MartinAleksa  
png  ROC_effHS_pt20_extr_JES_preliminary.png  r1  manage  27.9 K  20161018  14:44  MartinAleksa  
eps  Reta.eps  r1  manage  56.4 K  20180704  15:42  PeterJohannesFalke  Shower shape variable R_eta 
Reta.pdf  r1  manage  14.7 K  20180704  15:42  PeterJohannesFalke  Shower shape variable R_eta  
png  Reta.png  r1  manage  68.6 K  20180704  15:42  PeterJohannesFalke  Shower shape variable R_eta 
eps  Reta_log.eps  r1  manage  55.8 K  20180704  15:42  PeterJohannesFalke  Shower shape variable R_eta 
Reta_log.pdf  r1  manage  14.6 K  20180704  15:42  PeterJohannesFalke  Shower shape variable R_eta  
png  Reta_log.png  r1  manage  62.6 K  20180704  15:42  PeterJohannesFalke  Shower shape variable R_eta 
eps  Total_Noise.eps  r1  manage  10.7 K  20170912  19:35  SteffenStaerz  Total Noise (HEC) 
Total_Noise.pdf  r1  manage  14.2 K  20170912  19:35  SteffenStaerz  Total Noise (HEC)  
png  Total_Noise.png  r1  manage  114.4 K  20170912  19:35  SteffenStaerz  Total Noise (HEC) 
eps  cnf_poster.eps  r1  manage  8.8 K  20150223  17:28  MartinAleksa  Phase I Upgrade Demonstrator Plots (eps) 
png  cnf_poster.png  r1  manage  12.4 K  20150223  17:08  MartinAleksa  Phase I Upgrade Demonstrator Plots 
eps  comparison_energy_log.eps  r1  manage  56.8 K  20180704  12:08  PeterJohannesFalke  Measured energy comparison by layer 
comparison_energy_log.pdf  r1  manage  16.7 K  20180704  12:08  PeterJohannesFalke  Measured energy comparison by layer  
png  comparison_energy_log.png  r1  manage  79.9 K  20180704  12:08  PeterJohannesFalke  Measured energy comparison by layer 
eps  comparison_timing_log.eps  r1  manage  55.8 K  20180704  14:36  PeterJohannesFalke  Measured timing resolution by layer 
comparison_timing_log.pdf  r1  manage  16.6 K  20180704  14:36  PeterJohannesFalke  Measured timing resolution by layer  
png  comparison_timing_log.png  r1  manage  83.4 K  20180704  14:36  PeterJohannesFalke  Measured timing resolution by layer 
eps  cone.eps  r1  manage  58.5 K  20180704  15:37  PeterJohannesFalke  Analysis of shower variables 
cone.pdf  r1  manage  15.0 K  20180704  15:37  PeterJohannesFalke  Analysis of shower variables  
png  cone.png  r1  manage  77.3 K  20180704  15:37  PeterJohannesFalke  Analysis of shower variables 
eps  coneOverTotal.eps  r1  manage  56.9 K  20180704  15:37  PeterJohannesFalke  Analysis of shower variables 
coneOverTotal.pdf  r1  manage  14.8 K  20180704  15:37  PeterJohannesFalke  Analysis of shower variables  
png  coneOverTotal.png  r1  manage  68.8 K  20180704  15:37  PeterJohannesFalke  Analysis of shower variables 
eps  coneOverTotal_log.eps  r1  manage  57.8 K  20180704  15:38  PeterJohannesFalke  Analysis of shower properties 
coneOverTotal_log.pdf  r1  manage  15.0 K  20180704  15:38  PeterJohannesFalke  Analysis of shower properties  
png  coneOverTotal_log.png  r1  manage  67.2 K  20180704  15:38  PeterJohannesFalke  Analysis of shower properties 
eps  cone_log.eps  r1  manage  58.0 K  20180704  15:37  PeterJohannesFalke  Analysis of shower variables 
cone_log.pdf  r1  manage  14.9 K  20180704  15:37  PeterJohannesFalke  Analysis of shower variables  
png  cone_log.png  r1  manage  71.9 K  20180704  15:37  PeterJohannesFalke  Analysis of shower variables 
eps  difference_back.eps  r1  manage  9.2 K  20170912  14:01  SteffenStaerz  Demonstrator energy measurement 
difference_back.pdf  r1  manage  13.8 K  20170912  14:01  SteffenStaerz  Demonstrator energy measurement  
png  difference_back.png  r2 r1  manage  25.7 K  20170912  14:55  SteffenStaerz  Demonstrator energy measurement 
