# Changes between Version 2 and Version 3 of AcceptanceTerm

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Timestamp:
04/12/12 09:30:42 (8 years ago)
Comment:

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 v2 === Definition === 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 $x$ 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 $x$ In the computation of the likelihood of the MatrixElement, this acceptance term induce the following term: %$\int Acc(x) P(x,\alpha)dx$% In the computation of the likelihood of the MatrixElement, this acceptance term induce the following term: $\int Acc(x) P(x,\alpha)dx$ This could be estimated easily, by MC, as the number of accepted events on the number of generated events. %$\frac{N_{accepted}}{N_{generated}}$% This could be estimated easily, by MC, as the number of accepted events on the number of generated events. $\frac{N_{accepted}}{N_{generated}}$ === How to compute Acceptance term with pythia/PGS. ===