LArCaloPublicResults2015

Introduction
Proton Collisions Public Plots LHC Run 2 (2015 - 2018)
Beam Splash Events 2015

Introduction

Approved plots that can be shown by ATLAS speakers at conferences and similar events.
Please do not add figures on your own. Contact the responsible LAr project leader in case of questions and/or suggestions.

Proton Collisions Public Plots LHC Run 2 (2015 - 2018)

LAr data quality inefficiency in Run 2

LAr data quality inefficiency in Run 2 – Defect rejection In LAr, data not suitable for physics analysis during a “long” time period (i.e. more than 1 minute) can be rejected by assigning a defect to a luminosity block, the length of which is usually one minute. Consequently this luminosity block is no longer considered in the GRL. This plot show the data rejection by defect assignment for the 4 run-2 datasets for the most optimal processing (latest reprocessing for 2015-2017 and Tier0 processing for 2018). Minor evolutions with the originally approved plots can be explained by : the new reprocessings, the use of offline luminosity (instead of online luminosity). Only marginal recoveries are expected in case of a future reprocessing. The frozen defect tags are the following ones: 2015: DetStatus-v89-pro21-02 2016: DetStatus-v89-pro21-01 2017: DetStatus-v99-pro22-01 2018: DetStatus-v102-pro22-04 The inefficiencies are computed for the official ATLAS GRL available at the location http://atlas.web.cern.ch/Atlas/GROUPS/DATABASE/GroupData/GoodRunsLists/ The runs completely discarded due to a major hardware failure (solenoid off, LAr front end crate off...) are not included, hence slightly overestimating the relative DQ efficiency. The sources of data loss are: Data corruption (< permil): usually a desynchronization of one or several Front End Boards lasting more than 1 minute / luminosity block (otherwise treated by time veto). HV non nominal (< permil only in 2016): non nominal/ramping FCal high voltage at start of 3 runs for specific performance/efficiency studies. HV trip (sizable only in 2015): a sudden drop of high voltage makes unreliable the energy correction. The installation of new power supplies in 2016 allowed to largely reduce the number of high voltage trips. Trigger misconfiguration (relevant only in 2016): this is a single run failure in 2016 due to an inappropriate set of online calorimeter parameters directly affecting the data rate transferred to the trigger system. Due to this feature, some emergency measures were taken on the trigger system side to cope with the unexpectedly high data rate. Coverage (varying depending on specific hardware failures): an area of > 512 calorimeter cells (out of a total of ~182 000) is inactive. These are due to single hardware failures (front end cooling or back end electronics) requiring several hours for a fix. 3 major events during the whole LHC Run 2. noisy channels (~ permil): noisy channels are treated by masking them either permanently either selectively (when the quality factor indicates a pulse shape incompatible with a liquid-argon ionization). When too many contiguous cells are noisy, this method may induce an ineffective detection area and there is no other solution than rejecting the whole affected dataset. noise bursts (~ permil): noise bursts are well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). It is usually treated by applying a time veto. When the noise burst is not identified in due time, the veto windows can not be defined and the whole LB must be rejected by assigning a defect.	eps version, pdf version
LAr data quality inefficiency in Run 2 – Veto rejection In LAr, data not suitable for physics analysis during a “short” time period (i.e.less than 1 minute) can be rejected by defining time veto windows in the COOL database, prior to the data processing. This information is propagated via the EventInfo that is used to filter the data event by event. These plots show the data rejection by time veto for the 4 run-2 datasets for the most optimal processing (latest reprocessing for 2015-2017 and Tier0 processing for 2018). Minor evolutions with the originally approved plots can be explained by : the new reprocessings, the use of offline luminosity (instead of online luminosity). Only marginal recoveries are expected in case of a future reprocessing. The COOL tags used for processing are the following ones: 2015: LARBadChannelsOflEventVeto-RUN2-UPD4-06 2016: LARBadChannelsOflEventVeto-RUN2-UPD4-06 2017: LARBadChannelsOflEventVeto-RUN2-UPD4-08 2018: LARBadChannelsOflEventVeto-RUN2-UPD4-10 The inefficiencies are computed for the official ATLAS GRL available at the location http://atlas.web.cern.ch/Atlas/GROUPS/DATABASE/GroupData/GoodRunsLists/ The runs completely discarded due to a major hardware failure (solenoid off, LAr front end crate off...) are not included, hence slightly overestimating the relative DQ efficiency. The main sources of data loss are: Data corruption: usually a desynchronization of one or several Front End Boards. The data acquired between the desynchronization and the automatic resynchronization are rejected. Neither instantaneous-luminosity dependency nor suspicious Front End Board have been identified. noise bursts: noise bursts are since the LHC-Run 1 well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). The typical cumulated energy may reach several tens of TeVs. They are routinely identified and the corresponding data are rejected with a safety margin of typically few milliseconds before and after the phenomenon occurrence. Mni noise bursts: mini noise bursts are more recent phenomena that affect a small region of the detector (mainly middle sampling layer of EM barrel) during a very short time (typically several tens of nanoseconds). The typical cumulated energy may reach several tens of GeVs. They are routinely identified and the corresponding data are rejected with a safety margin of typically few milliseconds before and after the phenomenon occurrence. They appear only in 2016 when the instantaneous luminosity reaches 5–6x10³³ cm² s^-1. This explains why the rate is larger in 2016 as a conservative approach was adopted first. The rate was reduced in 2017 and 2018 with an improved software identification and an high voltage tuning that allowed to mitigate their rate.	eps version, pdf version

LAr data quality inefficiency in Run 2 – Defect rejection

In LAr, data not suitable for physics analysis during a “long” time period (i.e. more than 1 minute) can be rejected by assigning a defect to a luminosity block, the length of which is usually one minute. Consequently this luminosity block is no longer considered in the GRL.
This plot show the data rejection by defect assignment for the 4 run-2 datasets for the most optimal processing (latest reprocessing for 2015-2017 and Tier0 processing for 2018).
- Minor evolutions with the originally approved plots can be explained by : the new reprocessings, the use of offline luminosity (instead of online luminosity).
- Only marginal recoveries are expected in case of a future reprocessing.
- The frozen defect tags are the following ones:
  - 2015: DetStatus-v89-pro21-02
  - 2016: DetStatus-v89-pro21-01
  - 2017: DetStatus-v99-pro22-01
  - 2018: DetStatus-v102-pro22-04
The inefficiencies are computed for the official ATLAS GRL available at the location http://atlas.web.cern.ch/Atlas/GROUPS/DATABASE/GroupData/GoodRunsLists/
- The runs completely discarded due to a major hardware failure (solenoid off, LAr front end crate off...) are not included, hence slightly overestimating the relative DQ efficiency.

The sources of data loss are:
- Data corruption (< permil): usually a desynchronization of one or several Front End Boards lasting more than 1 minute / luminosity block (otherwise treated by time veto).
- HV non nominal (< permil only in 2016): non nominal/ramping FCal high voltage at start of 3 runs for specific performance/efficiency studies.
- HV trip (sizable only in 2015): a sudden drop of high voltage makes unreliable the energy correction. The installation of new power supplies in 2016 allowed to largely reduce the number of high voltage trips.
- Trigger misconfiguration (relevant only in 2016): this is a single run failure in 2016 due to an inappropriate set of online calorimeter parameters directly affecting the data rate transferred to the trigger system. Due to this feature, some emergency measures were taken on the trigger system side to cope with the unexpectedly high data rate.
- Coverage (varying depending on specific hardware failures): an area of > 512 calorimeter cells (out of a total of ~182 000) is inactive. These are due to single hardware failures (front end cooling or back end electronics) requiring several hours for a fix. 3 major events during the whole LHC Run 2.
- noisy channels (~ permil): noisy channels are treated by masking them either permanently either selectively (when the quality factor indicates a pulse shape incompatible with a liquid-argon ionization). When too many contiguous cells are noisy, this method may induce an ineffective detection area and there is no other solution than rejecting the whole affected dataset.
- noise bursts (~ permil): noise bursts are well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). It is usually treated by applying a time veto. When the noise burst is not identified in due time, the veto windows can not be defined and the whole LB must be rejected by assigning a defect.

eps version, pdf version

LAr data quality inefficiency in Run 2 – Veto rejection

In LAr, data not suitable for physics analysis during a “short” time period (i.e.less than 1 minute) can be rejected by defining time veto windows in the COOL database, prior to the data processing. This information is propagated via the EventInfo that is used to filter the data event by event.
These plots show the data rejection by time veto for the 4 run-2 datasets for the most optimal processing (latest reprocessing for 2015-2017 and Tier0 processing for 2018).
- Minor evolutions with the originally approved plots can be explained by : the new reprocessings, the use of offline luminosity (instead of online luminosity).
- Only marginal recoveries are expected in case of a future reprocessing.
- The COOL tags used for processing are the following ones:
  - 2015: LARBadChannelsOflEventVeto-RUN2-UPD4-06
  - 2016: LARBadChannelsOflEventVeto-RUN2-UPD4-06
  - 2017: LARBadChannelsOflEventVeto-RUN2-UPD4-08
  - 2018: LARBadChannelsOflEventVeto-RUN2-UPD4-10
The inefficiencies are computed for the official ATLAS GRL available at the location http://atlas.web.cern.ch/Atlas/GROUPS/DATABASE/GroupData/GoodRunsLists/
- The runs completely discarded due to a major hardware failure (solenoid off, LAr front end crate off...) are not included, hence slightly overestimating the relative DQ efficiency.

