Machine Learning-Assisted BeamOperation at SuperKEKB and Linac at KEK

Shinnosuke Kato (The University of Tokyo, KEK )Gaku Mitsuka , Naoko Iida,
Takuya Natsui , Masanori Satoh (KEK Acc.)

2023/09/14

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Luminosity is important for a new physics
Belle II detector𝒆− (HER)

Generated・decay particles

𝒆+ (LER)𝒆−SuperKEKB

𝒆+LinacAccumulating statistics N to search for a new physics→Requires high peak luminosity and long-term stability
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IBIC2023 Shinnosuke Kato

damping ring2

Motivation to introduce machine learning
Target profile of integrated luminosity

BelleⅡ/SuperKEKB Run timebreakdown (Feb. 21 - Jun. 22, 2022)
Peak luminosity
Integrated luminosity

100 times
larger20 times
LS2 (2028？)
larger thanthe present

1.9%0.8%
1.9%

Physics run
Acceleratortuning

29.3%

424 fb-1

Scheduledmaintenance

66.1%

Baking runothers

We want to make accelerator tuning efficient using machine learning
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Linac study for SuperKEKB injection tuning
SuperKEKB
Main Ring

2023
4e-e+

5678910112024

121Long shutdown
SuperKEKB LER SuperKEKB HER(We don’t test MLoperation restart operation restartassisted injection tuning)
Linacstudy

Linacstudy

SuperKEKB Injection tuningFor machine learning……

• How efficiently and quickly can we optimize the beam?
• What characteristics does the optimization have?
• Which parameters are important to optimize ?
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Outline• Introduction• Experiment setup of this study• How to optimize the machine parameters at the KEK LINAC• Two types of optimization algorithms• Results of optimization (beam charge maximizing)• 𝑒 − beam
• 𝑒 + beam

• Summary and prospects

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Introduction to the KEK LINAC

KEKOutline of the KEK LINAC

About 500m
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Components used in this study
Pulse magnets
ready for installation(taken in 2017)

Pulse steering magnet

LINAC

Ordinary BPM usedin the KEK LINAC

BPM signal processing system

1% uncertainty
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Experiment setup of this study
Tuning parameters
Applied currents to6 steering magnets:
𝐼𝑥0 , 𝐼𝑦0 , 𝐼𝑥1 , 𝐼𝑦1 , 𝐼𝑥2 , 𝐼𝑦2 (A)LINAC

Evaluation parameter
Beam charge of 14
BPMs’ average: Q (nC)

Loopagain !

Get beamcharge

Put currentof magnets

Machine learning…

2023/09/14…

…

Accumulate data
Trial1 𝐼𝑥0 ~𝐼𝑦2 ↔ Q′′
Trial2 𝐼𝑥0~𝐼𝑦2↔ Q′

Optimization
algorism(next page in detail)IBIC2023 Shinnosuke Kato

Estimate best
current at that time
to maximize charge
8Two types of algorithm to optimize(a)Bayesian optimization (BoTorch)(b)CMA-ES

Optimize black-box functions using Gaussian
process. Below figure is looking for the minimum.

One of evolutionary computation algorithms.

1st trial

10th trial

https://en.wikipedia.org/wiki/CMAES#/media/File:Concept_of_directional_optimization_in_CMA-ES_algorithm.png

Recently proposed

Conventional

We tested the both algorithms in this study.
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Detail of optimization for 𝒆 beam
Date

Expert tuned value(a)Bayesian June 2nd 11 am to 3 pm

9.3 nC(b)CMA-ES June 12th 11 am to 2 pm

8.9 nC“Previousbest value”

par2“Random”

par1
2023/09/14par2“Random”= First 10 trials are randomly initialized.
“Previous best value”= First 10 trials initialized using the top 10 results
giving the best beam charge taken from the last run

par1
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To what level and how quickly can algorithms optimize ?
• Peak hold charge is the maximum value of the beam charge.
• Iterating 100 trials and repeated the runs 5 times (identified by color)

Experttuned value(a)9.3 nC(b)
8.9 nC35

7min

Both algorithms were able to optimize even “Random” !
In Bayes opt., all runs are maximized in about 35 trials (7 minutes) !
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What characteristics do they have?
𝑡

Travel distance is formulated as 𝑄𝑡𝑑 = ෍ 𝑞 𝑡 − 𝑞(𝑡 − 1)1

Q td represents how exploitative or exploratory per trials.
steep slope → exploratory shallow slope → exploitative
Too exploratory
Search for
unnecessary regions

Too exploitative
Stuck in local
maximum

In “Random”, characteristics of Bayes opt. are influenced by the initial values.
In “Previous best value”, no big difference between Bayes opt. and CMA-ES.
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Which parameters are important to optimize ?
• “Importance” describes which parameters give the significant change in charge.
• It can be quantified using the fANOVA method. [Hutter, ICML 2014]• Importance sum is normalized to 1. (Σ(Importance) = 1)

𝑰𝒙𝟎 , 𝑰𝒚𝟎 (most upstream magnets) have higher importance in the both.
Agree with experts’ rule of thumb “Sequentially tuned from upstream”
Horizontal collimator in the R-sector → High importance of 𝐼𝑥0
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+
Detail of optimization for 𝒆 beam•••

Maximize the positron beam charge by adjusting the steering magnets close
to the tungsten target (only “Random”)
Optimize total 6 parameters using the Bayesian optimization
Iterate 100 trials and repeat the runs 5 times

Tungsten target

6 magnets (3 X + 3 Y) before 𝒆+ target
and 1 BPM after 𝒆+ target
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+
Result of optimization for 𝒆 beam
Peak hold

ImportanceExpert tuned
Value (4.6 nC)

The 𝒆− beam has been optimized also for 𝒆+ production!
Agree with experts’ rule of thumb “Both 𝐼3 and 𝐼4 are equally important”
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Summary and prospects• Using the Linac electron beam, we studied the possibility of using
machine learning to adjust the SuperKEKB beam.
• Bayesian optimization achieved the maximum charge in 7 minutes,
while experts took 30 minutes.
We also clarified the characteristics and important parameters.
• Optimization worked in the two different tests, so it would work forSuperKEKB beam-injection tuning.
• In this December after restarting operation, we will apply
ML-assisted methods to SuperKEKB.
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