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 PhysicsInterlaken, September 26-30, 2004

Outline:•The ATLAS trigger

• Fast algorithm•relevant physics performances

•Implementation in the Online framework

•Latency of the algorithm

•Conclusions

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

The LHC challenge to ATLAS Trigger/DAQ

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 : 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 ATLAS Trigger

75 kHz

~ 2 kHz

~ 200 Hz

Rate

Target processing time

~ 2 s

~ 10 ms

2.5 μs

Level-1

Hardware trigger

High Level Triggers

(HLT)

Level-2 + Event Filter

Software trigger

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:

1) “Global Pattern Recognition” involving trigger chambers and positions of MDT tubes (no use of drift time);

2) “Track fit” involving drift time measurements, performed for each MDT chamber;

3) Fast “pt estimate” via a Look-up-table (LUT) with no use of time consuming fit methods.

Result ,,direction of flight into the spectrometer, and pt at the interaction vertex.

muo

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After L1 emulation 1 hit from each Trigger 1 hit from each Trigger Station is required to start the Station is required to start the Pattern Recognition on MDT Pattern Recognition on MDT data.data.

1 hit from each Trigger 1 hit from each Trigger Station is required to start the Station is required to start the Pattern Recognition on MDT Pattern Recognition on MDT data.data.

Global Pattern recognition:seeded by the trigger chamber data

Use the L1 simulation Use the L1 simulation code to select the RPC code to select the RPC

Trigger Pattern Trigger Pattern

Valid coincidence in the Low-Pt CMA

Define “-roads” around this trajectory in each chamber;

Collect hit tubes within the roads using theresidual of the muon tube.

Apply a contiguity algorithm to furtherremove background hits inside the roads.

(muon hits) = 96% backgr. hits ~ 3%

Low pt (~ 6 GeV) High pt (~ 20 GeV)

Muon Roads and “contiguity algorithm”

Track Fit

Use drift time measurement to fitthe best straight line crossing allpoints.

Compute the track bending usingthe sagitta method: three pointsrequired

For a given chamber the sagitta is: s ~ 150 m for muon pt = 20 GeV s ~ 500 m for muon pt = 6 GeV

small effects respect to sm

Use linear relation between 1/s

and pT to estimate pT.

Prepare Look Up Tables (LUT) as a set of relations between valuesof s and pt for different regions (s = f ( , , pt)).

30 x 60 ( , ) tables for each detector octant.

PT estimate

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.

Trigger rates (barrel)Low pt

(6 GeV)

L1 rate (KHz) L2 rate(KHz)

K/ decays 7.9 3.1

b decays 1.7 1.0

c decays 1.0 0.5

Fake L1 1.0 Negligible

Total 10.6 4.6

High pt

(20 GeV)

L1 rate (KHz) L2 rate(KHz)

K/ decays 1.1 0.06

b decays 0.8 0.09

c decays 0.4 0.04

W decays 0.06 0.05

Fake L1 negligible negligible

Total 2.4 0.24

HLT Event Selection Software

HLTSSW

Steering Monitoring Service

1..*

MetaData Service

1..*ROB DataCollector

DataManager

HLTAlgorithms

Processing Task

Event DataModel

L2PU Application

<<import>>

Event DataModel

Reconstr. Algorithms

<<import>>

StoreGateAthena/Gaudi

<<import>><<import>>

Interface

Dependency

Package

Event Filter

HLT Core Software

Offline Core Software Offline Reconstruction

HLT Algorithms

HLT Data Flow Software

HLT Selection Software Framework ATHENA/GAUDI Reuse offline components Common to Level-2 and EF

Offline algorithms used in EF

Bytestream modelStandardization 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.

PADPAD

CM … up to 8CM … up to 8CMCMCMCM

Fired channelFired channelFired channelFired channelFired channelFired channel

CSM = MDT chamberCSM = MDT chamber

MdtAmtHittMdtAmtHittMdtAmtHittMdtAmtHittMdtAmtHitMdtAmtHit

Standard MDT data access scheme:use LVL1 Muon RoI info

muo

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7 MDT chambers 7 MDT chambers

to be accessedto be accessed

LVL1 RoILVL1 RoIMDT chamber MDT chamber

accessed accessed

This tail is critical for the MDT converter timing

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After L1 emulation

Width < 50 cm

Width ~ 5 cm

Width < 40 cm

Optimized MDT data access scheme:use Muon Roads

3 MDT chambers to 3 MDT chambers to be accessed; up to 6 be accessed; up to 6 in case Roads overlap in case Roads overlap two chambers.two chambers.

