Search NASASearch

DOE OSTI · 2584103

Optimizing 4d Emittance Measurements Using the Pinhole Scan Technique

Abstract

Accurate measurement of electron beam emittance is essential for optimizing high-brightness electron sources. The Pinhole Scan Technique measures the 4D phase space and hence the emittance by measuring the beam profile after clipping the beam using a pinhole followed by a drift section and then scanning the beam over the pinhole. This technique has been implemented in low energy (< 200 keV) beamlines at both Cornell University and Arizona State University. However, the technique poses several practical challenges. In this work, we analyze and address key issues affecting the 4D phase space and emittance measurements using this technique. We identify and investigate sources of inaccuracies like the pinhole aspect ratio, beam divergence, position-momentum correlations in the phase space, and the point-spread-function of the detector and suggest techniques to minimize them. Our findings offer a pathway to more accurate 4D phase space characterization in advanced electron beam systems.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Owusu, Peter P. [Arizona State University, Tempe, AZ, USA], Zhang, C. [Cornell Univ., Ithaca, NY (United States)], Bartnik, A. [Cornell Univ., Ithaca, NY (United States)], Kalpada, T. [Arizona State Univ., Tempe, AZ (United States)], Anawalt, J. [Arizona State Univ., Tempe, AZ (United States)], Maxson, J. [Cornell Univ., Ithaca, NY (United States)], Karkare, S. [Arizona State Univ., Tempe, AZ (United States)]. 2025-08-12. Optimizing 4d Emittance Measurements Using the Pinhole Scan Technique. https://doi.org/10.2172/2584103

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

KEEP EXPLORING

Related reports

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