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548 pages (+700 figures) 50 minutes  11 pages/minute !

548 pages (+700 figures) 50 minutes  11 pages/minute !. Physics TDR Volume 1 Content. A detailed description of how data is reconstructed Documents final detector design and configuration Changes since Detector TDRs, new forward detectors,… Validates our detailed simulation

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548 pages (+700 figures) 50 minutes  11 pages/minute !

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  1. 548 pages (+700 figures) 50 minutes  11 pages/minute !

  2. Physics TDR Volume 1 Content • A detailed description of how data is reconstructed • Documents final detector design and configuration • Changes since Detector TDRs, new forward detectors,… • Validates our detailed simulation • Testbeam comparisons and tunings • Refines our operation procedures since the Detector TDRs and implements them using Monte Carlo data • Calibration, alignment, synchronization, monitoring • Describes our physics object reconstruction tools • The software tools we expect to use on LHC data! • Presents our reconstruction performance based on full detailed simulations (GEANT4, pile-up, alignment uncertainties,…) • Including techniques to measure the reconstruction performance from data • Describes our software foundation • All the tools to do the above CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  3. Content, Cont’d • In other words, it encompasses those things we should be doing to prepare for LHC data • We continue where we left off in the DAQ/HLT TDR Vol.2 and further develop our software reconstruction algorithms for offline analysis (as well as further improve upon some HLT algorithms) • We complete the picture painted by our Computing TDR with a detailed description of our software foundation, including calibration & alignment • A comprehensive set of changes were introduced to the software framework and services in the last year to prepare for data-taking • Vol.1 of the Physics TDR forms the basis to study the physics capability of CMS in Vol.2 (due in May) CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  4. PTDR Volume 1 Chapters • Introduction • Software Components • Muon • ECAL • HCAL • Tracker • Forward Detectors • Luminosity Measurement • Muon id and reconstruction • Electron/photon id and reconstruction • Jets and MET reconstruction • Heavy-flavour tagging (b/tau) Software: framework, EDM, simulation, calibration, DQM, reconstruction, visualization Detector performance and operations Physics reconstruction tools CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  5. A walk through the chapters…

  6. Ch.2: Software

  7. Software: Framework Architecture Design • Architecture of the CMS framework: • Provide a clear data Model •  Use Data only accessed through the Event • Allow for modular testing and explicit scheduling •  Use a component architecture • Track provenance of data •  Data Files Keep track of how their data was produced • Browse- able POOL/ROOT File from Production •  Keep Event Data as simple as possible CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  8. Simulation • Event generator framework interfaces multiple packages • including the Genser distribution provided by LCG-AA • Detector Simulation (OSCAR) uses GEANT4 since end of 2003 (new development since DAQ TDR) • ~108 events fully simulated up to now since mid-2004 • Only 1/106 crashes in latest productions • Digitization tested and tuned with Testbeam (see later…) Detector Simulation Generation Digitization Hit collection. Hit object with timing, position, energy loss info. Based on GEANT4 MC truth collection include info from particle gun or physics generator about vertices and particles. Stored in HepMC format. Digi Collection Digi objects which include realistic modeling of electronic signal. CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  9. Fast Simulation (FAMOS) Transverse shower profile for 50 GeV  Energy deposition in a 5x5 crystal matrix for 50 GeV electrons Agreement to 1–3% Factor 4–10 speed-up Histograms = full GEANT4 simulation Red markers = GFLASH shower parameterization Longitudinal shower profile for 50 GeV  CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  10. Software: Data Tiers • CMS plans to implement a hierarchy of Data Tiers • Raw Data: as from the Detector • RECO: contains the objects created by Reconstruction • Full Event: contains the previous RAW+RECO • AOD: a subset of the previous, sufficient for a large majority of “standard” physics analyses • Contains tracks, vertices etc and in general enough info to (for example) apply a different b-tagging • Can contain very partial hit level information RAW CMS: ~1.5 MB/event RECO CMS: ~ 250 kB/event AOD CMS: ~ 50 kB/event CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  11. Calibration & Alignment: Online-to-Offline IP5 Offline, T0 Offline, Grid Online Configuration DB Condition DB Equip.Man. DB Offline Condition DB for GRID Offline Condition DB for GRID Offline Condition DB for GRID Offline Condition DB for GRID CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  12. “Alarm” “System ok” Software: Data Quality Monitoring “DQM” Monitoring producers, collectors, servers Monitoring information • Clear separation of creation of • monitoring information from • collection, processing • Used from all CMS detectors Monitoring consumer: Client Tools Database • “Comparison-to-reference” • Collation of similar objects • Configuration • Reference objects • Historic plots • Etc… CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  13. “Monitoring producer” (and collector): CERN • “Monitoring consumers” (clients): • one at CERN, one at Florida (US) • You are looking at web browser • running in Florida office Live cosmic test data for endcap muon detector Software: DQM Web Interface CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  14. Software: Visualization Cosmic muons at SX5 Drift Tube HCAL Can also visualize geometry, simulation CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  15. Ch.3: Muon

