PID study proposal
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Revision as of 09:02, 20 May 2020 by Sdobbs (Talk | contribs) (Created page with " === PID Studies === For each final state * Determine p/theta range of each final state particle * Compare PID variable distributions between data and MC ** First stage: 1D d...")
PID Studies
For each final state
- Determine p/theta range of each final state particle
- Compare PID variable distributions between data and MC
- First stage: 1D distributions integrated over all kinematics
- Optional: !D distributions from different p/theta bins
- Determine selection criteria which are 99% and 95% efficient
Systematic Studies
How to determine systematic uncertainty in efficiency due to PID cuts (assumes you have a final state with some clean peak: rho, phi, pi0, eta, eta'...):
- make tight PID cuts on all particles except the one you are testing the efficiency of - call this particle P
- make two sets of invariant mass distributions for whatever peak you have
- masses for events in which P satisfies the standard PID requirements
- masses for events in which P fails the standard PID requirements
- Fit each mass distribution to get the yields: N(pass) and N(fail)
- efficiency of the cut is N(pass) / [N(pass) + N(fail)]
- compare this efficiency between data and MC to determine how well it is modeled
Note this probably only works for the timing PID right now. Will need a separate set of files to test CDC dE/dx, but for now just look at the distributions. The biggest contributor here is probably the rate of events without enough hits to properly calculate dE/dx