The main sources of data loss are:
- Data corruption: usually a desynchronization of one or several Front End Boards. The data acquired between the desynchronization and the automatic resynchronization are rejected. Neither instantaneous-luminosity dependency nor suspicious Front End Board have been identified.
- noise bursts: noise bursts are since the LHC-Run 1 well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). The typical cumulated energy may reach several tens of TeVs. They are routinely identified and the corresponding data are rejected with a safety margin of typically few milliseconds before and after the phenomenon occurrence.
- Mni noise bursts: mini noise bursts are more recent phenomena that affect a small region of the detector (mainly middle sampling layer of EM barrel) during a very short time (typically several tens of nanoseconds). The typical cumulated energy may reach several tens of GeVs. They are routinely identified and the corresponding data are rejected with a safety margin of typically few milliseconds before and after the phenomenon occurrence.
  They appear only in 2016 when the instantaneous luminosity reaches 5–6x10³³ cm² s^-1. This explains why the rate is larger in 2016 as a conservative approach was adopted first. The rate was reduced in 2017 and 2018 with an improved software identification and an high voltage tuning that allowed to mitigate their rate.

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LAr data quality inefficiency in 2017 - Tier0 processing

LAr Data Quality Plots 2017: These plots show the data rejection as a function of the data taking period (assessment based on the Tier 0 output, i. e. without reprocessing). In ATLAS, a period corresponds to a data set acquired under similar operating conditions. The integrated luminosity varies widely from one period to another, ranging between 2.4 fb^-1 and 15 fb^-1. Full details can be found here. In LAr, data not suitable for physics analysis are rejected via two complementary means: by assigning a defect to a luminosity block, the length of which is usually one minute. Consequently this luminosity block is no longer considered in the GRL. by defining time veto windows in the COOL database, prior to the data processing. This information is propagated via the EventInfo that is used to filter the data event by event in each analysis. The main sources of data loss in 2017 are: coverage (0.27 % via defect assignment): this is a single run failure due to a water leak that lead to a shutdown of a back end readout crate during ~ 6 hours. noisy channels (0.19 % in time veto): noisy channels are treated by masking them either permanently either selectively (when the quality factor indicates a pulse shape incompatible with a liquid - argon ionization). When too many contiguous cells are noisy, this method may induce an ineffective detection area and there is no other solution than rejecting the whole affected dataset. noise bursts (0.13 % in defect assignment): noise bursts are well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). It is usually treated by applying a time veto. When the noise burst is not identified in due time, the veto windows can not be defined and the whole LB must be rejected by assigning a defect.	eps version, pdf version eps version, pdf version eps version, pdf version

LAr Data Quality Plots 2017: These plots show the data rejection as a function of the data taking period (assessment based on the Tier 0 output, i. e. without reprocessing).

In ATLAS, a period corresponds to a data set acquired under similar operating conditions. The integrated luminosity varies widely from one period to another, ranging between 2.4 fb^-1 and 15 fb^-1. Full details can be found here.
In LAr, data not suitable for physics analysis are rejected via two complementary means:
- by assigning a defect to a luminosity block, the length of which is usually one minute. Consequently this luminosity block is no longer considered in the GRL.
- by defining time veto windows in the COOL database, prior to the data processing. This information is propagated via the EventInfo that is used to filter the data event by event in each analysis.
The main sources of data loss in 2017 are:
- coverage (0.27 % via defect assignment): this is a single run failure due to a water leak that lead to a shutdown of a back end readout crate during ~ 6 hours.
- noisy channels (0.19 % in time veto): noisy channels are treated by masking them either permanently either selectively (when the quality factor indicates a pulse shape incompatible with a liquid - argon ionization). When too many contiguous cells are noisy, this method may induce an ineffective detection area and there is no other solution than rejecting the whole affected dataset.
- noise bursts (0.13 % in defect assignment): noise bursts are well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). It is usually treated by applying a time veto. When the noise burst is not identified in due time, the veto windows can not be defined and the whole LB must be rejected by assigning a defect.

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LAr BCID-Dependent Baseline Correction

Average transverse energy per unit η-φ area and unit μ (number of interactions per bunch crossing) as a function of the distance (in BCID, bunch crossing identifier, in 25 ns units) from the beginning of the bunch train in different regions of the ATLAS LAr calorimeter. Data taken in September 2017 with a filling scheme with 48 colliding bunches spaced by 25ns per train are compared to data taken with a filling scheme with 8 colliding bunches spaced by 25 ns with 125 ns between them (8b4e). With long trains, after 20 BCID the average shift is close to 0 thanks to the bipolar shaping applied in the readout and the total contribution from out-of-time pile-up compensates the in-time pile-up contribution. For distances from the beginning of the train smaller than the typical drift time in LAr gaps (450 ns in the EM barrel calorimeter, less in the FCAL) an average positive energy from pile-up is expected as the contributions from out-of-time pile-up does not cancel the in-time pile-up contribution. With the 8b4e filling scheme this cancellation is never perfectly achieved. The data are compared to predictions computed using the pulse shape, the luminosity per bunch and the optimal filter coefficients used to estimate the pulse energy. Some structures observed in the prediction and data before the correction are related to variations in the luminosity from bunch to bunch. These predictions are used to correct the expected average energy shift per calorimeter cell. In the EM calorimeter, the residual systematic shift on the transverse energy after the correction is less than 10 MeV at μ=40 for the typical size of the cluster used to measure electron and photon energies. Reconstruction of higher level physics objects like jets include some further event-by-event pileup mitigation techniques that further reduce the impact of residual systematic energy shifts after the correction.	eps version, pdf version eps version, pdf version eps version, pdf version eps version, pdf version eps version, pdf version eps version, pdf version eps version, pdf version eps version, pdf version

Average transverse energy per unit η-φ area and unit μ (number of interactions per bunch crossing) as a function of the distance (in BCID, bunch crossing identifier, in 25 ns units) from the beginning of the bunch train in different regions of the ATLAS LAr calorimeter.

Data taken in September 2017 with a filling scheme with 48 colliding bunches spaced by 25ns per train are compared to data taken with a filling scheme with 8 colliding bunches spaced by 25 ns with 125 ns between them (8b4e).

With long trains, after 20 BCID the average shift is close to 0 thanks to the bipolar shaping applied in the readout and the total contribution from out-of-time pile-up compensates the in-time pile-up contribution. For distances from the beginning of the train smaller than the typical drift time in LAr gaps (450 ns in the EM barrel calorimeter, less in the FCAL) an average positive energy from pile-up is expected as the contributions from out-of-time pile-up does not cancel the in-time pile-up contribution. With the 8b4e filling scheme this cancellation is never perfectly achieved.

The data are compared to predictions computed using the pulse shape, the luminosity per bunch and the optimal filter coefficients used to estimate the pulse energy. Some structures observed in the prediction and data before the correction are related to variations in the luminosity from bunch to bunch. These predictions are used to correct the expected average energy shift per calorimeter cell.

In the EM calorimeter, the residual systematic shift on the transverse energy after the correction is less than 10 MeV at μ=40 for the typical size of the cluster used to measure electron and photon energies.

Reconstruction of higher level physics objects like jets include some further event-by-event pileup mitigation techniques that further reduce the impact of residual systematic energy shifts after the correction.

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LAr Pulse Shape Plots and Impact on Pile-Up Correction:

LAr pulse shape determination from special run:

LAr pulse shape is measured using special runs taken with few colliding bunches at the beginning of the 2016 data taking period with randomly triggered events in a 32 BCID window around the colliding bunches.
4 time samples (25 ns apart) are readout for each event
Averaging many events the average pulse shape over the full LAr drift time can thus be obtained
These pulse shapes are normalized to 1 at the maximum and then φ-symmetrized per η cell and layer (or small group of cells in η)
See this presentation for more details.

Pile-up correction:

Because of the LHC bunch train structure and bunch-to-bunch luminosity variations, the average energy from pileup is not exactly 0 as it would be in the ideal case of infinite trains (thanks to the bipolar shaping of the LAr response)
A correction is computed to correct for these shifts. The inputs of this corrections are the luminosity per bunch, the pulse shape per layer and cell in eta (symmetrized in azimuth φ), the optimal filter coefficients used to reconstruct the pulse amplitude and a normalization factor giving the average energy deposited per cell for a single inelastic collision (initially tuned from single bunch data at high mu)
The pulse shapes currently used in this correction (for release 20.7) were the same as the "ideal" pulse shapes used in MC simulation.
Residual systematic effects are visible
The correction has been recomputed using the measured pulse shape from the data special run (for the layers 0-1-2 of the EM barrel and layers 1-2 of the EM endcap, where clean measurements were performed from the special run)
This improves significantly the accuracy of the BCID dependent pileup correction: the new correction matches better the observer data. These improved pulse shapes will be used in release 21 reprocessing

Pulse Shapes: Typical ionization pulse shapes in the EM barrel presampler and middle layer. The pulse shapes are extracted from special runs with isolated crossing bunches collected in 2016. The data are recorded by a random 200 Hz trigger within a 32 BCID window surrounding the filled bunches and transmitting four samples. The pulse shapes are extracted for each layer and η region of the calorimeter and are averaged over φ.	eps - pdf eps - pdf
Example of Pile-Up Correction: Average transverse energy per unit η-φ area and unit μ (number of interactions per bunch crossing) as a function of the distance (in BCID, bunch crossing identifier, in 25 ns units) from the beginning of the bunch train in the ATLAS EM calorimeter for 0.8<\|η\|<1.0. After 20 BCID, the average shift is close to 0 thanks to the bipolar shaping applied in the readout and the total contribution from out-of-time pile-up compensates the in-time pile-up contribution. For distances from the beginning of the train smaller than the typical drift time in LAr gaps ( 450 ns in the barrel calorimeter) an average positive energy from pile-up is expected as the contributions from out-of-time pile-up does not cancel the in-time pile-up contribution. ZeroBias data (trigger randomly proportionally to the instantaneous luminosity) taken in October 2016 with 48 bunches trains are compared to predictions computed using the pulse shape, the luminosity per bunch and the optimal filter coefficients used to estimate the pulse energy. The predictions using the measured pulse shapes in the 2016 special runs are compared to predictions using the same pulse shape as in the simulation. The plots show the new and the old correction as a function of BCID (top) and the difference between ZeroBias data and both corrections (bottom).	eps - pdf eps - pdf

Pulse Shapes: Typical ionization pulse shapes in the EM barrel presampler and middle layer. The pulse shapes are extracted from special runs with isolated crossing bunches collected in 2016. The data are recorded by a random 200 Hz trigger within a 32 BCID window surrounding the filled bunches and transmitting four samples. The pulse shapes are extracted for each layer and η region of the calorimeter and are averaged over φ.

