1 / 27

A Level-2 trigger algorithm for the identification of muons in the ATLAS Muon Spectrometer

A Level-2 trigger algorithm for the identification of muons in the ATLAS Muon Spectrometer. Alessandro Di Mattia on behalf of the Atlas TDAQ group Computing in High Energy Physics Interlaken, September 26-30, 2004. Outline: The ATLAS trigger m Fast algorithm relevant physics performances

naida-mckenzie
Download Presentation

A Level-2 trigger algorithm for the identification of muons in the ATLAS Muon Spectrometer

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A Level-2 trigger algorithm for the identification of muons in the ATLAS Muon Spectrometer Alessandro Di Mattia on behalf of the Atlas TDAQ group Computing in High Energy Physics Interlaken, September 26-30, 2004

  2. Outline: • The ATLAS trigger • mFast algorithm • relevant physics performances • Implementation in the Online framework • Latency of the algorithm • Conclusions

  3. Challenge to the ATLAS Trigger/DAQ • interaction rate 109 Hz, offline computing can handle O(102 Hz). • cross section of physics processes vary over many order of magnitude: • Inelastic: 109 Hz • W → l n: 102 Hz • tt production: 10 Hz • Higgs (100 GeV): 0.1 Hz • Higgs (600 GeV):10-2 Hz • ATLAS has O(108) read-out channels → average event size ~1.5 MByte The LHC challenge to ATLAS Trigger/DAQ LHC: proton-proton collisions @ ECM = 14 TeV starting 2007 L = 1034 cm-2 s-1 23 collisions per bunch crossing @ 25 ns interval 1 year at L = 1034 cm-2 s-1∫Ldt ≈100 fb -1

  4. The ATLAS Trigger Level-1 Hardware trigger 75 kHz 2.5 μs High Level Triggers (HLT) Level-2 + Event Filter Software trigger ~ 2 kHz ~ 10 ms ~ 200 Hz ~ 2 s Rate Target processing time

  5. Standalone muon reconstruction at Level-2 Task of the Level-2 muon trigger: • Confirm the Level-1 trigger with a more precise pt estimation within a “Region of interest (RoI)”. • Contribute to the global Level-2 decision. To perform the muon reconstruction RoI data are gathered together and processed in three steps: • “Global Pattern Recognition” involving trigger chambers and positions of MDT tubes (no use of drift time); • “Track fit” involving drift time measurements, performed for each MDT chamber; • Fast “pt estimate” via a Look-up-table (LUT) with no use of time consuming fit methods. Result h,f,direction of flight into the spectrometer, and pt at the interaction vertex.

  6. Approximated Muon trajectory muon Use the L1 simulation code to select the RPC Trigger Pattern Valid coincidence in the Low-Pt CMA Global Pattern recognition:seeded by the trigger chamber data After L1 emulation 1 hit from each Trigger Station is required to start the Pattern Recognition on MDT data.

  7. Muon Roads and “contiguity algorithm” Define “m-roads” around this trajectory in each chamber; Collect hit tubes within the roads using the residual of the muon tube. Apply a contiguity algorithm to further remove background hits inside the roads. e (muon hits) = 96% backgr. hits ~ 3% Low pt (~6 GeV) High pt (~20 GeV)

  8. Track Fit Use drift time measurement to fit the best straight line crossing all points. Compute the track bending using the sagitta method: three points required For a given chamber the m sagitta is: sm ~ 150 mm for muon pt = 20 GeV sm ~ 500 mm for muon pt = 6 GeV  small effects respect to sm

  9. Use linear relation between 1/s and pT to estimate pT. PT estimate Prepare Look Up Tables (LUT) as a set of relations between values of sandpt for different h, f regions (s = f (h , f , pt)). 30 x 60 (h , f) tables for each detector octant. Performances including background simulation for the high luminosity environment Resolution comparable with the ATLAS reconstruction program (factor of about 2). Track finding efficiency of about 97% for muons.

