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Dolinski, M.J. (ORCID:0000000277162126)

Publications and source records attributed to Dolinski, M.J. (ORCID:0000000277162126).

Ultra-pure nickel for structural components of low-radioactivity instruments

The next generation of nuclear and particle physics rare-event search experiments demand structural materials combining ultra-low levels of radioactive contamination with exceptional mechanical strength. Here, this study evaluates chemical vapor deposition (CVD) nickel as a low radioactive background candidate structural material for such applications. Manufacturer-supplied CVD Ni grown on aluminum substrates was assayed via inductively coupled plasma mass spectrometry (ICP-MS) employing isotope-dilution. These material assays produced measured bulk concentration of 232 Th, 238 U, and nat K at the levels of ~70 ppq, ≲100 ppq, and ~900 ppt, respectively, which is the lowest reported in nickel. Surface-etch profiling uncovered higher concentrations of these contaminants extending ~10 μ m beneath the surface, likely associated with the aluminum growth substrate. Additionally, the CVD Ni underwent tensile testing alongside standard Ni samples. CVD Ni exhibited a planar tensile strength of ~600 MPa, significantly surpassing standard nickel. However, heat treatment was found to reduce the tensile strength to levels comparable to standard Ni, with implications for high-temperature weld joining methods. The results reported are compared to the one other well documented usage of CVD Ni in a low radioactive background physics research experiment and a discussion is provided on how the currently reported results may arise from changes in CVD fabrication or testing process. These results establish CVD Ni as a promising low-radioactivity structural material, while outlining the need for further development in surface cleaning and weld-joining techniques to fully realize its potential in large-scale, low radioactive background rare-event search experiments.

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Machine learning for single-ended event reconstruction in PROSPECT experiment

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologies offer promising applications for other segmented particle detectors, underscoring their versatility and impact.

47 OTHER INSTRUMENTATION