  16. Muon Test Beams and Simulation Validation Cathode Strip Chambers: Drift Tube and CSC test beams from 2004 simulated in OSCAR and visualized in IGUANA m MB3 MB1 CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  17. DT Test Beam Results “Digi” . Tbeam data - simulation TDC recorded Drift Time Track segments s= 1.3 mrad s= 190 mm Rec.hits CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  18. CSC Test Beam Results with HCAL p/m Puncthrough in ME1/1 EM secondaries CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  19. Muon Detector Track-Based Alignment & Mass Reconstruction Misalignment estimator for a single MB1 chamber from track extrapolation (internally to muon system); 2 days of data taking at L=1034cm-2s-1 (similar approach as with Tracker) Reconstructed Z invariant mass spectrum; 1 muon in endcap & 1 muon in barrel-endcap overlap region; 1 day of data taking at 2 1033; QCD bacgkround and pile-up effects included Can determine momentum scale CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  20. Muon Reconstruction (Momentum Res.) • Stand-alone Muon Reconstruction • Muon system only • Global MuonReconstruction • Muon system + silicon tracker CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  21. Effect of Misalignment Reconstructed invariant mass of a 1 TeV Z’ +- First data taking  = 1mm Long Term scenario  = 200µm Perfect detector CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  22. Muon Isolation • 3 different algorithms applicable at HLT and offline: • calorimetry • pixel tracks • tracks Background efficiency CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  23. Muon Identification • Quantify the “muon compatibility” of any reconstructed track. • Useful to increase efficiency in reconstructing high multiplicity muon topologies (HZZ4µ) and in soft-lepton b-tags,… • Propagate silicon track outwards into calorimeters and muon systems. • Search in cone around extrapolated track for associated energy deposits in ECAL, HCAL, and HO (or hits and segments in the muon detectors). • Define probabilities corresponding to the compatibility of track with muon hypothesis based on these quantities. Muon system Calorimeter Single pT = 10 GeV/c muons and pions CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  24. Ch.4,10: ECAL, e/

  25. Energy (GeV) ECAL test beam results • Supermodule in H4 beam in 2004 • Demonstrate expected performance Central impact 20 x 20 mm2 impact, centred on corner CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  26.  = 4.2% ECAL calibration • Intercalibration of the ECAL is a technical challenge • Start from ~10% RMS variation of crystal light yield (barrel); ~25% RMS variation of VPT output (endcap) • Reduce this <4% before LHC startup with: • Test beam calibration of a few supermodules • Use of lab. measurements • Cosmic muon data • Very rapidly improve this to <2%: • Phi-independence of energy deposition Phi symmetry Lab. measurements CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  27. ECAL calibration [2] • In-situ inter-calibration based on E/p measurements using abundant single isolated electrons (We, Zee, …) • Main issue is event selection • Need well measured tracks/clusters, small bremsstrahlung in material • Detailed studies of stability of results versus selection cuts Similar results for the endcap Should provide H mass resolution <1% in barrel Intercalibration using We CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  28. Beyond Intercalibration… • We use “algorithmic corrections” to obtain an energy measurement • In particular, correction of supercluster energy measurement, used for electrons and converted photons • At present these corrections are obtained from MC-truth • We are starting to look at how these things can be controlled with data • In Vol.1: first attempt at a complete overview of calibration issues Use of electrons in Zee Use of photons in Z CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  29. Electron Reconstruction • Developments focused on low pT (non-triggering) e– • Driven by needs of such physics channels as HZZ*4e • Main improvements: • Tuning of supercluster algorithm parameters • Tuning of pixel-matching cuts • Use of (new/modified) GSF tracking algorithm • Provides new variables, like Pin/Pout (indicates brem.) • Classification of electrons • for corrections, error estimation, and selection cut tuning Different classes Occurence in  CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  30. Electron Reconstruction, continued • Use of both E and P to obtain best 4-vector estimate CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  31. Barrel Efficiency Efficiency Rejection Rejection Photon Isolation • Study of a wide range of variables: • Track isolation variables • ECAL isolation variables • HCAL isolation variables • Very high rejection factors needed for H • Example shown used neural net for combining parameters NN •  conversion tool available, and parameterization of energy uncertainty CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  32. Ch.5,11: HCAL, Jet/MET

