Towards tailored beam distributions for fixed target experiments at CERN

The time-of-flight (nTOF) facility at CERN uses neutrons produced by a proton beam interacting with a fixed target. To prevent target damage, an upper bound on the peak energy density has been imposed. Adhering to this constraint requires a large beam size. Similarly, at CERN’s North Area, a large beam size is required at the septa splitting the beam towards different experiments. However, both cases suffer from limitations associated to losses of the primary beam, leading to poor transmission efficiency and high radioactive activation. This paper proposes an alternative approach by manipulating the beam distribution. Given the absence of strong nonlinear elements in both transfer lines, the focus shifts to tailoring the distribution before extraction. Particle tracking simulations are presented alongside experimental results, characterizing the phase space distribution as a function of machine parameters. Advanced deep learning methods that enable efficient exploration of the parameter space are also discussed.

Publication Details

Journal
International Linear Collider
Published
2025-11-05
DOI
https://doi.org/10.18429/jacow-ipac2025-tupb018
Primary Topic
Nuclear physics research studies
Type
article
Field-Weighted Citation Impact
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article

Towards tailored beam distributions for fixed target experiments at CERN

International Linear Collider
Nuclear physics research studies
article

Towards tailored beam distributions for fixed target experiments at CERN

article en

Abstract

The time-of-flight (nTOF) facility at CERN uses neutrons produced by a proton beam interacting with a fixed target. To prevent target damage, an upper bound on the peak energy density has been imposed. Adhering to this constraint requires a large beam size. Similarly, at CERN’s North Area, a large beam size is required at the septa splitting the beam towards different experiments. However, both cases suffer from limitations associated to losses of the primary beam, leading to poor transmission efficiency and high radioactive activation. This paper proposes an alternative approach by manipulating the beam distribution. Given the absence of strong nonlinear elements in both transfer lines, the focus shifts to tailoring the distribution before extraction. Particle tracking simulations are presented alongside experimental results, characterizing the phase space distribution as a function of machine parameters. Advanced deep learning methods that enable efficient exploration of the parameter space are also discussed.

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