Error propagation in Amplitude Analysis

The following is a review of error propagation needed to compute the errors on the normalization integrals and the intensity sum that is based on them. Consider the estimator for the intensity for a given PWA solution, based on a sum over a Monte Carlo sample with Ngen phase space events generated and N reconstructed and passing all cuts. $$ I=\frac{1}{N_{gen}}\sum_i^N{ \sum_{\gamma,\delta}{\rho_{\gamma\delta} \sum_{\alpha,\beta}^n{ u_\alpha u_\beta^* A_\alpha^{\gamma}(x_i) A_\beta^{\delta *}(x_i) } } } $$

where the PWA sum is over n coherent amplitudes, and indices &gamma;, &delta; represent the spins of the external particles (incoming photon, incoming and outgoing nucleon), with &rho; representing their collective spin-density matrix. When amplitude analysis fits contain amplitudes with no free parameters, it is convenient to rearrange the summations above, to pre-compute the sum over the intensities of the events:

$$ I=\sum_{\gamma,\delta}{\rho_{\gamma\delta} \sum_{\alpha,\beta}^n{ u_\alpha u_\beta^* \left[ \frac{1}{N_{gen}}\sum_i^N{ A_\alpha^{\gamma}(x_i) A_\beta^{\delta *}(x_i) }     \right] } } = \sum_{\alpha,\beta}^n{ u_\alpha u_\beta^* \left[ \sum_{\gamma,\delta}{\rho_{\gamma\delta} \frac{1}{N_{gen}}\sum_i^N{ A_\alpha^{\gamma}(x_i) A_\beta^{\delta *}(x_i) }   }\right] } = \sum_{\alpha,\beta}^n{ u_\alpha u_\beta^* I_{\alpha\beta} } $$

storing the term in square brackets, a matrix indexed by &alpha;,&beta;, for contractions with varying free PWA parameters u in the course of a fit.

When considering the uncertainty on the overall integral, both the errors on the u parameters and those from the finite MC statistics will contribute. The part of the error on the intensity coming from the finite MC statistics is computed using the usual rules for error propagation. A subtle point easy to miss at this stage is that the errors on the I &alpha;&beta; are correlated because they are computed on the same MC sample. Therefore, while the I &alpha;&beta; is rank 2 in the partial wave index, its error is rank 4.

$$ \sigma_{MC}^2= \sum_i^N{ \left| \frac{1}{N_{gen}} \sum_{\gamma,\delta}{\rho_{\gamma\delta} \sum_{\alpha,\beta}^n{ u_\alpha u_\beta^* A_\alpha^{\gamma}(x_i) A_\beta^{\delta *}(x_i) } }  \right|^2 } $$

= \sum_{\alpha,\beta,\alpha',\beta'}^n{ u_\alpha u_\beta^* u_{\alpha'} u_{\beta'}^* \left[ \sum_{\gamma,\delta,\gamma',\delta'}{ \rho_{\gamma\delta}\rho_{\gamma'\delta'} \frac{1}{N_{gen}^2} \sum_i^N{ A_\alpha^{\gamma}(x_i) A_\beta^{\delta *}(x_i) A_{\alpha'}^{\gamma'}(x_i) A_{\beta'}^{\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:

$$ \sigma_{fit}^2= \left| \sum_k^n{ \sigma_{u_k} \frac{\partial}{\partial u_k}\left(   \sum_{\alpha,\beta}^n{u_\alpha u_\beta^* I_{\alpha\beta}}  \right) } \right|^2 $$

=\left( \sum_k^n{ \sigma_{u_k} \sum_{\gamma,\delta}{ \rho_{\gamma\delta}    \sum_{\alpha,\beta}^n{\delta_{k\alpha} u_\beta^* J^{\gamma\delta}_{\alpha\beta}}  } }\right) \left( \sum_{k'}^n{ \sigma^*_{u_{k'}} \sum_{\gamma',\delta'}{ \rho_{\gamma'\delta'}    \sum_{\alpha',\beta'}^n{\delta_{k'\alpha'} u_{\beta'} J^{\gamma'\delta'*}_{\alpha'\beta'}}  } }\right) $$

= \sum_{\gamma,\delta,\gamma',\delta'}{ \rho_{\gamma\delta} \rho_{\gamma'\delta'} \sum_{\alpha,\beta,\alpha',\beta'}^n{ \left(\sigma_{u_\alpha}\sigma^*_{u_{\alpha'}}\right) \left(u_\beta^* J^{\gamma\delta}_{\alpha\beta}\right) \left(u_{\beta'} J^{\gamma'\delta'*}_{\alpha'\beta'} \right) } } $$

= \sum_{\gamma,\delta,\gamma',\delta'}{ \rho_{\gamma\delta} \rho_{\gamma'\delta'} \sum_{\alpha,\alpha'}^n{ \left(\sigma_{u_\alpha}\sigma^*_{u_{\alpha'}}\right) G^{\gamma\delta*}_\alpha G_{\alpha'}^{\gamma\delta} } } $$

The product of &sigma; terms in the summation is represented by the error matrix derived from the fit. G was defined as

$$ G_\alpha^{\gamma\delta}=\sum_\beta{ u_\beta J_{\alpha\beta}^{\gamma\delta}} $$

The overall uncertainty in the integral I defined in the beginning comes out to:

$$ \sigma_I=\sqrt{\sigma^2_{MC} + \sigma^2_{fit}} $$