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



Newswise — Fixed number of protons And neutrons – the constituent elements of nuclei – can reorganize 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 this study, researchers successfully used this approach to study the sulfur-38 fingerprint. They also used machine learning and other cutting-edge tools to analyze data.

The impact

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 atoms. nuclei. The study is also important for the first successful implementation of a specific project. machine learningapproach based on data classification. Scientists take this approach to address other experimental design challenges.


The researchers used a measure that included a machine learning (ML) assisted in the 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 neutron-rich sulfur-38 nucleus. 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 necessary to excite it to higher quantum levels. The reaction and measurement exploited a heavy ion beam produced by the ATLAS installation (a Department of Energy user facility), a target produced by the Center for Accelerator and Target Science (CATS)the detection of electromagnetic decays (gamma rays) using the Gamma Energy Tracking Network (GRETINA)and the detection of nuclei produced using the Fragment Mass Analyzer (FMA).

Due to the complexity of the experimental parameters – which oscillated between sulfur production yields 38 nuclei in the response and optimal parameters for detection – the research adapted and implemented ML techniques throughout 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 classify sulfur-38. nuclei against all others isotopes produced by the nuclear reaction.


This work was supported by the Department of Energy’s Office of Science, the Office of Nuclear Physics, and by the National Research Council of Canada.

Link to journal: Physical Exam C, June 2023


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