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.
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=\frac{1}{N_{gen}}\sum_i^N{ \sum_{\gamma,\delta}{\rho_{\gamma\delta} \left| \sum_\alpha^n{ u_\alpha A_\alpha^{\gamma \delta}(x_i) } \right|^2 } }= \frac{1}{N_{gen}}\sum_i^N{ \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) } } } }
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] } } = \sum_{\gamma,\delta}{\rho_{\gamma\delta} \sum_{\alpha,\beta}^n{ u_\alpha u_\beta^* J_{\alpha \beta} } } }
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 } }
- 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 = \sum_{\gamma,\delta,\gamma',\delta'}{ \rho_{\gamma\delta}\rho_{\gamma'\delta'} \sum_{\alpha,\beta,\alpha',\beta'}^n{ u_\alpha u_\beta^* u_{\alpha'} u_{\beta'}^* \left[ \frac{1}{N_{gen}^2} \sum_i^N{ A_\alpha^{\gamma \delta}(x_i) A_\beta^{\gamma \delta *}(x_i) A_{\alpha'}^{\gamma' \delta'}(x_i) A_{\beta'}^{\gamma' \delta' *}(x_i) } \right] } } }
The relevant piece to pre-compute over the event set for error calculation is shown in brackets. Turning our attention now to the contribution to error on the production parameters u:
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_{fit}^2= \sum_{k,l}^n{ \sigma_{u_k}\sigma_{u_l} \frac{\partial}{\partial u_k}\left( \sum_{\gamma,\delta}{\rho_{\gamma\delta} \sum_{\alpha,\beta}^n{ u_\alpha u_\beta^* J_{\alpha\beta} } } \right) \frac{\partial}{\partial u_l}\left( \sum_{\gamma',\delta'}{\rho_{\gamma'\delta'} \sum_{\alpha',\beta'}^n{ u_{\alpha'} u_{\beta'}^* J_{\alpha'\beta'} } } \right) } } The product of σ terms in the summation are the error matrix derived from the fit.