  33. HCAL Detector Description • Final geometry, trigger and readout description updated with respect to the HCAL TDR CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  34. HCAL Calibration Monitoring • Overview of Calibration Systems and Database Information used for Event Reconstruction HCAL calib data and connections to DBs Source response in HB, ~5% precision on calib. CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  35. HCAL Performance from Test Beam • Test Beam Analysis Results (1996, 2002, 2004) • Longitudinal Shower Shape and Magnetic Field Brightening • Response and Resolution • Low Energy e/pi Response • Response Compensation 5 GeV pions Raw Corrected ECAL vs. HCAL 15 GeV pions Shower Depth CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  36. Tower Thresholds and Jet/MET Reco. • Projective Calorimeter Towers are constructed from ECAL+HCAL with multiple threshold schemes • The performance of the jet/MET reconstruction was studied to understand the trade-off between jet reconstruction efficiency and fake rate, as well as noise contributions to the response • Acceptable threshold dialing is equally important for online DAQ bandwidth control (zero suppression schemes) Thresholds per calorimeter subsystem in tower building, Noise-In-Cone(NIC), Jet-Energy-Loss(JEL) and MET Comparison of Jet Reco Efficiency for 4 Threshold Schemes (adjusted to be equal at 20 GeV) Efficiency Turn-on is Resolution Dominated CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  37. Jet Algorithms and Resolutions • Several Algorithms implemented and studied • Iterative Cone, MidPointCone (Split/Merge), Inclusive-kT ET -Resolution HBHEHF φ-Resolution Calo only Central Jets Calo+Tracking ET -Resolution CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  38. MET Reconstruction • Performance compared with UA1 and CDF • Linkage established between low-energy minbias events and hard QCD dijet events going from 30 GeV up to 4 TeV QCD Minbias Hard QCD Eventswith p/u QCD Resolution Tails Stochastic term ~ 0.6–0.7 CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  39. Jet Energy Scale Calibration • Full Chain of Jet Calibration Techniques studied • Gamma+Jet, Dijet Balancing, Dijet W Mass from Top Events, … Dijet W Top Events Dijet Balancing 5%  3% Overall JES Uncertainty CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  40. Ch.6,12: Tracker, b/

  41. Tracker Alignment with MILLEPEDE Algorithm • Original Millepede method solves matrix eqn. A x = B, by inverting huge matrix A.This can only be done for < 12000 alignment parameters. • New Millepede method instead minimises |A x – B|. Is expected to work for our 100000 alignment parameters. • Both successfully aligned ~12% of Tracker Modules using 2 million Z + - events.Results identical, but new method 1500 times faster ! CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  42. Track reconstruction efficiency Kalman Filter based Track Reconstruction Material effects important (GSF approach might be a possibility…) Reconstruction in HI collisions also efficient  µ CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  43. Resolutions (Pt)/Pt (%) 100 GeV/c 1 GeV/c (Z0)(m) (d0)(m) 1 GeV/c 1 GeV/c 100 GeV/c 100 GeV/c CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  44. Track Reconstruction without Pixel Detector • Track reconstruction now possible using Strip Tracker alone ! • (n.b. no Pixel Detector present in 2007 pilot run). • The non-Pixel tracking is only 30% slower than normal. Only 4% of its tracks are fake and it gives better efficiency at high rapidity. CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  45. Vertex Reconstruction • Several vertex fits available: least-squares, robust, kinematic + beam-spot find. • Primary vertex fits possible using pixel hits only for HLT etc. • Primary Vertex resolution for BsJ/  improved by “Adaptive” (robust) vertex fit compared to normal least-squares one. CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  46. B tagging • Three main categories • Impact parameter based • Trk counting • Probability • Secondary vertex based • Lepton tag based • Muon • Electron • Two main application areas: • Offline • High Level Trigger • b-tagging efficiency can be measured using top decays c g uds CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  47. Tau tagging • Several tagging techniques that exploit the isolation of the decay products • Charged tracks • Neutral pions • Mass tag • Secondary vertex • Impact parameter • Developed for HLT, and being refined for offline • Make use of Zttm-jet vs. Zmm for determining the efficiency from data 3-D flight path, cut on impact parameter significance CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  48. Ch.7: Forward Detectors

  49. Forward physics program • Diffractive hard scattering (jets, J/,W, Z, bottom, top production) • structure of diffraction and diffractive phenomena (gaps) • Central exclusive production (Higgs?) • low-x measurements and QCD studies • Two photon physics • New phenomena in the forward region (centauros, DCCs, anomalous WW prod.) • pA and AA centrality measurement • Understanding of cosmic ray phenomena ZDC ZDC CMS Physics TDR Vol.1 Darin Acosta, University of Florida

  50. Forward Detectors •  CASTOR Calorimeter • ZDC Calorimeter (at 140 m) ZDC location Tungsten/ quartz fibres 1 TeV neutron shower in ZDC CASTOR 5.25< <6.5 Tungsten/ quartz plates Energy resolution Common runs planned with TOTEM: Roman Pots and T1/T2 CMS Physics TDR Vol.1 Darin Acosta, University of Florida

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