arXiv subjects

Andreas Adelmann

Publications and source records attributed to Andreas Adelmann.

At least 37 records · Page 2Linked to original sources

arXiv2022

Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators

Particle accelerators are complex facilities that produce large amounts of structured data and have clear optimization goals as well as precisely defined control requirements. As such they are naturally amenable to data-driven research methodologies. The data from sensors and monitors inside the accelerator form multivariate time series. With fast pre-emptive approaches being highly preferred in accelerator control and diagnostics, the application of data-driven time series forecasting methods is particularly promising. This review formulates the time series forecasting problem and summarizes existing models with applications in various scientific areas. Several current and future attempts in the field of particle accelerators are introduced. The application of time series forecasting to particle accelerators has shown encouraging results and the promise for broader use, and existing problems such as data consistency and compatibility have started to be addressed.

physics.acc-ph↗

arXiv2022

IsoDAR@Yemilab: A Report on the Technology, Capabilities, and Deployment

IsoDAR@Yemilab is a novel isotope-decay-at-rest experiment that has preliminary approval to run at the Yemi underground laboratory (Yemilab) in Jeongseon-gun, South Korea. In this technical report, we describe in detail the considerations for installing this compact particle accelerator and neutrino target system at the Yemilab underground facility. Specifically, we describe the caverns being prepared for IsoDAR, and address installation, hielding, and utilities requirements. To give context and for completeness, we also briefly describe the physics opportunities of the IsoDAR neutrino source when paired with the Liquid Scintillator Counter (LSC) at Yemilab, and review the technical design of the neutrino source.

physics.ins-det↗

arXiv2022

Report of the Snowmass'21 Workshop on High-Power Cyclotrons and FFAs

This whitepaper summarizes and the state of the field of high-power cyclotrons and FFAs as discussed by international experts during a three-day workshop of the same name. The workshop was held online from Sep 7 to Sep 9, 2021 as part of the US Snowmass'21 Community Exercise, specifically the Accelerator Frontier (AF) and the subpanel Accelerators for Neutrinos (AF02). Thus, we put emphasis on the application of high-power cyclotrons in particle physics, specifically neutrino physics, and as drivers for muon production. In the introduction, we discuss the role of cyclotrons for particle physics, and later we highlight existing and planned experiments in the corresponding sections. However, as these same accelerators have important applications in the fields of isotope production - both for research and medicine - and possibly even in energy research, by providing beam to demonstrator experiments in the areas of Accelerator Driven Systems (ADS), we include these far-reaching topics to provide a full picture of the status and applications of high-power cyclotrons. Furthermore, Fixed Field Alternating Gradient accelerators (FFAs) have recently seen renewed interest. They are in many respects (basic operating principles) similar to cyclotrons and have thus been included in this workshop and whitepaper as well. We are discussing current projects and whether FFAs have the prospect of becoming high-intensity machines.

physics.acc-ph↗

arXiv2022

New directions for surrogate models and differentiable programming for High Energy Physics detector simulation

The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').

hep-ph↗

arXiv2022

muEDM: Towards a search for the muon electric dipole moment at PSI using the frozen-spin technique

The search for a permanent electric dipole moment (EDM) of the muon is an excellent probe for physics beyond the Standard Model of particle physics. We propose the first dedicated muon EDM search employing the frozen-spin technique at the Paul Scherrer Institute (PSI), Switzerland, with a sensitivity of $6 \times 10^{-23}~e\!\cdot\!\mathrm{cm}$, improving the current best limit set by the E821 experiment at Brookhaven National Laboratory by more than three orders of magnitude. In preparation for a high precision experiment to measure the muon EDM, several R&D studies have been performed at PSI: the characterisation of a possible beamline to host the experiment for the muon beam injection study and the measurement of the multiple Coulomb scattering of positrons in potential detector materials at low momenta for the positron tracking scheme development. This paper discusses experimental concepts and the current status of the muEDM experiment at PSI.

