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ATLAS reconstruction software

ATLAS reconstruction software. David Rousseau- LAL/Orsay. ATLAS detector. Muon spectrometer. Tile calo. Lar had calo. Lar em calo. Si tracker. Straw tracker. H  gg no pile-up. H  gg high luminosity ( L=10^34) 23 interactions per bunch crossing 1000 charged tracks in tracker acceptance.

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ATLAS reconstruction software

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  1. ATLAS reconstruction software David Rousseau- LAL/Orsay

  2. ATLAS detector Muon spectrometer Tile calo Lar had calo Lar em calo Si tracker Straw tracker David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  3. Hggnopile-up David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  4. Hgghigh luminosity (L=10^34)23 interactions per bunch crossing1000 charged tracks in tracker acceptance David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  5. Tracking • 3-layers pixel Si detector (middle one missing at startup) • 4 layers of stereo strip Si detectors • Straw tracker (typically 30 straws on track) • More details in Alessia Tricomi’s talk David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  6. Impact parameter resolution 1/pT resolution David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  7. B-jet tagging • Impact-parameter of tracks combined in likelihood-ratio • Both transverse and z impact parameter used • Severe requirements on track quality (e.g innermost pixel cluster inambiguous) David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  8. WH event H bb Wl nl M(H)=120 GeV/c2 David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  9. u jet rejection @ eb=60% More material: IP resolution degradation More fake high IP tracks David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  10. More non-b tracks More fake high IP tracks B-track narrow jet - Impact parameter resolution of b-tracks degraded by multiple scattering David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  11. ttH event H bb tt  W(l nl )bW(qq)b M(H)=120 GeV/c2 Same b tagging performance (for given pseudo-rapidity and Pt)   factorization hypothesis holds David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  12. b tagging in Heavy Ion collision • Heavy Ion physics was not even considered when Atlas was designed • ~10.000 tracks in tracker acceptance (10 times more than high luminosity pile-up) • But track density comparable to density in high Pt jets…so • with little extra work on tracking algorithm, tracks can be found • b tagging even doable: • Ru=35 @ eb=50% , 11 @ eb=60% David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  13. E/g identification Electron bremsstrahlung: need special tracking • High pT (>7GeV): • Find e.m cluster (sliding window) • E.m shower shape cuts • Track finding • 0 track: photon (typical jet rejection 2000) • 1 track: E/p matching + Transition Radiation hits counting: electron (typical jet rejection 100000) Low pT electron (b tagging, J/y): • Start from the track and search energy deposition in e.m. calorimeter David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  14. Soft electron ID • Tagging variables pi fraction of E in 3rd sampling fraction of E in 1st sampling e shower isolation energy weightedwidth diff between shower and impact position Et(calo)/pt transverse impactparameter # of TR hits David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  15. Soft electron ID • Electron in b jets Hbb (mH=120 GeV) without noise Pion rejection with noise Efficiency David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  16. E/g reconstruction • E.m. clusters reconstructed with sliding window algorithm • Rectangle clusters used • Robust against • Electronic noise • Pile-up • Underlying event • Material effects • Calibration/linearity • Typically 3 eta cells x 5 phi cells • Cell granularity 0.025 pseudo rapidity/radian • Larger in phi to accomodate B field David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  17. Em cluster reconstruction E measured vs g conversion radius * Conversions can be reconstructed David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  18. g pointing LHC: luminous region sz~5cm David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  19. Mass resolution Hgg Primary vertex from tracks Primary vertex from calo pointing s=1.18 GeV s=1.31 GeV  1 conv  mH (GeV) mH (GeV) Primary vertex finding: not always possible with low Pt(H) and high lumi No pile-up, no el. noise David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  20. Intermezzo… • ATLAS reconstruction has evolved from feedback on detector design to evaluation of detector performance and physics reach and recently: • foresee treatment of real Atlas data • migration fortran to C++ completed • keep on improving or new algorithms • Use Athena/Gaudi (with LHCb) flexible framework: • Separation between Data and Algorithms • Run-time configuration and dynamic library loading • Athena also used for MC generation, Geant4 simulation (coming), high level trigger, fast simulation, user analysis • Spring 2003 : Data Challenge 1, several millions event simulated (G3) and reconstructed world-wide (tens Terabytes of data) David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  21. Flow example Raw ByteStream LArCell Calo Clustering BS decoding Missing ET RawChannels First data