Estimation of Maxwellian averaged cross-sections with machine learning methods
ORAL
Abstract
Neutron capture cross-sections are crucial for many applications in nuclear physics, stellar astrophysics, and nuclear engineering are but acquiring the cross-section data can be expensive and difficult to perform. In particular, data for unstable (or even low natural abundance) nuclei are costly to obtain, limiting the amount of available experimental data. Thus, capture cross-section data for these nuclei are dependent on numeric estimates with varying degrees of success. In this work, machine learning methods are employed to develop a low order regression model for the temperature dependence of Maxwellian averaged cross-sections (MACS). We then use a neural network to learn the isotopic dependence of the regression model features from nuclei in a curated training set. The resulting model can be used to predict the temperature dependence of the MACS for all nuclei. Since the training set necessarily consists of data from stable nuclei, it is expected that the model works best near the valley of stability.
*The work at Brookhaven National Laboratory was sponsored by the Office of Nuclear Physics, Office of Science of the U.S. Department of Energy under Contract No. DE-SC0012704 with Brookhaven Science Associates, LLC. This research is an activity of the Intentional Forensics Venture project and was funded by the National Nuclear Security Administration, Defense Nuclear Nonproliferation Research and Development (NNSA DNN R&D). The views expressed in the article do not necessarily represent the views of the U.S. Department of Energy or the U.S. Government.
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Presenters
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Christian Stanley
- Indiana University, Indianapolis