Transcript Document

Muon Reconstruction in the
ATLAS experiment
Michela Biglietti
Dottorato in Fisica Fondamentale e Applicata, XVI ciclo
Università di Napoli “Federico II”
1
The Large Hadron Collider
 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
1011
Bunches per ring
2835
Design luminosity
W (E/m)4R-1
15 m  12
cm
Protons per bunch
Beam lifetime
Currently under construction in the LEP
tunnel
scheduled to start in the 2007
4 experiments : Atlas, CMS, LHCb, Alice
10 hours
1034 cm-2 s-1
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Physics @ 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
3
The 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 search
g
t H
g
q
W,Z
q
H
Production cross sec.
Higgs boson signal needs to be
extracted from a background of
several orders of magnitude larger.
 Low mass region (mH<130 GeV)
 H  gg, 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.
Decay BR
H  ZZ  4l “golden channel”
H  ZZ      is one of
the most promising
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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 criteria
 Large acceptance
 Very good e.m. calorimetry for detection of e and g
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 leptonmomentum measurements, for b quark tagging,
reconstruction of B decay at lower luminosity
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Conventions
Z
X
Y
 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 
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The Muon
Spectrometer
 view
 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
 Dpt/pt 10% for pt = 1Tev
RZ view
 Momentum and mass
resolution of 1% for
reconstructed 4-muons final
state
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The Muon Precision Chambers
Precise measurements in the bending direction
 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
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The Muon Trigger Chambers
For bunch crossing identification and second coordinate ()
measurements. Trigger system covers the region with ||<2.4
Barrel RPCs (Restistive Plate
Chambers): on both sides of
middle MDT stations and above
or below the outer MDT station.
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
 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 Collaboration
1700 members from 144 institutions and 33 countries
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Offline 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



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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++ techology
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Object Oriented Programming
Features
 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 Atlas
Data flow
Atlas
Sim. and rec. algorithms
dataObject
MC truth & simulation
Raw digits
Detector descriptor
…
Detector
element
Tracks
Em cluster
Tracking
Calorimetry
Muon
Calo Jets
Muon
E/g
identification
Event
Combined
Muon
Analysis
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Offline Reconstruction in Atlas
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
Message
Service
JobOptions
Service
Particle Prop.
Service
Other
Services
Converter
Converter
Converter
Application
Manager
Event Data
Service
Persistency
Service
Data
Files
Transient
Event Store
Algorithm
Algorithm
Algorithm
Detec. Data
Service
Transient
Detector
Store
Persistency
Service
Data
Files
Histogram
Service
Transient
Histogram
Store
Persistency
Service
Data
Files
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Offline Reconstruction in Atlas
Software organization inside Athena
The detector description, the
even structure and the
implementation of recostruction
algorithms are separated
Packages should be made of
many indipendent Athena topalgorithms
Algorithm
1
Transient objects are passed
via the Transient Data Store
DataObj
Algorithm
3
Algs
1
Algs
2
Event
Algs
3
DataObj
DataObj
Algorithm
2
Algorithms are only coupled
through the data
DataObj
DataObj
DataObj
T
D
S
Algorithms and data objects
should be placed in different
packages
Algorithmic packages depend
on data, not viceversa
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Muon Reconstruction
 At every interaction the signals from each subdetector 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 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
Reconstruction 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
 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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MOORE
Reconstruction
Strategy
 Searches for  regions of
activity
 From the RPC/TGC 
measurements “- Segments” are
created
rpc
 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
rpc
MDT
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MOORE 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.
MDT mutilayer
 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.
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Architecture and Design
MooAlgs
RPC/TGC digits 
MooMakePhiSegments
PhiSegments
MooMakeRZSegments
MDT digits
MooMakeRoads
CrudeRZSegments
MooRoads
MooMakeiPatTracks
MooStatistics
MooiPatTracks
MooMakeNtuples
Ntuples


Each step is
driven by an
Athena topalgorithm
Transient objects
are passed via
TDS
Independent
algorithms, the
only coupling is
through the
transient objects
Results : less dependencies, code
is more maintainable, modular,
easier to develop new
reconstruction approaches
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Architecture and Design (2)
Packages
organization
MooEvent
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Efficiency vs PT
Single muon
studies
 (%)
A Muon track consists of
hits from at least 2 stations
and is successfully fitted.
PT (GeV)
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Efficiency vs , cot
N event
N event
PT = 20 GeV
(rad)
cot
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PT = 20 GeV
Pt resolut ion 20 gev
N event
N event
PT resolution
PT = 100 GeV
Pt resolution 100 gev
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Effect of dead material
pull = (Xgen – Xrec)/rec
Including in the fit the material crossed by the track (chambers + toroids) .
Get full information from AMDB (via “trmusc” from MUONBOX)
No material
Material included in the fit
N event
N event
NO Material Effects in the fit
1./PT Pull
20 GeV
 = 1.0
1./PT Pull
20 GeV
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Combined 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 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
Reconstruction/MuonIdentificaton
 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
 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
Muonspectrometer
inner layer
Energy loss
and multiple
scattering
calorimeter
Beam spot
 MS track is express at vertex

2
fit for matching of inner
detector and muon
spectrometer tracks parameters
 Final fit
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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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Association of the
reconstructed
muon Track (from Moore)
with
the Truth Event track
(from MC/simulation).
Calculation of the difference
between the energy at
the vertex and the energy
at the entrance of the Muon
Spectrometer
Energy loss
from truth
N event
A First approach
Single 
Pt = 20 Gev
GeV
Need to parametrise calorimeter effects
36
Correction
on PT
Moore
track at
MS
entrance
Single 
Pt = 20 Gev
Muid
track at
vertex
N event
MuonIdentification
: First Look
cot
pull at
vertex
Single 
Pt = 20 Gev
GeV
37
MuonIdentification
First Look
N event
N event
 - pull at vertex
Single +
Pt = 20 Gev
Single Pt = 20 Gev
38
MuonIdentification
First Look
Moore PT pull at the
entrance of muon
spectrometer
N event
N event
MuID PT pull at vertex
Single 
Pt = 20 Gev
Single 
Pt = 20 Gev
39
Plans 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
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Following Moore design …
Moore Tracks
MuidStandAlone
MuidComb
CaloObjects
Stand alone
MuidTracks
In.Det.Tracks
MuidNtuples
Combined
MuidTracks
Ntuples
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