20.5 QC3 fit g20

20.5.1 kcot 1 par

\[ K_{df}^{iso}=\frac{P[0] M_0^2 }{E^2-M_r^2}+ P[2] \]

\[ \frac{k}{m} \cot \delta= \frac{1}{a_0m} \] The best fit:

\[\begin{gather} \chi^2/d.o.f.=1.01546 \\ P[0]=204.692\pm (23) \\ P[1]=9.12076\pm (0.0015) \\ P[2]=2491.55\pm (6.7e+02) \\ P[3]=-0.149458\pm (0.002) \\ \end{gather}\] {\[\begin{gather} C=\begin{pmatrix} 1& -1.77& 9.49& 3.95\\ -1.77& 1& 0.000297& -8.63e-05\\ 9.49& 0.000297& 1& -254\\ 3.95& -8.63e-05& -254& 1\\ \end{pmatrix} \end{gather}\]}

20.5.2 kcot 1 par kiso 2 par

\[ K_{df}^{iso}=\frac{P[0] M_0^2 }{E^2-M_r^2} \]

\[ \frac{k}{m} \cot \delta= \frac{1}{a_0m} \] The best fit:

\[\begin{gather} \chi^2/d.o.f.=1.34375 \\ P[0]=175.826\pm (19) \\ P[1]=9.12114\pm (0.00045) \\ P[2]=-0.145773\pm (0.0018) \\ \end{gather}\] {\[\begin{gather} C=\begin{pmatrix} 1& 4.79& 1.31\\ 4.79& 1& 0.000135\\ 1.31& 0.000135& 1\\ \end{pmatrix} \end{gather}\]}

20.5.3 kcot 1 par kiso 2 par and covariance

\[ K_{df}^{iso}=\frac{P[0] M_0^2 }{E^2-M_r^2} \]

\[ \frac{k}{m} \cot \delta= \frac{1}{a_0m} \] The best fit:

\[\begin{gather} \chi^2/d.o.f.=1.57684 \\ P[0]=178.79\pm (17) \\ P[1]=9.11996\pm (0.0013) \\ P[2]=-0.144965\pm (0.0017) \\ \end{gather}\] {\[\begin{gather} C=\begin{pmatrix} 1& -0.473& 5.8\\ -0.473& 1& 8.36e-05\\ 5.8& 8.36e-05& 1\\ \end{pmatrix} \end{gather}\]}

20.5.4 kcot 1 par kiso 3 par and covariance

\[ K_{df}^{iso}=\frac{P[0] M_0^2 }{E^2-M_r^2}+P[2] \]

\[ \frac{k}{m} \cot \delta= \frac{1}{a_0m} \] The best fit:

\[\begin{gather} \chi^2/d.o.f.=1.24315 \\ P[0]=210.135\pm (23) \\ P[1]=9.12063\pm (0.0013) \\ P[2]=2227.37\pm (6e+02) \\ P[3]=-0.148414\pm (0.0016) \\ \end{gather}\] {\[\begin{gather} C=\begin{pmatrix} 1& 0.0606& 0.629& -0.0076\\ 0.0606& 1& 0.188& -0.0878\\ 0.629& 0.188& 1& -0.254\\ -0.0076& -0.0878& -0.254& 1\\ \end{pmatrix} \end{gather}\]}