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Example of Pile-Up Correction: Average transverse energy per unit η-φ area and unit μ (number of interactions per bunch crossing) as a function of the distance (in BCID, bunch crossing identifier, in 25 ns units) from the beginning of the bunch train in the ATLAS EM calorimeter for 0.8<|η|<1.0. After 20 BCID, the average shift is close to 0 thanks to the bipolar shaping applied in the readout and the total contribution from out-of-time pile-up compensates the in-time pile-up contribution. For distances from the beginning of the train smaller than the typical drift time in LAr gaps ( 450 ns in the barrel calorimeter) an average positive energy from pile-up is expected as the contributions from out-of-time pile-up does not cancel the in-time pile-up contribution. ZeroBias data (trigger randomly proportionally to the instantaneous luminosity) taken in October 2016 with 48 bunches trains are compared to predictions computed using the pulse shape, the luminosity per bunch and the optimal filter coefficients used to estimate the pulse energy. The predictions using the measured pulse shapes in the 2016 special runs are compared to predictions using the same pulse shape as in the simulation. The plots show the new and the old correction as a function of BCID (top) and the difference between ZeroBias data and both corrections (bottom).

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IBL Services Impact on FCal:

Average Cell Energy Spread in 2012 Data: FCal cell energy spread in the first FCal module normalized to the average cell energy spread over cells with the same \|η\| within ±0.04 for minimum bias data collected in 2012 at s^1/2 = 8 TeV. Deviations from 1 indicate non-uniformities in φ. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The dark areas in the central regions along the horizontal and vertical figure axes and along the two diagonals are likely due to φ non-uniform material.	png - eps - pdf \| png - eps - pdf
Average Cell Energy Spread in 2015 Data: FCal cell energy spread in the first FCal module normalized to the average cell energy spread over cells with the same \|η\| within ±0.04 for zero bias data collected in 2015 at s^1/2 = 13 TeV. Deviations from 1 indicate non-uniformities in φ. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The alternating dark and light regions close to the inner bore not present in the 2012 data reflect the impact of the non-uniform distribution of the IBL services in front of the FCal. In addition the same dark areas in the central regions along the horizontal and vertical figure axes and along the two diagonals as in 2012 are visible.	png - eps - pdf \| png - eps - pdf
Average Cell Energy Spread in 2016 Data: FCal cell energy spread in the first FCal module normalized to the average cell energy spread over cells with the same \|η\| within ±0.04 for zero bias data collected in 2015 at s^1/2 = 13 TeV. Deviations from 1 indicate non-uniformities in φ. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The alternating dark and light regions close to the inner bore not present in the 2012 data reflect the impact of the non-uniform distribution of the IBL services in front of the FCal. In addition the same dark areas in the central regions along the horizontal and vertical figure axes and along the two diagonals as in 2012 are visible.	png - eps - pdf \| png - eps - pdf
Average Cell Energy in MC with Smeared IBL Services: Average FCal cell energy in the first FCal module normalized to the average cell energies over cells with the same \|η\| within ±0.04 for minimum bias simulations at s^1/2 = 13 TeV. The IBL Services are simulated as perfectly φ-symmetric. Deviations from 1 indicate non-uniformities in φ due to non-uniform material in front of the FCal. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). Since all material in front of the FCal is simulated φ-symmetric no significant deviations from 1 are observed.	png - eps - pdf \| png - eps - pdf
Average Cell Energy in MC with Straight IBL Services: Average FCal cell energy in the first FCal module normalized to the average cell energies over cells with the same \|η\| within ±0.04 for minimum bias simulations at s^1/2 = 13 TeV. The IBL Services are simulated as 14 straight cable bundles with φ-periodic locations. Deviations from 1 indicate non-uniformities in φ due to non-uniform material in front of the FCal. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The impact of the 14 straight IBL cable bundles is visible as alternating dark and light regions. The contrast is largest at the inner, high \|η\| detector region.	png - eps - pdf \| png - eps - pdf
Average Cell Energy in MC with Wavy IBL Services: Average FCal cell energy in the first FCal module normalized to the average cell energies over cells with the same \|η\| within ±0.04 for minimum bias simulations at s^1/2 = 13 TeV. The IBL Services are simulated as 14 wavy cable bundles with φ-periodic locations. Deviations from 1 indicate non-uniformities in φ due to non-uniform material in front of the FCal. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The impact of the 14 wavy IBL cable bundles is visible as alternating dark and light regions. The contrast is largest at the inner, high \|η\| detector region.	png - eps - pdf \| png - eps - pdf

LAr Mini Noise Bursts Plots for 2016 Data:

Mini Noise Bursts: Example of a region affected by so-called “mini noise bursts”, repeated bursts of coherent noise confined to a single FEB. The figure to the leG shows the cell occupancy at E_cell > 600 MeV for EMBC layer 2 cells in 455 pb^-1 of data from run 306310 in 2016 (CosmicCalo stream). These small bursts of noise can occur at a frequency of once per minute, and last less than 10 microseconds. Such events are flagged and removed by applying event veto periods. The figures show the impact of this cleaning procedure (top figure before cleaning, bottom figure after cleaning). Here all severe mini noise bursts are completely removed, with some residual noise remaining below the flagging threshold. During 2015+2016 a total of 0.11 % of luminosity was removed due to mini noise bursts.	eps - pdf eps - pdf

Mini Noise Bursts: Example of a region affected by so-called “mini noise bursts”, repeated bursts of coherent noise confined to a single FEB. The figure to the leG shows the cell occupancy at E_cell > 600 MeV for EMBC layer 2 cells in 455 pb^-1 of data from run 306310 in 2016 (CosmicCalo stream). These small bursts of noise can occur at a frequency of once per minute, and last less than 10 microseconds. Such events are flagged and removed by applying event veto periods. The figures show the impact of this cleaning procedure (top figure before cleaning, bottom figure after cleaning). Here all severe mini noise bursts are completely removed, with some residual noise remaining below the flagging threshold. During 2015+2016 a total of 0.11 % of luminosity was removed due to mini noise bursts.

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LAr Data Quality Plots for 2016 Data and Comparison with 2015 Data:

LAr Data Quality Plots 2016: These plots show the data rejection as a function of the data taking period following the release 21 reprocessing campaign. In ATLAS, a period corresponds to a data set acquired under similar operating conditions. The integrated luminosity varies widely from one period to another, ranging between 500 pb^-1 and 6 fb^-1. Full details can be found here In LAr, data not suitable for physics analysis are rejected via two complementary means: by assigning a defect to a luminosity block, the length of which is usually one minute. Here, the luminosity block is no longer considered in the GRL. by defining time veto windows in the COOL database, prior to the data processing. Here, the information is propagated via the EventInfo that is used to filter the data event by event in each analysis. The main sources of data loss in 2016 are: trigger mis-configuration (0.42 % via defect assignment): this is a single run failure due to an inappropriate set of online calorimeter parameters directly affecting the data rate transferred to the trigger system. Due to this feature, some emergency measures were taken on the trigger system side to cope with the unexpectedly high data rate. These data are not recoverable. noise bursts (0.08 % in defect assignment): noise bursts are well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). It is usually treated by applying a time veto. Due to the sharp instantaneous luminosity increase in early 2016, the identification of noise bursts was degraded. The efficiency loss was not recoverable prior to the data processing. As a result a DQ defect was defined a posteriori to cope with this. The problem was fixed during Summer 2016, and so the data rejected due to this problem should be largely recovered in the data reprocessing campaign during 2017. mini noise bursts (0.10 % in time veto): mini noise bursts affect very specific and confined parts of the detector during a very short time (typically a micro second). These are routinely treated by applying time vetoes.	eps - pdf eps - pdf eps - pdf
LAr Data Quality Plots - Comparison 2015 and 2016: These plots show the data rejection, following the release 21 reprocessing, as a function of the data taking year. In LAr, data not suitable for physics analysis are rejected via two complementary means: by assigning a defect to a luminosity block, the length of which is usually one minute. Here, the luminosity block is no longer considered in the GRL. by defining time veto windows in the COOL database, prior to the data processing. Here, the information is propagated via the EventInfo that is used to filter the data event by event in each analysis. The main sources of data loss in 2016 are: trigger mis-configuration (0.42 % via defect assignment): this is a single run failure due to an inappropriate set of online calorimeter parameters directly affecting the data rate transferred to the trigger system. Due to this feature, some emergency measures were taken on the trigger system side to cope with the unexpectedly high data rate. These data are not recoverable. noise bursts (0.08 % in defect assignment): noise bursts are well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). It is usually treated by applying a time veto. Due to the sharp instantaneous luminosity increase in early 2016, the identification of noise bursts was degraded. The efficiency loss was not recoverable prior to the data processing. As a result a DQ defect was defined a posteriori to cope with this. The problem was fixed during Summer 2016, and so the data rejected due to this problem should be largely recovered in the data reprocessing campaign during 2017. mini noise bursts (0.10 % in time veto): mini noise bursts affect very specific and confined parts of the detector during a very short time (typically a micro second). These are routinely treated by applying time vetoes.	eps - pdf eps - pdf

LAr Data Quality Plots 2016: These plots show the data rejection as a function of the data taking period following the release 21 reprocessing campaign. In ATLAS, a period corresponds to a data set acquired under similar operating conditions. The integrated luminosity varies widely from one period to another, ranging between 500 pb^-1 and 6 fb^-1. Full details can be found here

In LAr, data not suitable for physics analysis are rejected via two complementary means:

by assigning a defect to a luminosity block, the length of which is usually one minute. Here, the luminosity block is no longer considered in the GRL.
by defining time veto windows in the COOL database, prior to the data processing. Here, the information is propagated via the EventInfo that is used to filter the data event by event in each analysis.

