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Machine Learning Optimization of E-Beam Transport for a Superradiant FEL

Amir Weinberg, Leon Feigin, Ariel Nause, Avraham Gover • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2608.01874

Beamline Design & Simulation (39.6%) Novel Applications (38.1%)

Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments

Haoyi Jia, Sagar Addepalli, Julia Gonski • arXiv (2026)

Anomaly Detection & Fault Prediction (31.0%)

Particle tracking with physics-informed deep learning methods

Matthias Remta, Anja Beck, Shanthalakshmi Kilambi, Thomas Zhang, Francesco Velotti • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2608.14247

Novel Applications (51.3%) Tools & Libraries (45.8%)

Relative and absolute dosimetric commissioning of the ELIMAIA--ELIMED laser-driven proton beamline at 23.45 MeV

G. A. P. Cirrone, G. Petringa, A. Kurmanova, R. Catalano, A. Amato, A. Pappalardo, M. G. Guarrera, F. Schillaci, P. Blaha, F. Grepl, M. Tryus, A. Velyhan, L. Giuffrida, D. Margarone, G. Cuttone • arXiv (2026)

Beam Diagnostics (37.9%) By Facility Type (31.5%)

Bayesian Optimization of Molybdenum-99 Production by Laser Wakefield Acceleration Using Coupled PIC and Monte Carlo Simulations

Bruno Silveira Nunes, Nilson Dias Vieira Junior, Mirko Salomón Alva Sánchez, Alexandre Bonatto, Ricardo Elgul Samad • arXiv (2026)

Beamline Design & Simulation (32.9%) Surrogate Models (32.1%)

Flow-based surrogate models for particle tracking

Matthias Remta, Yann Dutheil, Francesco Velotti • arXiv (2026)

Surrogate Models (50.0%) Novel Applications (39.6%)

On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider

Abadjiev, Daniel, Howard, Eliza, You, Tsz Ngong, Michaud, Ryan, Roberts, Benjamin Ryan, Rosser, Benjamin, Di Petrillo, Karri Folan, Badea, Anthony, Berry, Doug, Das, Arghya Ranjan, Dickinson, Jennet, Di Guglielmo, Giuseppe, Gupta, Harshul, Fahim, Farah, Gandrakota, Abhijith, Gray, Lindsey, Hirschauer, James, Jiang, David, Kuang, Shiqi, Lipton, Ron, Littmann, Mira, Liu, Miaoyuan, Manganelli, Nicholas, Maksimovic, Petar, Mills, Corrinne, Neubauer, Mark S., Nicholas, Aidan, Parpillon, Benjamin, Pearkes, Jannicke, Quinn, Adam, Shekar, Danush, Silvestre, Ricardo, Syal, Chinar, Swartz, Morris, Tran, Nhan, Trivedi, Amit, Ulmer, Keith, Wadud, Mohammad Abrar, Weiss, Benjamin • arXiv (2026)

Novel Applications (43.7%) Tools & Libraries (40.2%)

Machine Learning Based ROI Segmentation for Beam Imaging Diagnostics at Accelerators

Prachiti Sujit Chandratreya, Frank Mayet, Sergey Tomin, Jitendra Kumar • arXiv (2026)

Beam Diagnostics (52.4%) By Facility Type (39.4%)

Deep-Learning-Enhanced Beam Diagnostics and High-Resolution Image Denoising for Next-Generation Particle Accelerators

Francis Osswald, M. Chahbaoui, X. Liang • EPJ Research Infrastructures (2026) • DOI: 10.1007/s41781-026-00171-w

Novel Applications (44.4%) Data Management (38.4%)

Automating Accelerator Tuning at GSI/FAIR

Sabrina Appel, Hendrik Alsmeier, M. Bajzek, Oliver Boine‐Frankenheim, Lisa Dingeldein, Rolf Findeisen, Benjamin Halilović, Simon Hirlaender, Sebastian Hirt, Daniel Kallendorf, E. Kazantseva, O. Kazinova, Maximilian Kraeft, Eric Lenz, N. Madysa, A. Obertelli, Maik Pfefferkorn, S. Piétri, Christoph Reinwald, M. Schlaich, Stefan Sorge, H. Weick, F. Wienholtz • EPJ Research Infrastructures (2026) • DOI: 10.1007/s41781-026-00175-6