hep-ex↗

arXiv2021

Input Beam Matching and Beam Dynamics Design Optimization of the IsoDAR RFQ using Statistical and Machine Learning Techniques

We present a novel machine learning-based approach to generate fast-executing virtual radiofrequency quadrupole (RFQ) particle accelerators using surrogate modelling. These could potentially be used as on-line feedback tools during beam commissioning and operation, and to optimize the RFQ beam dynamics design prior to construction. Since surrogate models execute orders of magnitude faster than corresponding physics beam dynamics simulations using standard tools like PARMTEQM and RFQGen, the computational complexity of the multi-objective optimization problem reduces significantly. Ultimately, this presents a computationally inexpensive and time efficient method to perform sensitivity studies and an optimization of the crucial RFQ beam output parameters like transmission and emittances. Two different methods of surrogate model creation (polynomial chaos expansion and neural networks) are discussed and the achieved model accuracy is evaluated for different study cases with gradually increasing complexity, ranging from a simple FODO cell example to the full RFQ optimization. We find that variations of the beam input Twiss parameters can be reproduced well. The prediction of the beam with respect to hardware changes, e.g. of the electrode modulation, are challenging on the other hand. We discuss possible reasons.

physics.acc-ph↗

arXiv2021

Benchmarking Collective Effects of Electron Interactions in a Wiggler with OPAL-FEL

OPAL-FEL is a recently developed tool for the modeling of particle accelerators containing wigglers or undulators. It extends the well established 3D electrostatic particle-tracking code OPAL, by merging it with the finite-difference time-domain electromagnetic solver MITHRA. We present results of two benchmark cases where OPAL-FEL simulations are compared to experimental results. Both experiments concern electron beamlines where the longitudinal phase space is modulated with a short magnetic wiggler. Good agreement was found in both the space charge and radiation dominated regimes.

physics.acc-ph↗

arXiv2021

Retrieval of aerosol properties from in situ, multi-angle light scattering measurements using invertible neural networks

Atmospheric aerosols have a major influence on the earths climate and public health. Hence, studying their properties and recovering them from light scattering measurements is of great importance. State of the art retrieval methods such as pre-computed look-up tables and iterative, physics-based algorithms can suffer from either accuracy or speed limitations. These limitations are becoming increasingly restrictive as instrumentation technology advances and measurement complexity increases. Machine learning algorithms offer new opportunities to overcome these problems, by being quick and precise. In this work we present a method, using invertible neural networks to retrieve aerosol properties from in situ light scattering measurements. In addition, the algorithm is capable of simulating the forward direction, from aerosol properties to measurement data. The applicability and performance of the algorithm are demonstrated with simulated measurement data, mimicking in situ laboratory and field measurements. With a retrieval time in the millisecond range and a weighted mean absolute percentage error of less than 1.5%, the algorithm turned out to be fast and accurate. By introducing Gaussian noise to the data, we further demonstrate that the method is robust with respect to measurement errors. In addition, realistic case studies are performed to demonstrate that the algorithm performs well even with missing measurement data.

physics.ao-ph↗

arXiv2021

Fast, efficient and flexible particle accelerator optimisation using densely connected and invertible neural networks

Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. Finding optimized operation points for these complex machines is a challenging task, however, due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on deep and invertible neural networks, that can replace the expensive physics computer models. These models are employed in multi-objective optimisations to find Pareto optimal operation points for two fundamentally different types of particle accelerators. Our approach reduces the time-to-solution for a multi-objective accelerator optimisation up to a factor of 640 and the computational cost up to 98%. The framework established here should pave the way for future on-line and real-time multi-objective optimisation of particle accelerators.

physics.acc-ph↗

arXiv2021

Beam stripping interactions in compact cyclotrons

Beam stripping losses of H- ion beams by interactions with residual gas and electromagnetic fields are evaluated. These processes play an important role in compact cyclotrons where the beam is produced on an internal ion source, and they operate under high magnetic field. The implementation of stripping interactions into the beam dynamics code OPAL provides an adequate framework to estimate the stripping losses for compact cyclotrons such as AMIT. The analysis is focused on optimizing the high energy beam current delivered to the target. The optimization is performed by adjusting parameters of the ion source to regulate the vacuum level inside the accelerator and minimize the beam stripping losses.

