1. 1 Muon Reconstruction in the ATLAS experiment Michela Biglietti Dottorato in Fisica Fondamentale e Applicata, XVI ciclo Università di Napoli “Federico II”

2. 2 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, 10 9 events/s (most soft hadronic interactions) Energy per proton7 TeV Bunch spacing25 ns Bunch size 15  m  12 cm Protons per bunch10 11 Bunches per ring2835 Beam lifetime10 hours Design luminosity10 34 cm -2 s -1  Currently under construction in the LEP tunnel  scheduled to start in the 2007  4 experiments : Atlas, CMS, LHCb, Alice W  (E/m) 4 R -1

3. 3 Physics @ LHC  Total p-p cross-section  80 mb  10 9 events/s  Most are large distance, soft collisions  QCD background  S/B very low (exe:  (H m=150Gev )/  (jet pt=700Gev ) ~10 -5 )  Pile up  Hard interactions overlapped with ~ 25 soft collisions  Need of good trigger system and fast detector response

4. 4 The LHC physics programme  Factory of all SM and new particles with masses in the TeV range  SM Higgs boson search  Exp limit (LEP): m H >113.5 Gev/c 2  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 = 10 33 cm -2 sec -1 ) b quark identification is not hidden by pile-up  LHCb  New physics

5. 5 SM Higgs boson search  Low mass region (m H <130 GeV)  H  , H  bb  Intermediate mass region (130 GeV < m H < 2 m Z )  H  WW ( * ), H  ZZ*  High mass region (m H > 2 m Z )  H  WW, H  ZZ, H  tt The channels experimentally most promising are those with leptons in final state. H  ZZ  4 l “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 g t H H q q W,Z

6. 6 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)  10 8 electronic channels

7. 7 Atlas 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 P T trigger capability  Efficient tracking at high luminosity for lepton- momentum measurements, for b quark tagging, reconstruction of B decay at lower luminosity

8. 8 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

9. 9 The Muon 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

10. 10 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

11. 11 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

12. 12 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

13. 13 The Atlas Collaboration 1700 members from 144 institutions and 33 countries

14. 14 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  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

15. 15 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

16. 16 Offline Reconstruction in Atlas Data flow Tracking Calorimetry Muon Tracks Em cluster Muon Calo Jets … Combined Muon Analysis Raw digits Detector element E/  identification Event MC truth & simulation Atlas Sim. and rec. algorithms dataObject Detector descriptor

17. 17 Offline Reconstruction in Atlas Converter Algorithm Event Data Service Persistency Service Data Files Algorithm Transient Event Store Detec. Data Service Persistency Service Data Files Transient Detector Store Message Service JobOptions Service Particle Prop. Service Other Services Histogram Service Persistency Service Data Files Transient Histogram Store Application Manager Converter 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

18. 18 Offline 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 Algorithm 1 Algorithm 2 Algorithm 3 DataObj TDSTDS Algs 2 Event Algs 1 Algs 3 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

19. 19 Muon 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 a 0, z 0, , cot , ± 1/P T  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

20. 20 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)

21. 21 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)

22. 22 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 ~10 7 events)  cavern “background events”  10 5 H  4 , A/H  2   10 6 Z  for calibration  ~10 7 events  Productions to be done in Roma, Napoli, Lecce

23. 23 MOORE Reconstruction Strategy  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 MDT

24. 24 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.  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

25. 25 Architecture and Design MooMakePhiSegments RPC/TGC digits PhiSegments MooMakeRZSegments MDT digits MooMakeRoads CrudeRZSegments MooMakeiPatTracks MooRoads MooiPatTracks MooMakeNtuples Ntuples MooAlgs MooStatistics 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 : less dependencies, code is more maintainable, modular, easier to develop new reconstruction approaches

26. 26 Architecture and Design (2) MooEvent Packages organization

27. 27 Efficiency vs P T Single muon studies P T (GeV)  (%) A Muon track consists of hits from at least 2 stations and is successfully fitted.

28. 28 Efficiency vs , cot  P T = 20 GeV  (rad) N event cot 

29. 29 P T resolution Pt resolut ion 20 gevPt resolution 100 gev P T = 20 GeV P T = 100 GeV N event

30. 30 Effect 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./P T Pull 20 GeV NO Material Effects in the fit Material included in the fit 1./P T Pull 20 GeV pull = (X gen – X rec )/  rec  = 1.0 N event

31. 31 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)

32. 32 Combined Muons  p T > ~100 GeV: profit from greatly superior Muon Spectrometer momentum precision  ~20 < p T < ~100 GeV:  combination more precise than Inner Detector or Muon Spectrometer alone  p T no parameter improvement over indet measurement  Reduce decay-in-flight background.

33. 33 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)

34. 34 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

35. 35 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

36. 36 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 at the vertex and the energy at the entrance of the Muon Spectrometer Energy loss from truth GeV Single  Pt = 20 Gev N event Need to parametrise calorimeter effects

37. 37 MuonIdentification : First Look Single  Pt = 20 Gev cot  pull at vertex N event GeV Correction on P T Muid track at vertex Moore track at MS entrance Single  Pt = 20 Gev

38. 38 MuonIdentification First Look Single  + Pt = 20 Gev  - pull at vertex N event Single  - Pt = 20 Gev

39. 39 MuonIdentification First Look Moore P T pull at the entrance of muon spectrometer MuID P T pull at vertex Single  Pt = 20 Gev Single  Pt = 20 Gev N event

40. 40 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

41. 41 Following Moore design … MuidStandAlone Moore Tracks CaloObjects MuidComb Stand alone MuidTracks MuidNtuples Ntuples In.Det.Tracks Combined MuidTracks