  10. Trigger rates (barrel)

  11. Event Filter HLTSSW Processing HLT Core Software Task 1..* Steering Monitoring Service HLT Algorithms L2PU Application Data Manager HLT Algorithms ROB Data Collector 1..* Event Data Model MetaData Service <<import>> <<import>> <<import>> <<import>> Package Athena/ Gaudi Event Data Model Reconstr. Algorithms StoreGate Interface Offline Reconstruction Offline Core Software Dependency HLT Event Selection Software HLT Data Flow Software HLT Selection Software • Framework ATHENA/GAUDI • Reuse offline components • Common to Level-2 and EF Offline algorithms used in EF

  12. CSM = MDT chamber PAD MdtAmtHit MdtAmtHitt MdtAmtHitt CM CM CM … up to 8 Fired channel Fired channel Fired channel Bytestream model Standardization of data access forces to model the data according to detector regions …. but …. bytestream should be optimized for a fast access to the detector data. RPC bytestream:the detector regions can’t be easily mapped on the readout structure because this latter is geared towards the trigger needs. Use an ad hoc solution: PAD -> Coincidence Matrix -> Fired CMA channel Data are strictly limited to the needed ones: no overhead introduced in the data decoding. MDT bytestream:readout structure mapped on the MDT chambers. CSM -> AMT hit (AMT data word) Data access according to chambers is not efficient:optimization needed.

  13. 7 MDT chambers to be accessed muon LVL1 RoI MDT chamber accessed This tail is critical for the MDT converter timing Standard MDT data access scheme:use LVL1 Muon RoI info

  14. 3 MDT chambers to be accessed; up to 6 in case Roads overlap two chambers. Approximated Muon trajectory muon Width < 50 cm MDT chamber accessed Width ~5 cm Width < 40 cm Only three MDT chambers are accessed in most of the cases. Optimized MDT data access scheme:use Muon Roads After L1 emulation

  15. Further optimization A considerable fraction of the data access time is taken by the “data preparation”. Data preparation is for: • associating space point to detector hits; • resolving ambiguites in some special detector regions (RPC data only); • providing refined info to the reconstruction: t0 subtraction (to MDT drift time), calibration of the space-time relationship of MDT tubes. To optimize this process, the data preparation is performed inside the algorithm using a standalone detector description that provides • description of the readout xxx xxxx • description of the detector geometry • offline versus online map xxx xxxx Advantages: • prepare only the data needed for reconstruction; • use code optimized for speed: • detector geometry organized according to readout hierarchy; • minimal use of STL, no memory allocation on demand; • minimize the dependencies towards the offline code: ease the integration

  16. mFast sequence diagram IDC for MDT CSM CSM CSM IDC for RPC mFast execution Amt Amt Amt Amt Amt Amt Amt Amt Amt PAD CM CM CM … up to 8 Fired channel Fired channel RoI reconstruction Filling histos for monitoring mFast sequences Framework infrastructure RPC data access Level-1 emulation RPC pattern recognition MDT data access MDT pattern recognition Feature extraction Monitoring PAD Id Fired channel PAD Trigger pattern Muon roads CSM CSM CSM Prepared digits Muon Features

  17. mFast and Total latency time mFast takes ~ 10% of the Level-2 latency. Cavern background does not increase the processing time. mFast • Optimized coderun on (XEON @ 2.4GHz). • Signal: single muon, pt=100 GeV • Cavern Background: High Lumi x 2 • The total latency shows timings made on the same event sample before and after optimizing the MDT data access. Optimized version: • total data access time ~ 800 ms; • data access takes the same cpu time of mFast; Total First implementation

  18. Conclusions • mFast is suitable to perform the muon trigger selection in ATLAS L2: BARREL RESULTS: • mFast reconstructs muon tracks into Muon Spectrometer and measures the PT at the interaction vertex with a resolution of5.5%at6 GeVand4%at20 GeV; • mFast allows to reduce the LVL1 trigger rate from 10.6 kHz to 4.6 kHz (6 GeV), and from 2.4 kHz to 0.24 kHz (20 GeV). • algorithm fully implemented in the Online framework. • algorithm and data access time match the L2 trigger latency:now ready to undergo a next optimization phase more devoted to standardize the software components.

  19. Backup transparencies

  20. Requirements for implementation Trigger architecture • L2 latency time set to 10 ms; • Thread Safety • Data access in restricted Geometrical Region (RoI seeding); • Hide aspects of data access behind offline Storgate interfaces; • Use RDO (Raw Data Object) as the atomic data component: • translate the bytestream Raw data into RDO; • conversion mechanism integrated into the data access. • Standardize the data access for every subdetector: • general region lookup to implement RoI mechanism, • common interfaces for detector specific code, e.g. RDO converters, • force a common structure for the RDOs, as far as it is possible: fit it into detector modules. • ROB (ReadOut Buffer) access and data preparation/conversion on demand; Software design

