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Tracker Simulations

Tracker Simulations. Michael Hebert UC Irvine January 12, 2005 RSVP Simulations & Backgrounds Review. Requirements – Resolution. The Tracker must provide precise momentum measurements to separate conversion signal events from muon decay in orbit (DIO) in a 1 T field.

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Tracker Simulations

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  1. Tracker Simulations Michael Hebert UC Irvine January 12, 2005 RSVP Simulations & Backgrounds Review

  2. Requirements – Resolution • The Tracker must provide precise momentum measurements to separate conversion signal events from muon decay in orbit (DIO) in a 1 T field. • The end point energy for DIO electrons coincides with the conversion signal, the end point spectrum falls as the 5th power of E, thus the level of DIO background in the signal region is sensitive to the resolution function; both the central (gaussian) region and high side tails • To obtain a signal / noise ratio of 20 for a nominal conversion signal of Rme = 10-16 requires an energy FWHM < 1 MeV The resolution is dominated by multiple scattering, thus material must be kept to a minimum Chamber r – f res. of 0.2 mm and z res. of 1.5 mm are adequate and achievable DIO Background convolved with detector response Acceptance wrt perfect detector for fixed 20:1 S/N GEANT conversion signal simulation Michael Hebert, UC Irvine Tracker Simulations

  3. Requirements – Rate Handling, Efficiency • A beam intensity of ~1011 stopped muons / sec implies that the detector must be capable of operating in a high rate environment. • This has driven the choice of 5 mm diameter straws and fast gas as the basic detector element. Note that the multiple scattering contribution to the resolution drives us to use thin walled (25 mm) straws • The majority of DIO electrons are at low pTrelative to signal events, thus the active elements of the detector are at large radius with respect to the stopping target • The detector must have reasonable efficiency to make the most of the sensitivity provided by the intense stopped muon beam. This drives a close packed straw array. • Reconstruction efficiency will be discussed in detail later in the talk Michael Hebert, UC Irvine Tracker Simulations

  4. Two Geometry Options • Two geometries under consideration (details follow) • “Longitudinal” (or simply “L”) Tracker with straws (nearly) parallel to the DS axis • “Transverse” (or “T”) Tracker with straws perpendicular to the DS axis • The nature of the event information and the engineering challenges associated with the construction are quite different for each arrangement • We have formed a “Tracking Chamber Advisory Committee” to evaluate both variants and recommend a choice to the MECO Executive Committee • We do not have an ETA on this decision yet, although (currently) this system is not on the MECO critical path Michael Hebert, UC Irvine Tracker Simulations

  5. Longitudinal Tracker Geometry: Octagon with eight vanes – rotated by a few mrad in plane Straws: 2.6 m length  5mm diameter, 25 mm thickness – 2800 total Three layers per plane, outer two resistive, inner conducting Pads: 30 cm  5mm wide cathode strips affixed to outer straws 16640 total pads Position Resolution: 0.2 mm (r,f)  1.5 mm (z) per hit is goal Readout Channels: ~20k Michael Hebert, UC Irvine Tracker Simulations

  6. Transverse Tracker Geometry: 18 Modules of three planes each, 30° rotation between successive planes Straws: 70 – 130 cm length  5mm diameter, 15 or 25 mm thickness 12960 total straws One layer per plane, all straws are conducting Position Resolution: 0.2 mm (x,y) Readout Channels: 13k 136 cm Michael Hebert, UC Irvine Tracker Simulations

  7. Tracker Studies • Existing Efforts • L Tracker prototypes at Houston and Osaka using different straw types • Front-end electronics work at Houston that is largely applicable to either the L or T variants • Simulations of performance in the presence of backgrounds at UCI (L Tracker) and NYU (T Tracker) • New Initiatives this year • T Tracker mechanical design at Syracuse University • Additional front-end electronics at UVA, LBL • Simulation software coordination via Berkeley • Trigger/DAQ/Back-end electronics design at BU, UMass, NYU Michael Hebert, UC Irvine Tracker Simulations

  8. L Tracker Test Beam Performance • Cathode Pad Resolution • Seamless Straw (4MW/sq) Resolution s = 0.4 mm at 60° • Spiral Straw (0.5MW/sq) Resolution s =1.1 mm at 60° • Design goal (s = 1.5 mm) is exceeded • Seamless Straw Anode Performance • Drift Distance Resolution s = 70 mm at 60° • Efficiency > 95% except near walls • Design goal (s = 0.2 mm) is exceeded Cathode Pad Resolution Spiral straw s(mm) Seamless straw Angle (deg.) Michael Hebert, UC Irvine Tracker Simulations