Novel Applications (51.6%) Tools & Libraries (48.1%)

LinacNet: A Composable Particle-Cloud Surrogate Model for Linear Accelerators, and Its Path to Larger Machines

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag • Research Square (2026) • DOI: 10.21203/rs.3.rs-10486818/v1

Novel Applications (47.6%) Tools & Libraries (45.1%) Surrogate Models (50.0%)

Longitudinal Phase Space Tomography via Machine Learning at the Los Alamos Neutron Science Center

Christopher Leon, Alexander Scheinker, Petr M. Anisimov • Research Square (2026) • DOI: 10.21203/rs.3.rs-10697074/v1

Novel Applications (36.0%) Beam Diagnostics (35.9%)

Gaussian Process Regression and Bayesian Optimization for a 40-90 MeV Laser-Plasma Injector for the cSTART Storage Ring

David Squires, Elias Sailer, Joseph Natal, Alexander Saw, Nathan Ray, M. Fuchs, Thiemo Schmelzer • KITopen (2026) • DOI: 10.5445/ir/1000196455

Beamline Design & Simulation (43.0%) Novel Applications (37.0%)

Exploration of Real Time Inference for MI-RR Deblending on GPU/TPU Systems

Yu, Kellen • InspireHEP (2026)

Data Management (40.7%) Tools & Libraries (34.9%)

Machine learning for slow-extraction uniformity at the Fermilab Delivery Ring

Prescott, Matthew J. • InspireHEP (2026)

Data Management (32.1%) Novel Applications (31.1%)

📊 Publication Statistics & Trends

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Publication Trend Over Time

Papers by Category

Recent Activity (Last 6 Months)

Top Publication Venues

Anomalies

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The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń, Ernesto De Matteis • arXiv (2017)

Novel Applications (41.1%) Anomaly Detection & Fault Prediction (38.2%)

Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator

Willem Blokland, Pradeep Ramuhalli, Charles Peters, Yigit Yucesan, Alexander Zhukov, Malachi Schram, Kishansingh Rajput, Torri Jeske • arXiv (2021)

Novel Applications (46.8%) Anomaly Detection & Fault Prediction (45.1%)

Anomaly Detection in Particle Accelerators using Autoencoders

Jonathan P. Edelen, Nathan M. Cook • arXiv (2021)

Anomaly Detection & Fault Prediction (41.9%) Novel Applications (41.6%)

Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning

Majdi I. Radaideh, Chris Pappas, Mark Wezensky, Pradeep Ramuhalli, Sarah Cousineau • arXiv (2022)

Anomaly Detection & Fault Prediction (41.9%) Novel Applications (31.9%)

Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression

Sichen Li, Jochem Snuverink, Fernando Perez-Cruz, Andreas Adelmann • arXiv (2023)

Statistics & Trends (41.5%) Anomaly Detection & Fault Prediction (41.3%)

Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source

Ryan Humble, William Colocho, Finn O'Shea, Daniel Ratner, Eric Darve • arXiv (2023)

Anomaly Detection & Fault Prediction (39.3%) Novel Applications (31.8%)

Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators

Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta • arXiv (2024)

Anomaly Detection & Fault Prediction (48.8%) Novel Applications (37.5%)

Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology

Zhiyuan Chen, Wei Lu, Radhika Bhong, Yimin Hu, Brian Freeman, Adam Carpenter • arXiv (2024)

Anomaly Detection & Fault Prediction (46.1%) Novel Applications (42.2%)

Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory

Monibor Rahman, Adam Carpenter, Khan Iftekharuddin, Chris Tennant • arXiv (2024)

Anomaly Detection & Fault Prediction (40.5%) Data Management (32.9%)

Automated Anomaly Detection on European XFEL Klystrons

Antonin Sulc, Annika Eichler, Tim Wilksen • arXiv (2024)

Anomaly Detection & Fault Prediction (47.9%) RF Systems (42.5%)

A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL

Boukela, Lynda, Eichler, Annika, Branlard, Julien, Jomhari, Nur Zulaiha • arXiv (2024)