MDT chamberMDT chamberaccessedaccessed

Only three MDT chambers are accessed in most of the cases.

Further optimizationA 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

1) description of the readout xxx xxxx

2) description of the detector geometry

3) 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

CSMCSM

Fast sequence diagram

RPC dataaccess

Level-1emulation

RPC patternrecognition

MDT dataaccess

Featureextraction

MDT patternrecognition

Monitoring

PAD Id

FastFastexecutionexecution

RoIreconstruction

IDC for RPCIDC for RPC

PADPAD

CM … up to 8CM … up to 8CMCMCMCM

Fired channelFired channelFired channelFired channelFired channelFired channel

PADPAD Triggerpattern

Muonroads

IDC for MDTIDC for MDT

CSMCSM

AmtAmtAmtAmtAmtAmt

CSMCSM CSMCSM

AmtAmtAmtAmtAmtAmtAmtAmtAmtAmtAmtAmt

CSMCSMCSMCSM

Muon Features

Prepared digits

Frameworkinfrastructure

Fastsequences

Filling histos for monitoring

Fast and Total latency time

• Optimized code run 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 s;– data access takes the same cpu time of

Fast;

TotalTotal

Fast Fast Fast takes Fast takes ~ 10% of the Level-2 ~ 10% of the Level-2 latency.latency.

Cavern background does not Cavern background does not increase the processing time.increase the processing time.

First implementation

Conclusions

• Fast is suitable to perform the muon trigger selection in ATLAS L2:BARREL RESULTS:– Fast reconstructs muon tracks into Muon Spectrometer and measures the PT at

the interaction vertex with a resolution of 5.5% at 6 GeV and 4% at 20 GeV;

– Fast 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.

Backup transparencies

Requirements for implementation

• 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 Software designdesign

TriggerTriggerarchitecturearchitecture

Level-1 RoI is the intersection of a CMA processing RPC eta-view with a CMA processing RPC phi-view inside 1 PAD.

RPC bytestreamRPC 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

Pivot plane

Confirm plane low pt

No way to fit RPC bytestream into RPC detector modules!

Shown are odd number CMAs only, CMAs overlap in confirm planes, but not in the pivot plane.

RPC RDO Definition• Needed different types:

– “bare” RDO as persistent representation of bytestream; contains raw data from the Level-1 and are used by Fast 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.

PADPAD

CM … up to 8CM … up to 8CMCMCMCM

Fired channelFired channelFired channelFired channelFired channelFired channel

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”);

MDT bytestream

To fit MDT bytestream into MDT detector modules is trivial.

MDT RDO definition• Need different types:

– “bare” RDO as persistent representation of bytestream; contains MDT raw data and are used by Fast 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.

CSM = MDT chamberCSM = MDT chamber

MdtAmtHittMdtAmtHittMdtAmtHittMdtAmtHittMdtAmtHitMdtAmtHit

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.

Bytestream dataflow

ROD emulation

RoI B. emulation RoI

Bytestream

RDO Converter RIO Converter

RIO

Fast EF andoffline rec.

Fast 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.

Readout CablingDetector GeometryOnline vs Offline map

L2 Detector Description

Offline DetectorDescription

RDO

use

use

use

Simulation

use

• 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.

Fast implementation

ProcessTYP

ProcessBase

ProcessStd

Pure virtual implementation

Concrete imp. of the data structure I/O and printouts

Concrete imp. of the task type

Minimal use of STL containers.Minimal use of STL containers.No memory allocation on No memory allocation on

demand.demand.

Minimal use of STL containers.Minimal use of STL containers.No memory allocation on No memory allocation on

demand.demand.

Sequence

name: string type: integer data: struct <TYPE>

Methods: giveData() start()

ProcessStd

name: stringtype: integerdata&: struct<TYPE>

Methods: run() printout()

runs