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Performance of the Resistive Plate Chambers as LVL1 ATLAS muon trigger

Performance of the Resistive Plate Chambers as LVL1 ATLAS muon trigger. Michele Bianco INFN Lecce & Physics Department, Salento University on behalf the ATLAS Muon Comunity. Outline. The RPC and LVL1 muon trigger in the ATLAS barrel region

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Performance of the Resistive Plate Chambers as LVL1 ATLAS muon trigger

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  1. Performance of the Resistive Plate Chambers as LVL1 ATLAS muon trigger Michele Bianco INFN Lecce & Physics Department, Salento University on behalf the ATLAS Muon Comunity Michele Bianco ICATPP 09

  2. Outline • The RPC and LVL1 muon trigger in the ATLAS barrel region • Cosmics data analysis and results for RPC detector • LVL1 trigger timing and performances • DCS and monitoring software status • Conclusions Michele Bianco ICATPP 09

  3. The ATLAS Muon Trigger in barrel region Resistive Plate Chambers(RPC) will be used as Muon Trigger Detector in the barrel region (-1 < h < 1) • More than 1100 RPC units • 368.416 Read-out channels • 26 different chambers type • Total surface ~ 4000 m2 Muon Trigger Segmentation in Barrel region 16 Physical Sectors (Large and Small) 64 Trigger Sectors 396 Trigger Towers Michele Bianco ICATPP 09

  4. The ATLAS Muon Spectrometer Muon Chamber during the installation MDT Chamber RPC Chamber Michele Bianco ICATPP 09

  5. The ATLAS Resistive Plate Chambers Gaseous detector, operated at atmospheric pressure ATLAS RPC works in saturated avalanche regime Gas mixture:C2H2F4 94.7% - C4H105% - SF6 0.3% Each unit contains 2 layers of gas volume. 2mm gas gap, bakelite resistivity ~ 1-4x1010 cm handfread-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. • Provide the 2nd-coordinate measurement with a 8-10mm resolution Michele Bianco ICATPP 09

  6. OK pT>(pT)thr KO pT>(pT)thr pT<(pT)thr pT<(pT)thr OK KO ATLAS Muon LVL1 trigger strategy • The COINCIDENCE WONDOWS depends on: • Trigger pT threshould • η coorinates • Muon spectrometer layout At each strip on pivot plane are associted COINCIDENCE WONDOWS on HighPT and LowPt planes RPC3 High-pT RPC2 RPC2 Pivot RPC1 Low-pT Coincidence window Michele Bianco ICATPP 09

  7. ATLAS Muon LVL1 trigger strategy Muon selection mechanism is based on the allowed geometrical road (Coincidence Windows) • Two threshold regimes: • Low-Pt : muon trigger (6<pt<10 GeV) majority 3/4 • High-Pt: muon trigger (>10 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 Michele Bianco ICATPP 09

  8. Trigger segmentation • Organized in 64 trigger sector: 32 Side A + 32 Side C • An Atlas geometrical sector correspond to 4 trigger sectors • Each trigger sector contains 6-7 trigger tower • 1 Trigger Tower = 1 Low Pt PAD + 1 High Pt PAD • Each PAD contain 2 η-CM and 2 φ –CM • The overlap of an η-CM with a φ-CM correspond to a RoI Michele Bianco ICATPP 09

  9. RPC Detector Analysis Strategy • In order to ensure redundancy/robustness, a twofold strategy are used for RPC detector 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 granularity Michele Bianco ICATPP 09

  10. Tracking with MDT, Quality Cuts • Event selection and track quality: • Events with only 1 track • c2/d.o.f. < 20 • At least 2 f hits on track Michele Bianco ICATPP 09

  11. RPC efficiency with MDT tracks Efficiency distribution HV = 9600 V, Vth= 1000 mV • Low Panel Efficiency related to HV channel off • Efficiency not corrected for dead strips. ATLAS Preliminary HV = 9600 V Vth= 1000 mV Efficiency vs sector ATLAS Preliminary • Cluster size for h and f panels • h view cluster size is a little bit lower wrt f view. This is as expected, due to difference in detector costruction Michele Bianco ICATPP 09

