Data-driven modeling of beam loss in the LHC

TL;DR

This paper presents a data-driven, autoregressive modeling approach using Kalman Filters to predict beam losses in the LHC, accounting for complex non-linear effects and uncertainties for improved accelerator operation.

Contribution

It introduces an autoregressive model with Kalman Filter estimation to better predict beam losses, overcoming limitations of previous regression models.

Findings

Effective modeling of beam loss dynamics using autoregressive methods.

Improved prediction accuracy over traditional models.

Enhanced understanding of control parameter influence on losses.

Abstract

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscillation amplitudes or large momentum error are scraped from the beams. The level of particle losses typically is optimized manually by changing multiple control parameters, among which are, for example, currents in the focusing and defocusing magnets along the collider. It is generally challenging to model and predict losses based on the control parameters due to various (non-linear) effects in the system, such as electron clouds, resonance effects, etc, and multiple sources of uncertainty. At the same time understanding the influence of control parameters on the losses is extremely important in order to improve the operation and performance, and future design of accelerators. Existing results showed that it is hard to generalize the models, which assume the regression model of losses depending on control parameters, from fills carried out throughout one year to the data of another year. To circumvent this, we propose to use an autoregressive modeling approach, where we take into account not only the observed control parameters but also previous loss values. We use an equivalent Kalman Filter (KF) formulation in order to efficiently estimate models with different lags.