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,111g}In 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.