  12. 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. Michele Bianco ICATPP 09

  13. RPC StandAlone Track Quality • 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 Michele Bianco ICATPP 09

  14. RPC StandAlone Tracking Results RPC Efficiency measured for all strips panels, with the RPC standalone tracking dead strips not removed. HV = 9600 Volts, Vth 1000 mV. Average Efficiency = 91.5 % Fitted Efficiency = 94.4 % ATLAS Preliminary RPC panel noise distribution measured for all strips panels, with the RPC standalone package, ATLAS Preliminary HV = 9600 Volts, Vth 1000 mV. Michele Bianco ICATPP 09

  15. Other Off-line StandAlone Monitoring Results Rocks + concrete layers ATLAS Cosmics muon map reconstructed by Off-line RPC standalone muon monitoring extrapolated to surface . The tracking is based only on RPC space points, which are defined by orthogonal RPC cluster hits. Main shafts and elevator shafts are clearly visible. Michele Bianco ICATPP 09

  16. RPC trigger coverage status Trigger Coverage > 97% 5/396 Trigger towers with readout problems Few other holes due to HV problems (recoverable changing trigger majority) Michele Bianco ICATPP 09

  17. 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 configurations (cosmic, beam). • The strip propagation is relevant for the trigger time spread ( max 12ns ), read out cable were optimized to reduce this spread. • All these delays have to be corrected in the pipelines of different element. For a good detector timing 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 wrt each other Local alignment Global alignment Michele Bianco ICATPP 09

  18. 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 = 1000 mV Michele Bianco ICATPP 09

  19. Time alignment inside Sector Logic and between Sector • The misalignment between trigger sectors is the combination of the delay and time of flight. • With cosmics is very difficult to disentangle the 2 components using only RPC. • The best way to check it is to use only pointing tracks (known time of flight) and look at relative alignment. • Dedicated runs were taken using Transition Radiation Tracker (TRT) as source of external trigger (its small radius allow to select pointing tracks easily). • Misalignment between trigger tower inside same Trigger Sector and misalignment between different Trigger Sector have been significantly reduced via an iterative procedure. Trigger Time read-out for, each trigger tower, along RPC trigger sectors. RPC trigger distribution wrt TRT trigger signal. Michele Bianco ICATPP 09

  20. SL N SL N+1 CMA 0 CMA 0 CMA 1 CMA 1 Ly1 Ly0 Pivot Ly1 Conf Ly0 I.P. Trigger Road Analysis RPC spatial correlation between trigger strip (Pivot) and confirm strip (LowPt) in phi view for a programmed trigger road in cosmics data. It is possible to see the trigger road projective pattern by the deviation of the data points from the dashed line. Strip number 0 corresponds to the center of the geometrical sector. Along the phi view (non bending view), trigger road are used to reduce the background, requiring pointing tracks. Michele Bianco ICATPP 09

  21. Detector Control System ( 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 a dedicated Data Base. Michele Bianco ICATPP 09

  22. 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. Michele Bianco ICATPP 09

  23. 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 algorithms have been developed inside the RPC monitoring package to completely monitoring the RPC detector: RPC, RPCLV1, MDTvsRPC Michele Bianco ICATPP 09

  24. 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 RPC has three different have three different sources of DQA: DCS, On-line and Off-line monitoring In 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, it allow to apply automatically pre-defined algorithm to check reference histograms. DQA results grouped as the DAQ partition are collected in specific DB. The DataQuality Off-line is totally based on RPC off-line monitoring performed at Tier0 Michele Bianco ICATPP 09

  25. Conclusion • RPC detector have been installed and commissioned since long time. • Long Cosmic Data Taking allowed to perform a complete detector characterization. • Two different Off-line strategies of detector performance analysis has been developed to assure a complete characterization. • Offline RPC monitoring fully integrated in ATLAS Software Framework. • Detector behavior during the run is fully monitored via DCS system. Michele Bianco ICATPP 09

  26. Backup slides Michele Bianco ICATPP 09

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