Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2001194

Automated integration gate selection for Gaussian mixture model pulse shape discrimination

Kaplan, Alan D.·Blair, Brenton·Glenn, Andrew·Wurtz, Ron
View original source ↗Read source document ↗Export metadata ↓

Abstract

Pulse shapes differ between neutron and gamma particles when measured with detector devices employing pulse shape discriminating (PSD) scintillators. Digitized waveforms can be used in detection systems to perform pulse shape discrimination for this application. Prior Gaussian Mixture Model (GMM) methods require access to the pulse full-waveform. Reducing the waveform to a smaller set of combined samples reduces computational cost while affecting PSD performance. In this work, we develop a method for selecting the best performing combination of integration gates, or contiguous summed segments of the digitized pulse for PSD. The method uses a discrimination score based on the GMM PSD approach. Furthermore, the final selection is performed using Bayesian Optimization. PSD detection results are compared with varying numbers of selected gates on time-of-flight (TOF) data. This method can be used to fully automate the selection of gates in an unsupervised (without ground truth labels) setting.

Explore related subjects

42 ENGINEERING73 NUCLEAR PHYSICS AND RADIATION PHYSICSBayesian optimizationGate selectionGaussian mixture modelNeutron detectionPileupPulse shape discrimination

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kaplan, Alan D., Blair, Brenton, Glenn, Andrew, Wurtz, Ron. 2023-07-01. Automated integration gate selection for Gaussian mixture model pulse shape discrimination. https://doi.org/10.1016/j.nima.2023.168486

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

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