r(t) relations from inclusive mdt tubes drift time distributions -- update --
DESCRIPTION
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, - PowerPoint PPT PresentationTRANSCRIPT
R(t) RelationsR(t) Relations
from inclusive MDT tubes from inclusive MDT tubes drift time distributionsdrift time distributions
-- update ---- update --
M.M. BaroneBarone
Software and AnalSoftware and Analyyssiis Meetings MeetingATLAS/FrascatiATLAS/Frascati
LNF -- October 18, 2004LNF -- October 18, 2004
LNF, 18/20/2004 2
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 + t = R(t)
The Method
R
muon track
• R is the distance of minimum approach of the muon track to the sensing wire
Watch out!Incorrect R-t association for events wheredelta-rays are produced
• RTRUE(t) = “correct” relation between t and R ( no delta-rays )• Use of RTRUE(t) to determine the distribution
that hypothetically would correspond to the inclusive time distribution
LNF, 18/20/2004 3
- The method performs very well: precision < 10 microns using two MonteCarlo samples with different gas mixture
Results from MonteCarlo (Garfield)
Similar excellent performances expected even with real data, provided that the appropriate distribution is used
RTRUE (t) – R(t)
LNF, 18/20/2004 4
- Are delta-rays properly simulated by the Garfield program?
Comparison of the delta-ray content:GarfieldGarfield vs X5 dataX5 data (with external tracker)
% of delta-rays
Garfield does not simulate delta-ray production in the tube walls
Use of X5 data to Use of X5 data to determine the determine the
distributiondistribution
Delta rays
LNF, 18/20/2004 5
- distribution : from X5
- inclusive time distribution t : from H8
- R(t) = (t)
…………. Our usual procedure, but…Our usual procedure, but…
Procedure
R (mm)
LNF, 18/20/2004 6
• Garfield shows that the method is very sensitive to variations of the t0
• Before:Before: t0 and tmax as the starting and final point of the t distribution => very critical and affecting the achievable precision Time window: t0 t0 ≤≤ t ≤ tmax t ≤ tmax
• Now: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)(t0 – 20ns) ≤ t ≤ (tmax+40ns)
Integration method
LNF, 18/20/2004 7
• R(t) as input for the tracking program (Athena)• our R(t)• R(t) obtained with Calib program
Tracking with Athena
LNF, 18/20/2004 8
# of segments <=2
our R(t)
R(t) Calib
R (mm)
resid
ual (
mm
)
PRELIMINAR
LNF, 18/20/2004 9
- 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 investigatedto 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
Conclusions
LNF, 18/20/2004 10
Supporting plotsSupporting 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
- distribution integral (X5)
- inclusive t distribution integral (H8)
LNF, 18/20/2004 14
# of segments – BIL(2 multilayers)
10000 events
216
LNF, 18/20/2004 15
R(t) Calib
R(mm) vs t (ns)t (ns)
R (mm)
LNF, 18/20/2004 16
residual (mm)residual (mm) vs R(mm)
residual (mm) vs R (mm)
# of segments <=2
LNF, 18/20/2004 17
our R(t)
R(mm) vs t (ns)t (ns)
R (mm)
LNF, 18/20/2004 18
residual (mm) residual (mm) vs R(mm)
residual (mm) vs R (mm)
# of segments <=2