The main sources of data loss in 2016 are:

trigger mis-configuration (0.42 % via defect assignment): this is a single run failure due to an inappropriate set of online calorimeter parameters directly affecting the data rate transferred to the trigger system. Due to this feature, some emergency measures were taken on the trigger system side to cope with the unexpectedly high data rate. These data are not recoverable.
noise bursts (0.08 % in defect assignment): noise bursts are well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). It is usually treated by applying a time veto. Due to the sharp instantaneous luminosity increase in early 2016, the identification of noise bursts was degraded. The efficiency loss was not recoverable prior to the data processing. As a result a DQ defect was defined a posteriori to cope with this. The problem was fixed during Summer 2016, and so the data rejected due to this problem should be largely recovered in the data reprocessing campaign during 2017.
mini noise bursts (0.10 % in time veto): mini noise bursts affect very specific and confined parts of the detector during a very short time (typically a micro second). These are routinely treated by applying time vetoes.

eps - pdf

eps - pdf

eps - pdf

LAr Data Quality Plots - Comparison 2015 and 2016: These plots show the data rejection, following the release 21 reprocessing, as a function of the data taking year.

In LAr, data not suitable for physics analysis are rejected via two complementary means:

by assigning a defect to a luminosity block, the length of which is usually one minute. Here, the luminosity block is no longer considered in the GRL.
by defining time veto windows in the COOL database, prior to the data processing. Here, the information is propagated via the EventInfo that is used to filter the data event by event in each analysis.

The main sources of data loss in 2016 are:

trigger mis-configuration (0.42 % via defect assignment): this is a single run failure due to an inappropriate set of online calorimeter parameters directly affecting the data rate transferred to the trigger system. Due to this feature, some emergency measures were taken on the trigger system side to cope with the unexpectedly high data rate. These data are not recoverable.
noise bursts (0.08 % in defect assignment): noise bursts are well known phenomena that affect a large fraction of the detector during a very short time (typically a micro second). It is usually treated by applying a time veto. Due to the sharp instantaneous luminosity increase in early 2016, the identification of noise bursts was degraded. The efficiency loss was not recoverable prior to the data processing. As a result a DQ defect was defined a posteriori to cope with this. The problem was fixed during Summer 2016, and so the data rejected due to this problem should be largely recovered in the data reprocessing campaign during 2017.
mini noise bursts (0.10 % in time veto): mini noise bursts affect very specific and confined parts of the detector during a very short time (typically a micro second). These are routinely treated by applying time vetoes.

eps - pdf

eps - pdf

LAr Noise Bursts Plots for 2015 Data:

Noise Bursts: Like already experienced during LHC Run 1, during proton collisions, the LAr Calorimeter records very rare events, which show a substantial fraction of cells with unexpected signal shapes and high signals in at least 5 front-end boards of the end-cap calorimeter. Such events are called noise bursts and appear less than once per minute. Due to flagging of such events and applying veto-periods 0.03 % of luminosity has been removed in 2015 (0.2 % in 2012). In an attempt to further understand the reason for these events, tests were performed during the 2015 data taking which showed a strong correlation between these noise bursts and the LAr purity system. Data with the LAr purity system HV switched OFF show a much smaller number of noise bursts. This effect is shown in the plot, that shows the lost luminosity fraction in 2.8 fb^-1 of proton data recorded with and without LAr purity HV. When the purity probes are switched OFF, the rate is well reduced and becomes independent of the instantaneous luminosity.	eps - pdf

Noise Bursts: Like already experienced during LHC Run 1, during proton collisions, the LAr Calorimeter records very rare events, which show a substantial fraction of cells with unexpected signal shapes and high signals in at least 5 front-end boards of the end-cap calorimeter. Such events are called noise bursts and appear less than once per minute. Due to flagging of such events and applying veto-periods 0.03 % of luminosity has been removed in 2015 (0.2 % in 2012). In an attempt to further understand the reason for these events, tests were performed during the 2015 data taking which showed a strong correlation between these noise bursts and the LAr purity system. Data with the LAr purity system HV switched OFF show a much smaller number of noise bursts. This effect is shown in the plot, that shows the lost luminosity fraction in 2.8 fb^-1 of proton data recorded with and without LAr purity HV. When the purity probes are switched OFF, the rate is well reduced and becomes independent of the instantaneous luminosity.

eps - pdf

LAr Time Resolution Plots for 2015 Data:

Time resolution as a function of energy for High and Medium gain in Electromagnetic Barrel (EMB) Slot 12 (0.4 < \|η\| < 0.8) with 3.3 fb^-1 of collision data at s^1/2 = 13 TeV. The data has been calibrated with a 7 step procedure in which the following are corrected for: time of flight from the primary vertex to the calorimeter cell, average time per cable passage through cryostat wall per run, average time per Front End Board (FEB), average time per cell, average time as a function of energy, cross talk related to position within the cell (δφ, δη), and cross talk between layers using fractional energy deposits in layer 1 and layer 3. The corrections were measured with a W → eν data set and applied here to an independent Z → ee data set. The data is fit to an assumed functional form: σ(t) = p₀/E ⊕ p₁. The coefficients p₀, p₁ multiply the noise term and constant term respectively. A calculated correlated contribution of ∼ 200 ps to the constant term of the time resolution can be attributed to the beamspread. If it is assumed that the LAr Calorimeter contributes only to the uncorrelated part of the constant term, the correlated contribution can be subtracted in quadrature from the p₁ fit values. This yields an uncorrelated contribution to the constant term of the time resolution from the LAr Calorimeter of ∼ 170 ps in EMB Slot 12 Medium Gain.	eps - pdf
Time resolution as a function of energy for High and Medium gain Electromagnetic Endcap (EMEC) Slot 11 (1.5 < \|η\| < 2.0) with 3.3 fb^-1 of collision data at s^1/2 = 13 TeV. The data has been calibrated with a 7 step procedure in which the following are corrected for: time of flight from the primary vertex to the calorimeter cell, average time per cable passage through cryostat wall per run, average time per Front End Board (FEB), average time per cell, average time as a function of energy, cross talk related to position within the cell (δφ, δη), and cross talk between layers using fractional energy deposits in layer 1 and layer 3. The corrections were measured with a W → eν data set and applied here to an independent Z → ee data set. The data is fit to an assumed functional form: σ(t) = p₀/E ⊕ p₁. The coefficients p₀, p₁ multiply the noise term and constant term respectively. A calculated correlated contribution of ∼ 200 ps to the constant term of the time resolution can be attributed to the beamspread. If it is assumed that the LAr Calorimeter contributes only to the uncorrelated part of the constant term, the correlated contribution can be subtracted in quadrature from the p₁ fit values. This yields an uncorrelated contribution to the constant term of the time resolution from the LAr Calorimeter of ∼ 65 ps in EMEC Slot 11 Medium Gain.	eps - pdf

Time resolution as a function of energy for High and Medium gain in Electromagnetic Barrel (EMB) Slot 12 (0.4 < |η| < 0.8) with 3.3 fb^-1 of collision data at s^1/2 = 13 TeV. The data has been calibrated with a 7 step procedure in which the following are corrected for: time of flight from the primary vertex to the calorimeter cell, average time per cable passage through cryostat wall per run, average time per Front End Board (FEB), average time per cell, average time as a function of energy, cross talk related to position within the cell (δφ, δη), and cross talk between layers using fractional energy deposits in layer 1 and layer 3. The corrections were measured with a W → eν data set and applied here to an independent Z → ee data set. The data is fit to an assumed functional form: σ(t) = p₀/E ⊕ p₁. The coefficients p₀, p₁ multiply the noise term and constant term respectively. A calculated correlated contribution of ∼ 200 ps to the constant term of the time resolution can be attributed to the beamspread. If it is assumed that the LAr Calorimeter contributes only to the uncorrelated part of the constant term, the correlated contribution can be subtracted in quadrature from the p₁ fit values. This yields an uncorrelated contribution to the constant term of the time resolution from the LAr Calorimeter of ∼ 170 ps in EMB Slot 12 Medium Gain.

eps - pdf

Time resolution as a function of energy for High and Medium gain Electromagnetic Endcap (EMEC) Slot 11 (1.5 < |η| < 2.0) with 3.3 fb^-1 of collision data at s^1/2 = 13 TeV. The data has been calibrated with a 7 step procedure in which the following are corrected for: time of flight from the primary vertex to the calorimeter cell, average time per cable passage through cryostat wall per run, average time per Front End Board (FEB), average time per cell, average time as a function of energy, cross talk related to position within the cell (δφ, δη), and cross talk between layers using fractional energy deposits in layer 1 and layer 3. The corrections were measured with a W → eν data set and applied here to an independent Z → ee data set. The data is fit to an assumed functional form: σ(t) = p₀/E ⊕ p₁. The coefficients p₀, p₁ multiply the noise term and constant term respectively. A calculated correlated contribution of ∼ 200 ps to the constant term of the time resolution can be attributed to the beamspread. If it is assumed that the LAr Calorimeter contributes only to the uncorrelated part of the constant term, the correlated contribution can be subtracted in quadrature from the p₁ fit values. This yields an uncorrelated contribution to the constant term of the time resolution from the LAr Calorimeter of ∼ 65 ps in EMEC Slot 11 Medium Gain.

eps - pdf

LAr Front-End Electronics Timing Plots 2015:

The following plots show the energy weighted average times per front- end board (FEB) in the four different LAr detectors: EM Barrel (EMB), EM EndCap (EMEC), Hadronic EndCap (HEC) and Forward Calorimeter (FCal) after the last FEB timing adjustment in 2015. Approximately 700 pb^-1 of proton-proton collisions data at s^1/2 = 13 TeV were used to extract corrections to the FEB fine-delay constants that are uploaded before each run and have originally been computed using 2011, 2012 and beam splash data. All signals above an energy threshold (between 1 and 10 GeV depending on the layer and the detector region) in medium and high electronics gain and with a quality factor below 4000 have been used after energy weighting. Offline corrections to harmonize the timing in medium and high electronics gain have been extracted and applied. These corrections to the FEB fine-delay constants along with the medium vs. high gain offline corrections have been applied starting from Oct. 18, 2015. The remaining 1.6 fb^-1 of proton-proton collisions data recorded after that date were used to measure the FEB timing after the corrections. The results are presented in the following plots.

