Instrumentation & Measurement Magazine 24-9 - 48

The initial results discussed
in [21]-[24] show very
clearly the capability of
ML-based regression models
[25]-[29] to predict
individual quadrupolar errors.
More than this, they
can boost the quality of
optics corrections, as they
provide insight into the
sources of local errors in
the accelerator. Some novel
findings are presented in
[30] and cover cases where
the data are from numerical
simulations and beam
measurements.
Fig. 1. Layout of the LHC ring (from [21]).
However, even excellent theoretical optics are of little use if
excellent measurements and corrections techniques are not
at hand. For this reason, strong efforts have been devoted
in recent years to the development and improvement of the
techniques for measurement and correction of the linear optics,
with outstanding results [17]-[20] that have been of
paramount importance for achieving the excellent LHC performance.
Further improvements would require tackling two
major points: devising approaches to detect faulty Beam Position
Monitor (BPM) measurements to exclude them from the
further analyses; and building effective models to describe
how the magnetic field errors distributed along the accelerator
circumference influence the optics measurements. ML techniques
provide tools to deal with both points and have been
the subject of active research efforts in recent years.
Both SL and UL paradigms are applied to measurements
of linear optics and its correction at the LHC. Supervised
methods are used to build regression models with the goal of
reconstructing errors of individual magnets from optics disturbances
generated by the errors, whereas correction techniques
available to date compute circuits strength settings to compensate
the measured optics deviations from the design values.
48
The detection of faulty
BPMs in optics measurements,
responsible for
nonphysical outliers in the
value of the optical functions
derived from BPM
data, is better performed
by means of UL tools. A decision-tree-based
Isolation
Forest (IF) algorithm [31]
has considerably improved
the measured data used
to evaluate optical functions,
with an important
side effect of minimizing
the human effort to clean
the measured data.
Anomaly-detection techniques have also been applied to
clean measured data with a comparison to clustering techniques,
as presented in [32], while extensive studies on
cleaning techniques for the LHC optics measurements, comprising
recent progress and plans for the future, are discussed
in [33].
LHC Collimation System
Commissioning with Beam
The LHC equipment must be protected from any damage or
down-time caused by beam losses, for which a robust collimation
system is used. It comprises around 100 collimators
located along the 27 km ring, each made of two parallel absorbing
jaws. The four jaw ends are individually movable by
means of dedicated stepper motors, which totals about 400 degrees
of freedom.
Collimator settings are determined, following a beambased
alignment (BBA) procedure established in [34], which
is used to estimate the beam center and size at the collimator
locations. This procedure moves collimator jaws separately towards
the beam halo, while monitoring the measured beam
loss signal on a dedicated Beam Loss Monitoring (BLM) device
IEEE Instrumentation & Measurement Magazine
December 2021

Instrumentation & Measurement Magazine 24-9

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