Measurement of Proton-Induced Reactions on Lanthanum from 55–200 MeV by Stacked-Foil Activation

Jonathan T. Morrell, Ellen M. O'Brien, Michael Skulski,Andrew S. Voyles, Dmitri G. Medvedev,Veronika Mocko,Lee A. Bernstein, C. Etienne Vermeulen

arxiv(2024)

引用 0|浏览0
暂无评分
摘要
Cerium-134 is an isotope desired for applications as a chemical analogue to the promising therapeutic radionuclide ^225Ac, for use in bio-distribution assays as an in vivo generator of the short-lived positron-emitting isotope ^134La. In the 50-100 MeV energy range relevant to the production of ^134Ce by means of high-energy proton bombardment of lanthanum, existing cross section data are discrepant and have gaps at important energies. To address these deficiencies, a series of 17 ^139La foils (99.919 abundance) were irradiated in two stacked-target experiments: one at the LANL's Isotope Production Facility with an incident proton energy of 100 MeV, and a second at BNL's Brookhaven Linac Isotope Producer with an incident proton energy of 200 MeV - a complete energy range spanning approximately 55-200 MeV. Cross sections are reported for 30 products of ^139La(p,x) reactions (representing up to 55 24 residual products measured in the ^natCu and ^natTi foils that were used as proton flux monitors. The measured production cross sections for ^139La reactions were compared to literature data as well as default calculations from the nuclear reaction modeling codes TALYS, EMPIRE and ALICE, as well as the TENDL-2023 library. The default calculations typically exhibited poor predictive capability, due to the complexity of multiple interacting physics models in this energy range, and deficiencies in preequilibrium reaction modeling. Building upon previous efforts to evaluate proton-induced reactions in this energy range, a parameter adjustment procedure was performed upon the optical model and the two-component exciton model using the TALYS-2.0 code. This resulted in an improvement in ^139La(p,x) cross sections for applications including isotope production, over default predictions.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要