Sensitivity of 107,109Ag(α, xn) cross sections to statistical-model inputs.

The α-induced reactions on silver isotopes leading to the production of the medically relevant radionuclides 108m,109g,110m,110g,111gIn have been systematically analyzed using the TALYS 2.0 code. A total of 192 combinations of nuclear reaction model parameters - comprising level-density models (LDM), α-optical model potentials (αOMP), and pre-equilibrium (PE) models - were evaluated through χ2 minimization against the available experimental data. The results reveal a pronounced channel-dependent sensitivity of the statistical-model ingredients. The 107Ag(α, 3n)108mIn and 109Ag(α, 3n)110gIn reactions are primarily governed by the level-density model. For the 107Ag(α, 2n)109gIn reaction, the sensitivities to the LDM and the pre-equilibrium mechanism are comparable, indicating that both model ingredients play nearly equal roles in describing the experimental data. In contrast, the agreement with the experimental data for the 107Ag(α, n)110mIn and 109Ag(α, 2n)111gIn reactions are most sensitive to the PE mechanism, with the α-optical model potential providing a secondary contribution and the LDM contributing only minimally. These findings demonstrate that the relative importance of the statistical-model ingredients varies significantly among the investigated reaction channels. Additionally, discrepancies between the present TALYS calculations and evaluated libraries such as TENDL-2023 are attributed to the absence of parameter optimization in the current approach. Overall, the present analysis indicates that the relative importance of the statistical-model ingredients depends on the reaction channel and that no single parameter combination provides the best description of all investigated reactions. The observed channel-dependent sensitivities provide useful guidance for selecting and evaluating TALYS model ingredients for the studied reaction channels. Future work incorporating additional experimental datasets and model-uncertainty quantification would further assess the robustness of the preferred parameter combinations.