Exploring Reinforcement Learning for Optimal Bunch Merge in the AGS
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43 PARTICLE ACCELERATORS↗
Engineering topics
Edelen, A.
Publications and source records attributed to Edelen, A..
Collaboration for Advanced Modeling of Particle Accelerators
The pverarching purpose is to accelerate and expand the scope of discoveries from high energy physics (HEP) particle accelerators by enabling the design of accelerators that are significantly more compact and cheaper to build and run. This will be realized through (i) developing high-performance computing (HPC) accelerator and beam modeling capabilities to design the full range of systems required (ii) developing community simulation ecosystems that seamlessly integrate accelerator elements to facilitate the design and control of next-generation accelerators.
43 PARTICLE ACCELERATORS↗
Advanced Modeling of Conventional Particle Accelerators
SciDAC-5 goals: Deliver particle accelerator and beam simulations tools that go beyond the current state of the art, up to the realization of virtual twins of particle accelerators, enabling design and modeling of particle accelerators at unprecedented speed, levels of accuracy, and realism; and apply these tools to key accelerator facilities relevant to DOE HEP (such as PIP-II/DUNE, FACET-II).
43 PARTICLE ACCELERATORS↗
Advanced Modeling of Plasma-based Particle Accelerators
Plasma-based acceleration (PBA) driven by an intense laser (LWFA) or particle beam (PWFA) can produce ultra-high accelerating fields in excess of a GV/cm. PBA could substantially reduce the size and cost of future linear collider facilities if deployed successfully. PBA enables compact tabletop accelerators that can provide lower energy GeV-class beams in a laboratory setting. PBA enables high quality beam generation suitable for x-ray free electron lasers (XFEL) via controllable methods of self-injection. Challenges in modeling and optimization of multi-stage PBA motivate the need for exascale computing and state-of-the-art PIC codes.
43 PARTICLE ACCELERATORS↗
Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations
Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional (> $2D$) beam properties. In this Letter, we introduce a general-purpose algorithm that combines neural networks with differentiable particle tracking to efficiently reconstruct high-dimensional phase space distributions without using specialized beam diagnostics or beam manipulations. Furthermore, we demonstrate that our algorithm accurately reconstructs detailed 4D phase space distributions with corresponding confidence intervals in both simulation and experiment using a single focusing quadrupole and diagnostic screen. This technique allows for the measurement of multiple correlated phase spaces simultaneously, which will enable simplified 6D phase space distribution reconstructions in the future.
47 OTHER INSTRUMENTATION↗
Submacropulse electron-beam dynamics correlated with higher-order modes in a Tesla-type cryomodule
Not Available
43 PARTICLE ACCELERATORS↗
PyEmittance: A General Python Package for Particle Beam Emittance Measurements With Adaptive Quadrupole Scans [Poster]
We present PyEmittance: a new Python package for general particle beam emittance measurements that offers adaptive quadrupole scans and is designed for robust and flexible integration with Python-based software, with online accelerators, and with simulation software. It is open source software, and can be installed via the command pip install pyemittance.
43 PARTICLE ACCELERATORS↗
Differentiable Preisach Modeling for Characterization and Optimization of Particle Accelerator Systems with Hysteresis
Future improvements in particle accelerator performance are predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and material components of accelerators are often neglected in online accelerator models used to inform control algorithms, even though reproducibility errors from systems exhibiting hysteresis are not negligible in high precision accelerators. Here, we combine the classical Preisach model of hysteresis with machine learning techniques to efficiently create nonparametric, high-fidelity models of arbitrary systems exhibiting hysteresis. We experimentally demonstrate how these methods can be used in situ, where a hysteresis model of an accelerator magnet is combined with a Bayesian statistical model of the beam response, allowing characterization of magnetic hysteresis solely from beam-based measurements. Finally, we explore how using these joint hysteresis-Bayesian statistical models allows us to overcome optimization performance limitations that arise when hysteresis effects are ignored.
43 PARTICLE ACCELERATORS↗
Physics model-informed Gaussian process for online optimization of particle accelerators
Not Available
43 PARTICLE ACCELERATORS↗
Recovering the phase and amplitude of X-ray FEL pulses using neural networks and differentiable models
Dynamics experiments are an important use-case for X-ray free-electron lasers (XFELs), but time-domain measurements of the X-ray pulses themselves remain a challenge. Shot-by-shot X-ray diagnostics could enable a new class of simpler and potentially higher-resolution pump-probe experiments. Here, we report training neural networks to combine low-resolution measurements in both the time and frequency domains to recover X-ray pulses at high-resolution. Critically, we also recover the phase, opening the door to coherent-control experiments with XFELs. The model-based generative neural-network architecture can be trained directly on unlabeled experimental data and is fast enough for real-time analysis on the new generation of MHz XFELs.
42 ENGINEERING↗
DIRECT OBSERVATIONS OF SUBMICROPULSE ELECTRON-BEAM EFFECTS FROM SHORT-RANGE WAKEFIELDS IN TESLA-TYPE SUPERCONDUCTING RF CAVITIES
Experiments were performed at The Fermilab Accelerator Science and Technology (FAST) facility to elucidate the effects of short-range wakefields (SRWs) in TESLA-type rf cavities. FAST has a unique configuration of a photocathode rf gun beam injecting two TESLA-type single cavities (CC1 and CC2) in series prior to the cryomodule. To investigate short-range wakefield effects, we have steered the beam to minimize the signals in the higher-order mode (HOM) detectors of CC1 and CC2 for a baseline, and then used a vertical corrector between the two cavities to steer the beam off axis at an angle into CC2. A Hamamatsu synchroscan streak camera viewing a downstream OTR screen provided an image of y-t effects within the micropulses with ~10-micron spatial resolution and 2-ps temporal resolution. At 500 pC/b, 50 b, and 4 mrad off-axis steering into CC2, we observed an ~ 100-micron head-tail centroid shift in the streak camera image y(t)-profiles. This centroid shift value is 5 times l arger than the observed HOM-driven centroid oscillation within the macropulse, and it is consistent with a calculated short-range wakefield effect using ASTRA simulations.
43 PARTICLE ACCELERATORS↗