Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2007501

Summary Report for the 2019 Radiation Detection for Nuclear Security Summer School

McDonald, Benjamin S.·Runkle, Robert C.·Baciak, James E.·Nixon, Amy R.
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Pacific Northwest National Laboratory (PNNL) hosted students from across the United States at the 6th Radiation Detection for Nuclear Security Summer School 17-28 June 2019. The summer school provided students with a unique understanding of nuclear security challenges faced in the field and exposed them to the technical foundations, analyses, and insight that will be required by future leaders in technology development and implementation. The course heavily emphasized laboratory and field demonstrations including measurements of special nuclear material. Student evaluations and feedback from student advisors indicates that the summer school achieved its objectives of 1) exposing students to the range of nuclear security applications for which radiation detection is necessary, 2) articulating the relevance of student research into the broader context, and 3) growing student interest in careers in nuclear security.

Explore related subjects

61 RADIATION PROTECTION AND DOSIMETRYRadiation DetectionRadiation Detection and Nuclear Security Summer SchoolSummer School

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

McDonald, Benjamin S., Runkle, Robert C., Baciak, James E., Nixon, Amy R.. 2020-01-01. Summary Report for the 2019 Radiation Detection for Nuclear Security Summer School. https://doi.org/10.2172/2007501

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Deep Learning enabled spectral energy conversion for in situ exposure measurements

A detector-specific deep learning (DL) approach is presented for spectra-to-exposure conversion using large-format sodium iodide (NaI(Tl)) detectors deployed for in situ environmental radiation measurements in emergency response scenarios. Accurate determination of exposure from NaI spectra is challenging due to poor energy resolution, partial energy absorption, and the strong sensitivity of traditionally deployed analytical conversion methods to calibrated source geometry and pre-deployment assumptions. Here, to address these limitations, a multi-layer perceptron model was trained on a hybrid in situ /Monte Carlo dataset constructed to span a broad range of photon energies, spatial extents, and realistic deployment variability, representative of general in situ emergency response conditions. The DL model was evaluated against commonly fielded analytical approaches under matched simulation conditions, including a single-factor method, a G-function method, and a modeled pressurized ion chamber (PIC) baseline. This study was intentionally computational in scope to enable controlled, like-for-like comparisons between conversion techniques while minimizing confounding real-world variability. Comparison to the modeled PIC provides contextual benchmarking and is not intended as a field inter-comparison with deployed instruments. Across the evaluated 20 keV to 3 MeV energy range, the DL approach consistently exhibited higher accuracy and reduced variance relative to the analytical methods against a deterministically calculated exposure. This may indicate improved robustness to spectral complexity without reliance on source-, geometric-, or spectral region-specific optimization. While results do not represent real-world validation, the presented work demonstrates that deep learning may effectively learn the nonlinear detector response-to-exposure relationship for asymmetric NaI(Tl) detectors and offers a promising pathway for improving in situ exposure estimation using spectroscopic systems already integrated into initial real-time emergency response operations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

ACRRF High-Bay Dose Calculations using MCNP (Part B)

ACRR radiation outputs through vertical cavities have been documented in two reports. In Part B, the maximum dose from the unshielded central cavity, FREC-II, and NRS is calculated to inform the safety basis. The maximum dose is ≈1430 mrem per 300 MJ, or ≈40,930 rem/hr for full-power operation. A verification study completes the V&V of the modeling. Supplementary studies of variance-reduction techniques, model sensitivities, and aircraft dose above the ACRRF are included.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

Logistics for the 2026 International Nuclear Accident Dosimetry Intercomparison Exercise

The purpose of this exercise is to test participants’ nuclear accident dosimeters (NADs) and personnel to the requirements established by American National Standards Institute (ANSI), Health Physics Society (HPS) standard ANSI/HPS-N13.3-2013 (R2025) Dosimetry for Criticality Accidents. IER-634 is the second nuclear accident dosimetry exercise taking place at the Armed Forces Radiobiology Research Institute (AFRRI) in Bethesda, MD, a Department of Defense (DOD) facility. This effort continues use of the high-dose neutron exposure test bed maintained by the DOD and represents the continued collaboration between the DOD and Department of Energy (DOE). Previous work at AFRRI included a reactor characterization (IER 484) in 2023 and a nuclear accident intercomparison exercise (IER-602) in 2024.

61 RADIATION PROTECTION AND DOSIMETRY↗