Simulations and measurements of beam loss patterns at the CERN Large Hadron Collider
R. Bruce, R.W. Assmann, V. Boccone, C. Bracco, M. Brugger, M. Cauchi,, F. Cerutti, D. Deboy, A. Ferrari, L. Lari, A. Marsili, A. Mereghetti, D., Mirarchi, E. Quaranta, S. Redaelli, G. Robert-Demolaize, A. Rossi, B., Salvachua, E. Skordis, C. Tambasco, G. Valentino, T. Weiler

TL;DR
This paper presents detailed simulations and measurements of beam loss patterns at CERN's LHC, demonstrating the effectiveness of the collimation system and the accuracy of the simulation tools used for future accelerator design.
Contribution
It provides a comprehensive numerical simulation framework for beam loss distribution in the LHC, validated against experimental measurements, enhancing understanding of beam control and safety.
Findings
Simulation results agree within a factor of 2 with measurements.
The simulation accounts for over 5000 magnets and complex particle interactions.
Results support the use of these tools for future accelerator design.
Abstract
The CERN Large Hadron Collider (LHC) is designed to collide proton beams of unprecedented energy, in order to extend the frontiers of high-energy particle physics. During the first very successful running period in 2010--2013, the LHC was routinely storing protons at 3.5--4 TeV with a total beam energy of up to 146 MJ, and even higher stored energies are foreseen in the future. This puts extraordinary demands on the control of beam losses. An un-controlled loss of even a tiny fraction of the beam could cause a superconducting magnet to undergo a transition into a normal-conducting state, or in the worst case cause material damage. Hence a multi-stage collimation system has been installed in order to safely intercept high-amplitude beam protons before they are lost elsewhere. To guarantee adequate protection from the collimators, a detailed theoretical understanding is needed. This…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