Average time per front end board (FEB) in the LAr electromagnetic barrel (EMB) with 1.6 fb^-1 of collision data at s^1/2 = 13 TeV collected in 2015. The average time for one FEB is the result of a Gaussian fit on medium and high gain entries for all channels of this FEB. All but one FEB are well aligned. The outlier at 4 ns can be traced back to a hardware problem on this respective FEB. An offline correction was applied for this outlier FEB that corrects any bias of the timing and the energy calculation for all signals above 3 σ_noise.	eps - pdf
Average time per front end board (FEB) in the LAr electromagnetic end-cap (EMEC) with 1.6 fb^-1 of collision data at s^1/2 = 13 TeV collected in 2015. The average time for one FEB is the result of a Gaussian fit on medium and high gain entries for all channels of this FEB. All FEBs are well aligned since the distribution is centered at zero and no significant outliers exist	eps - pdf
Average time per front end board (FEB) in the LAr hadronic end- cap (HEC) with 1.6 fb^-1 of collision data at s^1/2 = 13 TeV collected in 2015. The average time for one FEB is the result of a Gaussian fit on medium and high gain entries for all channels of this FEB. All FEBs are well aligned since the distribution is centered at zero and no significant outliers are observed.	eps - pdf
Average time per front end board (FEB) in the LAr forward calorimeter (FCal) with 1.6 fb^-1 of collision data at s^1/2 = 13 TeV collected in 2015. The average time for one FEB is the result of a Gaussian fit on medium and high gain entries for all channels of this FEB. The observed systematic shift (Mean = 0.63 ns) is well below 1 ns and hence negligible for the energy calculation (it has been corrected for the 2015 proton run at s^1/2 = 5 TeV and the heavy-ion run).	eps - pdf

Beam Splash Events 2015

The plots in the following show energy distributions recorded by the ATLAS LAr calorimeters during the beam splash events from April 7, 2015. During these beam splashes one LHC beam was hitting the tertiary collimator approximately 175 m upstream of ATLAS immediately after injection and hence was producing a splash of particles heading towards the ATLAS detector. Splashes from beam 1 which traverse the ATLAS detector from A-side to C-side (opposite orientation of z-axis) were followed by beam 2 splashes (C-side to A-side) on the same day. Public ATLAS event displays from 2015 splashes can be found here.

Average LAr cell energy sums obtained during beam splashes on April 7, 2015: Average LAr cell energy sums (without FCal) distributed in a hypothetical tower grid with Δη x Δφ = 0.025 x 0.025 for 63 beam splash events from April 2015. From left to right the plots show the summed average energies in the endcap C, in the barrel and in the endcap A. For η<0 and the endcap C 30 events, where the particles entered from the postive η (A) side are averaged over while the average for η>0 and the endcap A uses 33 events, where the particles entered from the negative η (C) side. In total the displayed LAr layers recorded 3.5 PeV (7.0 PeV) on average per event for particles entering from the A (C) side. The visible regular eight-fold pattern in φ stems from the material in the endcap toroid magnets shadowing the incoming particles. (see for more details)	eps\|eps\|eps (all three: eps) - pdf\|pdf\|pdf (all three: pdf)
LAr cell energies recorded during a splash event of beam 1 on April 7, 2015: LAr cell energy sums (without FCal) distributed in a hypothetical tower grid with Δη x Δφ = 0.025 x 0.025 for a beam splash event from April 2015. The particles entered from the positive η (A) side. From left to right the plots show the summed energies in the endcap C, in the barrel and in the endcap A. In total the displayed LAr layers recorded 2.085 PeV in this event. The visible regular eight-fold pattern in φ stems from the material in the endcap toroid magnet shadowing the incoming particles. The inner wheel HEC cells on the incoming (A) side have underestimated energies due to the gain switch decision which is based on the signal amplitude measured at the expected timing for collision events. The pulses on the incoming side are hence too early leading to a wrong gain decision and saturation in some cases and therefore a sharp energy drop for η>2.5, while in reality the energy flow is smooth in η. (see for more details)	eps\|eps\|eps (all three: eps) - pdf\|pdf\|pdf (all three: pdf)
LAr cell energies recorded during a splash event of beam 2 on April 7, 2015: LAr cell energy sums (without FCal) distributed in a hypothetical tower grid with Δη x Δφ = 0.025 x 0.025 for a beam splash event from April 2015. The particles entered from the negative η (C) side. From left to right the plots show the summed energies in the endcap C, in the barrel and in the endcap A. In total the displayed LAr layers recorded 8.243 PeV in this event. The visible regular eight-fold pattern in φ stems from the material in the endcap toroid magnet shadowing the incoming particles. The inner wheel HEC cells on the incoming (C) side have underestimated energies due to the gain switch decision which is based on the signal amplitude measured at the expected timing for collision events. The pulses on the incoming side are hence too early leading to a wrong gain decision and saturation in some cases and therefore a sharp energy drop for η<-2.5, while in reality the energy flow is smooth in η. (see for more details)	eps\|eps\|eps (all three: eps) - pdf\|pdf\|pdf (all three: pdf)
LAr electromagnetic barrel (EMB) front-end board (FEB) timing distribution obtained with data of splash events on April 7, 2015: Timing in the LAr Calorimeter is measured from beam splash events taken in April 2015. For each front-end board, the average time of the 128 channels is calculated. Time of flight corrections are applied to account for splash events originating away from the interaction point and traveling nearly parallel to the beam axis. The barrel region is used to measure a reference time. Shown is the distribution of average timing in the FEBs of the electromagnetic barrel with respect to the reference time.	eps - pdf
LAr electromagnetic end-cap (EMEC) front-end board (FEB) timing distribution obtained with data of splash events on April 7, 2015: Timing in the LAr Calorimeter is measured from beam splash events taken in April 2015. For each front-end board, the average time of the 128 channels is calculated. Time of flight corrections are applied to account for splash events originating away from the interaction point and traveling nearly parallel to the beam axis. The barrel region is used to measure a reference time. Shown is the distribution of average timing in the FEBs of the electromagnetic end-cap with respect to the reference time.	eps - pdf
LAr hadronic end-cap (HEC) front-end board (FEB) timing distribution obtained with data of splash events on April 7, 2015: Timing in the LAr Calorimeter is measured from beam splash events taken in April 2015. For each front-end board, the average time of the 128 channels is calculated. Time of flight corrections are applied to account for splash events originating away from the interaction point and traveling nearly parallel to the beam axis. The barrel region is used to measure a reference time. Shown is the distribution of average timing in the FEBs of the hadronic end-cap with respect to the reference time.	eps - pdf
LAr forward calorimeter (FCAL) front-end board (FEB) timing distribution obtained with data of splash events on April 7, 2015: Timing in the LAr Calorimeter is measured from beam splash events taken in April 2015. For each front-end board, the average time of the 128 channels is calculated. Time of flight corrections are applied to account for splash events originating away from the interaction point and traveling nearly parallel to the beam axis. The barrel region is used to measure a reference time. Shown is the distribution of average timing in the FEBs of the forward calorimeter with respect to the reference time.	eps - pdf

Major updates:
-- MartinAleksa - 2015-05-04

Responsible: MartinAleksa
Subject: LAr Public Plots Run 2

Page variables

Local page variables

This section contains local page variables that you can use in editing this page to avoid hardcoding long paths etc.

To use the variable, just enclose it with %, like: %IMG{name}% which will turn into automatically.

If you require to introduce a new variable, simply add it to the list below.

Set IMGNAME = %IF{"attachments[name='%DEFAULT{default="fig1"}%.png']" then="%DEFAULT{default="fig1"}%.png" else="%IF{"attachments[name='%DEFAULT{default="fig1"}%.jpg']" then="%DEFAULT{default="fig1"}%.jpg" else="%IF{"attachments[name='%DEFAULT{default="fig1"}%.jpeg']" then="%DEFAULT{default="fig1"}%.jpeg" else="File '%DEFAULT{default="fig1"}%' not found!"}%"}%"}%
Set IMGPATH = /twiki/pub/AtlasPublic/LArCaloPublicResults2015/%IMGNAME{%DEFAULT{default="fig1"}%}%
Set IMG =
Set PDF = pdf version
Set EPS = eps version
Set PLOT = %IMG{%DEFAULT{default="fig1"}%}%
%EPS{%DEFAULT{default="fig1"}%}%, %PDF{%DEFAULT{default="fig1"}%}%
Set HALFPLOT = %IMG{"%DEFAULT{default="fig1"}%" IMGSIZE="175"}%
%EPS{%DEFAULT{default="fig1"}%}%, %PDF{%DEFAULT{default="fig1"}%}%

Page variables

Local page variables

This section contains local page variables that you can use in editing this page to avoid hardcoding long paths etc.

To use the variable, just enclose it with %, like: %IMG{name}% which will turn into automatically.

If you require to introduce a new variable, simply add it to the list below.