eps  difference_front.eps  r1  manage  9.0 K  20170912  14:01  SteffenStaerz  Demonstrator energy measurement 
difference_front.pdf  r1  manage  13.7 K  20170912  14:01  SteffenStaerz  Demonstrator energy measurement  
png  difference_front.png  r2 r1  manage  25.4 K  20170912  14:55  SteffenStaerz  Demonstrator energy measurement 
eps  difference_middle.eps  r1  manage  9.8 K  20170912  14:02  SteffenStaerz  Demonstrator energy measurement 
difference_middle.pdf  r1  manage  13.9 K  20170912  14:02  SteffenStaerz  Demonstrator energy measurement  
png  difference_middle.png  r2 r1  manage  28.3 K  20170912  14:55  SteffenStaerz  Demonstrator energy measurement 
eps  difference_presampler.eps  r1  manage  9.5 K  20170912  14:02  SteffenStaerz  Demonstrator energy measurement 
difference_presampler.pdf  r1  manage  13.9 K  20170912  14:02  SteffenStaerz  Demonstrator energy measurement  
png  difference_presampler.png  r2 r1  manage  26.6 K  20170912  14:55  SteffenStaerz  Demonstrator energy measurement 
eps  energyResol.eps  r1  manage  56.9 K  20180704  15:38  PeterJohannesFalke  Analysis of shower properties 
energyResol.pdf  r1  manage  14.3 K  20180704  15:38  PeterJohannesFalke  Analysis of shower properties  
png  energyResol.png  r1  manage  69.8 K  20180704  15:38  PeterJohannesFalke  Analysis of shower properties 
eps  energyResol_log.eps  r1  manage  57.8 K  20180704  15:38  PeterJohannesFalke  Analysis of shower properties 
energyResol_log.pdf  r1  manage  14.5 K  20180704  15:38  PeterJohannesFalke  Analysis of shower properties  
png  energyResol_log.png  r1  manage  71.8 K  20180704  15:38  PeterJohannesFalke  Analysis of shower properties 
eps  extractionMethods_front_average.eps  r1  manage  68.2 K  20180305  14:24  SteffenStaerz  Demonstrator pulse shape extraction methods 
extractionMethods_front_average.pdf  r1  manage  25.8 K  20180305  14:24  SteffenStaerz  Demonstrator pulse shape extraction methods  
png  extractionMethods_front_average.png  r1  manage  57.6 K  20180305  14:24  SteffenStaerz  Demonstrator pulse shape extraction methods 
eps  extractionMethods_front_hist.eps  r1  manage  68.9 K  20180305  14:24  SteffenStaerz  Demonstrator pulse shape extraction methods 
extractionMethods_front_hist.pdf  r1  manage  25.9 K  20180305  14:24  SteffenStaerz  Demonstrator pulse shape extraction methods  
png  extractionMethods_front_hist.png  r1  manage  57.7 K  20180305  14:24  SteffenStaerz  Demonstrator pulse shape extraction methods 
eps  extractionMethods_front_maxE.eps  r1  manage  68.4 K  20180305  14:24  SteffenStaerz  Demonstrator pulse shape extraction methods 
extractionMethods_front_maxE.pdf  r1  manage  25.5 K  20180305  14:24  SteffenStaerz  Demonstrator pulse shape extraction methods  
png  extractionMethods_front_maxE.png  r1  manage  58.6 K  20180305  14:24  SteffenStaerz  Demonstrator pulse shape extraction methods 
eps  f3.eps  r1  manage  55.7 K  20180704  15:42  PeterJohannesFalke  Shower shape variable f3 
f3.pdf  r1  manage  14.6 K  20180704  15:42  PeterJohannesFalke  Shower shape variable f3  
png  f3.png  r1  manage  62.0 K  20180704  15:42  PeterJohannesFalke  Shower shape variable f3 
eps  f3_log.eps  r1  manage  56.3 K  20180704  15:42  PeterJohannesFalke  Shower shape variable f3 
f3_log.pdf  r1  manage  14.7 K  20180704  15:42  PeterJohannesFalke  Shower shape variable f3  
png  f3_log.png  r1  manage  63.3 K  20180704  15:42  PeterJohannesFalke  Shower shape variable f3 
eps  fig1.eps  r2 r1  manage  980.0 K  20151112  16:59  MartinAleksa  Demonstrator SC Plots (eps) 
fig1.pdf  r2 r1  manage  70.2 K  20151112  17:00  MartinAleksa  Demonstrator SC Plots (pdf)  
