Transcript ppt

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
Explorer
http://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
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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:
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AliTOFhits2SDigits.C
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AliTOFanalyzeSDigitsV2.C
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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:
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AliTOFSDigits2Digits.C (only digitization, no
merging)
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AliTOFanalyzeDigits.C
Merging/Digitization (3)
UML diagram
CPU time estimation for sdigitization
and merging
Sdigitization
~3s/ event
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LEGENDA:
event=central
Hijing event in theta
range [45°,135°]
Digitization only
~1s/event
Reconstruction/PID QA and test
macros
Reconstruction
Class:
AliTOFReconstructioner
PID ('last step'
efficiency data
added)
Inherits from TTask
Class: AliTOFPID
Output: TNtuple object
(assignment of time of
flight to tracks)
Inherits from TTask
QA and test macros:
QA and test macros:
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AliTOFtestRecon.C
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AliTOFanalyzeMatchin
g.C
Output: TH1F objects
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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
Shaker
Unit [MeV/c*c]
Unit [MeV/c*c]
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Pions : s(m)~90
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Pions : s(m)~90
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Kaons: s(m)~56
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Kaons: s(m)~53
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Protons:s(m)~33
"
Protons:s(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
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Area=3s(y)*3s(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:
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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-)
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Use a straight line approximation, assume as velocity
the speed of the light -as in the gamma case - and
derive the 'time zero'
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No reconstruction needed at all