Instrumentation & Measurement Magazine 24-9 - 50

of the two beams in parallel was tested in a machine development
(MD) study, with another MD scheduled to probe
the parallel fully automatic software including angular alignments.
The alignment time of the collimators at injection was
reduced by 71.4% with respect to the semi-automatic alignment
in 2017 (from nearly 3 hours to just 50 minutes) [42], as
shown in Fig. 4. This represents a major step in terms of operational
efficiency enhancement and will become the default
software for starting the LHC in 2022.
Fig. 3. Precision distribution for each model considered and their Ensemble
(LR: Logistic Regression, NN: Neural Network, SVM: Support Vector Machine,
DT: Decision Tree, RF: Random Forest, GB: Gradient Boost, ALL: Ensemble)
(from [36]).
hyper-parameters. The SFS algorithm checks all combinations
of features by introducing one at a time and retaining
the best one for future combinations.
The five topmost characteristics [36] were applied for training
and comparing six ML models for binary classification,
namely Logistic Regression, Neural Network, Support Vector
Machine, Decision Tree, Random Forest, and Gradient Boost.
False detection of an alignment spike is to be considered worse
than not detecting an alignment one, hence the main performance
metric used was precision. Fig. 3 shows the distribution
of precision obtained by each model and their Ensemble, i.e.,
a new model in which the others are gathered together by a
mechanism of majority vote. The differences in the mean precision
of the results obtained from each model are included in
Fig. 3, a different color implying that the mean significantly
differs.
The Support Vector Machine achieved the best precision,
featuring a mean
like the Ensemble. The Ensemble
model was added
to the BBA software [39],
including the necessary
threshold-selection algorithm
[40] and crosstalk
analysis [41], which transformed
the semi-automatic
alignment into a fully automatic
one. This new
alignment software was
successfully used throughout
2018 LHC operation.
The first version was tested
during beam commissioning,
with the collimators
of the two beams being
automatically aligned in
sequence, at injection and
top energy. The possibility
to align the collimators
50
Beam Lifetime and Losses Optimization
Using ML to build a rigorous model of particle losses occurring
in the machine would offer valuable insights into the detail of
the beam dynamics, which would help enhance the LHC performance,
also allowing the exploration of ML applications for
future colliders, such as the CERN FCC [10].
The main aim consists of developing an approach that is
able to identify the set of operational parameters that provide
the maximum beam intensity lifetimes under the constraint of
a given, specific machine configuration [43]. The ultimate goal
is the comparison of the model obtained from experimental
data, which would be a data-driven surrogate model, against
results from particle tracking simulations: an efficient optimization
algorithm could then be applied to establish the best
parameters for operation.
This problem has been tackled with a SL approach: the
model output is the beam lifetime, while the physical accelerator
parameters, such as tune, chromaticity, and magnet
currents are the inputs. The used data cover an entire operational
year, but only a reduced subset of the complete machine
cycle, corresponding to the end of the injection plateau just
prior to launching the energy ramp, is considered; such a severe
data reduction will be progressively removed in future
analyses.
Fig. 4. Evolution of the time required for the collimators' alignment at injection (from [42]). The first 2018 data indicate
that the alignment had been carried out in sequence for the two beams, whereas the second 2018 data show parallel
alignment of the two beams.
IEEE Instrumentation & Measurement Magazine
December 2021

Instrumentation & Measurement Magazine 24-9

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