png  fig1.png  r2 r1  manage  84.7 K  20151112  16:58  MartinAleksa  Demonstrator SC plots (png) 
eps  fig2.eps  r2 r1  manage  20.1 K  20151112  16:59  MartinAleksa  Demonstrator SC Plots (eps) 
fig2.pdf  r2 r1  manage  10.0 K  20151112  17:00  MartinAleksa  Demonstrator SC Plots (pdf)  
png  fig2.png  r2 r1  manage  63.0 K  20151112  16:58  MartinAleksa  Demonstrator SC plots (png) 
eps  fig3.eps  r2 r1  manage  20.8 K  20151112  16:59  MartinAleksa  Demonstrator SC Plots (eps) 
fig3.pdf  r2 r1  manage  10.5 K  20151112  17:00  MartinAleksa  Demonstrator SC Plots (pdf)  
png  fig3.png  r2 r1  manage  64.2 K  20151112  16:58  MartinAleksa  Demonstrator SC plots (png) 
eps  fig4.eps  r2 r1  manage  29.8 K  20151112  16:59  MartinAleksa  Demonstrator SC Plots (eps) 
fig4.pdf  r2 r1  manage  14.3 K  20151112  17:00  MartinAleksa  Demonstrator SC Plots (pdf)  
png  fig4.png  r2 r1  manage  66.1 K  20151112  16:58  MartinAleksa  Demonstrator SC plots (png) 
eps  fig5.eps  r2 r1  manage  26.6 K  20151112  16:59  MartinAleksa  Demonstrator SC Plots (eps) 
fig5.pdf  r2 r1  manage  12.9 K  20151112  17:00  MartinAleksa  Demonstrator SC Plots (pdf)  
png  fig5.png  r2 r1  manage  69.0 K  20151112  16:58  MartinAleksa  Demonstrator SC plots (png) 
eps  fig6.eps  r2 r1  manage  115.9 K  20151112  16:59  MartinAleksa  Demonstrator SC Plots (eps) 
fig6.pdf  r2 r1  manage  84.1 K  20151112  17:00  MartinAleksa  Demonstrator SC Plots (pdf)  
png  fig6.png  r2 r1  manage  104.8 K  20151112  16:58  MartinAleksa  Demonstrator SC plots (png) 
eps  fig7.eps  r2 r1  manage  9.5 K  20151112  16:59  MartinAleksa  Demonstrator SC Plots (eps) 
fig7.pdf  r2 r1  manage  5.0 K  20151112  17:00  MartinAleksa  Demonstrator SC Plots (pdf)  
png  fig7.png  r2 r1  manage  27.5 K  20151112  16:58  MartinAleksa  Demonstrator SC plots (png) 
eps  fig8.eps  r2 r1  manage  10.1 K  20151112  16:59  MartinAleksa  Demonstrator SC Plots (eps) 
fig8.pdf  r2 r1  manage  5.3 K  20151112  17:00  MartinAleksa  Demonstrator SC Plots (pdf)  
png  fig8.png  r2 r1  manage  29.0 K  20151112  16:58  MartinAleksa  Demonstrator SC plots (png) 
eps  intr_electr_res_fl_prel.eps  r1  manage  20.2 K  20170914  14:31  SteffenStaerz  Quantization noise 
intr_electr_res_fl_prel.pdf  r1  manage  25.1 K  20170914  14:31  SteffenStaerz  Quantization noise  
png  intr_electr_res_fl_prel.png  r1  manage  43.5 K  20170914  14:31  SteffenStaerz  Quantization noise 
eps  intr_electr_res_ml_prel.eps  r1  manage  20.4 K  20170914  14:31  SteffenStaerz  Quantization noise 
intr_electr_res_ml_prel.pdf  r1  manage  25.0 K  20170914  14:31  SteffenStaerz  Quantization noise  
png  intr_electr_res_ml_prel.png  r1  manage  45.2 K  20170914  14:31  SteffenStaerz  Quantization noise 
eps  noise_tot_ratio_sFCal_SmallGaps_over_FCal_mu200new_prelim.eps  r1  manage  22.1 K  20161018  14:43  MartinAleksa  
noise_tot_ratio_sFCal_SmallGaps_over_FCal_mu200new_prelim.pdf  r1  manage  21.8 K  20161018  14:43  MartinAleksa  
png  noise_tot_ratio_sFCal_SmallGaps_over_FCal_mu200new_prelim.png  r1  manage  23.0 K  20161018  14:43  MartinAleksa  
eps  phase2_ann_LSTM_singlecell_archi.eps  r1  manage  123.5 K  20210621  18:13  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
phase2_ann_LSTM_singlecell_archi.pdf  r1  manage  43.0 K  20210621  18:13  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_LSTM_singlecell_archi.png  r1  manage  31.5 K  20210621  18:13  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_LSTM_slidewindow_archi.eps  r1  manage  335.6 K  20210621  18:14  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
