Information and Statistics in Nuclear Experiment and Theory (ISNET)

Journal article, 2024

As with all empirical sciences, nuclear physics operates in the virtuous cycle of the scientific method: observations inspire theoretical models; models lead to new predictions; predictions are tested in experiments; experiments lead to new observations; and so on. Evaluating what we are inferring, and how certain we are of it, is key to this process.
These requirements, and a general interest in applying novel statistical, mathematical, and computational techniques, led to the formation of a dedicated research community entitled “Information and Statistics in Nuclear Experiment and Theory (ISNET)” (https://isnet-series.github.io/), which now includes more than 300 members. While the community’s interests lean toward nuclear theory, the unifying theme for this group is the inference of knowledge from data.
Input from beyond nuclear research has been critical to the success of the ISNET workshops. The most important contributions have come from statisticians and applied mathematicians, many of whom hail from the uncertainty quantification (UQ) community.