Error propagation in Amplitude Analysis
The following is a review of error propagation needed in amplitude analysis.
Consider a Monte-Carlo (MC) integral over the intensities of N detected events out of Ngen generated.
where we take n coherent amplitudes and allow incoherent sums indexed by γ, δ to allow for applications like spin-density matrices (ρ). When amplitude analysis fits contain amplitudes with not free parameters, it is convenient to rearrange the summations above, to pre-compute the sum over the intensities of the events:
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle I=\sum_{\gamma,\delta}{\rho_{\gamma\delta} \sum_{\alpha,\beta}^n{ u_\alpha u_\beta^* \left[ \frac{1}{N_{gen}}\sum_i^N{ A_\alpha^{\gamma \delta}(x_i) A_\beta^{\gamma \delta *}(x_i) } \right] } } }
storing the term in square brackets, a matrix indexed by α,β, for contractions with varying free production parameters u in the course of a fit.
When considering the uncertainty on the overall integral, both the errors on u parameters and those from the finite MC set of events will contribute. A single detected event (i) can be viewed as one sample of a Poisson process, having therefore an uncertainty of σi=1. An integral over such events is then a weighted sum of such samples, having therefore a contribution to the variance: Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \sigma_{MC}^2= \sum_i^N{ \sigma_i^2 \left| \frac{1}{N_{gen}} \sum_{\gamma,\delta}{\rho_{\gamma\delta} \sum_{\alpha,\beta}^n{ u_\alpha u_\beta^* A_\alpha^{\gamma \delta}(x_i) A_\beta^{\gamma \delta *}(x_i) } } \right|^2 } }