Conveners
Poster/Demos: Flash Talks
- Tia Miceli (Fermilab)
- Jason St. John (Fermilab)
Poster/Demos: Live Demos and Posters & Snacks
- There are no conveners in this block
Compiled slide show for this year's poster/demo contributions.
Machine Learning (ML) has gained significant prominence in the field of engineering due to its adaptability and versatility. An example of its practical application is in anomaly detection, which serves the fundamental purpose of providing a binary response to the question, "Has an issue arisen?". Most machine learning and anomaly detection projects strive to provide generalisable solutions...
The Collider-Accelerator Department’s (C-AD) Controls Group at Brookhaven National Laboratory produces and implements tools to analyze data after magnet quench events. Diodes are used in the circuitry to protect quenching magnets from damage. Intermittently failing diodes can be difficult to identify as they may not always impact beam. Accelerator physicists have studied the voltage tap...
We've introduced Machine Learning methods to accelerator operations at SACLA/SPring-8.
One of them is an automatic beam tuning based on Bayesian Optimization.
In the initial test, we tried to maximize the pulse energy by using the optimizer.
Then we've introduced a new high-resolution single-shot inline spectrometer (resolution of a few eV) to maximize the spectral brightness.
Today the...
Magnet control is important to improve beam quality as its misalignments cause beam degradation and prevent the beam from reaching the desired specifications (e.g., polarization). Magnet misalignment measurements serve as the reference values in operations and provide a foundation for effective control. However, use of the historical measurement data may cause a significant deviation from the...
Differentiable modelling has garnered significant interest in the accelerator physics community, but literature is lacking on its specific application to synchrotron dynamics. In principle, access to the gradients should reduce the number of trials required in an optimisation loop. As a 'real test case', we want to optimise the best set of beam perturbations to achieve the goals of 'Pulse...
In recent years, Bayesian optimization has been attracting attention as a tuning method for accelerators. However, the number of iterations required increases as the number of parameters increases. Therefore, there is a limit to the number of parameters that can be optimized in a realistic amount of time. In this study, we proposed a new method that combines the dimensionality reduction method...
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard method for this task, but they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation...
High intensity beams, through electron cloud and impedance based mechanisms, cause an increase in vacuum pressure in the SPS kicker magnets. These magnets are pulsed at high voltage in order to quickly deflect the beam. However, if the vacuum inside their aperture deteriorates, it can lead to an electrical breakdown and potential damage to the kicker itself.
Conversely, a breakdown in a...
SLAC and RadiaSoft are partnering to provide integration support for two parallel workflows that support end-to-end modeling and machine learning integration for accelerators. LUME, Light Source Unified Modeling Environment, has been developed by SLAC to facilitate end-to-end modeling for machine tuning and optimization. This workflow includes the integration of machine learning surrogate...
We present experience with deploying several ML-based methods for control and optimization of the PETRA III storage ring.
First, we discuss the recent progress with the compensation of influence of insertion devices (IDs) on beam orbit using Deep Neural Networks. Different models were trained to predict the distortion in the closed orbit induced by movements of the IDs in the context of a...
Physics-based simulation tools are essential to the design and operation of modern particle accelerators. Although accurate, these tools tend to be expensive to evaluate, aren’t always compatible with modern implementations of automatic differentiation, and have a hard time incorporating data from real-world machines. Surrogate models have the potential to solve these problems by turning...
Neural networks have a strong history as universal function approximators and as such have seen extensive use as surrogate models for computationally expensive physics simulations. However, achieving predictions for sparse or step functions are difficult, particularly within the spatial domain. This is of particular concern when these sparse events in the spatial dimension are used to...
Multi-Objective Genetic Algorithm(MOGA) is one of promising approach for optimizing nonlinear beam dynamics in accelerators. For explorative problems that have many variables and local optima, however the performance of MOGA is not always satisfactory. To improve the efficiency of optimization in linac beam line, we propose a novel integration of MOGA and neural networks. The neural network is...
The response matrix is the closed orbit distortion at each BPM responses to the change in every corrector. For a large ring, the response matrix has tens of thousands of data points which can fully include the linear optics of the ring. LOCO use response matrix for lattice calibration and error correction. For 4 th generation diffraction limitation ring which uses many strong sextupoles and...
The Linear IFMIF Prototype Accelerator (LIPAc) is designed to validate the main key technical solutions for the particle accelerator of the International Fusion Materials Irradiation Facility, which will answer the need of the fusion community for a high energy (14.1 MeV) high intensity neutron source. LIPAc is jointly developed under the EU-Japan Broader Approach agreement to accelerate 125mA...
Design optimization for a cyclotron is important for obtaining a high accelerating voltage to increase efficiency. A different cavity geometry made from the same material usually will have a different quality factor, which might affect the turn separation, especially if the electric field at the accelerator zones changes. For this purpose, a neural network is trained to give predictions of...
