Genetic algorithms as data-optimization tool in nuclear-reaction modeling for medical radionuclide production

by Luciano Canton (Istituto Nazionale di Fisica Nucleare)

Monday 18 Jul 2022, 11:00 → 13:00 Europe/Rome

C. Villi meeting room

Genetic algorithms define an heuristic optimization strategy inspired by Darwin’s theory of natural selection. The algorithm adopts the mechanisms of natural evolution where the fittest individuals of a population are selected for reproduction in order to produce offsprings for the next generation. Genetic algorithms represent a very flexible and powerful optimization strategy applicable in various fields such as operations research,  logistics, financial markets, manufacturing systems, engineering design, data clustering and mining,  image processing, neural networks, and medical science.

In the current research, these algorithms have  been used to optimize the input parameters of the nuclear reaction codes, with the goal to adequately fit the first-time measured cross sections by the REMIX collaboration, relevant for Sc47 production  from protons on enriched Titanium targets. This is important for the project because  the accurately fit cross sections allow to precisely analyze the investigated production routes in terms of yields, activities and purities, and even to assess how the contamination by undesired co-produced rdionuclides increase the dose imparted to the patient. All these information can be useful for selecting  the irradiation conditions that best meet the stringent criteria imposed by Pharmacopeia for clinical applications. In addition, the measurement and optimization process of cross sections relevant for radionuclide production improve the overall quality of nuclear data as well as the associated Reference Input Parameter Library for nuclear reaction codes. In this Talk I will discuss the basic ideas behind the use of genetic algorithms and the preliminary results in computing such optimization of cross section for the REMIX project.