  9. Tracker Simulations • Effects Modeled • GEANT3 detailed geometries including straw walls, wires, gas, manifolds • Noise hits fromg, p, n, at the nominal rate and doubled • Resolution – 200 mm per straw for T tracker 200 mm (r-j) 1.5 mm (z) per cluster for L tracker, both with tails out to 2.5 mm at 1% • Delta ray effects on rates • Energy loss (Landau fluctuations) and Molieré scattering in the stopping target • Beginning to work with GEANT4 as well • Remaining to be done • Hit digitization in the L Tracker, straws and pads • L Tracker cluster recognition • B field effects on drift performance • Support structures Michael Hebert, UC Irvine Tracker Simulations

  10. L Tracker Detector Rates vs. Time Rate [MHz] Rate [kHz] Full time between proton pulses Detection time interval 25 20 15 10 5 0 800 700 600 500 400 300 200 100 0 m-capture protons beam electrons m- decay in flight 0 400 800 1200 700 900 1100 1300 time with respect to proton pulse arrival time [ns] Very high rate from beam electrons at short times – potential problems with chamber operation Protons from m capture are very heavily ionizing – potential problems with noise, crosstalk Rates are similar (not identical) for T tracker, both highly dependent on radial position near inner edge Michael Hebert, UC Irvine Tracker Simulations

  11. Tracker Reconstruction • Codes for reconstructing events in each geometry have been developed independently at different institutions, often with different underlying assumptions • We have begun integrating the two approaches, we are still in the early stages. See Yury’s talk for more on this effort. • All current tracker reconstruction efforts have been based solely upon drift distance or straw positions. Additional information remains to be exploited: • Both trackers: • Pulse height to remove protons – for “free” via the use of the Elefant readout chip. • L tracker • Pulse-height for Compton electron rejection • Pulse-height for proton rejection, r-f and z hit correlation • Comparison of r-f and z hit timing to match hits in the two views – good to a few ns • Straw number correlation with z position for valid helices, i.e. order of r-f hits in each module is correlated with z position due to tilt and to lesser extent to energy loss • Use of local (cluster) track angle and timing information in pattern recognition to reduce combinatoric search (improves speed) • Some of these implemented in background rejection already (e.g. local track angle to reduce mis-reconstruction background) Michael Hebert, UC Irvine Tracker Simulations

  12. L Tracker Pattern Recognition & Fitting • Look for circles in r-f hits • Match with z hits – all combinations are tried • Hit positions are currently taken from detector crossing points, but these will ultimately be obtained via cluster finding • Following application of pre-selection cuts (following slide) raw helix info is passed to a maximum likelihood fitter. Michael Hebert, UC Irvine Tracker Simulations

  13. L Tracker Event Selection • The energy of the GEANT primary electron at the entrance of the electron calorimeter is at least 75 MeV. • The agreement of the projected entry position at the electron calorimeter for the fitted track is within 20 cm of that given by the GEANT primary electron. • The maximum scattering angle at each detector element < 0.1 rad • 45 º < pitch angle < 60º • The fitting uncertainty is less than 600 MeV. • The total number of clusters is at least 6 • There are no missing clusters on the track where it intercepts any detector element. Michael Hebert, UC Irvine Tracker Simulations

  14. Mis-reconstruction • Mis-reconstructed tracks like this one are potentially a source of background from very low energy DIO electrons that reconstruct many s above the actual energy • Difficult to study, since orders of magnitude more low energy electrons • Technique of increasing rates, then reducing observed background by weighting factor of (rT/rMC)n, where rT and rMC are the true and MC rates, and n is the number of noise signals used in the background event • Ovne additional cut: event rejected if a lower momentum track is found real e- from - decay fake high p track Michael Hebert, UC Irvine Tracker Simulations

  15. L Tracker Resolution • 2  107 reconstructed conversion events • The intrinsic resolution is 180 keV Michael Hebert, UC Irvine Tracker Simulations

  16. L Tracker Signal vs. Background • DIO contribution is known spectrum convolved with “measured” signal resolution function • The acceptance is 19% for NBack / Nsignal = 0.05 • For B(m→e) =10-16 this amounts to 5 conversion events and 0.25 DIO background in the signal region Michael Hebert, UC Irvine Tracker Simulations

  17. Summary of L Tracker Results • Additional studies of straw material thickness, overall detector length, and minimum number of clusters on the candidate track Michael Hebert, UC Irvine Tracker Simulations

  18. T-Tracker Pattern Recognition • Single Orientation View • To reconstruct helix projection 4 hits are needed • Sample event • 4 different views (different colors) Michael Hebert, UC Irvine Tracker Simulations

