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TOF, Status of the Code
F. Pierella, Bologna University and INFN
TOF Offline Group
ALICE Offline Week, June 2002
For participants in virtual rooms
URL for this presentation
Explorerhttp://www.bo.infn.it/alice/pierella/Doc/June02E.html
Netscape
http://www.bo.infn.it/alice/pierella/Doc/June02.html
Contents
Activity during the past 2 month:
Geometry
SDigitization
Merging/Digitization
CPU time estimation for Sdigitization and Merging
Reconstruction/PID QA and test macros
Probabilities for PID
Propagation from TPC to TOF using Kalman
AliTOFV2
AliTOFV3
Conclusions & outlook on 'time zero'
Geometry
Review on TOF geometry
Some volume overlaps has been fixed;
review on materials.
Cooling tubes and FE card has been introduced in the GEANT description of the TOF detector
Geometry (2)
Proof
Sdigitization (1)
UML diagram: ClassDef(AliTOFSDigitizer,2)
Sdigitization (2)
Summary
Class: AliTOFSDigitizer
Inherits from TTask
Output: TClonesArray of AliTOFSDigit The AliTOF fSDigits data member is transient
QA and test macros: AliTOFhits2SDigits.C AliTOFanalyzeSDigitsV2.C AliTOFanalyzeSDigits.C (to be used if fSDigits is
persistent)
Sdigitization (3)
TDC distribution (1 TDC bin= 50ps) as an example (25 central HIJING events in the theta range [45°-135°])
Merging/Digitization (1)
Algorithm description:
Sdigits from different files (e.g. for BKG and SGN) are merged (i.e. 'summed' if necessary, using the AliTOFHitMap) and collected in a tmp array;
from this array they are converted into TOF digits.
No noise added (for the time being) due to the negligeable expected noise level of 1Hz/pad:
taking into account a readout window of 500ns (and the total number of readout channels) the expected noise value is 0.08
Merging/Digitization (2)
Summary:
Class: AliTOFDigitizer
Inherits from AliDigitizer
Output: TClonesArray of AliTOFdigit
QA and test macros: AliTOFSDigits2Digits.C (only digitization, no
merging) AliTOFanalyzeDigits.C
Merging/Digitization (3)
UML diagram
CPU time estimation for sdigitization and merging
Sdigitization
~3s/ event LEGENDA:
event=central Hijing event in theta range [45°,135°]
Digitization only
~1s/event
Reconstruction/PID QA and test macros
ReconstructionClass: AliTOFReconstructioner
Inherits from TTask
Output: TNtuple object (assignment of time of flight to tracks)
QA and test macros:
AliTOFtestRecon.C AliTOFanalyzeMatchin
g.C
PID ('last step' efficiency data added)
Class: AliTOFPID
Inherits from TTask
Output: TH1F objects
QA and test macros:
AliTOFtestPID
Probabilities for PID (1)
Definition of probability from TOF-PID (Hijing)
Probabilities for PID (2)
the same but for Shaker (different 'model' -> different amplitudes) (how to avoid model dependency in defining probability?)
Probabilities for PID (3) (Sigmas comparison in Shaker & Hijing)
Hijing
Unit [MeV/c*c] Pions : (m)~90 Kaons: (m)~56 Protons:(m)~33
Shaker
Unit [MeV/c*c] Pions : (m)~90 Kaons: (m)~53 Protons:(m)~32
Probability to be pion
1.5GeV/c<p<2.GeV/c (Pb-Pb Hijing)
Probability to be pion (2)
1.5GeV/c<p<2.GeV/c (pp, PYTHIA)
Probability to be kaon
1.5GeV/c<p<2.GeV/c (Pb-Pb) (fit problem)
Probability to be kaon (2)
1.5GeV/c<p<2.GeV/c (pp)
Probability to be proton
1.5GeV/c<p<2.GeV/c (fit problem)
Probabilities for PID (2)
... in different momentum range
Propagation from TPC to TOF using Kalman
This exercise started before the TRD tracking was ready
We plan to use the backpropagation from TRD to TOF detector (very short distance compared to the previous TPC->TOF)
Preliminary results for the area spread by the track propagation (it results less than the statistical method -see TOF TDR Addendum Chapter 5, Section 5.5-)
Propagation from TPC to TOF using Kalman (2)
From TPC reconstructed tracks (and back propagated in TPC!)
I step : propagation through the outer wall of the TPC (radiation length from TPC TDR)
II step: propagation in air (for the time being, applied to events with no TRD)
III step: propagation through the outer wall of the TOF
IV step: derive the area spread by the track Area=3(y)*3(z)
Back Propagation in TPC
Area after back propagation in TPC
Propagation from TPC to TOF using Kalman (4)
Area after propagation to TOF(~4 TOF pads)
AliTOFT0V2 (1)
Algorithm description:
Combinatorial method as described in TOF TDR Addendum (Chapter 5, Section 5.7)
BUT, now applied to reconstructed tracks (i.e. including also the tracks with a wrong time of flight assignment) - with p>1GeV/c, to have the larger matching efficiency -
In any case (preliminary!) a better resolution than 50ps can be reached
And, (no surprise!) by using the library (not an interpreted code as in the past) the computing time is reduced by a factor 10.
AliTOFT0V2 (2)
Preliminary result for time zero (B=0.4T)
AliTOFT0V2 (3)
Same as previous slide but at B=0.2T
AliTOFT0V3 (1)
Implementation of the following idea: "Assume for all (high statistics) reconstructed tracks the pion mass and derive the time zero by meaning the zero time of all tracks"
Preliminary result: the zero time mean distribution is narrow (~60ps) BUT it is not centered around zero (as it as to be in MC) due to the systematic wrong mass assumption (need for truncated mean analysis).
AliTOFT0V3 (2)
Preliminary result for 100 HIJING events
AliTOFT0V3 (3)
Preliminary result for 100 SHAKER events
Conclusions & Outlook on 'time zero'
Several ideas for 'time zero' determination are under investigation
the most interesting and fascinating one is the following:
Take the earliest signals on TOF (they are mainly due to electrons from prompt gamma conversion in the TOF volume - to be verified and how to tag them?-> may be using TOF signals not matched with the TRD reconstructed tracks-)
Use a straight line approximation, assume as velocity the speed of the light -as in the gamma case - and derive the 'time zero'
No reconstruction needed at all