Anomaly Detection & Fault Prediction (43.4%) Novel Applications (36.9%)

AI-Enabled Operations at Fermi Complex: Multivariate Time Series Prediction for Outage Prediction and Diagnosis

Jain, Milan, Mutlu, Burcu O., Stam, Caleb, Strube, Jan, Schupbach, Brian A., John, Jason M.St., Pellico, William A. • arXiv (2025)

Anomaly Detection & Fault Prediction (45.2%) Tools & Libraries (33.8%)

Coincident Learning for Beam-based RF Station Fault Identification Using Phase Information at the SLAC Linac Coherent Light Source

Jia Liang, William Colocho, Franz-Josef Decker, Ryan Humble, Ben Morris, Finn H. O'Shea, David A. Steele, Zhe Zhang, Eric Darve, Daniel Ratner • arXiv (2025)

Anomaly Detection & Fault Prediction (45.0%) Beam Diagnostics (38.1%)

Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source

Cencillo, Alberto D., Concepción, Leonardo, Luengo, Julián, Triguero, Isaac • arXiv (2026)

Anomaly Detection & Fault Prediction (32.3%)

Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments

Haoyi Jia, Sagar Addepalli, Julia Gonski • arXiv (2026)

Anomaly Detection & Fault Prediction (31.0%)

RBF neural net based classifier for the AIRIX accelerator fault diagnosis

J. C. Ribes, G. Delaunay, J. Delvaux, E. Merle, M. Mouillet • arXiv (2000)

Anomaly Detection & Fault Prediction (42.2%) Novel Applications (34.5%)

Development of an Experimental Test Stand and Machine Learning Surrogate Models for Capturing Complex Responses in Pulsed Septa

Kawa, Krzysztof, Szumlak, Tomasz, Yagci, Omer Yusuf, Balhan, Bruno, Lackner, Friedrich • IEEE Trans.Appl.Supercond. (2026) • DOI: 10.1109/tasc.2026.3660099

Novel Applications (36.1%) Anomaly Detection & Fault Prediction (36.1%) Surrogate Models (50.0%)

Deep learning framework for fault detection in accelerators

Piekarski, Michal • JACoW (2024) • DOI: 10.18429/jacow-ibic2024-wep47

Beam Diagnostics (43.4%) Anomaly Detection & Fault Prediction (41.9%) Tools & Libraries (50.0%)

Machine learning using beam loss monitors for DIAMOND-II

Lehmann, Corey, Bobb, Lorraine, Burrows, Philip • JACoW (2026) • DOI: 10.18429/jacow-ibic2025-mopco04

Anomaly Detection & Fault Prediction (47.3%) Beam Diagnostics (43.1%)

Anomaly detection of slow-moving variables at LANSCE for improved beam quality

Huang, En-Chuan, Yampolsky, Nikolai • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop007

Anomaly Detection & Fault Prediction (52.2%) Beam Diagnostics (44.5%)

Machine learning-enhanced infrared imaging for temperature anomaly detection in power supplies

Mohsen, Osama, Lobach, Ihar, Borland, Michael, Sun, Yine • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop059

Anomaly Detection & Fault Prediction (45.9%) Novel Applications (37.0%)

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

Hal Ferguson • ODU Digital Commons (Old Dominion University) (2026) • DOI: 10.25777/2pjk-8q69

Anomaly Detection & Fault Prediction (36.9%) Novel Applications (30.3%)

Control

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Neural Networks for Modeling and Control of Particle Accelerators

A. L. Edelen, S. G. Biedron, B. E. Chase, D. Edstrom, S. V. Milton, P. Stabile • arXiv (2016)

Novel Applications (53.9%) Operations & Control (53.8%)

Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities

Jorge Alberto Diaz Cruz, Sandra Biedron, Manel Martinez-Ramon, Salvador Sosa, Reza Pirayesh • arXiv (2019)

Optimization & Control (40.3%) RF Systems (36.9%)

Machine learning for design optimization of storage ring nonlinear dynamics

Faya Wang, Minghao Song, Auralee Edelen, Xiaobiao Huang • arXiv (2019)

