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Transcript of 1 Muon Reconstruction in the ATLAS experiment Michela Biglietti Dottorato in Fisica Fondamentale e...
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Muon Reconstruction in the Muon Reconstruction in the ATLAS experiment ATLAS experiment
Michela Biglietti Dottorato in Fisica Fondamentale e Applicata, XVI ciclo
Università di Napoli “Federico II”
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The The LLarge arge HHadron adron CColliderollider Proton - proton collider Centre of mass energy of 14 Tev
(7+7) previous accelerations in the,
linac (50 MeV), PS (25 GeV) and SPS (450 GeV)
Circumference of 27 km 23 collision per crossing, 109
events/s (most soft hadronic interactions)
Energy per proton 7 TeV
Bunch spacing 25 ns
Bunch size 15 m 12 cm
Protons per bunch 1011
Bunches per ring 2835
Beam lifetime 10 hours
Design luminosity 1034 cm-2 s-1
Currently under construction in the LEP tunnel scheduled to start in the 20074 experiments : Atlas, CMS, LHCb, Alice
W (E/m)4R-1
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Physics @ LHCPhysics @ LHC
Total p-p cross-section 80 mb 109 events/s Most are large distance, soft collisions
QCD background S/B very low
(exe: (Hm=150Gev)/(jetpt=700Gev) ~10-5 ) Pile up
Hard interactions overlapped with ~ 25 soft collisions
Need of good trigger system and fast detector response
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The LHC physics programmeThe LHC physics programme Factory of all SM and new particles with masses in the TeV
range SM Higgs boson search
Exp limit (LEP): mH>113.5 Gev/c2 LHC will be able to observe a SM Higgs up 1 TeV and to measure his
mass and couplings with high precision SUSY particles search Precision measurements
huge production of W, Z, b and t particles• exe: tt cross section ~ 1 nb (0.8 event/s)
B physics low luminosity running (L = 1033 cm-2 sec-1)
• b quark identification is not hidden by pile-up LHCb
New physics
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SM Higgs boson searchSM Higgs boson search
Low mass region (mH<130 GeV)
H , H bb
Intermediate mass region (130 GeV < mH< 2 mZ)
H WW(*), H ZZ*
High mass region (mH > 2 mZ )
H WW, H ZZ, H tt
The channels experimentally most promising are those with leptons in final state.
H ZZ 4l “golden channel”H ZZ is one of the most promising
Production cross sec.
Decay BR
Higgs boson signal needs to be extracted from a background of several orders of magnitude larger.
g
gt H
H
q
q
W,Z
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The Atlas Apparatus The Atlas Apparatus General purpose
apparatus Lenght of 46 m,
diameter of 22 m Onion shell structure,
two endcaps ad one barrel
Inner tracker, calorimeters, muon spectrometer
Inner tracker cointained in a solenoid (max 2 T), muon spectrometer in a toroid (air core, max 3.9 T for barrel, 4.1 T for endcap)
108 electronic channels
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Atlas design criteriaAtlas design criteria
Large acceptance Very good e.m. calorimetry for detection of e and
and energy measurements, hermeticity. High precision muon momentum measurements
(accurate tracking in the inner detector for low pt muons and large level arm of the muon spectrometer), low PT trigger capability
Efficient tracking at high luminosity for lepton-momentum measurements, for b quark tagging, reconstruction of B decay at lower luminosity
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Conventions Conventions z direction along the beam
pipe x-y define the plane
transverse to the beam direction
Positive x-axis points from the interaction point to the centre of the LHC ring, positive y-axis points from the interaction point upward
Cylindrical coordinates useful : , , R
Pseudorapidity : = -ln(tan(/2))
cot
X
YZ
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The MuonThe Muon Spectrometer Spectrometer
16 sectors in (small and large) Instrumented with trigger and
precision chambers Muon binding
|| < 0.7 from barrel toroid 1.4<||<2.7 from two endcap
magnet 0.7<||<1.4 transition region
Open structure of magnets minimizes the effect of multiple scattering and energy loss
Design performances pt/pt 10% for pt = 1Tev Momentum and mass
resolution of 1% for reconstructed 4-muons final state
view
RZ view
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The Muon Precision Chambers The Muon Precision Chambers
MDTs (Monitored Drift Chambers) Basic element is a tube with a diameter of 3 cm and a variable
lenght, from 70 cm to 630 cm Tubes arranged in multilayer of 3 (4 for the inner stations) Single wire resolution 80 m
CSCs (Catod Strip Chambers) MWPC with segmented cathode strips read-out both orthogonal
(precision measurements) and parallel to the anode wires In the innermost ring of the endcap region, 2 < || < 2.7 (faster, for
high multiplicity) Spatial resolution 60 m, small drift time (30 ns), time
resolution 7 ns
Precise measurements in the bending direction
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The Muon Trigger Chambers The Muon Trigger Chambers
Barrel RPCs (Restistive Plate
Chambers): on both sides of middle MDT stations and above or below the outer MDT station.
