operations and performance of the resistive plate chambers detector supplying the first level...

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 AsmundisIstituto Nazionale di fisica nucleare,Sezione di Napoli, on behalf the ATLAS Muon Community

14-10-2009 Riccardo de Asmundis 1

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 performancesDetector Control System and monitoring

software statusConclusions


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 368.416 Read-out

channels26 different chambers

typeTotal surface ~ 4000 m2

Muon Trigger Segmentation in Barrel region16 Sector (Large and Small)64 Sector Logic396 trigger Towers

The ATLAS Resistive Plate Chambers


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

Gaseous detector, operated at atmospheric pressure ATLAS RPC works in saturated avalanche regime.

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

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%


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)

Pump module

Gas system: the monitor


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

Example of an analytical Report

Muon trigger strategy

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


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

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


RPC Detector Analysis StrategyIn 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) Advantage :

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 granularity14-10-2009 Riccardo de Asmundis 10

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


MDT Tracking Results


• 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

• Low Panel Efficiency is related to HV channel off

• Efficiency is not correct for dead strips.

Efficiency distribution

HV = 9660 V, Vth= 1000 mV

Efficiency vs sector HV = 9660 V Vth= 1000 mV

RPC StandAlone Tracking Results


RPC Efficiency measured for all strips panels, with the RPC standalone package, dead strips not removed.

HV = 9600 Volts, Vth 1000 mV. Average Efficiency = 91.5 %Fitted Efficiency = 94.5 %

RPC panel noise distribution measured for all strips panels, with the RPC standalone package.

HV = 9600 Volts, Vth 1000 mV.

Other Off-line StandAlone Monitoring Result


Rocks +concrete layers

ATLASCosmics muon map reconstructed by Off-line RPC standalone muon monitoring extrapolated to surface.Main shafts and elevator shafts are clearly visible.

RPC trigger coverage status


Trigger Coverage > 97%5/396 Trigger towers not initialized (easily recoverable).Few other holes due to HV problems (recoverable changing trigger majority).

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

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.

Trigger Road Analysis


SL N SL N+1

Pivot

Conf

I.P.

Ly1

Ly1Ly0

Ly0

CMA 0CMA 1 CMA 0CMA 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.

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.


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.

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

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 analysisRPCs 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.

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.


operations and performance of the resistive plate chambers detector supplying the first level...

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