Optimization & Control (43.7%) Novel Applications (31.2%)

Online tuning and light source control using a physics-informed Gaussian process Adi

A. Hanuka, J. Duris, J. Shtalenkova, D. Kennedy, A. Edelen, D. Ratner, X. Huang • arXiv (2019)

Optimization & Control (46.2%) Operations & Control (42.3%)

Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia, Oliver Stein, Erik Bründermann, Willi Kuropka, Hannes Dinter, Frank Mayet, Thomas Vinatier, Florian Burkart, Holger Schlarb • arXiv (2023)

Reinforcement Learning & Autonomous Systems (57.9%) Optimization & Control (46.4%)

RL-ABC: Reinforcement Learning for Accelerator Beamline Control

Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin, Alexey Petrenko, Denis Derkach • arXiv (2026)

Reinforcement Learning & Autonomous Systems (53.2%) Operations & Control (43.6%)

Batch spacing optimization by reinforcement learning

Remta, Matthias, Velotti, Francesco, Rezagholi, Sharwin • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/g9wr-197z

Reinforcement Learning & Autonomous Systems (61.3%) Operations & Control (52.5%)

Application of ensemble machine learning algorithms and filtering techniques in slow orbit feedback systems of electron storage rings

Fan, Jiaqi, Liu, Weibin, Wang, Jiuqing, Wei, Yanru, Wei, Yuanyuan, Ji, Daheng • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/physrevaccelbeams.28.042801

Optimization & Control (36.3%) Reinforcement Learning & Autonomous Systems (35.4%)

Continuous data-driven control of the GTS-LHC ion source at CERN

Kain, Verena, Rodriguez Mateos, Borja, Bruchon, Niky, Hirlaender, Simon, Küchler, Detlef • JACoW (2024) • DOI: 10.18429/jacow-ecris2024-mop11

Data Management (53.3%) Operations & Control (53.0%)

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

Whitbeck, A., Berlioz, J., Danison-Fieldhouse, K., Hazelwood, K., Khan, M., Mitrevski, J., Narayanan, A., John, J.St., Tran, N., Ji, J., Walter, M. • Journal (2025) • DOI: 10.18429/jacow-napac2025-mop039

Novel Applications (37.3%) Optimization & Control (36.1%)

Machine learning-enhanced deterministic controls in lasers and accelerators

Dan Wang, Alessio Amodio, Alex Picksley, A. J. Gonsalves, Curtis J. Berger, Hai-En Tsai, Jeroen van Tilborg, Mahek Logantha, N. R. Vora, Qiang Du, Russell Wilcox, Samuel K. Barber, Tong Zhou, Z. Eisentraut • JACOW (2026) • DOI: 10.18429/jacow-napac2025-weyn01

Operations & Control (47.0%) Reinforcement Learning & Autonomous Systems (44.2%)

Machine Learning-Driven Beam Tuning Using Adaptive Region Bayesian Optimization at INFN-LNL

Ysabella Kassandra Ong, Luca Bellan, D. Bortolato, Maurizio Montis, M. Comunian, Natalia Milas, Ryoichi Miyamoto, Domenic Nicosia, Francesco Grespan, E. Fagotti, A. Pisent • Instruments (2026) • DOI: 10.3390/instruments10020033

Novel Applications (46.2%) Optimization & Control (41.2%)

Novel Applications

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Machine learning applied to single-shot x-ray diagnostics in an XFEL

A. Sanchez-Gonzalez, P. Micaelli, C. Olivier, T. R. Barillot, M. Ilchen, A. A. Lutman, A. Marinelli, T. Maxwell, A. Achner, M. Agåker, N. Berrah, C. Bostedt, J. Buck, P. H. Bucksbaum, S. Carron Montero, B. Cooper, J. P. Cryan, M. Dong, R. Feifel, L. J. Frasinski, H. Fukuzawa, A. Galler, G. Hartmann, N. Hartmann, W. Helml, A. S. Johnson, A. Knie, A. O. Lindahl, J. Liu, K. Motomura, M. Mucke, C. O'Grady, J-E. Rubensson, E. R. Simpson, R. J. Squibb, C. Såthe, K. Ueda, M. Vacher, D. J. Walke, V. Zhaunerchyk, R. N. Coffee, J. P. Marangos • arXiv (2016)