For bunch crossing identification and second coordinate ()
measurements. Trigger system covers the region with ||<2.4
Endcap TGCs (Thin Gap Chambers) : 3 stations close the MDT middle station. Consists of MWPC (wires for trigger signal, parallel to those of MDTs ) with read-out strips orthogonal to the wires for the second coordinate measurement
Time resolution 1 ns
Spatial resolution 1 cm
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HEP Computing HEP Computing
In the past elementary particle experiments the dominant programming language was Fortran Introduced when experiment were small
• Small detectors, small number of workers
Today experiments are HUGE Stringent demands not only on the detector’s hardware but
also on software needed to simulate, reconstruct and analyse physic events
Need to change from procedural to object-oriented programming
… but sometimes Fortran is hard to kill … Strong links with the past We have inherited too many useful and working tools
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The Atlas CollaborationThe Atlas Collaboration1700 members from 144 institutions and 33 countries
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Offline Software in ATLASOffline Software in ATLAS Goals
Detector response simulation and geometry description Reconstruction of physically interpretable objects from raw data Storage ( 100 Mbyte/s ) Analysis Visualization …
Features High complexity Long lifetime (20 years!) Large data volumes Many developers, most of them are not expert in programming
Needs of Flexibility , mantainaibility, uniformity, modularity, reusability,
distribuited development mechanisms … Choice to use OO/C++ techologyChoice to use OO/C++ techology
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Object Oriented Programming Object Oriented Programming FeaturesFeatures An OO application is a collection of collaborating objects that interact
to each other by exchanges messages Encapsulation
Implementation details are hidden Clients only see object’s interface, i.e. his behaviour
Polymorphism and Inheritance Different kinds of objects can belong to a abstract common class and
have similar features and a common interface The “shared operation” behavior depends on the type of the object
Abstraction Real objects are abstracted into classes, similarities among objects are
implemented in terms of interface, using polymorphism and inheritance Reduction of complexity, increase of modularity, flexibility, robustness
and code reuse Object Orientation is the widest used technology for large
software projects C++ is a mature, standard and widely used OO language
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Offline Reconstruction in AtlasOffline Reconstruction in Atlas
Data flow
Tracking
Calorimetry
Muon
Tracks
Em cluster
Muon
Calo Jets
…
Combined Muon
Analysis
Raw digits
Detector element E/
identificationEvent
MC truth & simulation
Atlas Sim. and rec. algorithms
dataObject
Detector descriptor
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Offline Reconstruction in AtlasOffline Reconstruction in Atlas
Converter
Algorithm
Event DataService
PersistencyService
DataFiles
AlgorithmAlgorithm
Transient Event Store
Detec. DataService
PersistencyService
DataFiles
Transient Detector
Store
MessageService
JobOptionsService
Particle Prop.Service
OtherServices
HistogramService
PersistencyService
DataFiles
TransientHistogram
Store
ApplicationManager
ConverterConverter
Necessity of a framework: a template application into which developers plug in their code, using mechanisms defined by the framework, collections of functionality, common vocabulary …
Athena
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Offline Reconstruction in AtlasOffline Reconstruction in Atlas
Packages should be made of many indipendent Athena top-algorithms
Transient objects are passed via the Transient Data Store
Algorithms are only coupled through the data
Algorithm1
Algorithm2
Algorithm3
DataObj
DataObj
DataObj
DataObj
DataObj
DataObj
T
D
S
Algs2Event
Algs1
Algs3
Algorithms and data objects should be placed in different packages
Algorithmic packages depend on data, not
viceversa
Software organization inside Athena
The detector description, the even structure and the implementation of recostruction algorithms are separated
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Muon ReconstructionMuon Reconstruction At every interaction the signals from each sub-detector
that pass the trigger selection are recorded for processing by the offline reconstruction software
