Changes between Version 2 and Version 3 of AcceptanceTerm
 Timestamp:
 04/12/12 09:30:42 (7 years ago)
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AcceptanceTerm
v2 v3 3 3 === Definition === 4 4 5 In general, the probability that an event is accepted depends on the characteristics of the measured event, and not on the process that produced it. The measured probability density $\bar{P}(x,\alpha)$ can be related to the produced probability density $P(x,\alpha)$: %\[\bar{P}(x,\alpha)=Acc(x) P(x,\alpha)\]%where $ Acc(x)$ is the detector acceptance, which depends only on $ x $5 In general, the probability that an event is accepted depends on the characteristics of the measured event, and not on the process that produced it. The measured probability density $\bar{P}(x,\alpha)$ can be related to the produced probability density $P(x,\alpha)$: $\bar{P}(x,\alpha)=Acc(x) P(x,\alpha)$ where $ Acc(x)$ is the detector acceptance, which depends only on $ x $ 6 6 7 In the computation of the likelihood of the MatrixElement, this acceptance term induce the following term: %\[\int Acc(x) P(x,\alpha)dx\]%7 In the computation of the likelihood of the MatrixElement, this acceptance term induce the following term: $\int Acc(x) P(x,\alpha)dx$ 8 8 9 This could be estimated easily, by MC, as the number of accepted events on the number of generated events. %\[\frac{N_{accepted}}{N_{generated}}\]%9 This could be estimated easily, by MC, as the number of accepted events on the number of generated events. $\frac{N_{accepted}}{N_{generated}}$ 10 10 11 11 === How to compute Acceptance term with pythia/PGS. === … … 29 29 30 30 31