In the same spirit as the HEP Living Review, the accelerator physics community needs to accurately track the ML contributions to the field.
Number of papers in Arxiv: 97
- Offset Finding of Beamline Parameters on the METRIXS Beamline at BESSY II Using Machine Learning (2025)
- Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment (2025)
- Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach (2025)
- Bayesian optimization of beam injection and storage in the PSI muEDM Experiment (2025)
- Generative machine learning-based 6-dimensional phase space reconstruction using bunch compressors at PAL-XFEL (2025)
- Explainable physics-based constraints on reinforcement learning for accelerator controls (2025)
- Emittance Minimization for Aberration Correction II: Physics-informed Bayesian Optimization of an Electron Microscope (2024)
- Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power (2024)
- Microsecond-Latency Feedback at a Particle Accelerator by Online Reinforcement Learning on Hardware (2024)
- Machine Learning for Reducing Noise in RF Control Signals at Industrial Accelerators (2024)
- Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation (2024)
- Beamline Steering Using Deep Learning Models (2024)
- Dynamic Exclusion of Low-Fidelity Data in Bayesian Optimization for Autonomous Beamline Alignment (2024)
- Bayesian optimization of laser wakefield acceleration in the self-modulated regime (SM-LWFA) aiming to produce molybdenum-99 via photonuclear reactions (2024)
- Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab (2024)
- Injection Optimization at Particle Accelerators via Reinforcement Learning: From Simulation to Real-World Application (2024)
- Bayesian optimization scheme for the design of a nanofibrous high power target (2024)
- Long Short-Term Memory Networks for Anomaly Detection in Magnet Power Supplies of Particle Accelerators (2024)
- Automated Anomaly Detection on European XFEL Klystrons (2024)
- Accelerator beam phase space tomography using machine learning to account for variations in beamline components (2024)
- Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory (2024)
- Efficient 6-dimensional phase space reconstruction from experimental measurements using generative machine learning (2024)
- Multi-Objective Bayesian Active Learning for MeV-ultrafast electron diffraction (2024)
- Leveraging Prior Mean Models for Faster Bayesian Optimization of Particle Accelerators (2024)
- Optimizing Dynamic Aperture Studies with Active Learning (2024)
- Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology (2024)
- Machine-learning approach for operating electron beam at KEK $e^-/e^+$ injector Linac (2024)
- Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations (2024)
- Bayesian Optimization Algorithms for Accelerator Physics (2023)
- Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning (2023)
- Machine Learning For Beamline Steering (2023)
- Uncertainty Aware Deep Learning for Particle Accelerators (2023)
- Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source (2023)
- Machine Learning Based Alignment For LCLS-II-HE Optics (2023)
- Time-drift Aware RF Optimization with Machine Learning Techniques (2023)
- Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions (2023)
- Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning (2023)
- Detecting resonance of radio-frequency cavities using fast direct integral equation solvers and augmented Bayesian optimization (2023)
- Optimization of the injection beam line at the Cooler Synchrotron COSY using Bayesian Optimization (2023)
- Bayesian optimization of laser-plasma accelerators assisted by reduced physical models (2022)
- A machine-learning based closed orbit feedback for the SSRF storage ring (2022)
- Data-driven Science and Machine Learning Methods in Laser-Plasma Physics (2022)
- Bayesian Optimization of the Beam Injection Process into a Storage Ring (2022)
- Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning (2022)
- Neural Networks as Effective Surrogate Models of Radio-Frequency Quadrupole Particle Accelerator Simulations (2022)
- Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration (2022)
- Machine learning-based analysis of experimental electron beams and gamma energy distributions (2022)
- Uncertainty Aware ML-based surrogate models for particle accelerators: A Study at the Fermilab Booster Accelerator Complex (2022)
- Automatic setup of 18 MeV electron beamline using machine learning (2022)
- Diagnostics for Linac Optimization With Machine Learning (2022)
- Transverse phase space tomography in the CLARA accelerator test facility using image compression and machine learning (2022)
- Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization (2022)
- Optimizing a Superconducting Radiofrequency Gun Using Deep Reinforcement Learning (2022)
- Adaptive Machine Learning for Time-Varying Systems: Towards 6D Phase Space Diagnostics of Short Intense Charged Particle Beams (2022)
- SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics (2022)
- Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities (2022)
- Anomaly Detection at the European XFEL using a Parity Space based Method (2022)
- Anomaly Detection in Particle Accelerators using Autoencoders (2021)
- Input Beam Matching and Beam Dynamics Design Optimization of the IsoDAR RFQ using Statistical and Machine Learning Techniques (2021)
- Online-compatible Unsupervised Non-resonant Anomaly Detection (2021)
- Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator (2021)
- Quantifying Uncertainty for Machine Learning Based Diagnostic (2021)
- Beam Measurements and Machine Learning at the CERN Large Hadron Collider (2021)
- Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning (2021)
- Machine learning enabled fast evaluation of dynamic aperture for storage ring accelerators (2021)
- Turn-Key Constrained Parameter Space Exploration for Particle Accelerators Using Bayesian Active Learning (2021)
- Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks (2021)
- Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster (2021)
- SPIRAL2 Cryomodules Models: a Gateway to Process Control and Machine Learning (2021)
- Improving Surrogate Model Accuracy for the LCLS-II Injector Frontend Using Convolutional Neural Networks and Transfer Learning (2021)
- Machine learning-based direct solver for one-to-many problems on temporal shaping of relativistic electron beams (2021)
- Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators (2021)
- Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL (2020)
- Beyond optimization -- supervised learning applications in relativistic laser-plasma experiments (2020)
- Machine learning assisted non-destructive transverse beam profile imaging (2020)
- Multi-Objective Bayesian Optimization for Accelerator Tuning (2020)
- Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning (2020)
- Machine learning for beam dynamics studies at the CERN Large Hadron Collider (2020)
- Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks (2020)
- Multiobjective optimization of the dynamic aperture for SLS 2.0 using surrogate models based on artificial neural networks (2020)
- Introduction to Machine Learning for Accelerator Physics (2020)
- Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory (2020)
- Machine learning for design optimization of storage ring nonlinear dynamics (2019)
- Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities (2019)
- Bayesian optimization of a free-electron laser (2019)
- RF design of APEX2 two-cell continuous-wave normal conducting photoelectron gun cavity based on multi-objective genetic algorithm (2019)
- Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems (2019)
- Opportunities in Machine Learning for Particle Accelerators (2018)
- Multi-objective shape optimization of radio frequency cavities using an evolutionary algorithm (2018)
- Online storage ring optimization using dimension-reduction and genetic algorithms (2018)
- Machine learning for analysis of plasma driven Ion source (2017)
- Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets (2017)
- Machine learning applied to single-shot x-ray diagnostics in an XFEL (2016)
- Optimizing the lattice design for a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms (2016)
- A Genetic Algorithm for Chromaticity Correction in Diffraction Limited Storage Rings (2016)
- Initial experimental results of a machine learning-based temperature control system for an RF gun (2015)
- Improving the nonlinear performance of the HEPS baseline design with genetic algorithm (2015)