The CERN SPS Beam Dump System (SBDS) disposes the beam in the SPS at end of cycled operation or in case of machine malfunctioning, with its kicker magnets deviating the beam to an absorber block and diluting the particle density. This is a critical system, as its malfunctioning can lead to absorber block degradation, unwanted activation of the surroundings or even damage to the vacuum chamber....
Multi-objective optimisations are extensively used during the design phase of the modern storage rings, to optimise at the same time the simulated Touschek lifetime and injection efficiency. Online optimisations of either the measured lifetime or the measured injection efficiency have been also extensively used at the ESRF and in several other accelerators. Online multi-objective optimisations...
To meet the requirement of electron beam characteristics at linac entrance of CEPC and PWFA. A method of searching in a high-dimensional parameter space was performed using a multi-objective genetic algorithm. A deep Gaussian process was adopted as an surrogate model to solve high-dimensional parameter optimization problem. geometric parameters of radio frequency gun and beam element...
The RHIC heavy ion program relies on a series of RF bunch merge gymnastics to combine individual source pulses into bunches of suitable intensity. Intensity and emittance preservation during these gymnastics require careful setup of the voltages and phases of RF cavities operating at several different harmonic numbers. The optimum setting tends to drift over time, degrading performance and...
Machine learning has been widely used in many fields including science and technology. This paper will focus on the orbit correction by the algorithm of neural networks, a subset of machine learning, in Taiwan photon Photon Source. The training data for neural network is collected by machine study and accelerator toolbox (AT).
The 8 GeV proton-storage Recycler Ring (RR) is essential for reaching megawatt beam intensity goals for the DUNE neutrino beam at Fermilab. Custom shims on each RR permanent magnet were designed to cancel manufacturing defects and bring magnetic fields to the design values. Remaining imperfections cause the observed tune variation vs energy to deviate from what is calculated using the design...
Low energy linear accelerators can generate intense ultra-short THz pulses of coherent synchrotron radiation (CSR) by using chicanes and/or undulators to bend the path of the electron bunch. Additionally, potential users of the THz light might have particular requests for their experiments, which calls for a way to more flexibly tailor the emitted spectrum.
It's often a complex and...
One of the key metrics determining the capabilities of Free Electron Laser (FEL) facilities is the intensity of photon beam they can provide to experiments. However, in day-to-day operations, tuning to maximise the FEL intensity is one of the most difficult and time-consuming tasks. Skilled human operators still need large amounts of the available beam time, which are then not available for...
The Institute of Modern Physics is developing the Fourth generation of Electron Cyclotron Resonance (FECR), which requires Nb3Sn superconducting hexapole magnets with higher magnetic fields and composite structures. For Nb3Sn superconducting magnets, they exhibit significant thermal magnetic instability, known as "flux jump". This characteristic can generate random voltage spikes during the...
Motion control is assuming an increasingly pivotal role within modern large accelerator facilities, such as 4th generation storage ring-based light sources, SRF accelerators, and high-performance photon beamlines. For very high-Q SRF linacs, such as LCLS-II, the precise management of cavity resonance becomes indispensable for maintaining stable operations. Failing to do so would entail a...
Beam commissioning is a key procedure to achieve high quality beam. Conventional “Monkey ump” tuning is time-consuming and inefficient. Reinforcement learning (RL) can swiftly make decisions based on the current system state and control requirements, providing an efficient control solution for accelerator systems.
High Intensity Proton Injector (HIPI) accelerator requires a rapid and...
Over last decades, most synchrotron radiation light source designs are based on planar storage rings. Under the linear uncoupled condition, we can describe the physics of these storage rings using the auxiliary functions such as Twiss parameters. We can also give the effects of coupling on emittance by some approximations under the hypothesis of weak coupling. However, in recent years, the...
Non-linear optics commissioning for the LHC has faced challenges with higher order errors using a diverse array of correction techniques. Feed down of these errors complicates the correction process, demanding significant time and effort. As machine complexity increases and IP beta functions decrease, there is a growing need for efficient and reliable correction methods. This study explores...
Reinforcement Learning (RL) is a unique learning paradigm that is particularly well-suited to tackle complex control tasks, can deal with delayed consequences, and learns from experience without an explicit model of the dynamics of the problem. These properties make RL methods extremely promising for applications in particle accelerators, where the dynamically evolving conditions of both the...
Autonomous tuning of particle accelerators is an active and challenging field of research with the goals of reducing tuning times and enabling novel accelerator technologies for novel applications. Large language models (LLMs) have recently made enormous strides towards the goal of general intelligence, demonstrating that they are capable of solving complex task based just a natural language...
Bayesian optimization (BO) is an effective tool in performing online control of particle accelerators. However, BO algorithms can struggle in high dimensional or tightly constrained parameter spaces due to its’ inherent bias towards over-exploration, leading to slow convergence times for relatively simple problems and high likelihoods of constraint violations. In this work, we describe the...
