weighting events properly in multiple-sample
Dear MG-Team:
Consider the simple problem,
I want to model a pt distribution of a given final particle (i.e it behaves, as many other observables, 1/pT) and I want the larger pt bins to have a statiscal error as small as lower pt bins have (so, I want the full distribution to have a similar statistical error in the full pt range). I had done this kind of study using pythia, in that case I cut on a few space paramente (let say transfer P_T in the parton interaction, pT_hard) and generate a few samples of events (10<PT_hard<50, pT_hard>50, PT_hard>100) not having the same number of events each. The issue is that in Pythia I had three final global parameters of the simulation: x-section of each sample (sigma), the tried parton configurations (ntrials) and the successes (the amplirtude of the sample I wanted, the nevents in the MG run_card). Then observable distribution for each sample needed to be weigthed before summation. In pythia's case the weight factor
sigma/ntrials
in such a way the histograms look smooth and independent of the successes asked por each sample. And this the point, in the results I get in MG simulation from unweightedevents root files the ntrials is never stored, just the success (even the weight in the tree is the average value of the x-section of succeses). So when I tried to add up al my samples in the final analysis I get the typical bumps in the distributions at the cuts I set if I cut on a given final P_T, which tells me I am making the weighting process wrong.
This is a quetsion actually for the ExRootAnalysis part but it spread from parton simulation to expermiental observables. The issue is the following:
1- How to properly weight observable distributions when adding diffrent samples of a given reaction?
I know the question looks simple but I can not figure out how to get it work properly with MG or at least find the suited explanation for weighting process in MG. Probably I am missing something .
any help is welcome and thanks in advance ,
arian
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