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Thompson, Scott J.

Publications and source records attributed to Thompson, Scott J..

Stable and tunable MeV $$\gamma$$-ray generation via dual-laser inverse Thomson scattering from a laser-plasma accelerator

Abstract Inverse Thomson scattering from laser-plasma accelerators offers a pathway to compact, tunable MeV $$\gamma$$ -ray sources for reduced-dose radiography and enhanced performance in nuclear resonance fluorescence (NRF)-based isotope identification. However, photon yield and spectral quality are often limited by constraints on interaction geometry and scatter-laser tunability. Here we demonstrate a MeV $$\gamma$$ -ray source based on a dual-laser inverse Thomson scattering configuration driven by a 100-TW laser-plasma accelerator. Electron beams tunable from 122 to 204 MeV with $$<5$$ mrad divergence and $$<1$$ mrad pointing stability generate $$\gamma$$ rays with peak energies from 276 keV to 1.2 MeV and yields up to $$2\times 10^{7}$$ photons per shot. By independently controlling the interaction position and the scatter-pulse duration, we experimentally match the scatter pulse to the walk-off-limited interaction length. Extending the scatter pulse to 200 fs increases photon production by approximately $$15\%$$ while maintaining operation in the linear Thomson regime, thereby preserving narrow spectral bandwidth and controlled radiation divergence. Radiographic characterization demonstrates MeV-level penetration and $$\approx 0.1$$ mm spatial resolution, while stable operation is sustained over multi-hour timescales across multiple days. These results show that interaction-length optimization provides a scalable strategy for improving photon yield, spectral control, and operational stability in compact laser-plasma-accelerator-driven $$\gamma$$ -ray sources.

Tsai, Hai-En↗

Explosion Detection Using Smartphones: Ensemble Learning with the Smartphone High-Explosive Audio Recordings Dataset and the ESC-50 Dataset

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion”, “ambient”, or “other” with true positive rates (recall) greater than 96% for all three categories.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Expansion of the Fuel Motion Monitoring System

The Idaho National Laboratory (INL) Transient Reactor Test Facility (TREAT) Fuel Motion Monitoring System (FMMS) is a diagnostic tool used to visualize the movement and location of fuel within the nuclear reactor during transient experiments. TREAT is designed to test the behavior of nuclear fuel under accident conditions, such as a rapid power increase. The TREAT FMMS, also referred to as the “hodoscope” because of the system’s massive steel collimator component, is located on the North beam port of the reactor. While the FMMS system is capable of incorporating a total of 360 sensors, initial refurbishment efforts resulted in a array of 96 fast neutron detectors. This initial array was configured to provide a narrow (2 channels) full-length view of experiment vessels with an extended 4-channel–wide region in the center of the array capable of providing a full view of all initially planned experiments. Planning and efforts to expand the FMMS array began immediately following the restart of TREAT. An additional 96 detectors were evaluated, characterized, and prepared for installation at TREAT during the summer of 2018. Unfortunately, funding reductions forced the project towards a 5-year hiatus. In October of 2023 funding from the Department of Energy’s Nuclear Science User Facilities enabled the expansion to recommence. This report serves to document the activities performed to expand the FMMS detector array to 192 channels in order to provide a broader view of the larger and more sophisticated test capsules currently planned for future irradiation in the TREAT reactor. This expansion also included doubling the data acquisition capability, addressing the new heat load on the system, and synchronizing the time for all digital components.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