  21. RPC bytestream RPC bytestream reflects the organization of the trigger logic: • ROD -> Rx -> PAD -> Coincidence Matrix (CMA) -> CMA channel • 1 ROD = 2 Sector Logic = 2 Rx; RPC detector are read by 64 Logic Sector; • Up to 7 PAD into a Rx; up to 8 CMA into a PAD (4 per view); • CMA channel = 32/64 depending on the CMA side (Pivot/Confirm); 1 CMA  coincidences between RPC planes in a 3-dimensional area Confirm plane high pt Level-1 RoI is the intersection of a CMA processing RPC eta-view with a CMA processing RPC phi-view inside 1 PAD. Pivot plane Confirm plane low pt Shown are odd number CMAs only, CMAs overlap in confirm planes, but not in the pivot plane. No way to fit RPC bytestream into RPC detector modules!

  22. PAD CM CM CM … up to 8 Fired channel Fired channel Fired channel RPC RDO Definition • Needed different types: • “bare” RDO as persistent representation of bytestream; contains raw data from the Level-1 and are used by mFast to run the Level-1 emulation on one RoI; • “prepared” RDO (or RIO – Reconstruction Input Object) are obtained from the RDO with some manipulation of the data to resolve the overlap regions and to associate space positions to the hits. Used by the offline reconstruction. BARE:Convenient way to organizing RDOs in IDC is according to PAD. Data requests are simplified thanks to the close correspondence between PAD and RoI. PAD -> Coincidence Matrix -> Fired CMA channel Data are strictly limited to the needed ones: no overhead introduced in the data decoding. PREPARED:Stored in Storegate in hierarchical structure as defined by offline identifiers up to the RPC chamber modules.

  23. MDT bytestream MDT bytestream organization: ROD -> Chamebr System Module (CSM) -> TDC ->TDC channel • 1 ROD = 1 trigger tower (f x h x r = 1 x 2 x 3); • 1 CSM read 1 MDT chamber; one CSM can have up to 18 TDC; • 1 AMT (Atlas Muon TDC) can have up to 24 channel (= “tubes”); To fit MDT bytestream into MDT detector modules is trivial.

  24. CSM = MDT chamber MdtAmtHit MdtAmtHitt MdtAmtHitt MDT RDO definition • Need different types: • “bare” RDO as persistent representation of bytestream; contains MDT raw data and are used by mFast to confirm the Level-1 RoI. • “prepared” RDO contains refined info (drift time, calibarted time, radius, error). BARE: Convenient way to organizing RDOs in IDC is according to CSM, because can be closely matched both to a detector element and to the trigger tower read-out. No ordering is foreseen for AMT data words. CSM -> AMT hit (AMT data word) Data access according to chambers is not efficient: optimization needed. PREPARED: Stored in Storegate with the same structure as RDO but contains a list of offline MDT digits.

  25. Optimization of MDT data access • Standard implementation of MDT data access was not efficient: • ~7 chambers required per RoI; • … but typically only 3 chambers have muon hits; • direct impact on the timing performance because: • MDT occupancy dominated by Cavern Background; • MDT converter time scales linearly with the chamber occupancy; • A more efficient access schema has be implemented using: • Muon Roads – refinement of the RoI region available after L1-emulation. The widths of Muon Roads are smaller than the chamber size. • An optimized way for accessing the detector elements–selects detector elements according to the station (Innermost, Middle, Outermost), to the sector and to the track path.

  26. Bytestream dataflow L2 Detector Description Readout Cabling Detector Geometry Online vs Offline map RoI EF and offline rec. mFast RoI B. emulation use RDO RIO ROD emulation use use RDO Converter RIO Converter use Simulation Bytestream Offline Detector Description mFast uses a dedicated detector description code to reconstruct RDOs: • standalone implementation to ease the integration in HLTSSW; • detector geometry organized according to readout hierarchy; • minimal use of STL container.

  27. mFast implementation Sequence ProcessStd Pure virtual implementation ProcessBase name: string type: integer data: struct <TYPE> Methods: giveData() start() name: string type: integer data&: struct<TYPE> Methods: run() printout() runs Concrete imp. of the data structure I/O and printouts ProcessStd Concrete imp. of the task type ProcessTYP • The processing tasks are implemented by “process” classes • acts on “C style” data structure; no use of offline EDM. • process versioning implemented through inheritance • The “sequence” classes manage the execution of processes and publish the data sctucture towards the processes and the sequences • provide interfaces to framework components: MessageSvc, TimerSvc, etc. Minimal use of STL containers. No memory allocation on demand.

More Related