Predicting the transverse emittance of space charge dominated beams using the phase advance scan technique and a fully connected neural network

Mayet, Frank; Hachmann, Max; Floettmann, K.; Burkart, Florian; Dinter, H.; Kuropka, Willi; Vinatier, Thomas; Assmann, R.

doi:10.1103/physrevaccelbeams.25.094601

Cited by 10 publications

(2 citation statements)

References 32 publications

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“…In Ref. 35 , a method was developed for predicting the transverse emittance of space charge-dominated beams and demonstrated. In Ref.…”

Section: Review Of Deep Learning For Acceleratorsmentioning

confidence: 99%

A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

Rautela,

Williams,

Scheinker

2024

Preprint

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Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations, and inherent uncertainties in the system. We propose a two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for learning the spatiotemporal dynamics of charged particles in accelerators. CLARM consists of a Conditional Variational Autoencoder (CVAE) transforming six-dimensional phase space into a lower-dimensional latent distribution and a Long Short-Term Memory (LSTM) network capturing temporal dynamics in an autoregressive manner. The CLARM can generate projections at various accelerator modules by sampling and decoding the latent space representation. The model also forecasts future states (downstream locations) of charged particles from past states (upstream locations). The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.

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“…In Ref. 35 , a method was developed for predicting the transverse emittance of space charge-dominated beams and demonstrated. In Ref.…”

Section: Review Of Deep Learning For Acceleratorsmentioning

confidence: 99%

A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

Rautela,

Williams,

Scheinker

2024

Preprint

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“…Neural networks are also being used for uncertainty aware anomaly detection to predict errant beam pulses [32], as virtual diagnostics for 4D tomographic phase space reconstructions [33], for predicting the transverse emittance of space charge dominated beams In Sections IV-B and IV-C we tune several components in this section of the accelerator. [34], and for high resolution longitudinal phase space virtual diagnostics [35]. Neural network-based deep reinforcement learning (RL) methods have been used for accelerator control [36], and in a sample efficient manner, which trains a policy based on data at two beam lines at CERN [37].…”

Section: B Accelerator Tuning and Optimizationmentioning

confidence: 99%

Practically Safe Extremum Seeking

Williams

Krstić

Scheinker

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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We demonstrate the recent designs of Safe Extremum Seeking (Safe ES) on the 1 kilometer-long charged particle accelerator at the Los Alamos Neutron Science Center (LANSCE). Safe ES is a modification of ES which, in addition to minimizing an analytically unknown cost, also employs a safety filter based on an analytically unknown control barrier function (CBF) safety metric.Accelerator tuning is necessitated by the accelerators being large, with many drifting parameters due to thermal effects and degradation. At the same time, safe operation (the maintenance of state constraints) is crucial, as damage brings astronomical costs, both financially and in operation downtime.Our measured (but analytically unknown) safety metric is the beam current. We perform multivariable Safe ES on three accelerator applications, in which we adapt 4, 6, and 3 magnet strength parameters, respectively. Two of the three applications are for validated simulation models of beamlines at LANSCE: the first for the Proton Radiography (pRad) beamline of 800 MeV protons for spot size tuning; the second on a high performance code, HPSim, for tuning the low energy beam transport (LEBT) region of of 750 keV protons. The third is an experimental tuning of the steering magnets in the LEBT at LANSCE.

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A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

Rautela,

Williams,

Scheinker

2024

Sci Rep

View full text Add to dashboard Cite

Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations, and inherent uncertainties in the system. We propose a two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for learning the spatiotemporal dynamics of charged particles in accelerators. CLARM consists of a Conditional Variational Autoencoder transforming six-dimensional phase space into a lower-dimensional latent distribution and a Long Short-Term Memory network capturing temporal dynamics in an autoregressive manner. The CLARM can generate projections at various accelerator modules by sampling and decoding the latent space representation. The model also forecasts future states (downstream locations) of charged particles from past states (upstream locations). The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.

show abstract

Predicting the transverse emittance of space charge dominated beams using the phase advance scan technique and a fully connected neural network

Cited by 10 publications

References 32 publications

A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

Practically Safe Extremum Seeking

A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

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