1. Operations and performance of the Resistive Plate Chambers detector supplying the first Level trigger in the barrel muon spectrometer of the ATLAS Experiment.
Riccardo de Asmundis
Istituto Nazionale di fisica nucleare,Sezione di Napoli,
on behalf the ATLAS Muon Community
Riccardo de Asmundis

2. Outline
The RPC as detectors for the level-1 Muon trigger in the barrel region: structure and technical's
Data analysis for Cosmics Rays and results for RPC detector
Level-1 trigger timing and performances
Detector Control System and monitoring software status
Conclusions
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3. The ATLAS Muon Spectrometer
Resistive Plate Chambers (RPC) are used as Muon Trigger Detector in the barrel region (-1 < h < 1)
More than 1100 RPC units
Read-out channels
26 different chambers type
Total surface ~ 4000 m2
Muon Trigger Segmentation in Barrel region
16 Sector (Large and Small)
64 Sector Logic
396 trigger Towers
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4. The ATLAS Resistive Plate Chambers
Gaseous detector, operated at atmospheric pressure ATLAS RPC works in saturated avalanche regime.
Each unit contains 2 layers of gas volume.
2mm gas gap, bakelite resistivity ~ 1-4x1010 cm
h and f read-out copper strips panels, pitch ranging from 26.4 to 37 mm
Main ATLAS RPC tasks:
Good time resolution for bunch-crossing identification (~ 1 ns).
High rate capability to sustain the high background level.
2nd-coordinate measurement with a 5-10mm resolution
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5. Some technicalities on the gas system
The gas is supplied thanks to a complex distribution system which provides to:
Produce the mixture as a fraction of the total available flow
Introduce moisture in a controlled way (ab. 40% rel.)
Circulate the mixture
Filters and purifiers the mixture in several stages
Provide for a complete online monitor
Gas mixture is: C2H2F4 94.7% - C4H10 5% - SF6 0.3%
Pump module
Humidifier
Exhaust
Mixer
Some data:
15 cubic meter of plant
Volume exchange in the detectors: every 2.5 hours  1 hour
Recirculation 5 m3/h; fresh IN 0.5 m3/h (10%, can be reduced)
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6. Gas system: the monitor
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7. Gas quality: the analysis station
Example of an analytical Report
Gas quality: the analysis station
Gas Chromatographic station
GC Perkin-Elmer Clarus 500
Two analytical columns for deep air separation (N2-O2)
Multi-port valve to select the sample (all significant points)
PC controlled
Continuously running
Fresh gas mixture used as reference
Calibration by external standard
Air contamination within 0.1 % due to small loss in the system
Ready for studies once the beam (and irradiation) are ON
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8. Muon trigger strategy
Muon selection mechanism is based on the allowed geometrical road (Coincidence Windows)
Two threshold regimes:
Low-Pt : muon trigger (6<pt<20 GeV) majority 3/4
High-Pt: muon trigger (>20 GeV) majority 1/2 + Low-Pt
Low Pt and High Pt trigger are separate but not independent.
Low Pt trigger result is needed for the High Pt decision.
The timing between Low Pt and High Pt has to be adjusted depending on the physics (cosmics or beam)
The High Pt PAD routes data out to trigger and readout
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9. Trigger Segmentation
Organized in 64 logical sectors: 32 Side A + 32 Side C
A geometrical sector corresponds to 4 logical sectors
Each logical sector contains 6- 7 trigger towers
1 Trigger Tower = 1 Low Pt PAD + 1 High Pt PAD
Each PAD contains 2 η-CM and 2 φ –CM
The overlap of an η-CM with a φ-CM corresponds to a RoI
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10. RPC Detector Analysis Strategy
In order to ensure redundancy/robustness, a twofold strategy is used for RPC detectors studies
Exploiting the precise tracking from the MDTs:
Advantage :
extrapolation to RPC layers takes into account materials and magnetic field
precise extrapolation allows to determine spatial resolution and to study small local effects
Disadvantage:
applicable only to runs with MDTs on
presently all RPC hits are used in reco, hence a bias is introduced in efficiency measurement (will be fixed)
Using standalone tracking (only RPC)
Does not depend on MDTs
Dedicated tracking algo avoids reconstruction bias on efficiency (by not using hits of a given layer)
Automatic run at Tier0 facility
Disadvantage :
Extrapolation precision limited by RPC granularity
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11. MDT Tracking Quality Cuts
Event selection and track quality:
Events with only 1 track
c2/d.o.f. < 20
At least 2 f hits on track
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12. MDT Tracking Results Efficiency vs sector Efficiency distribution
HV = 9660 V, Vth= 1000 mV
Low Panel Efficiency is related to HV channel off
Efficiency is not correct for dead strips.
Efficiency vs sector
HV = 9660 V Vth= 1000 mV
Cluster size for h and f panels
h view cluster size is a little bit lower with respect to f view. This is as expected, due to difference in the detector costruction
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13. RPC StandAlone Track Quality
Pattern recognition seeded by a straight line, which is defined by two RPC space points.
RPC space points not part of any previous tracks and inside a predefined distance from the straight line are associated to the pattern.
From cosmic data about 95 % percent of events have at least one RPC track.
Applying a quality cut of chi2/dof < 1 about 70 % of events have at least a good tracks and 10 % with more than one.
The detection efficiency is measured by repeating 6 times the RPC tracking.
The layer under test is removed from the pattern recognition and track fitting.
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14. RPC StandAlone Track Quality/2
70 % Events with at least a track after cuts on c2/d.o.f. < 1
Efficiency is measured by repeating 6 times the RPC tracking.
Monitoring of Time Tracks Residual, any cuts applied on time residual up to now
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15. RPC StandAlone Tracking Results
RPC Efficiency measured for all strips panels, with the RPC standalone package, dead strips not removed.
