1. Yet another approach to the ToF-based PID at PANDA
A.Kiselev, Yu.Naryshkin
PANDA Collaboration Meeting
Stockholm,

2. Layout of the talk Motivation of this work Suggested formalism
Monte-Carlo results
Discussion

3. ToF detectors at PANDA Forward Wall Barrel ToF (?) Side Wall
Continuous beam , no “start” counters  tstart unknown!

4. Algorithms considered so far
Three main procedures:
Reconstruction from one identified particle
Minimization mass residuals under start time variations
Mass hypothesis  find time offset
 The following 3 slides: Stefano Spataro, Sep’2008

5. Start time reconstruction: the 1-st way
identify one particle in the event
(assume its mass)
calculate its time-of-flight
(from momentum and path length)
calculate START time
of the reaction
recalculate tof of the
other particles
in the event

6. Start time reconstruction: the 2nd way
New algorithm was implemented (Alexander Schmah)
variation of start time tstart
calculate mass from tstart, momentum, tof, path length
find the closest realistic mass
construct residual for all the particles
Find tstart that minimizes the Q functional

7. Start time reconstruction: the 3rd way
use PID informations from other detectors or
mass hypothesis
per each particle calculate the time-of-flight
calculate the mean offset or
not stand-alone
Improvements for the global PID
…Work in progress

8. The “4-th way”: goals Quantify PID decision as much as possible
Provide statistically significant formulation
Have a better interface to tracking package
Be flexible in terms of input:
do not require ANY external PID info per default
be able to use such information, if available
Allow flexible output:
be able either to provide its own PID decision …
… or, alternatively, give input for the “global” PID

9. The “4-th way”: logic
consider all N tracks in a given event at once, that’s clear
assume each track can be pion/kaon/proton
evaluate all 3N {m1,…,mN} mass
configurations separately, one by one
obtain their c2 weights
(and tS offsets)
reject (reweight) some,
using external PID input
compare various mass configurations
obtain the
significance estimate
select the “most probable” one(s)

10. Formalism (simplified version)
how to evaluate a given {m1,…,mN} mass configuration?
Y (tS0) is distributed as c2 with N-1 degrees of freedom for “correct” mass configurations
Tends to have higher c2 values for the “wrong” ones

11. Standalone PID evaluation
calculate “weight” for each {m1,…,mN} configuration:
define “probability” of j-th track to be say a pion as
use Monte-Carlo to check selection efficiency &
proton contamination (in this report: PYTHIA
@10GeV/c, forward spectrometer tracks only)

12. Proton-pion separation
Pions, <p>=2.15 GeV/c
Protons, <p>=3.25 GeV/c
NB: fluxes are not accounted here!

13. Proton-pion separation, cont’d
 Consider modest (100ps) ToF resolution
Pions
Protons

14. Kaon separation <p>=2.7 GeV/c
NB: again, fluxes are not accounted here!

15. Formalism (advanced version)
Extra requirements to the tracking package:
append {tS} to the “standard” Kalman filter {x,y,sx,sy,1/p} parameter set
do track fitting for pion/kaon/proton hypotheses separately
provide {p,l,tS}i parameter set with cov.matrix on
per-track (and per-mass-hypothesis) basis
This allows to extend the suggested formalism in a way, that ToF-based PID 1) is not biased by unknown tS offsets during the 1-st tracking pass, 2) gives the most complete and statistically meaningful set of information to the “global” PANDA PID code

16. Formalism (advanced, in detail)
Kalman
filter c2
Track->event
matching
ToF
measurement
extended
parameter
set
 requires further studies!

17. Summary
The (relatively) new formalism to handle PANDA ToF-based PID problem is suggested
It allows to implement a statistically solid interface between tracking code, ToF measurements and the “global” PID