physics.acc-ph↗

arXiv2021

Order-of-Magnitude Beam Current Improvement in Compact Cyclotrons

There is great need for high intensity proton beams from compact particle accelerators in particle physics, medical isotope production, and materials- and energy-research. To address this need, we present, for the first time, a design for a compact isochronous cyclotron that will be able to deliver 10 mA of 60 MeV protons - an order of magnitude higher than on-market compact cyclotrons and a factor four higher than research machines. A key breakthrough is that vortex motion is incorporated in the design of a cyclotron, leading to clean extraction. Beam losses on the septa of the electrostatic extraction channels stay below 50 W (a factor four below the required safety limit), while maintaining good beam quality. We present a set of highly accurate particle-in-cell simulations, and an uncertainty quantification of select beam input parameters using machine learning, showing the robustness of the design. This design can be utilized for beams for experiments in particle and nuclear physics, materials science and medical physics as well as for industrial applications.

physics.acc-ph↗

arXiv2021

On Boundary Conditions in the sub-mesh interaction of the Particle-Particle-Particle-Mesh Algorithm

The Particle-Particle-Particle-Mesh algorithm elegantly extends the standard Particle-In-Cell scheme by direct summation of interaction that happens over distances below or around mesh size. Generally, this allows for a more accurate description of Coulomb interactions and improves precision in the prediction of key observables. Nevertheless, most implementations neglect electrostatic boundary conditions for the short-ranged interaction that are directly summed. In this paper a variational description of the Particle-Particle-Particle-Mesh algorithm will be developed for the first time and subsequently used to derive temporally and spatially discrete equations of motion. We show that the error committed by neglecting boundary conditions on the short scale is directly tied to the discretization error induced by the computational grid.

physics.comp-ph↗

arXiv2021

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also utilizes the advances of image classification techniques. Our best performing interlock-to-stable classifier reaches an Area under the ROC Curve value of $0.71 \pm 0.01$ compared to $0.65 \pm 0.01$ of a Random Forest model, and it can potentially reduce the beam time loss by $0.5 \pm 0.2$ seconds per interlock.

physics.acc-ph↗

arXiv2020

Global Sensitivity Analysis on Numerical Solver Parameters of Particle-In-Cell Models in Particle Accelerator Systems

Every computer model depends on numerical input parameters that are chosen according to mostly conservative but rigorous numerical or empirical estimates. These parameters could for example be the step size for time integrators, a seed for pseudo-random number generators, a threshold or the number of grid points to discretize a computational domain. In case a numerical model is enhanced with new algorithms and modelling techniques the numerical influence on the quantities of interest, the running time as well as the accuracy is often initially unknown. Usually parameters are chosen on a trial-and-error basis neglecting the computational cost versus accuracy aspects. As a consequence the cost per simulation might be unnecessarily high which wastes computing resources. Hence, it is essential to identify the most critical numerical parameters and to analyze systematically their effect on the result in order to minimize the time-to-solution without losing significantly on accuracy. Relevant parameters are identified by global sensitivity studies where Sobol' indices are common measures. These sensitivities are obtained by surrogate models based on polynomial chaos expansion. In this paper, we first introduce the general methods for uncertainty quantification. We then demonstrate their use on numerical solver parameters to reduce the computational costs and discuss further model improvements based on the sensitivity analysis. The sensitivities are evaluated for neighbouring bunch simulations of the existing PSI Injector II and PSI Ring as well as the proposed Daedalus Injector cyclotron and simulations of the rf electron gun of the Argonne Wakefield Accelerator.

physics.comp-ph↗

arXiv2020

Sparse Grids based Adaptive Noise Reduction strategy for Particle-In-Cell schemes