Novel Applications (30.3%) By Facility Type (29.7%)

Neural Networks for Modeling and Control of Particle Accelerators

A. L. Edelen, S. G. Biedron, B. E. Chase, D. Edstrom, S. V. Milton, P. Stabile • arXiv (2016)

Novel Applications (53.9%) Operations & Control (53.8%)

Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets

Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik • arXiv (2017)

Novel Applications (37.7%) Statistics & Trends (33.1%)

First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

A. L. Edelen, S. G. Biedron, S. V. Milton, J. P. Edelen • arXiv (2016)

Beam Diagnostics (42.3%) Novel Applications (42.2%)

Machine learning for analysis of plasma driven Ion source

N. Joshi, O. Meusel, H. Podlech • arXiv (2018)

Novel Applications (53.1%) Statistics & Trends (46.1%)

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń, Ernesto De Matteis • arXiv (2017)

Novel Applications (41.1%) Anomaly Detection & Fault Prediction (38.2%)

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

Auralee Edelen, Nicole Neveu, Yannick Huber, Mattias Frey, Christopher Mayes, Andreas Adelmann • arXiv (2020)

Novel Applications (51.5%) Tools & Libraries (51.0%)

Generative Adversarial Networks (GAN) for compact beam source modelling in Monte Carlo simulations

David Sarrut, Nils Krah, Jean-Michel Létang • arXiv (2019)

Novel Applications (35.9%) Statistics & Trends (31.5%)

Machine learning for design optimization of storage ring nonlinear dynamics

Faya Wang, Minghao Song, Auralee Edelen, Xiaobiao Huang • arXiv (2019)

Optimization & Control (43.7%) Novel Applications (31.2%)

Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin • arXiv (2020)

RF Systems (49.8%) Novel Applications (36.4%)

Introduction to Machine Learning for Accelerator Physics

Daniel Ratner • arXiv (2020)

Novel Applications (61.5%) Tools & Libraries (55.3%)

Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators

Andrei Ivanov, Ilya Agapov • arXiv (2020)

Novel Applications (49.3%) Beamline Design & Simulation (43.2%)

Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics

A. Hanuka, C. Emma, T. Maxwell, A. Fisher, B. Jacobson, M. J. Hogan, Z. Huang • arXiv (2020)

Beam Diagnostics (39.9%) Novel Applications (37.8%)

Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning

Xiaoying Pang, Sunil Thulasidasan, Larry Rybarcyk • arXiv (2020)

Reinforcement Learning & Autonomous Systems (65.2%) Novel Applications (52.3%)

Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster

Jason St. John, Christian Herwig, Diana Kafkes, Jovan Mitrevski, William A. Pellico, Gabriel N. Perdue, Andres Quintero-Parra, Brian A. Schupbach, Kiyomi Seiya, Nhan Tran, Malachi Schram, Javier M. Duarte, Yunzhi Huang, Rachael Keller • arXiv (2021)

Reinforcement Learning & Autonomous Systems (58.1%) Novel Applications (55.7%)

Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL

Simon Hirlaender, Niky Bruchon • arXiv (2022)

Reinforcement Learning & Autonomous Systems (58.2%) Novel Applications (51.5%)

BOOSTR: A Dataset for Accelerator Control Systems

Diana Kafkes, Jason St. John • arXiv (2021)

Novel Applications (53.2%) Data Management (51.3%)

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

Sichen Li, Mélissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani, Andreas Adelmann • arXiv (2021)

Novel Applications (46.5%) Statistics & Trends (43.6%)

Adaptive deep learning for time-varying systems with hidden parameters: Predicting changing input beam distributions of compact particle accelerators

Alexander Scheinker, Frederick Cropp, Sergio Paiagua, Daniele Filippetto • arXiv (2021)

Novel Applications (42.8%) Data Management (36.2%)

Improving Surrogate Model Accuracy for the LCLS-II Injector Frontend Using Convolutional Neural Networks and Transfer Learning

