Acceptance Correction
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Use g3leps with known SDM’s in the event generators to generate
ntuples.
Filtering the ntuples with standard analysis codes used for real data
and put into the weights of acceptance correction. Check whether
we are able to extract the input SDM’s.
Experience:
1. ML fitting framework is robust with the variations of starting
values of SDM’s in fitting if there is no acceptance effect.
2. ML fitting suffers less from the statistics than from the
acceptance.
3. MC tests only succeed for Egamma>2.2 and t>-0.2 where the
experiment acceptance is close to be of full coverage.
4. The weighting scheme in ML fitting is not effective as expected.
Need to be checked.