26 Jan, MEG Software Status Framework for MEG MC, Unification of LargePrototype/beam test, Schedule and DC reconstruction MEG Software Group

26 Jan, Framework for MEG MC

26 Jan, Framework for MEG MC(1) Unified framework with double option: –Tokyo or Pisa code, Framework based on: –Fortran 77, –CERNLIB, –ZEBRA, –GEANT3, Distributed via CVS.

26 Jan, Framework for MEG MC(2) Tree structure & skeleton … ready; Materials & tracking medium … merged; Event generator … stand alone & built-in; DC, TC, Magnet and B field … merged; ZEBRA structure … in progress; Scintillation photon tracking … in progress.

26 Jan, To be done: MEG MC Merge Liq. Xe Geometry; Test of reduced number of PMT in Liq. Xe; Digitization for all the sub detectors; Full reconstruction program; Offline database… ODB? Separate database (e.g.PostgreSQL, MySQL, etc.)?

26 Jan, LargePrototype/beam test

26 Jan, LargePrototype/beam test unification(1) MC Merge into the new framework … just finished. –Common output format for DATA/MC; –Scintillation photon tracking in Liq. Xe … Full ray tracing or geometrical tracking; –Reflection on the PMT quartz window … Fresnel or total reflection; –Scintillation light spectrum … Monochromatic, Gaussian, Basov et.al, etc. –Rayleigh scattering/absorption in Liq. Xe.

26 Jan, LargePrototype/beam test unification(2) Analyzer –NaI calibration/gain correction … merged; –NaI vertex/energy reconstruction … stand alone program, merging into analyzer being in progress.

26 Jan, To be done: LargePrototype/beam test NaI response simulation; LiH target and Vessel simulation; phase space simulation for  0 ; Offline database … ODB? Separate database (e.g. PostgreSQL, MySQL, etc.)?

26 Jan, Schedule/man power Will be discussed in the Software meeting and reported in the review meeting.

26 Jan, DC reconstruction Track Dictionary –Efficiency –Resolution L-R ambiguity solution

26 Jan, Several tracks with similar kinematics producing a single hit pattern Hit pattern Single and unique string (i.e. a dictionary key) Average (over the set) track parameters The dictionary concept Several tracks with similar kinematics producing a single hit pattern Several tracks with similar kinematics producing a single hit pattern Several tracks with similar kinematics producing a single hit pattern

26 Jan, The track dictionary is a ordered list of records: Key (hit pattern)  average track parameters + rms The track dictionary exploits the “digital” response of the spectrometer NO Tdrift used NO z measurements used yet

26 Jan, MC sample used to build the dictionary: Positrons from Michel decay; Unpolarized muons; Generator level cuts:  0.08 < |cosθ| < 0.35;  -60° < φ < 60°.

26 Jan, The population of the patterns is not uniform: 40% has 1 entry 43% has 2 ÷ 10 entries 13% has 11 ÷ 50 entries 4% more than 50 entries Number of events in a dictionary record generated events  patterns; efficiency = 95%

26 Jan, Momentum components for events in the dictionary Event by event distributions Average in each Dictionary record Track first turn has hits in at least three sectors Px / MeV Py / MeV Pz / MeV LEFT RIGHT

26 Jan, The comparison of the distributions of an average parameter in the dictionary with the actual parameter distribution shows: Px and Py have similar shapes; Pz a hit pattern in the spectrometer cannot tell the sign of Pz; the shape of the distribution of |Pz| is not well reproduced  poor |Pz| resolution of the dictionary.

p MC - σ Generate a sample of independent events For tracks in the dictionary acceptance (Nsectors > 2) find the dictionary key compare; Px with (key); normalize to RMS vertex X vertex Y vertex Z Px Py Pz What is the dictionary “resolution” for all parameters ?

26 Jan, To be done: dictionary Optimize stats. given by 1 – eff. ~ and by looking at RMS vs. stats. (intrinsic resolution of method); Add noise hits; Add inefficiency of Drift Chamber; Add drift time; Superimpose tracks.

20 Starting from digit ID and drift time, in each sector we have 4 possible solution (4 tangent segments) Digitization of the MC hit from x,y,z to: number of sector (1-17), number of chamber (1-2) number of wire (1-9) D.C.A(digit) smearing of 200  m  Tdrift (const V drift ) Z(digit) smearing of 300  m First reconstruction step drift circle LEFT – RIGHT AMBIGUITY SOLUTION

26 Jan, 2004 P Q T The assumption: track ~ circle with centre in C if PT and QT are straight segments tangent to C and intersecting in point T, then α = α´ The strategy: select the right tangents in two consecutive sectors by choosing the pair giving the minimum  ´ Intrinsic limitations: non uniform B implies that tracks are not exactly circles drift distance resolution ‘ C  ’’

26 Jan, The plot shows the distribution of   for 1000 tracks All possible combinations (23097) Exact combinations (3778) We need to define a cut on  which allows to keep high efficiency for correct left-right choices and to reject wrong combinations  rad rejects 57% of the incorrect solutions

26 Jan, efficiency With Δα = 0.24 rad we reach 90% of total efficiency in L – R solution. By definition, the total efficiency comes from two terms: -Tracks where the L – R ambigurity is solved in each sector (60%); -Tracks where the L – R ambigurity isn’t solved only in 1 sector (30%)  cut Efficiency vs 

26 Jan, To be done: L-R Ambiguity solving improve efficiency, evaluate timing; study the effect of resolution varying with the impact parameter; use the “calibrated” digits, (i.e. x,y.z as estimated after left-right ambiguity resolution) as starting points for F. Cei’s algorithm estimating track parameters.

A long term plan once a fit algorithm is defined Get dictionary output –if the hit pattern corresponds to a key –when/if the resolution is appropriate for the fit (save computing time) go to the track fit Solve left-right ambiguity –if the track is not found in the dictionary –if the hit pattern gives ambiguous track parameters (high combinatorial calculations only when needed) go to the track fit else