  19. T Tracker Pattern Recognition – 1 • Sample Event • 29 Real hits • 260 Background hits • Compton e rejection • Three Tracker hits + Calorimeter centroid • Calorimeter s = 15 mm • Tracker s = 1.5 mm 1 2 3 Michael Hebert, UC Irvine Tracker Simulations

  20. T Tracker Pattern Recognition – 2 • The Same Sample Event • 29 Real hits • 260 Background hits • Four Tracker hit combinations • p resolution from helix fit to hit straw centers s = 0.45 MeV/c • Restore all Tracker hits, use helix info to add/drop hits 1 4 Michael Hebert, UC Irvine Tracker Simulations

  21. T Tracker Pattern Recognition with Drift Time • Deterministic Annealing Filter • Combinatorial Collapse Filter Average loss of real hits  0.8 / event (2.7%) Average Remaining bkgd hits  0.4 / event Overall background rejection  260 / 0.4 = 650 1 5 Michael Hebert, UC Irvine Tracker Simulations

  22. Deterministic Annealing Filter • Left and Right points are projected on straw center layer using fitted helix • Initial prob of points are assumed to be equal • Calculate point prob  Gauss(Xi, Mean, Vn) • Kalman filter runs on all layers taking weighted mean according to point prob • If combined hit prob < Threshold  hit is rejected • Combinatorial Collapse Filter (CCF) treats Left-Right problem keeping a set of best choices • The number of collapsed components = 8 • The maximum number of components = 32 • Drift radius > 0.25 mm Michael Hebert, UC Irvine Tracker Simulations

  23. T-Tracker Event Selection • Calorimeter Energy > 80 MeV • Pitch Angle > 45° • At least 18 hit straws included in on track after background hit subtraction • Track extends to at least 48.25 cm radius, i.e. 10 cm into the active region of the detector • Several Kalman filter internal consistency checks, including a cut of 2 < 70 to remove very poor fits Michael Hebert, UC Irvine Tracker Simulations

  24. T Tracker Resolution with Background • Nominal background and 25 µm • Delta-ray and straw inefficiency • Kalman filter reconstruction • Tracker Resolution  = 0.19 MeV/c • Plot is the difference between reconstructed input momentum and actual input momentum Average straw rate 550 kHz Straw eff. Overall acceptance 100% 19.2 % 97% 18.7 % Michael Hebert, UC Irvine Tracker Simulations

  25. T Tracker DIO Reconstruction with Background • 1M DIO events (> 100 MeV/c) were generated  20 times more than expected Fit by Norm x (Emax - E)5 • 3 event found in search range > 103.5 MeV Background  3 / 20 = 0.2 event Muon Conversion Signal  5.5 events expected for Br = 10-16 Background/Signal Ratio = 0.03 (Same conditions) Michael Hebert, UC Irvine Tracker Simulations

  26. Summary of T Tracker Results • Full pattern recognition and momentum reconstruction based on Kalman filter was performed • Results at nominal and doubled (in parenthesis) rates without delta-ray and straw inefficiency • Looking at the impact of using 25 mm thickness straw material vs. 15 mm… Michael Hebert, UC Irvine Tracker Simulations

  27. Summary of T Tracker Results • Results at nominal background rate without and with delta-ray and straw inefficiencies of 3% Michael Hebert, UC Irvine Tracker Simulations

  28. Summary • Simulations of both the L and T Trackers indicate that either design will provide the required precise track momentum measurement in our high rate environment. • Although not every detail has yet been implemented in the code, none of what remains to be done is expected to alter the preceding conclusion. • We have demonstrated the basic L Tracker operating principles via prototypes tested with cosmics and test beams • We are initiating similar studies for the T Tracker • Thus we believe that we have two approaches that meet the physics requirements. The prototyping efforts and some engineering design will allow us to choose which approach to implement. Michael Hebert, UC Irvine Tracker Simulations

  29. END Michael Hebert, UC Irvine Tracker Simulations

  30. Proton Contribution to Tracker Rates Protons are very heavily ionizing – up to 50 times minimum ionizing Protons below ~18 MeV absorbed in target Rate hitting tracker reduced by absorbers – further optimization possible Studies of response of chambers to low energy protons planned at TUNL at Duke University 105 103 10 all generated protons 100 10 1 protons hitting tracker 0 20 40 60 80 100 Proton energy [MeV] Michael Hebert, UC Irvine Tracker Simulations

  31. k Kalman Filter • State vector x • Measured vector m k • Multiple scattering and Energy losses Progressive procedure adding more and more planes Multi-helix trajectory Michael Hebert, UC Irvine Tracker Simulations

  32. Event Viewer • Sample event Michael Hebert, UC Irvine Tracker Simulations

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