Set IMGNAME = fig1.png
Set IMGPATH = /twiki/pub/AtlasPublic/LArCaloPublicResultsUpgrade/fig1.png
Set IMG =
Set PDF = pdf version
Set EPS = eps version
Set PLOT =
eps version, pdf version
Set HALFPLOT =
eps version, pdf version

Attachments

Topic attachments
I	Attachment	History	Action	Size	Date	Who	Comment
eps	Av_FEB_BeamSplash_EMB.eps	r1	manage	8.4 K	2015-05-12 - 08:48	MartinAleksa	Timing plots EMEC, EMB
pdf	Av_FEB_BeamSplash_EMB.pdf	r1	manage	14.3 K	2015-05-12 - 08:48	MartinAleksa	Timing plots EMEC, EMB
png	Av_FEB_BeamSplash_EMB.png	r1	manage	11.1 K	2015-05-12 - 08:48	MartinAleksa	Timing plots EMEC, EMB
eps	Av_FEB_BeamSplash_EMEC.eps	r1	manage	8.4 K	2015-05-12 - 08:48	MartinAleksa	Timing plots EMEC, EMB
pdf	Av_FEB_BeamSplash_EMEC.pdf	r1	manage	14.3 K	2015-05-12 - 08:48	MartinAleksa	Timing plots EMEC, EMB
png	Av_FEB_BeamSplash_EMEC.png	r1	manage	11.0 K	2015-05-12 - 08:48	MartinAleksa	Timing plots EMEC, EMB
eps	Av_FEB_BeamSplash_FCAL.eps	r1	manage	7.8 K	2015-05-12 - 08:49	MartinAleksa	Timing plots splashes FCal and HEC
pdf	Av_FEB_BeamSplash_FCAL.pdf	r1	manage	14.1 K	2015-05-12 - 08:49	MartinAleksa	Timing plots splashes FCal and HEC
png	Av_FEB_BeamSplash_FCAL.png	r1	manage	10.5 K	2015-05-12 - 08:49	MartinAleksa	Timing plots splashes FCal and HEC
eps	Av_FEB_BeamSplash_HEC.eps	r1	manage	7.8 K	2015-05-12 - 08:49	MartinAleksa	Timing plots splashes FCal and HEC
pdf	Av_FEB_BeamSplash_HEC.pdf	r1	manage	14.1 K	2015-05-12 - 08:49	MartinAleksa	Timing plots splashes FCal and HEC
png	Av_FEB_BeamSplash_HEC.png	r1	manage	10.5 K	2015-05-12 - 08:49	MartinAleksa	Timing plots splashes FCal and HEC
eps	Av_FEB_EMB.eps	r1	manage	9.3 K	2016-01-25 - 17:39	MartinAleksa	FEB timing plots 2015
gif	Av_FEB_EMB.gif	r1	manage	10.9 K	2016-01-25 - 17:39	MartinAleksa	FEB timing plots 2015
pdf	Av_FEB_EMB.pdf	r1	manage	9.4 K	2016-01-25 - 17:42	MartinAleksa	FEB timing plots pdf (2015)
eps	Av_FEB_EMEC.eps	r1	manage	9.1 K	2016-01-25 - 17:39	MartinAleksa	FEB timing plots 2015
gif	Av_FEB_EMEC.gif	r1	manage	10.6 K	2016-01-25 - 17:39	MartinAleksa	FEB timing plots 2015
pdf	Av_FEB_EMEC.pdf	r1	manage	9.5 K	2016-01-25 - 17:42	MartinAleksa	FEB timing plots pdf (2015)
eps	Av_FEB_FCAL.eps	r1	manage	8.5 K	2016-01-25 - 17:39	MartinAleksa	FEB timing plots 2015
gif	Av_FEB_FCAL.gif	r1	manage	9.9 K	2016-01-25 - 17:39	MartinAleksa	FEB timing plots 2015
pdf	Av_FEB_FCAL.pdf	r1	manage	9.2 K	2016-01-25 - 17:42	MartinAleksa	FEB timing plots pdf (2015)
eps	Av_FEB_HEC.eps	r1	manage	8.7 K	2016-01-25 - 17:39	MartinAleksa	FEB timing plots 2015
gif	Av_FEB_HEC.gif	r1	manage	10.0 K	2016-01-25 - 17:39	MartinAleksa	FEB timing plots 2015
pdf	Av_FEB_HEC.pdf	r1	manage	9.4 K	2016-01-25 - 17:42	MartinAleksa	FEB timing plots pdf (2015)
eps	AvgSpalashesApr2015Composite_Barrel_EMEC_HEC_LArOnly.eps	r1	manage	7480.9 K	2015-05-05 - 09:14	MartinAleksa	LAr splash plots with all sides
pdf	AvgSpalashesApr2015Composite_Barrel_EMEC_HEC_LArOnly.pdf	r1	manage	819.8 K	2015-05-05 - 09:14	MartinAleksa	LAr splash plots with all sides
eps	AvgSpalashesApr2015Composite_Barrel_LArOnly.eps	r1	manage	1182.6 K	2015-05-04 - 18:06	MartinAleksa	LAr Beam Splas Plots
pdf	AvgSpalashesApr2015Composite_Barrel_LArOnly.pdf	r1	manage	164.0 K	2015-05-04 - 18:07	MartinAleksa	LAr Beam Splash Plots
eps	AvgSpalashesApr2015Composite_EMEC_HEC_SumA_LArOnly.eps	r1	manage	1543.1 K	2015-05-04 - 18:06	MartinAleksa	LAr Beam Splas Plots
pdf	AvgSpalashesApr2015Composite_EMEC_HEC_SumA_LArOnly.pdf	r1	manage	284.4 K	2015-05-04 - 18:07	MartinAleksa	LAr Beam Splash Plots
eps	AvgSpalashesApr2015Composite_EMEC_HEC_SumC_LArOnly.eps	r1	manage	1593.2 K	2015-05-04 - 18:06	MartinAleksa	LAr Beam Splas Plots
pdf	AvgSpalashesApr2015Composite_EMEC_HEC_SumC_LArOnly.pdf	r1	manage	284.1 K	2015-05-04 - 18:07	MartinAleksa	LAr Beam Splash Plots
eps	EMB_Presampler_PulseShape_Preliminary.eps	r1	manage	8.6 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
pdf	EMB_Presampler_PulseShape_Preliminary.pdf	r1	manage	14.8 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
png	EMB_Presampler_PulseShape_Preliminary.png	r1	manage	15.0 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
eps	EMB_Sampling2_PulseShape_Preliminary.eps	r1	manage	8.7 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
pdf	EMB_Sampling2_PulseShape_Preliminary.pdf	r1	manage	14.9 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
png	EMB_Sampling2_PulseShape_Preliminary.png	r1	manage	15.4 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
eps	FCalA1_MinBias_data2012_RMS_over_avg_ATLAS_Label.eps	r1	manage	766.9 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
pdf	FCalA1_MinBias_data2012_RMS_over_avg_ATLAS_Label.pdf	r1	manage	171.8 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
png	FCalA1_MinBias_data2012_RMS_over_avg_ATLAS_Label.png	r1	manage	47.0 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
eps	FCalA1_ZeroBias_data2015_RMS_over_avg_ATLAS_Label.eps	r1	manage	758.6 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
pdf	FCalA1_ZeroBias_data2015_RMS_over_avg_ATLAS_Label.pdf	r1	manage	170.4 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
png	FCalA1_ZeroBias_data2015_RMS_over_avg_ATLAS_Label.png	r1	manage	47.1 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	FCalA1_ZeroBias_data2016_RMS_over_avg_ATLAS_Label.eps	r1	manage	764.1 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
pdf	FCalA1_ZeroBias_data2016_RMS_over_avg_ATLAS_Label.pdf	r1	manage	171.6 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
png	FCalA1_ZeroBias_data2016_RMS_over_avg_ATLAS_Label.png	r1	manage	47.1 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	FCalA1_s2879_E_over_avg_ATLAS_Label.eps	r1	manage	793.8 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
pdf	FCalA1_s2879_E_over_avg_ATLAS_Label.pdf	r1	manage	175.4 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
png	FCalA1_s2879_E_over_avg_ATLAS_Label.png	r1	manage	47.2 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
eps	FCalA1_s2880_E_over_avg_ATLAS_Label.eps	r1	manage	793.3 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
pdf	FCalA1_s2880_E_over_avg_ATLAS_Label.pdf	r1	manage	176.5 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
png	FCalA1_s2880_E_over_avg_ATLAS_Label.png	r1	manage	47.5 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	FCalA1_s2882_E_over_avg_ATLAS_Label.eps	r1	manage	791.5 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
pdf	FCalA1_s2882_E_over_avg_ATLAS_Label.pdf	r1	manage	175.6 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
png	FCalA1_s2882_E_over_avg_ATLAS_Label.png	r1	manage	47.1 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	FCalC1_MinBias_data2012_RMS_over_avg_ATLAS_Label.eps	r1	manage	768.8 K	2017-02-15 - 10:22	MartinAleksa	LAr IBL Plots 2016
pdf	FCalC1_MinBias_data2012_RMS_over_avg_ATLAS_Label.pdf	r1	manage	172.2 K	2017-02-15 - 10:22	MartinAleksa	LAr IBL Plots 2016
png	FCalC1_MinBias_data2012_RMS_over_avg_ATLAS_Label.png	r1	manage	46.4 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	FCalC1_ZeroBias_data2015_RMS_over_avg_ATLAS_Label.eps	r1	manage	760.1 K	2017-02-15 - 10:22	MartinAleksa	LAr IBL Plots 2016
pdf	FCalC1_ZeroBias_data2015_RMS_over_avg_ATLAS_Label.pdf	r1	manage	170.8 K	2017-02-15 - 10:22	MartinAleksa	LAr IBL Plots 2016
png	FCalC1_ZeroBias_data2015_RMS_over_avg_ATLAS_Label.png	r1	manage	46.6 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	FCalC1_ZeroBias_data2016_RMS_over_avg_ATLAS_Label.eps	r1	manage	765.3 K	2017-02-15 - 10:22	MartinAleksa	LAr IBL Plots 2016
pdf	FCalC1_ZeroBias_data2016_RMS_over_avg_ATLAS_Label.pdf	r1	manage	171.9 K	2017-02-15 - 10:22	MartinAleksa	LAr IBL Plots 2016
png	FCalC1_ZeroBias_data2016_RMS_over_avg_ATLAS_Label.png	r1	manage	46.6 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	FCalC1_s2879_E_over_avg_ATLAS_Label.eps	r1	manage	793.0 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
pdf	FCalC1_s2879_E_over_avg_ATLAS_Label.pdf	r1	manage	175.3 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
png	FCalC1_s2879_E_over_avg_ATLAS_Label.png	r1	manage	46.8 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	FCalC1_s2880_E_over_avg_ATLAS_Label.eps	r1	manage	792.6 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
pdf	FCalC1_s2880_E_over_avg_ATLAS_Label.pdf	r1	manage	176.7 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
png	FCalC1_s2880_E_over_avg_ATLAS_Label.png	r1	manage	46.9 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	FCalC1_s2882_E_over_avg_ATLAS_Label.eps	r1	manage	792.9 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
pdf	FCalC1_s2882_E_over_avg_ATLAS_Label.pdf	r1	manage	175.9 K	2017-02-15 - 10:21	MartinAleksa	LAr IBL Plots 2016
png	FCalC1_s2882_E_over_avg_ATLAS_Label.png	r1	manage	46.5 K	2017-02-15 - 10:36	MartinAleksa	LAr IBL Plots 2016
eps	LAr-DQrun2-defects.eps	r1	manage	17.6 K	2019-01-29 - 11:11	SteffenStaerz	LAr Data Quality Run 2: Defects
pdf	LAr-DQrun2-defects.pdf	r1	manage	14.6 K	2019-01-29 - 11:11	SteffenStaerz	LAr Data Quality Run 2: Defects
png	LAr-DQrun2-defects.png	r1	manage	217.1 K	2019-01-29 - 11:11	SteffenStaerz	LAr Data Quality Run 2: Defects
eps	LAr-DQrun2-veto.eps	r1	manage	12.8 K	2019-01-29 - 11:18	SteffenStaerz	LAr Data Quality Run 2: Veto
pdf	LAr-DQrun2-veto.pdf	r1	manage	14.1 K	2019-01-29 - 11:18	SteffenStaerz	LAr Data Quality Run 2: Veto
png	LAr-DQrun2-veto.png	r1	manage	177.7 K	2019-01-29 - 11:18	SteffenStaerz	LAr Data Quality Run 2: Veto
eps	NB-lostlumi-vs-lumi-2016-2-22.eps	r1	manage	10.9 K	2016-04-28 - 11:47	MartinAleksa	Noise burst plots 2015
pdf	NB-lostlumi-vs-lumi-2016-2-22.pdf	r1	manage	15.1 K	2016-04-28 - 11:47	MartinAleksa	Noise burst plots 2015
png	NB-lostlumi-vs-lumi-2016-2-22.png	r1	manage	22.2 K	2016-04-28 - 11:47	MartinAleksa	Noise burst plots 2015
jpg	Screen_Shot_2017-02-15_at_15.27.54.jpg	r1	manage	283.4 K	2017-02-15 - 15:32	MartinAleksa	LAr IBL Screen Shots 2016