A charged particle moving in the detectors leaves a trace of hits
The goal of the reconstruction is to find a track associated to the hits and and perform a fit to obtain the best estimates of the set of parameters that describes the particle trajectories To define a 3D curve we need of 5 parameters: usually a0, z0, ,
cot, ±1/PT
The result of the fit is the best estimate of th track parameters and their covariace matrix at every position along the track
Track can be traced to the beam line to searches for matching to the vertex
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Muon Reconstruction in AtlasMuon Reconstruction in Atlas
Old package Muonbox in F90 Still working but hard to integrate with all the Atlas
software Lacks of flexibility and maintainaibility Potentially dangerous to use for the standard Atlas
muon reconstruction
Necessity to have a new C++ package MOORE (Muon OO REconstruction)
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Software for Muon Software for Muon Reconstruction and MeReconstruction and Me
My present work consists of contribute in developing the C++ stand alone
package for muon reconstruction (Moore)• Integration with Atlas offline software/reconstruction
framework• Architecture and design• Test
develop a package for combined muon reconstruction, Inner Detector + MuonSpectrometer (MuonIdentification)
This is finalised to physics studies (together with validation of software, check of the quality of simulated data producted, detector studies)
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Atlas Data Challenges Atlas Data Challenges Massive production of simulated physics events Needed for
software validation• Check of the full chain generation-simulation-offline reconstruction• Data storage
high level trigger studies detector performances studies physics studies
DC1 (July/August, October/November 2002 ) We are involved in muons-final states events production
Single ’s for several energies (in total ~107 events) cavern “background events” 105 H 4, A/H 2 106 Z for calibration ~107 events Productions to be done in Roma, Napoli, Lecce
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MOOREMOORE Reconstruction Reconstruction StrategyStrategy
Searches for regions of activity From the RPC/TGC measurements “-
Segments” are created
Searches for R-Z regions of activity For each “-Segment”, the associated MDTs is
found and a “crude” RZ Segments is built (essentially collections of z hits) .
rpc
rpcrpc
MDT
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MOOREMOORE Reconstruction Strategy Reconstruction Strategy Pattern recognition
and outer Roads– Inside MDTs the drift distance is
calculated from the drift time, by applying various corrections on it (TOF, second coordinate, propagation along the wire, Lorenz effect). From the 4 tangential lines the best one is found.
– All the “MDT segments” of the O station are combined with those of the M layer. The MDT hits of each combination are added to the phi-hits of the “Phi Segment”, forming “outer” track candidates. All the successfully fitted candidates are kept for further processing. Final tracks
The successful “outer” track is subsequently used to associate inner station MDT hits. A “final” track is defined as a successfully fitted collection of trigger hits and of MDT hits from at least two layers.
MDT mutilayer
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Architecture and Design Architecture and Design
MooMakePhiSegments
RPC/TGC digits
PhiSegments
MooMakeRZSegmentsMDT digits
MooMakeRoads CrudeRZSegments
MooMakeiPatTracks MooRoads
MooiPatTracks
MooMakeNtuples
Ntuples
MooAlgsMooAlgs
MooStatisticsMooStatistics
Each step is driven by an Athena top-algorithm
Transient objects are passed via TDS
Independent algorithms, the only coupling is through the transient objects
Results : Results : less dependencies, code less dependencies, code is more maintainable, modular, is more maintainable, modular, easier to develop new easier to develop new reconstruction approaches reconstruction approaches
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Efficiency vs PEfficiency vs PTT
Single muon studies
PT (GeV)
(%)
A Muon track consists ofhits from at least 2 stationsand is successfully fitted.
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PPTT resolution resolution
Pt resolut ion 20 gev Pt resolution 100 gev
PT = 20 GeV PT = 100 GeV
N e
ven
t
N e
ven
t
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Effect of dead materialEffect of dead material
No material
Including in the fit the material crossed by the track (chambers + toroids) .