We propose a sparse grids based adaptive noise reduction strategy for electrostatic particle-in-cell (PIC) simulations. Our approach is based on the key idea of relying on sparse grids instead of a regular grid in order to increase the number of particles per cell for the same total number of particles, as first introduced in Ricketson and Cerfon (Plasma Phys. and Control. Fusion, 59(2), 024002). Adopting a new filtering perspective for this idea, we construct the algorithm so that it can be easily integrated into high performance large-scale PIC code bases. Unlike the physical and Fourier domain filters typically used in PIC codes, our approach automatically adapts to mesh size, number of particles per cell, smoothness of the density profile and the initial sampling technique. Thanks to the truncated combination technique, we can reduce the larger grid-based error of the standard sparse grids approach for non-aligned and non-smooth functions. We propose a heuristic based on formal error analysis for selecting the optimal truncation parameter at each time step, and develop a natural framework to minimize the total error in sparse PIC simulations. We demonstrate its efficiency and performance by means of two test cases: the diocotron instability in two dimensions, and the three-dimensional electron dynamics in a Penning trap. Our run time performance studies indicate that our new scheme can provide significant speedup and memory reduction as compared to regular PIC for achieving comparable accuracy in the charge density deposition.

physics.comp-ph↗

arXiv2020

Multiobjective optimization of the dynamic aperture for SLS 2.0 using surrogate models based on artificial neural networks

Modern synchrotron light source storage rings, such as the Swiss Light Source upgrade (SLS 2.0), use multi-bend achromats in their arc segments to achieve unprecedented brilliance. This performance comes at the cost of increased focusing requirements, which in turn require stronger sextupole and higher-order multipole fields for compensation and lead to a considerable decrease in the dynamic aperture and/or energy acceptance. In this paper, to increase these two quantities, a multi-objective genetic algorithm (MOGA) is combined with a modified version of the well-known tracking code tracy. As a first approach, a massively parallel implementation of a MOGA is used. Compared to a manually obtained solution this approach yields very good results. However, it requires a long computation time. As a second approach, a surrogate model based on artificial neural networks is used in the optimization. This improves the computation time, but the results quality deteriorates. As a third approach, the surrogate model is re-trained during the optimization. This ensures a solution quality comparable to the one obtained with the first approach while also providing an order of magnitude speedup. Finally, good candidate solutions for SLS 2.0 are shown and further analyzed.

physics.acc-ph↗

arXiv2020

Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems

High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often limits their use in practice for design optimization and experiment planning. It also precludes their use as online models tied directly to accelerator operation. We introduce an approach based on machine learning to create nonlinear, fast-executing surrogate models that are informed by a sparse sampling of the physics simulation. The models are 10^6 to 10^7 times more efficient to execute.We also demonstrate that these models can be reliably used with multi-objective optimization to obtain orders-of-magnitude speedup in initial design studies and experiment planning. For example, we required 132 times fewer simulation evaluations to obtain an equivalent solution for our main test case, and initial studies suggest that between 330 to 550 times fewer simulation evaluations are needed when using an iterative retraining process. Our approach enables new ways for high-fidelity particle accelerator simulations to be used, at comparatively little computational cost.

physics.acc-ph↗

arXiv2020

Scientific opportunies for bERLinPro 2020+, report with ideas and conclusions from bERLinProCamp 2019

The Energy Recovery Linac (ERL) paradigm offers the promise to generate intense electron beams of superior quality with extremely small six-dimensional phase space for many applications in the physical sciences, materials science, chemistry, health, information technology and security. Helmholtz-Zentrum Berlin started in 2010 an intensive R\&D programme to address the challenges related to the ERL as driver for future light sources by setting up the bERLinPro (Berlin ERL Project) ERL with 50 MeV beam energy and high average current. The project is close to reach its major milestone in 2020, acceleration and recovery of a high brightness electron beam. The goal of bERLinProCamp 2019 was to discuss scientific opportunities for bERLinPro 2020+. bERLinProCamp 2019 was held on Tue, 17.09.2019 at Helmholtz-Zentrum Berlin, Berlin, Germany. This paper summarizes the main themes and output of the workshop.

physics.acc-ph↗