phase2_ann_LSTM_slidewindow_archi.pdf  r1  manage  98.1 K  20210621  18:14  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_LSTM_slidewindow_archi.png  r1  manage  159.9 K  20210621  18:14  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_ROC.eps  r1  manage  169.7 K  20210621  15:40  ArnoStraessner  PhaseII ANN performance and FPGA implementation 
phase2_ann_ROC.pdf  r1  manage  70.5 K  20210621  15:40  ArnoStraessner  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_ROC.png  r1  manage  103.4 K  20210621  15:40  ArnoStraessner  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_cnn_architecture.eps  r1  manage  253.6 K  20210621  15:40  ArnoStraessner  PhaseII ANN performance and FPGA implementation 
phase2_ann_cnn_architecture.pdf  r1  manage  80.2 K  20210621  15:40  ArnoStraessner  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_cnn_architecture.png  r1  manage  171.5 K  20210621  15:40  ArnoStraessner  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_cnn_gap_resolution.eps  r1  manage  127.1 K  20210621  18:13  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
phase2_ann_cnn_gap_resolution.pdf  r1  manage  34.3 K  20210621  18:13  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_cnn_gap_resolution.png  r1  manage  25.9 K  20210621  18:13  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_cnn_sequence.eps  r1  manage  136.0 K  20210621  15:40  ArnoStraessner  PhaseII ANN performance and FPGA implementation 
phase2_ann_cnn_sequence.pdf  r1  manage  55.2 K  20210621  15:40  ArnoStraessner  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_cnn_sequence.png  r1  manage  100.4 K  20210621  15:40  ArnoStraessner  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_hlsVScpp_reso.eps  r1  manage  14.0 K  20210621  18:13  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
phase2_ann_hlsVScpp_reso.pdf  r1  manage  14.9 K  20210621  18:13  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_hlsVScpp_reso.png  r1  manage  20.4 K  20210621  18:13  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_lstm_singlecell_gap_resolution.eps  r1  manage  131.6 K  20210621  18:14  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
phase2_ann_lstm_singlecell_gap_resolution.pdf  r1  manage  33.5 K  20210621  18:14  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_lstm_singlecell_gap_resolution.png  r1  manage  26.9 K  20210621  18:14  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_ofmax_gap_resolution.eps  r1  manage  139.5 K  20210621  18:15  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
phase2_ann_ofmax_gap_resolution.pdf  r1  manage  34.7 K  20210621  18:15  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_ofmax_gap_resolution.png  r1  manage  27.6 K  20210621  18:15  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_perf_summary_plot.eps  r1  manage  13.5 K  20210621  18:15  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
phase2_ann_perf_summary_plot.pdf  r1  manage  14.3 K  20210621  18:15  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_perf_summary_plot.png  r1  manage  16.4 K  20210621  18:15  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
eps  phase2_ann_vrnn_gap_resolution.eps  r1  manage  130.8 K  20210621  18:15  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
phase2_ann_vrnn_gap_resolution.pdf  r1  manage  33.2 K  20210621  18:15  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation  
png  phase2_ann_vrnn_gap_resolution.png  r1  manage  26.8 K  20210621  18:15  ThomasPhilippeCalvet  PhaseII ANN performance and FPGA implementation 
