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LT Separation Meeting - 26/08/22
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Stephen Kay
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Garth Huber
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Vijay Kumar
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Ali Usman
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Muhammad Junaid
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Garth has a set of slides - https://redmine.jlab.org/attachments/download/1593/LT_sep_iterations.pdf
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See also Bill's slides - https://redmine.jlab.org/attachments/download/1594/Kaon_LT_tutorial_18nov28.pdf
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Start from Bill's code - https://github.com/billlee77/omega_analysis
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- Prior to LT separation, need *final* normalised yields
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- Most of the work involved is actually in getting to this point!
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- If things are converging, iteration steps should be quick
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- This includes diamond cut already
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- One could analyse the variations across only the larger, high epsilon diamond
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- All efficincies included
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- LT
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- FADC DT
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- Cryotarget
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- Yield corrections
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- ALL tested for reliablity over a wide range and applied
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- All kinematic offsets determined and finalised
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- Need one thing kept constant between iterations
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- The normalised yields and distributions
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- Cross section varies across experimental acceptance
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- Need to choose a functional form that will reasonably account for this
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- Don't know this in advance
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- Make a choice, start iteration process with it
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- Can check previous analyses for guidance on forms to try
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- Can be different for each diamond plot
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- So long as it it well understood for each individual diamond
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- On diamond plot, t bins slice across diagonally
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- W and Q2 different for each t bin
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- Results quote different Q2/W value for each t bin
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- t min varies across the diamond
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- t min varies even within a t bin
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- In SIMC, replace physics_pion.f with physics_iterate.f
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- Need to make some good guesses of intial paramter values for your first iteration
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- Need some Q2 dependence, t dependence etc
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- Will likely have to go back and change these functions a lot
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- Each Q2/W should be done separately
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- Can't expect the procedure to work globally
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- *Keep notes organised!*
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- *Keep all output!*
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- Store each iteration in its own directory
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- Step 1 - SIMC distributions
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- Run SIMC for large #events
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- Generate spectrometer and physics variables using functional form and fitpar
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- Do this setting by setting for a given Q2/W diamond
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- E.g. Left/Centre/Right all different
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- Compare to data
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- Step 2 - Combine Left/Centre/Right settings at high and low epsilon for each W/Q2/t/phi/epsilon bin for data and MC
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- Get statistical errors for each bin
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- Get yield, error in yield for data and simulation
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- Calculate ratio and error in ratio for each bin
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- Process enough events to minimise SIMC statistical error!
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- Shouldn't be dominated by simulation statistical error!
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- Step 3 - Calculate average kinematics
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- Mean data values of W/Q2/Theta/epsilon for each t bin at high an low epsilon
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- Need this for data and MC
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- Values will differ between high and low epsilon slghtly
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- Will also change slightly as the model is iterated
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- Step 4a - Inspect and understand data closely
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- Examine variation on phi between data and MC within each t bin
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- Deviations between data/MC are usually indicated as wiggles in the ratio
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- Want R ~ 1 across a broad kinematic range
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- Some plots earlier on might look ok, only by examining the ratio very closely in individual bins can some differences be found
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- How things vary should guide what changes in the next iteration
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- E.g. Is it the interference terms that are off?
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- Step 4b - Even closer examination
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- Each t bin (for one epsilon), binned in theta
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- Interference terms depend upon theta
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- Interference terms vanish in parallel kinematics
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- Explicitly chose a functional form that makes this true
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- In lowest bin, should have minimal interference terms
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- Wiggles get larger as theta increases
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- Phi distributions for each t bin, subdivided into 8 theta bins
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- A lot of plots
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- More than you probably want to put in thesis
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- BUT a tech note/report is encouraged
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- Even here, you probably want to show only a few plots as an example
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- Step 5 - Calculate unseparated cross section
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- Using fitpar for the iteration, evaluate the model at *average* kinematics of the data for each t-bin
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- Calculate using formula and fit parameters, calculate the cross section at the average values of W/Q2/Theta and epsilon for each bin
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- Read over Blok PRC 78 (2008) 045202 carefully
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- Each t/phi bin at both high and low epsilon
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- The weight in SIMC is NOT the unseparated two fold cross section
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- It is the five fold cross section (d5sigma)
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- Fitting procedure iterated until the experimental cross section changed by less than a prescribed amount (~1%)
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- Step 6 - Fit Rosenbluth Eqn
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- Each t bin is fit separately
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- Fit result gives L, T, LT, TT cross sections for each t bin
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- Step 7 - Fit L, T, LT, TT values to get new fitpar
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- Compare t bins with each other
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- Fit with physics_iterate.f functions to give next iteration model parameters
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- Now, we're back at the top. Feed these back into step 1
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- Repeat steps 1-7 until separated cros sections are stable
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- *Don't re-run SIMC*
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- Recalculate weight for each event
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- Any theses older than Tanja's are likely using different procedures
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- Tanja's procedures were improved on earlier ones
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- Some slight modifications after this
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- Only Marco and Bill used Tanja's procedure
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- Evaluating if fit equations are ok
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- Procedure usually works ok, but for some kinematics, pi-/pi+ sigma wouldn't converge
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- Compare fitpar from different Q2/W and see if they were slowly varying
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- If not, could use their variation as a pointer to an alternative functional form variety to try
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- Things will behave in a consistent manner if the correct solution is found
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- General pointers
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- Read Blok paper carefully
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- Review Bill's slides
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- A single Q2/W iteration should only take 1-2 hours
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- Fitpar should converge in a few iterations to give 0.5<R<2
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- The main work is getting R to be acceptably flat
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