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