Get full information from AMDB (via “trmusc” from MUONBOX)
1./PT Pull
20 GeV
NO Material Effects in the fit Material included in the fit
1./PT Pull
20 GeV
pull = (Xgen – Xrec)/rec
= 1.0
N e
ven
t
N e
ven
t
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Combined Muon ReconstructionCombined Muon Reconstruction Improve muons identification efficiency
Discrimination of muons from rays in the muon spectrometer Reconstruction of low energy muons that do not reach the middle and
outer stations of the muon spectrometer Rejection of decay muons (from k and ) by requiring tracks originate
close the interaction point Discrimination of muons in hadronic jets from hadrons. An efficient muon
b-tagging requires a good muon identification for non isolated muons Improve track parameters
Achieve the best possible momentum resolution Reduce tails in the momentm resolution of the muon spectrometer,
resulted from fluctuation in energy loss in the calorimeter Improve charge determination for high energy muons
Understand the detector Check the calibration of calorimeter. Cross check the results from the inner detector and muon spectrometer
(for muons with momenta from 20 GeV to 70 GeV)
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Combined MuonsCombined Muons
pT > ~100 GeV: profit from greatly superior Muon Spectrometer momentum precision
~20 < pT < ~100 GeV: combination more
precise than Inner Detector or Muon Spectrometer alone
pT < ~ 20 GeV: purpose is purely identification => no parameter improvement over indet measurement Reduce decay-in-flight
background.
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Combined Combined Reconstruction/Reconstruction/MuonIdentificatonMuonIdentificaton Purpose: associate tracks found by Moore in Muon
Spectrometer with inner detector tracks and calorimeter information to identify muons at their production vertex with optimum parameter resolution
2 principle methods: Stand-alone muons – MS track and track-segment
parameters propagated to beam-axis Combined muons – match MS to ID tracks and fit
combined parameters
Input – results of Inner Detector, Calorimetry and Muon Spectrometer (Moore) reconstruction (as C++ objects through Athena framework interface)
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MuonIdentification Method MuonIdentification Method MS track and inner station
segment parameters propagated to beam-axis Angle resolutions dominated
by Coulomb scattering in calo Parametrise calorimeter
effects – function of p and (i.e. thickness)
or measure energy loss from calibration of observed energy deposition
MS track is express at vertex
2 fit for matching of inner detector and muon spectrometer tracks parameters
Final fit
calorimeter
Muonspectrometer inner layer
Beam spot
Energy loss and multiple scattering
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Track Combination and Final Fit Track Combination and Final Fit
From the point of view of interfaces, the track combination and final fit easy to perfom Muid and Moore track
both ihnerit from the base class Track
Inner Detector track is a (instance of) Track
The same happens to the Fitter objects
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A First approach A First approach
Association of the reconstructed muon Track (from Moore) with the Truth Event track(from MC/simulation). Calculation of the difference between the energy atthe vertex and the energy at the entrance of the Muon Spectrometer
Energy loss from truth
GeV
Single Pt = 20 Gev
N e
ven
t
Need to parametrise calorimeter effects
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MuonIdentificationMuonIdentification: First Look : First Look
Single Pt = 20 Gev
cot pull at vertex
N e
ven
t
GeV
Correction on PT
Muidtrack at vertex
Moore track atMS entrance
Single Pt = 20 Gev
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MuonIdentificationMuonIdentification First Look First Look
Single + Pt = 20 Gev
- pull at vertex
N e
ven
t
N e
ven
t
Single - Pt = 20 Gev
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MuonIdentificationMuonIdentificationFirst LookFirst Look
Moore PMoore PTT pull at the pull at theentrance of muon entrance of muon spectrometerspectrometer
MuID PMuID PTT pull at vertex pull at vertex
Single Pt = 20 Gev
Single Pt = 20 Gev
N e
ven
t
N e
ven
t
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Plans for futurePlans for future
Continue software developing Completation of Muid method
• Get calorimeter information for energy loss• Get inner detector track from framework• Implement a fit method for track matching at vertex
Improve MuonIdentification design, need to modularize of the code eliminate superfluos dependeces exploit the new Atlas software (event structure, detector
description, framework facilities, event display … ) separate framework interface object/algorithms/events
Physic studies based on DC1 data produced in our site