jpg	Screen_Shot_2017-02-15_at_15.28.43.jpg	r1	manage	279.8 K	2017-02-15 - 15:32	MartinAleksa	LAr IBL Screen Shots 2016
jpg	Screen_Shot_2017-02-15_at_15.28.57.jpg	r1	manage	288.0 K	2017-02-15 - 15:32	MartinAleksa	LAr IBL Screen Shots 2016
jpg	Screen_Shot_2017-02-15_at_15.29.13.jpg	r1	manage	284.6 K	2017-02-15 - 15:32	MartinAleksa	LAr IBL Screen Shots 2016
jpg	Screen_Shot_2017-02-15_at_15.29.28.jpg	r1	manage	287.3 K	2017-02-15 - 15:32	MartinAleksa	LAr IBL Screen Shots 2016
jpg	Screen_Shot_2017-02-15_at_15.29.50.jpg	r1	manage	287.7 K	2017-02-15 - 15:32	MartinAleksa	LAr IBL Screen Shots 2016
eps	afterCells_linear.eps	r1	manage	177.7 K	2017-02-14 - 17:05	MartinAleksa	LAr Mini Noise Burst Plots 2016
pdf	afterCells_linear.pdf	r1	manage	53.3 K	2017-02-14 - 17:05	MartinAleksa	LAr Mini Noise Burst Plots 2016
png	afterCells_linear.png	r1	manage	19.0 K	2017-02-14 - 17:05	MartinAleksa	LAr Mini Noise Burst Plots 2016
eps	beforeCells_linear.eps	r1	manage	177.6 K	2017-02-14 - 17:05	MartinAleksa	LAr Mini Noise Burst Plots 2016
pdf	beforeCells_linear.pdf	r1	manage	53.6 K	2017-02-14 - 17:05	MartinAleksa	LAr Mini Noise Burst Plots 2016
png	beforeCells_linear.png	r1	manage	19.1 K	2017-02-14 - 17:05	MartinAleksa	LAr Mini Noise Burst Plots 2016
jpg	compositelarbeamsplashplots2015.jpg	r1	manage	189.4 K	2015-05-04 - 18:05	MartinAleksa	LAr Beam Splash Plots
pdf	compositelarbeamsplashplots2015.pdf	r1	manage	823.3 K	2015-05-04 - 18:25	MartinAleksa	LAr Splashes Summary Files
eps	defects--Period--2017.eps	r1	manage	19.7 K	2018-02-14 - 15:42	SteffenStaerz	Defects Period 2017
pdf	defects--Period--2017.pdf	r1	manage	14.7 K	2018-02-14 - 15:42	SteffenStaerz	Defects Period 2017
png	defects--Period--2017.png	r1	manage	17.7 K	2018-02-14 - 15:42	SteffenStaerz	Defects Period 2017
eps	defects--Period--2017logy.eps	r1	manage	18.7 K	2018-02-14 - 15:44	SteffenStaerz	Defects Period 2017 log scale
pdf	defects--Period--2017logy.pdf	r1	manage	14.5 K	2018-02-14 - 15:44	SteffenStaerz	Defects Period 2017 log scale
png	defects--Period--2017logy.png	r1	manage	17.2 K	2018-02-14 - 15:44	SteffenStaerz	Defects Period 2017 log scale
eps	defects2016.eps	r1	manage	20.7 K	2017-07-25 - 14:37	MartinAleksa
pdf	defects2016.pdf	r1	manage	15.1 K	2017-07-25 - 14:37	MartinAleksa
png	defects2016.png	r1	manage	18.7 K	2017-07-25 - 14:37	MartinAleksa
eps	defects2016_2015.eps	r1	manage	14.5 K	2017-07-25 - 14:36	MartinAleksa
pdf	defects2016_2015.pdf	r1	manage	14.3 K	2017-07-25 - 14:36	MartinAleksa
png	defects2016_2015.png	r1	manage	19.9 K	2017-07-25 - 14:36	MartinAleksa
eps	defects2016log.eps	r1	manage	19.5 K	2017-07-25 - 14:36	MartinAleksa
pdf	defects2016log.pdf	r1	manage	14.8 K	2017-07-25 - 14:36	MartinAleksa
png	defects2016log.png	r1	manage	19.2 K	2017-07-25 - 14:36	MartinAleksa
eps	ed_diff_vs_bvid_Preliminary.eps	r1	manage	21.4 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
pdf	ed_diff_vs_bvid_Preliminary.pdf	r1	manage	21.8 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
png	ed_diff_vs_bvid_Preliminary.png	r1	manage	79.2 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
eps	emb_48b.eps	r1	manage	22.2 K	2017-09-26 - 11:09	SteffenStaerz	8b4e filling scheme EMB
pdf	emb_48b.pdf	r1	manage	26.5 K	2017-09-26 - 11:09	SteffenStaerz	8b4e filling scheme EMB
png	emb_48b.png	r1	manage	258.5 K	2017-09-26 - 11:09	SteffenStaerz	8b4e filling scheme EMB
eps	emb_8b.eps	r1	manage	24.5 K	2017-09-26 - 11:09	SteffenStaerz	8b4e filling scheme EMB
pdf	emb_8b.pdf	r1	manage	28.6 K	2017-09-26 - 11:09	SteffenStaerz	8b4e filling scheme EMB
png	emb_8b.png	r1	manage	281.8 K	2017-09-26 - 11:09	SteffenStaerz	8b4e filling scheme EMB
eps	emec_48b.eps	r1	manage	23.0 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme EMEC
pdf	emec_48b.pdf	r1	manage	26.8 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme EMEC
png	emec_48b.png	r1	manage	282.3 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme EMEC
eps	emec_8b.eps	r1	manage	25.2 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme EMEC
pdf	emec_8b.pdf	r1	manage	28.7 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme EMEC
png	emec_8b.png	r1	manage	298.4 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme EMEC
eps	et_vs_bcid_Preliminary.eps	r1	manage	17.9 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
pdf	et_vs_bcid_Preliminary.pdf	r1	manage	18.1 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
png	et_vs_bcid_Preliminary.png	r1	manage	68.1 K	2017-02-14 - 18:14	MartinAleksa	LAr Pulse Shape and BCID Correction
eps	fcal_48b.eps	r1	manage	22.6 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme FCAL
pdf	fcal_48b.pdf	r1	manage	26.6 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme FCAL
png	fcal_48b.png	r1	manage	277.2 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme FCAL
eps	fcal_8b.eps	r1	manage	24.8 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme FCAL
pdf	fcal_8b.pdf	r1	manage	28.6 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme FCAL
png	fcal_8b.png	r1	manage	298.8 K	2017-09-26 - 11:10	SteffenStaerz	8b4e filling scheme FCAL
eps	h_eResolution_EMB.eps	r1	manage	13.9 K	2016-04-28 - 11:33	MartinAleksa	Timing resolution plots 2015 (EMB and EMEC)
pdf	h_eResolution_EMB.pdf	r1	manage	17.3 K	2016-04-28 - 11:33	MartinAleksa	Timing resolution plots 2015 (EMB and EMEC)
png	h_eResolution_EMB.png	r1	manage	25.3 K	2016-04-28 - 11:33	MartinAleksa	Timing resolution plots 2015 (EMB and EMEC)
eps	h_eResolution_EMEC.eps	r1	manage	13.3 K	2016-04-28 - 11:33	MartinAleksa	Timing resolution plots 2015 (EMB and EMEC)
pdf	h_eResolution_EMEC.pdf	r1	manage	16.9 K	2016-04-28 - 11:33	MartinAleksa	Timing resolution plots 2015 (EMB and EMEC)
png	h_eResolution_EMEC.png	r1	manage	25.2 K	2016-04-28 - 11:33	MartinAleksa	Timing resolution plots 2015 (EMB and EMEC)
eps	hec_48b.eps	r1	manage	22.2 K	2017-09-26 - 11:11	SteffenStaerz	8b4e filling scheme HEC
pdf	hec_48b.pdf	r1	manage	26.3 K	2017-09-26 - 11:11	SteffenStaerz	8b4e filling scheme HEC
png	hec_48b.png	r1	manage	255.0 K	2017-09-26 - 11:11	SteffenStaerz	8b4e filling scheme HEC
eps	hec_8b.eps	r1	manage	24.5 K	2017-09-26 - 11:11	SteffenStaerz	8b4e filling scheme HEC
pdf	hec_8b.pdf	r1	manage	28.3 K	2017-09-26 - 11:11	SteffenStaerz	8b4e filling scheme HEC
png	hec_8b.png	r1	manage	282.8 K	2017-09-26 - 11:11	SteffenStaerz	8b4e filling scheme HEC
pdf	larbeamsplashplots2015.pdf	r1	manage	1649.7 K	2015-05-04 - 18:25	MartinAleksa	LAr Splashes Summary Files
jpg	larbeamsplashplots2015_beam1.jpg	r1	manage	196.3 K	2015-05-05 - 00:06	MartinAleksa
jpg	larbeamsplashplots2015_beam2.jpg	r1	manage	187.9 K	2015-05-05 - 00:06	MartinAleksa
eps	r260466_lb0747_e17219_splash_AllCells_minus_FCal_Barrel_EMEC_HEC_LArOnly.eps	r1	manage	7646.0 K	2015-05-05 - 09:14	MartinAleksa	LAr splash plots with all sides
pdf	r260466_lb0747_e17219_splash_AllCells_minus_FCal_Barrel_EMEC_HEC_LArOnly.pdf	r1	manage	855.3 K	2015-05-05 - 09:14	MartinAleksa	LAr splash plots with all sides
eps	r260466_lb0747_e17219_splash_AllCells_minus_FCal_Barrel_LArOnly.eps	r1	manage	1280.6 K	2015-05-04 - 18:05	MartinAleksa	LAr Beam Splash Plots
pdf	r260466_lb0747_e17219_splash_AllCells_minus_FCal_Barrel_LArOnly.pdf	r1	manage	177.6 K	2015-05-04 - 18:07	MartinAleksa	LAr Beam Splash Plots
eps	r260466_lb0747_e17219_splash_AllCells_minus_FCal_EMEC_HEC_SumA_LArOnly.eps	r1	manage	1544.6 K	2015-05-04 - 18:05	MartinAleksa	LAr Beam Splash Plots
pdf	r260466_lb0747_e17219_splash_AllCells_minus_FCal_EMEC_HEC_SumA_LArOnly.pdf	r1	manage	292.6 K	2015-05-04 - 18:07	MartinAleksa	LAr Beam Splash Plots
eps	r260466_lb0747_e17219_splash_AllCells_minus_FCal_EMEC_HEC_SumC_LArOnly.eps	r1	manage	1564.8 K	2015-05-04 - 18:05	MartinAleksa	LAr Beam Splash Plots
pdf	r260466_lb0747_e17219_splash_AllCells_minus_FCal_EMEC_HEC_SumC_LArOnly.pdf	r1	manage	292.2 K	2015-05-04 - 18:07	MartinAleksa	LAr Beam Splash Plots
eps	r260466_lb1103_e24650_splash_AllCells_minus_FCal_Barrel_EMEC_HEC_LArOnly.eps	r1	manage	7549.2 K	2015-05-05 - 09:14	MartinAleksa	LAr splash plots with all sides
pdf	r260466_lb1103_e24650_splash_AllCells_minus_FCal_Barrel_EMEC_HEC_LArOnly.pdf	r1	manage	817.8 K	2015-05-05 - 09:14	MartinAleksa	LAr splash plots with all sides
eps	r260466_lb1103_e24650_splash_AllCells_minus_FCal_Barrel_LArOnly.eps	r1	manage	1214.9 K	2015-05-04 - 18:05	MartinAleksa	LAr Beam Splash Plots
pdf	r260466_lb1103_e24650_splash_AllCells_minus_FCal_Barrel_LArOnly.pdf	r1	manage	164.2 K	2015-05-04 - 18:07	MartinAleksa	LAr Beam Splash Plots
eps	r260466_lb1103_e24650_splash_AllCells_minus_FCal_EMEC_HEC_SumA_LArOnly.eps	r1	manage	1517.8 K	2015-05-04 - 18:05	MartinAleksa	LAr Beam Splash Plots
pdf	r260466_lb1103_e24650_splash_AllCells_minus_FCal_EMEC_HEC_SumA_LArOnly.pdf	r1	manage	284.8 K	2015-05-04 - 18:07	MartinAleksa	LAr Beam Splash Plots
eps	r260466_lb1103_e24650_splash_AllCells_minus_FCal_EMEC_HEC_SumC_LArOnly.eps	r1	manage	1601.6 K	2015-05-04 - 18:05	MartinAleksa	LAr Beam Splash Plots
pdf	r260466_lb1103_e24650_splash_AllCells_minus_FCal_EMEC_HEC_SumC_LArOnly.pdf	r1	manage	283.4 K	2015-05-04 - 18:07	MartinAleksa	LAr Beam Splash Plots
eps	veto--Period--2017.eps	r1	manage	13.7 K	2018-02-14 - 15:44	SteffenStaerz	Veto Period 2017
pdf	veto--Period--2017.pdf	r1	manage	14.3 K	2018-02-14 - 15:44	SteffenStaerz	Veto Period 2017
png	veto--Period--2017.png	r1	manage	17.1 K	2018-02-14 - 15:44	SteffenStaerz	Veto Period 2017
eps	veto2016.eps	r1	manage	12.9 K	2017-07-25 - 14:37	MartinAleksa
pdf	veto2016.pdf	r1	manage	14.2 K	2017-07-25 - 14:37	MartinAleksa
png	veto2016.png	r1	manage	15.2 K	2017-07-25 - 14:37	MartinAleksa
eps	veto2016_2015.eps	r1	manage	10.8 K	2017-07-25 - 14:36	MartinAleksa
pdf	veto2016_2015.pdf	r1	manage	13.8 K	2017-07-25 - 14:36	MartinAleksa
png	veto2016_2015.png	r1	manage	16.1 K	2017-07-25 - 14:36	MartinAleksa

