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R(t) Relations from inclusive MDT tubes drift time distributions -- update --

R(t) Relations from inclusive MDT tubes drift time distributions -- update --. M. Barone Software and Anal y s i s Meeting ATLAS/Frascati LNF -- October 18, 2004. The Method. Use of the inclusive drift time spectrum to determine the R(t) relation,

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R(t) Relations from inclusive MDT tubes drift time distributions -- update --

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  1. R(t) Relationsfrom inclusive MDT tubes drift time distributions-- update -- M.Barone Software and Analysis Meeting ATLAS/Frascati LNF -- October 18, 2004

  2. The Method Use of the inclusive drift time spectrum to determine the R(t) relation, by associating the R position of a track to the corresponding drift time R + t = R(t) • R is the distance of minimum approach of the • muon track to the sensing wire Watch out! Incorrect R-t association for events where delta-rays are produced R • RTRUE(t) = “correct” relation between t and R ( no delta-rays ) • Use of RTRUE(t) to determine the distributionthat hypothetically would correspond to the inclusive time distribution muon track LNF, 18/20/2004

  3. Results from MonteCarlo (Garfield) • The method performs very well: precision < 10 microns • using two MonteCarlo samples with different gas mixture RTRUE (t) – R(t) Similar excellent performances expected even with real data, provided that the appropriate  distribution is used LNF, 18/20/2004

  4. Delta rays • Are delta-rays properly simulated by the Garfield program? Comparison of the delta-ray content: Garfield vs X5 data (with external tracker) % of delta-rays Garfield does not simulate delta-ray productionin the tube walls Use of X5 data to determine the  distribution LNF, 18/20/2004

  5. Procedure •  distribution : • from X5 • inclusive time distribution t : • from H8 • R(t) =  (t) • …………. Our usual procedure, but… R (mm) LNF, 18/20/2004

  6. Integration method • Garfield shows that the method is very sensitive to • variations of the t0 • Before:t0 and tmax as the starting and final point of • the t distribution => very critical and affecting • the achievable precision • Time window:t0 ≤ t ≤ tmax • Now:t0 and tmax values from the FermiDirac fit • (bending points of the rising and falling edge resp.) • Time window:(t0 – 20ns) ≤ t ≤ (tmax+40ns) LNF, 18/20/2004

  7. Tracking with Athena • R(t) as input for the tracking program (Athena) • our R(t) • R(t) obtained with Calib program LNF, 18/20/2004

  8. # of segments <=2 residual (mm) PRELIMINAR our R(t) R(t) Calib R (mm) LNF, 18/20/2004

  9. Conclusions • The method performs very well if the  distribution used is appropriate for the sample to be analyzed • R(t) relation determined applying thedistribution from X5 data to the inclusive time spectrum from H8 2004 data: • tracking highlights remaining problems, especially in the region abs(R)~ 10mm => to be investigated • Waiting to be able to quantify the number of delta rays in the H8 data we could use the R(t) relation from Calib to determine the  distribution once for all LNF, 18/20/2004

  10. Supporting plots LNF, 18/20/2004

  11. ∆R = R(t+∆t) - R(t) ∆t = 1 ns LNF, 18/20/2004

  12. X5 data - RTRUE (t) LNF, 18/20/2004

  13. inclusive t distribution • integral (H8) •  distribution integral • (X5) LNF, 18/20/2004

  14. # of segments – BIL (2 multilayers) 10000 events 216 LNF, 18/20/2004

  15. t (ns) R(mm) vs t (ns) R (mm) R(t) Calib LNF, 18/20/2004

  16. residual (mm) vs R(mm) residual (mm) residual (mm) vs R (mm) # of segments <=2 LNF, 18/20/2004

  17. t (ns) R(mm) vs t (ns) R (mm) our R(t) LNF, 18/20/2004

  18. residual (mm) vs R(mm) residual (mm) residual (mm) vs R (mm) # of segments <=2 LNF, 18/20/2004

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