Instrumentation & Measurement Magazine 24-9 - 47

Beam Measurements and
Machine Learning at the
CERN Large Hadron Collider
Pasquale Arpaia, Gabriella Azzopardi, Frédéric Blanc, Xavier Buffat, Loic Coyle, Elena Fol,
Francesco Giordano, Massimo Giovannozzi, Tatiana Pieloni, Roberto Prevete, Stefano Redaelli,
Belen Salvachua, Benoît Salvant, Michael Schenk, Matteo Solfaroli Camillocci, Rogelio Tomás,
Gianluca Valentino, Frederik Florentinus Van der Veken, and Jörg Wenninger
P
article accelerators are among the most complex
instruments conceived by physicists for the exploration
of the fundamental laws of nature. Of relevance
for particle physics are the high-energy colliders, such as the
CERN Large Hadron Collider (LHC), which hosts particle
physics experiments that are probing the Standard Model predictions
and looking for signs of physics beyond the standard
model.
Machine Learning for Beam Studies
The particle beams circulating in the colliders need to be controlled
in an accurate way with beam instrumentation, the key
to the performance of a collider. The amount of data generated
by beam instrumentation is so large that Machine Learning
(ML) is making its way into the domain of Accelerator Physics,
with several laboratories worldwide devoting intense efforts
in this domain.
The power of these tools has been exploited for advanced
analysis of colliders data for decades, but it is only in recent
years that ML techniques have found applications in the
field of Accelerator Physics. The first attempts to use these
techniques date to a few decades ago and dealt with beam diagnostics
and beam control systems [1], [2]. However, some
sizeable progress has been made only recently (see, e.g., [3]-[8]
and references therein). Nowadays, there is a general agreement
within the Accelerator Physics community of the need
for and usefulness of ML techniques, which resulted in the
publication of a white paper to review the state-of-the-art and
present recommendations to encourage the development of
such techniques in Accelerator Physics laboratories [9].
CERN has recently started focused efforts oriented towards
ML techniques for beam dynamics studies at the Large
Hadron Collider (LHC). The LHC complexity, in terms of the
number of operational systems, size of data collected, variety
of beam dynamics configurations and beam behaviors, is such
that ML becomes an efficient tool for data analysis. Indeed, a
wide spectrum of applications from beam measurements and
machine performance optimization to analysis of numerical
data from tracking simulations of nonlinear beam dynamics
December 2021
will be reviewed in this paper, paying attention to future developments
related to projects like the Future Circular Collider
(FCC) [10] whose complexity will far exceed that of the LHC.
The process of building a mathematical model built upon
sample data (training data), makes the core of ML, which aims for
predictions or decisions to be made without explicit programming
required [11], so appealing, in particular when there is a
large amount of data. ML includes a few learning paradigms,
such as Supervised Learning (SL), Unsupervised Learning
(UL), and Reinforcement Learning, and deals with tasks such as
classification, regression, clustering, anomaly detection, dimensionality
reduction, and reward maximization [12].
To train a mathematical model to successfully achieve a
particular task requires a few complex steps for ML, which
include data collection and curation, feature (input) engineering,
feature selection and dimensionality reduction, model
hyper-parameter optimization, and model training, with performance
evaluation being at the end of this process. In the SL
approach, training of ML algorithms is carried out on labelled
data sets where a ground-truth output exists for each input. In
UL [13], however, no ground-truth output is available, and the
algorithms aim to discover structure in the dataset. Reinforcement
Learning is typically used in control applications where
the goal is to execute a series of actions to maximize a given reward,
such as achieving a given beam parameter.
Overview of the LHC Ring and Optics
Measurement
Fig. 1 shows the schematic layout of the LHC ring (see [14] for
additional detail). The eight-fold symmetry is visible, together
with the main function of each long straight section. Note that
the RF accelerating systems share the straight section with
some key beam diagnostic devices, like transverse and longitudinal
beam profile monitors and beam current transformers.
During the LHC Run 2 (2015-2018), proton and ion (of atomic
number Z) beams were accelerated from 450 Z GeV to a maximum
of 6.5 Z TeV (see, e.g., [15]).
The study and optimization of the linear optics [16] has
been a priority for the tight link with the collider performance.
IEEE Instrumentation & Measurement Magazine
1094-6969/21/$25.00©2021IEEE
47

Instrumentation & Measurement Magazine 24-9

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