reduction TileCell Read Out Driver emulation Detailed Digitization Fast Digi Simulation MC Hits David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  22. Work model • Circa 1500 C++ classes in 300 packages maintained/developed by 100 people (only a few CERN based) • Structured CVS repository with one directory per detector specific software (Muon, Larg calorimeter…), and one per activity (Reconstruction, DetectorDescription…). Each directory managed by responsible person. • One major release every ~6 months • Developer release every three weeks (1-2 iterations allowed, some failure allowed) • Automatic nightly builds with latest tagged version of all software in view of the following release. David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  23. Jet reconstruction • Several algorithms explored • Cone jet (seeded/unseeded + split/merge) • kT jet • Weighting technique: • w= a + b/E + c ln(E) • Minimize resolution under constraint E=Etrue • Bins in pseudo-rapidity because effective length of calorimeters is non uniform (mainly 1/sin(q) but also passive material distribution) • « 2 » weights: one for e.m calo one for had calo • « 7 » weights : exploit calorimeters longitudinal segmentations David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  24. sE/E=100%/E 2% sE/E=80%/E 2% E/Etrue Jet relative energy resolution sE/E=65%/E 2% David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  25. Missing ET • Missing transverse energy essential tool for a wide range of physics • Computed from sum of cell transverse energy with optimised weights • Calorimeter acceptance very important: -5<pseudo-rapidity<5 David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  26. Missing ET resolution David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  27. Mass reco with missing ET No noise With noise Asymetric cut E>2sE Mass resolution (GeV) Mass resolution (GeV) Z  bbA   m(A)=450 GeV Z/A/H  1 2  X1 1 X2 2 Assume the two neutrinos are colinear to visible tau decays System solvable using Missing ET measurement David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  28. Tau identification • Especially important for At +t- and numerous supersymmetry channels • Hadronic tau decay characterised as a very narrow jet in particular: • use very fine granularity of first layer of Lar em calorimeter : Dh=0.003 • reconstructed track counting • Strong pT dependence: at higher pT, tau jet is narrower, background jet fatter • Correlated tagging variables combined in bins of jet pT (measured with weights optimised for tau’s ) • Depending of analysis, different working points in Rejection vs Efficiency plane David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  29. Tau Tagging variables Radius of em cluster Energy within 0.1<DR<0.2 Number of charged tracks t jet Number of em calo strip with energy above threshold Cluster eta width in em calo first layer Charged tracks total charge David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  30. Tau ID efficiency 0.5 At+t-l+nnh-n At+t-h+ nh- n t H+b t+(hn)b susy searches David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  31. Muon reconstruction • Identification of Region of Activity • 2.Reconstruction of local • straight track segments • Combination of three tracks segments • 4. Global fittaking into account multiple scattering and energy loss David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  32. Muon backtracking Backtracking from Muon System down to beam region through calorimeters taking into account E loss, multiple scattering and E loss fluctuations E loss from parametrization (from calo measurement possible but risk of pollution from nearby particle) Combination with inner detector track A.Farilla Gallipoli 06/03 S.Hassani Athens 05/03 David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  33. High pT efficiency pT(GeV) • Pattern recognition perturbed by possible em shower accompagning high pT muon • Under study: Mu System pattern recognition redone using Inner Detector measurement David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  34. Low pT efficiency pT(GeV) As pT decreases, the energy lost by the  in calorimeters becomes comparable to its energy, especially in the barrel Inner Station Segments Tracks • Under study :use of the Inner Station Segments could improve the identification efficiency up to 90% David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  35. m reconstruction Mass plots m(GeV) • J/→+- • Z→ + - • H → Z Z→+- + - m(GeV) David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

  36. Outlook • A complete spectrum of algorithms are available • Ongoing developments: • Cleaner modularization (toolbox) • Robustness (noisy/dead channels, misalignments) • Extend algorithms reach (e.g low pt, very high pt) • New algorithms • Next challenge: summer 2004, an ATLAS barrel wedge with all detectors in testbeam. Reconstruction and analysis using (almost) only atlas offline reconstruction. • Many thanks to my ATLAS colleagues, in particular for this talk: Nektarios Benekos, Frédéric Derue, Ambreesh Gupta, Michael Heldmann, Anna Kaczmarska, Jean-Francois Laporte, Jessica Leveque, Pavel Nevski, Frank Paige, Gilbert Poulard, Jean-Baptiste de Vivie, Silvia Resconi, Francesco Tartarelli, Monika Wielers David Rousseau, ATLAS reconstruction, HEP2003, Aachen, July 2003

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