eps  pulseRelEDev_Ebinned_middle_RMS.eps  r1  manage  55.7 K  20180704  12:08  PeterJohannesFalke  Measured energy comparison by layer 
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png  pulseRelEDev_Ebinned_middle_RMS.png  r1  manage  76.6 K  20180704  12:08  PeterJohannesFalke  Measured energy comparison by layer 
eps  pulseShape_back.eps  r1  manage  69.4 K  20180305  14:25  SteffenStaerz  Demonstrator pulse shapes 
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png  pulseShape_back.png  r1  manage  59.6 K  20180305  14:25  SteffenStaerz  Demonstrator pulse shapes 
eps  pulseShape_front.eps  r1  manage  68.9 K  20180305  14:27  SteffenStaerz  Demonstrator pulse shapes 
pulseShape_front.pdf  r1  manage  25.9 K  20180305  14:25  SteffenStaerz  Demonstrator pulse shapes  
png  pulseShape_front.png  r1  manage  57.7 K  20180305  14:25  SteffenStaerz  Demonstrator pulse shapes 
eps  pulseShape_middle.eps  r1  manage  68.9 K  20180305  14:27  SteffenStaerz  Demonstrator pulse shapes 
pulseShape_middle.pdf  r1  manage  25.9 K  20180305  14:27  SteffenStaerz  Demonstrator pulse shapes  
png  pulseShape_middle.png  r1  manage  58.2 K  20180305  14:27  SteffenStaerz  Demonstrator pulse shapes 
eps  pulseShape_presampler.eps  r1  manage  69.3 K  20180305  14:27  SteffenStaerz  Demonstrator pulse shapes 
pulseShape_presampler.pdf  r1  manage  26.1 K  20180305  14:27  SteffenStaerz  Demonstrator pulse shapes  
png  pulseShape_presampler.png  r1  manage  60.5 K  20180305  14:27  SteffenStaerz  Demonstrator pulse shapes 
eps  pulseTiming_Ebinned_middle_RMS.eps  r1  manage  53.1 K  20180704  14:36  PeterJohannesFalke  Measured timing resolution by layer 
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eps  pulseTiming_back.eps  r1  manage  31.5 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution 
pulseTiming_back.pdf  r1  manage  13.5 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution  
png  pulseTiming_back.png  r1  manage  36.7 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution 
eps  pulseTiming_front.eps  r1  manage  30.5 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution 
pulseTiming_front.pdf  r1  manage  13.5 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution  
png  pulseTiming_front.png  r1  manage  34.6 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution 
eps  pulseTiming_middle.eps  r1  manage  31.1 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution 
pulseTiming_middle.pdf  r1  manage  13.4 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution  
png  pulseTiming_middle.png  r1  manage  34.3 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution 
eps  pulseTiming_presampler.eps  r1  manage  31.2 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution 
pulseTiming_presampler.pdf  r1  manage  13.5 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution  
png  pulseTiming_presampler.png  r1  manage  35.9 K  20180305  14:29  SteffenStaerz  Demonstrator timing distribution 
eps  relEDev_Mean_etaDep_19.eps  r1  manage  84.6 K  20180704  15:36  PeterJohannesFalke  Energy overview plots 
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relEDev_RMS_etaDep_19.pdf  r1  manage  22.5 K  20180704  15:36  PeterJohannesFalke  Energy overview plots  
png  relEDev_RMS_etaDep_19.png  r1  manage  142.7 K  20180704  15:36  PeterJohannesFalke  Energy overview plots 
eps  sFCalReview_CellSig_VBF2600_prelim.eps  r1  manage  19.3 K  20161018  14:43  MartinAleksa  
sFCalReview_CellSig_VBF2600_prelim.pdf  r1  manage  16.2 K  20161018  14:43  MartinAleksa  