Lipi Gupta, Auralee Edelen, Nicole Neveu, Aashwin Mishra, Christopher Mayes, Young-Kee Kim • arXiv (2021)

Novel Applications (50.9%) Beamline Design & Simulation (46.4%) Surrogate Models (50.0%)

SPIRAL2 Cryomodules Models: a Gateway to Process Control and Machine Learning

Adrien Vassal, Adnan Ghribi, François Millet, François Bonne, Patrick Bonnay, Pierre-Emmanuel Bernaudin • arXiv (2021)

Beamline Design & Simulation (42.1%) Novel Applications (41.7%)

Adaptive Latent Space Tuning for Non-Stationary Distributions

Alexander Scheinker, Frederick Cropp, Sergio Paiagua, Daniele Filippetto • arXiv (2021)

Novel Applications (39.8%) Tools & Libraries (33.4%)

Uncertainty Quantification for Virtual Diagnostic of Particle Accelerators

Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka • arXiv (2021)

Novel Applications (42.6%) Statistics & Trends (40.0%)

Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks

Friedrich Bethke, Richard Pausch, Patrick Stiller, Alexander Debus, Michael Bussmann, Nico Hoffmann • arXiv (2021)

Surrogate Models (51.3%) Novel Applications (42.4%)

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

Renato Bellotti, Romana Boiger, Andreas Adelmann • arXiv (2021)

Novel Applications (59.7%) Tools & Libraries (55.4%)

Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning

Alexander Scheinker • arXiv (2021)

Novel Applications (47.9%) Tools & Libraries (42.8%)

Quantifying Uncertainty for Machine Learning Based Diagnostic

Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka • arXiv (2021)

Novel Applications (37.7%) Statistics & Trends (35.2%)

Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator

Willem Blokland, Pradeep Ramuhalli, Charles Peters, Yigit Yucesan, Alexander Zhukov, Malachi Schram, Kishansingh Rajput, Torri Jeske • arXiv (2021)

Novel Applications (46.8%) Anomaly Detection & Fault Prediction (45.1%)

Anomaly Detection in Particle Accelerators using Autoencoders

Jonathan P. Edelen, Nathan M. Cook • arXiv (2021)

Anomaly Detection & Fault Prediction (41.9%) Novel Applications (41.6%)

Physics-informed neural network method for modelling beam-wall interactions

Kazuhiro Fujita • arXiv (2022)

Beamline Design & Simulation (52.5%) Novel Applications (49.2%)

A Neural Network Model of a Quasi-Periodic Elliptically Polarizing Undulator in Universal Mode

Ryan Sheppard, Cameron Baribeau, Tor Pedersen, Mark Boland, Drew Bertwistle • arXiv (2022)

Novel Applications (50.5%) Reinforcement Learning & Autonomous Systems (38.6%)

Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron Laser

J. Zhu, N. M. Lockmann, M. K. Czwalinna, H. Schlarb • arXiv (2022)

Novel Applications (37.5%) Data Management (36.1%)

Differentiable Preisach Modeling for Characterization and Optimization of Accelerator Systems with Hysteresis

R. Roussel, A. Edelen, D. Ratner, K. Dubey, J. P. Gonzalez-Aguilera, Y. K. Kim, N. Kuklev • arXiv (2022)

Novel Applications (52.2%) Beamline Design & Simulation (51.3%)

Adaptive Machine Learning for Time-Varying Systems: Towards 6D Phase Space Diagnostics of Short Intense Charged Particle Beams

Alexander Scheinker, Spencer Gessner • arXiv (2022)

Novel Applications (51.4%) Statistics & Trends (43.0%)

Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-based Charged Particle Beams

Auralee Edelen, Christopher Mayes • arXiv (2022)

Novel Applications (52.4%) Beamline Design & Simulation (47.3%)

Optimizing a Superconducting Radiofrequency Gun Using Deep Reinforcement Learning

David Meier, Luis Vera Ramirez, Jens Völker, Bernhard Sick, Jens Viefhaus, Gregor Hartmann • arXiv (2022)

Reinforcement Learning & Autonomous Systems (44.6%) Novel Applications (41.3%)

Predicting Beam Transmission Using 2-Dimensional Phase Space Projections Of Hadron Accelerators