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Average Cell Energy Spread in 2012 Data: FCal cell energy spread in the first FCal module normalized to the average cell energy spread over cells with the same \|η\| within ±0.04 for minimum bias data collected in 2012 at s^1/2 = 8 TeV. Deviations from 1 indicate non-uniformities in φ. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The dark areas in the central regions along the horizontal and vertical figure axes and along the two diagonals are likely due to φ non-uniform material.	png - eps - pdf \| png - eps - pdf
Average Cell Energy Spread in 2015 Data: FCal cell energy spread in the first FCal module normalized to the average cell energy spread over cells with the same \|η\| within ±0.04 for zero bias data collected in 2015 at s^1/2 = 13 TeV. Deviations from 1 indicate non-uniformities in φ. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The alternating dark and light regions close to the inner bore not present in the 2012 data reflect the impact of the non-uniform distribution of the IBL services in front of the FCal. In addition the same dark areas in the central regions along the horizontal and vertical figure axes and along the two diagonals as in 2012 are visible.	png - eps - pdf \| png - eps - pdf
Average Cell Energy Spread in 2016 Data: FCal cell energy spread in the first FCal module normalized to the average cell energy spread over cells with the same \|η\| within ±0.04 for zero bias data collected in 2015 at s^1/2 = 13 TeV. Deviations from 1 indicate non-uniformities in φ. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The alternating dark and light regions close to the inner bore not present in the 2012 data reflect the impact of the non-uniform distribution of the IBL services in front of the FCal. In addition the same dark areas in the central regions along the horizontal and vertical figure axes and along the two diagonals as in 2012 are visible.	png - eps - pdf \| png - eps - pdf
Average Cell Energy in MC with Smeared IBL Services: Average FCal cell energy in the first FCal module normalized to the average cell energies over cells with the same \|η\| within ±0.04 for minimum bias simulations at s^1/2 = 13 TeV. The IBL Services are simulated as perfectly φ-symmetric. Deviations from 1 indicate non-uniformities in φ due to non-uniform material in front of the FCal. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). Since all material in front of the FCal is simulated φ-symmetric no significant deviations from 1 are observed.	png - eps - pdf \| png - eps - pdf
Average Cell Energy in MC with Straight IBL Services: Average FCal cell energy in the first FCal module normalized to the average cell energies over cells with the same \|η\| within ±0.04 for minimum bias simulations at s^1/2 = 13 TeV. The IBL Services are simulated as 14 straight cable bundles with φ-periodic locations. Deviations from 1 indicate non-uniformities in φ due to non-uniform material in front of the FCal. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The impact of the 14 straight IBL cable bundles is visible as alternating dark and light regions. The contrast is largest at the inner, high \|η\| detector region.	png - eps - pdf \| png - eps - pdf
Average Cell Energy in MC with Wavy IBL Services: Average FCal cell energy in the first FCal module normalized to the average cell energies over cells with the same \|η\| within ±0.04 for minimum bias simulations at s^1/2 = 13 TeV. The IBL Services are simulated as 14 wavy cable bundles with φ-periodic locations. Deviations from 1 indicate non-uniformities in φ due to non-uniform material in front of the FCal. The figure to the left shows the C-side (z < 0) and the figure to the right shows the A-side (z > 0). The impact of the 14 wavy IBL cable bundles is visible as alternating dark and light regions. The contrast is largest at the inner, high \|η\| detector region.	png - eps - pdf \| png - eps - pdf