Beam Dynamics Framework Incorporating Acceleration to Define the Minimum Aperture in Two Focusing Schemes for Proton Radiotherapy Linac

Physical Review Accelerators and Beams（2024）

Cited 0|Views5

Abstract

In this paper, a self-consistent transverse beam dynamics framework isdemonstrated, that incorporates acceleration into the transverse beam dynamicsstudies for a proton linac machine. Two focusing schemes are developed anddiscussed; the FODO-like scheme, and the minimum aperture scheme. The FODO-likescheme is a simple scheme, requiring only one quadrupole per cavity. The schemeis analytically solved to minimise the beam size at the cavity entrance/exitand ensures a constant beam size along the lattice, with respect to adiabaticdamping due to longitudinally accelerating rf cavities. The minimum aperturescheme describes the regime that matches the beam ellipse to the acceptanceellipse of a cavity, allowing for the smallest possible aperture, for a givencavity length. A simple approximation of an rf cavity map is determined toallow changes in particle energy along a lattice, and acceleration is assumedonly in the longitudinal direction.

Translated text

Key words

Radiotherapy Physics

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper