Machine learning techniques improve discovery of excited nuclear levels in sulfur 38

A fixed number of protons and neutrons – the building blocks of nuclei – can rearrange themselves within a single nucleus. The products of this rearrangement include electromagnetic transitions (gamma rays). These transitions connect excited energy levels called quantum levels, and the pattern of these connections provides a unique “fingerprint” for each isotope.

Determining these fingerprints provides a sensitive test of scientists’ ability to describe one of the fundamental forces, the strong (nuclear) force that holds protons and neutrons together.

In the laboratory, scientists can initiate the movement of protons and neutrons by injecting excess energy through a nuclear reaction.

In an article published in Physical examination C, researchers successfully used this approach to study the sulfur-38 fingerprint. They also used machine learning and other cutting-edge tools to analyze the data.

The results provide new empirical insights into the “fingerprint” of quantum energy levels in the sulfur-38 nucleus. Comparisons with theoretical models can lead to important new insights. For example, one of the calculations highlighted the key role played by a particular nucleon orbital in the model’s ability to reproduce the fingerprints of sulfur 38 as well as those of neighboring nuclei.

The study is also significant for the first successful implementation of a specific machine learning-based approach to classify data. Scientists take this approach to address other experimental design challenges.

The researchers used a measurement that included machine learning (ML)-assisted analysis of the collected data to better determine the unique quantum energy levels (a “fingerprint” formed by the rearrangement of protons and neutrons) in the rich nucleus. in sulfur-38 neutrons. .

The results doubled the amount of empirical information on this particular fingerprint. They used a nuclear reaction involving the fusion of two nuclei, one from a heavy ion beam and the second from a target, to produce the isotope and introduce the energy needed to excite it to higher quantum levels.

The reaction and measurement exploited a heavy ion beam produced by the ATLAS facility (a Department of Energy user facility), a target produced by the Center for Accelerator and Target Science (CATS), decay detection electromagnetics (gamma rays) using the Gamma-Ray Energy Tracking Array (GRETINA) and the detection of nuclei produced using the Fragment Mass Analyzer (FMA).

Due to the complexity of the experimental parameters, which depended on the production yields of the sulfur 38 nuclei in the reaction and the optimal detection parameters, the research adapted and implemented ML techniques throughout the data reduction .

These techniques have provided significant improvements over other techniques. The ML framework itself consisted of a fully connected neural network that was trained under supervision to rank sulfur-38 nuclei against all other isotopes produced by the nuclear reaction.