Deep Learning for Neutron Lifetime Measurement

The precise value of neutron lifetime τ_n to an uncertainty less than 1 s plays a critical role in the Standard Model of nuclear and particle physics, as well as cosmology. The UCNτ experiment at Los Alamos National Lab uses a magneto-gravitational trap to store ultracold neutrons (UCNs) and an in-situ neutron detector to count the number of UCNs that have not decayed after prescribed holding times. The lifetime, τ_n ,is extracted from blind, independent analyses of the experimental data by either pairing adjacent short and long holding time runs or by performing a global likelihood fit of all runs. While systematic uncertainties are accounted for as corrections to the estimated lifetime, the understanding of the underlying UCN distribution and evolution in phase space is desired. The UCNτ datasets show a time dependence in the neutron counts for a given holding period due to changes such as the quality of the solid deuterium crystal used to produce the UCN and the spallation neutron source intensity. We present results from using long short term memory (LSTM) neural networks for time-dependent experimental neutron lifetime data analysis, with a goal of better understanding the variation and evolution of the number of UCN initially loaded into the UCNtau trap. This work opens doors to physics-informed machine learning to enhance UCN lifetime experiments.

LA-UR-23-26634