png  sFCalReview_CellSig_VBF2600_prelim.png  r1  manage  26.8 K  20161018  14:43  MartinAleksa  
eps  sFCalReview_NConst_VBF2600_prelim.eps  r1  manage  28.8 K  20161018  14:43  MartinAleksa  
sFCalReview_NConst_VBF2600_prelim.pdf  r1  manage  23.8 K  20161018  14:43  MartinAleksa  
png  sFCalReview_NConst_VBF2600_prelim.png  r1  manage  28.1 K  20161018  14:43  MartinAleksa  
eps  scatter_back.eps  r1  manage  13.2 K  20170912  14:06  SteffenStaerz  Demonstrator energy measurement 
scatter_back.pdf  r1  manage  15.2 K  20170912  14:06  SteffenStaerz  Demonstrator energy measurement  
png  scatter_back.png  r2 r1  manage  83.9 K  20170912  14:55  SteffenStaerz  Demonstrator energy measurement 
eps  scatter_front.eps  r1  manage  14.0 K  20170912  14:06  SteffenStaerz  Demonstrator energy measurement 
scatter_front.pdf  r1  manage  15.4 K  20170912  14:06  SteffenStaerz  Demonstrator energy measurement  
png  scatter_front.png  r3 r2 r1  manage  87.0 K  20170912  14:55  SteffenStaerz  Demonstrator energy measurement 
eps  scatter_middle.eps  r1  manage  18.2 K  20170912  14:07  SteffenStaerz  Demonstrator energy measurement 
scatter_middle.pdf  r1  manage  16.6 K  20170912  14:07  SteffenStaerz  Demonstrator energy measurement  
png  scatter_middle.png  r2 r1  manage  95.1 K  20170912  14:55  SteffenStaerz  Demonstrator energy measurement 
eps  scatter_presampler.eps  r1  manage  14.6 K  20170912  14:07  SteffenStaerz  Demonstrator energy measurement 
scatter_presampler.pdf  r1  manage  15.4 K  20170912  14:07  SteffenStaerz  Demonstrator energy measurement  
png  scatter_presampler.png  r3 r2 r1  manage  85.0 K  20170912  14:55  SteffenStaerz  Demonstrator energy measurement 
eps  shaper.eps  r1  manage  13.8 K  20170912  15:29  SteffenStaerz  Total Noise, bipolar shaper 
shaper.pdf  r1  manage  14.4 K  20170912  15:29  SteffenStaerz  Total Noise, bipolar shaper  
png  shaper.png  r1  manage  14.5 K  20170912  15:29  SteffenStaerz  Total Noise, bipolar shaper 
eps  timing_Mean_etaDep_19.eps  r1  manage  85.1 K  20180704  15:35  PeterJohannesFalke  Timing overview plots 
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png  timing_Mean_etaDep_19.png  r1  manage  147.0 K  20180704  15:35  PeterJohannesFalke  Timing overview plots 
eps  timing_RMS_etaDep_19.eps  r1  manage  83.9 K  20180704  15:35  PeterJohannesFalke  Timing overview plots 
timing_RMS_etaDep_19.pdf  r1  manage  21.9 K  20180704  15:35  PeterJohannesFalke  Timing overview plots  
png  timing_RMS_etaDep_19.png  r1  manage  140.8 K  20180704  15:35  PeterJohannesFalke  Timing overview plots 
eps  total_noise_I05.eps  r2 r1  manage  142.9 K  20150223  23:27  MartinAleksa  Phase I Upgrade Demonstrator Plots (eps) 
png  total_noise_I05.png  r2 r1  manage  29.2 K  20150223  23:28  MartinAleksa  Phase I Upgrade Demonstrator Plots 
eps  totalnoise_demonstrator.eps  r2 r1  manage  142.9 K  20150223  23:28  MartinAleksa  Phase I Upgrade Demonstrator Plots (eps) 
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eps  wEta.eps  r1  manage  57.1 K  20180704  15:43  PeterJohannesFalke  Shower shape variable w_{eta,2} 
wEta.pdf  r1  manage  14.8 K  20180704  15:43  PeterJohannesFalke  Shower shape variable w_{eta,2}  
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eps  wEta_log.eps  r1  manage  56.5 K  20180704  15:43  PeterJohannesFalke  Shower shape variable w_{eta,2} 
wEta_log.pdf  r1  manage  14.7 K  20180704  15:43  PeterJohannesFalke  Shower shape variable w_{eta,2}  
png  wEta_log.png  r1  manage  64.9 K  20180704  15:43  PeterJohannesFalke  Shower shape variable w_{eta,2} 