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Wonders, Marc A.

Publications and source records attributed to Wonders, Marc A..

Report on Next-Gen AI for Proliferation Detection Workshop: Domain-Aware Methods

The emergence of artificial intelligence (AI) and machine learning (ML) in the modern world has impacted nearly every application imaginable. This includes nuclear proliferation detection, which offers the potential to improve existing capabilities as well as create new ones. Proliferation detection seeks to detect and characterize attempts by state and non-state actors to acquire nuclear weapons or associated technology, materials, or knowledge. Such a mission is vitally important for global stability and security but is notoriously difficult. By leveraging advances in AI, exciting opportunities exist to enhance the proliferation detection regime. The Data Science and AI portfolio within the National Nuclear Security Administration’s Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D) seeks to leverage the capabilities of the Department of Energy’s (DOE’s) national laboratories and other partners to develop AI systems that can accomplish otherwise impossible tasks in support of proliferation detection. As part of its efforts, the portfolio has created a series of workshops on Next-Gen AI for Proliferation Detection to help define the requirements for suitable AI systems, share successful research and best practices, and foster connection and understanding between the relevant parties including researchers and end-users. Each workshop in the series focuses on a specific and critical aspect of AI to enable it to accomplish proliferation detection objectives. The first workshop focused on explainability techniques; the second workshop and the topic of this report, covers methods for incorporating domain awareness into AI. The Next-Gen AI for Proliferation Detection Workshop: Domain-Aware Methods took place virtually over two days in February 2021 and included four keynote presentations, 22 technical presentations, and a concluding panel. The presentations, discussions, and workshop findings are summarized in this report.

97 MATHEMATICS AND COMPUTING↗

Optimization and characterization of a silicon photomultiplier-based ZnS(Ag) proton recoil fast neutron detector for nuclear fuel performance monitoring at TREAT

The restart of the Transient Reactor Test Facility (TREAT) at Idaho National Laboratory and consequent refurbishment of the Fuel Motion Monitoring System (FMMS), or Hodoscope, offers the opportunity to upgrade the detector system used for neutron imaging. Silicon photomultipliers (SiPMs) are a viable option for updating the Hodoscope to yield improved fuel monitoring capability. The Hodoscope uses ZnS(Ag) proton recoil scintillators (PRS) that provide good gamma-ray suppression and discrimination. Previous work showed that the Hamamatsu S13360-6075CS SiPM offers the best neutron detection and gamma-ray discrimination capability with the ZnS PRS. This work optimizes a SiPM-based detector and develops a PRS prototype for testing. Specifically, possible overvoltages for use are determined by confirming steady operation over extended measurement times. In addition, various SiPM-circuit implementations are tested to optimize the detector according to desired properties, and ultimately a PRS prototype is developed with modifiable components for versatile testing. Measurements of the neutron detection efficiency and gamma-ray rejection efficiency of SiPM-based PRS detectors and a reference PMT-based detector are also carried out. Neutron detection efficiency ranges between 1-2%, and detected gamma-ray rejection efficiency is on the order of 10 -7 . In conclusion, use of a low-pass filter only or a low-pass filter and 50-ω shunt resistor is recommended for the SiPM-based detector, and both configurations demonstrate improved performance over the PMT-based detector.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