HV = Volts, Vth mV.
Average Efficiency = 91.5 %
Fitted Efficiency = %
RPC panel noise distribution measured for all strips panels, with the RPC standalone package.
HV = Volts, Vth mV.
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16. Other Off-line StandAlone Monitoring Result
Rocks +
concrete layers
ATLAS
Cosmics muon map reconstructed by Off-line RPC standalone muon monitoring extrapolated to surface.
Main shafts and elevator shafts are clearly visible.
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17. RPC trigger coverage status
5/396 Trigger towers not initialized (easily recoverable).
Few other holes due to HV problems (recoverable changing trigger majority).
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18. LVL1 trigger timing and performances.
A correct timing-in means that we will trigger the μ, with the desired Pt, emerging from the IP at given BC and we will stamp it with the correct BC-ID.
The timing-in of the trigger requires to correct for:
The delay due to the propagation along cables, fibers and to the latencies of the different elements.
The Time of Flight, i.e. the physics to select, needs to know the physical “interesting” configurations
The strip propagation is relevant for the trigger time spread (max 12ns) and cannot be corrected for.
All these delays have to be corrected in the pipelines of the different parts
For a good detector timing it is necessary to ensure the correct alignment of:
✦ Layers within the same CM
✦ Views (φ CM - η CM) within the same PAD
✦ Towers (PADs) within the same Trigger sector
✦ Trigger Sectors with respect to each other
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19. Time alignment inside LowPt trigger tower
Distribution of the relative time between RPC layers of Low Pt non-bending view coincidence matrix delivering one and only one hardware trigger in the event.
Time alignment inside Low Pt trigger towers in phi view with cosmic data.
Entries are not a track time residuals.
The time is relative to the layer nearest to the IP.
HV = 9600 V, Vth = mV
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20. Time alignment inside the Sector Logic and between Sectors
The misalignment between trigger sectors is the combination of the delay and time of flight.
With cosmics rays it is very difficult to disentangle the 2 components using RPC only.
The best way to check is to use pointing tracks only (having a known time of flight) and look at the relative alignments.
Dedicated runs were taken using Transition Radiation Tracker (TRT) as source of external trigger (its small radius allows to select pointing tracks easily).
The misalignment between trigger towers inside the same Sector Logic and the misalignment between different Sectors Logic have been significantly reduced via an iterative procedure.
Trigger Time read-out for each trigger tower, along RPC trigger sectors.
RPC trigger distribution with respect toTRT trigger signal.
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21. Trigger Road Analysis
SL N
SL N+1
Pivot
Conf
I.P.
Ly1
Ly0
CMA 0
CMA 1
The RPC spatial correlation between trigger strip (Pivot) and confirm strip (LowPt) in the PHI view for a programmed trigger road in cosmics data.
It is possible to detect the trigger road projective pattern by looking at the deviation of the data points from the dashed line. Strip number 0 corresponds to the centre of the geometrical sector.
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22. DCS overview DCS system:
Controlling the detector power system (chamber HV, frontend LV)
Configuring and/or Monitoring the frontend electronics
Reading/Recording non event-based environmental and conditions data
Adjusting operations parameters to ensure efficient detector operation
Controlling which actions are allowed under what conditions to prevent configurations potentially harmful for the detector
Hierarchical approach:
Separation of frontend (process) and supervisory layer
Commercial SCADA System + CERN JCOP Framework + Muon specific developments, Scalable, Distributed
Performance monitoring:
Monitoring and historical trend for all monitored quantities.
Data Quality Assessment automatically generate and transferred in Cool Data Base.
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23. DCS overview
Overview of the whole detector via FSM: PS, Gas, Env. Sensors, DQ.
Alarms and watchdogs (safety scripts) for unattended operation: Mainframe connections, HV- GAS Igap currents.
Global Switch ON/OFF via FSM command for LV system
Advanced shifter and expert operations interfaces:
Gas channels, Stations status.
LVL1 crates.
DQA Monitoring.
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24. Off-line Monitoring at Tier0
A software package to debug, monitor, and asses data quality for the RPC detector, has been developed within the ATLAS software framework.
Run by run, all relevant quantities characterizing the RPC detector are measured and stored in a dedicate database.
These quantities are used for MonteCarlo simulations and off-line reconstruction by physics analysis groups.
The code was developed using C++ objet oriented framework and it is configurable via Python script.
Three families of Algorithms have been developed inside the RPC monitoring package to completely monitor the RPC detector:
RPC, RPCLV1, MDTvsRPC
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25. Data Quality framework
The status of ATLAS data taking is evaluated based on information from the data acquisition and trigger systems (TDAQ), and the analysis of events reconstructed online and offline at the Tier-0, constituting the Data Quality Assessment or DQA. DQA comprises data quality monitoring (DQM), evaluation, and flagging for future use in physics analysis
RPCs have three different sources of DQA: DCS, On-line and Off-line monitoring
In the DCS, threshold on active fraction of the detector is applied to generate the DQ Assesment.
On-line and Off-line monitoring use the ATLAS DQM Framework to generate the DQ Assesment; this allows to automatically apply pre-defined algorithms to check reference histograms.
DQA results, grouped as the DAQ partitions, are collected in specific DB.
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26. Conclusions
RPC detectors have been installed and commissioned since long time.
A constant activity is in act to keep track and maintain the reliability of the detectors.
Long time Cosmic Data Taking allowed to perform a complete detector characterization.
Two different Off-line strategies of performance analysis has been developed to assure a complete characterization.
Offline RPC monitoring is fully integrated in the ATLAS Software Framework, and the DataQuality Off-line is totally based on RPC off-line monitoring performed at Tier0 level.
Detector behavior during the runs is fully monitored via DCS system.
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