Anthony Tran, Yue Hao, Brahim Mustapha, Jose L. Martinex Marin • arXiv (2022)

Beamline Design & Simulation (40.5%) Novel Applications (37.7%)

Transverse phase space tomography in the CLARA accelerator test facility using image compression and machine learning

Andrzej Wolski, Mark A. Johnson, Matthew King, Boris L. Militsyn, Peter H. Williams • arXiv (2022)

Novel Applications (41.8%) Statistics & Trends (39.1%)

Diagnostics for Linac Optimization With Machine Learning

R. Sharankova, M. Mwaniki, K. Seiya, M. Wesley • arXiv (2022)

Novel Applications (48.9%) Statistics & Trends (46.7%)

Automatic setup of 18 MeV electron beamline using machine learning

Francesco Maria Velotti, Brennan Goddard, Verena Kain, Rebecca Ramjiawan, Giovanni Zevi Della Porta, Simon Hirlaender • arXiv (2022)

Novel Applications (44.3%) Reinforcement Learning & Autonomous Systems (43.1%)

Uncertainty Aware ML-based surrogate models for particle accelerators: A Study at the Fermilab Booster Accelerator Complex

Malachi Schram, Kishansingh Rajput, Karthik Somayaji Peng Li, Jason St. John, Himanshu Sharma • arXiv (2022)

Novel Applications (43.2%) Statistics & Trends (43.1%) Surrogate Models (50.0%)

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

Sichen Li, Andreas Adelmann • arXiv (2022)

Statistics & Trends (51.5%) Novel Applications (49.7%) Reviews (50.0%)

Machine learning-based analysis of experimental electron beams and gamma energy distributions

M. Yadav, M. Oruganti, S. Zhang, B. Naranjo, G. Andonian, Y. Zhuang, Ö. Apsimon, C. P. Welsch, J. B. Rosenzweig • arXiv (2023)

Novel Applications (44.8%) Statistics & Trends (40.1%)

Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning

Majdi I. Radaideh, Chris Pappas, Mark Wezensky, Pradeep Ramuhalli, Sarah Cousineau • arXiv (2022)

Anomaly Detection & Fault Prediction (41.9%) Novel Applications (31.9%)

Applications of object detection networks at high-power laser systems and experiments

Jinpu Lin, Florian Haberstroh, Stefan Karsch, Andreas Döpp • arXiv (2022)

Novel Applications (38.0%) Tools & Libraries (35.9%)

Neural Network Prior Mean for Particle Accelerator Injector Tuning

Connie Xu, Ryan Roussel, Auralee Edelen • arXiv (2022)

Novel Applications (47.5%) Surrogate Models (47.5%)

Applications of Differentiable Physics Simulations in Particle Accelerator Modeling

Ryan Roussel, Auralee Edelen • arXiv (2022)

Beamline Design & Simulation (57.4%) Novel Applications (54.4%)

Data-driven Science and Machine Learning Methods in Laser-Plasma Physics

Andreas Döpp, Christoph Eberle, Sunny Howard, Faran Irshad, Jinpu Lin, Matthew Streeter • arXiv (2023)

Novel Applications (42.5%) Statistics & Trends (37.5%)

Identification of Magnetic Field Errors in Synchrotrons based on Deep Lie Map Networks

Conrad Caliari, Adrian Oeftiger, Oliver Boine-Frankenheim • arXiv (2023)

Novel Applications (52.9%) Statistics & Trends (43.8%)

Physics-constrained 3D Convolutional Neural Networks for Electrodynamics

Alexander Scheinker, Reeju Pokharel • arXiv (2023)

Novel Applications (33.3%) Tools & Libraries (26.1%)

Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions

Steven Goldenberg, Malachi Schram, Kishansingh Rajput, Thomas Britton, Chris Pappas, Dan Lu, Jared Walden, Majdi I. Radaideh, Sarah Cousineau, Sudarshan Harave • arXiv (2023)

Novel Applications (34.8%) Statistics & Trends (32.8%)

Time-drift Aware RF Optimization with Machine Learning Techniques

R. Sharankova, M. Mwaniki, K. Seiya, M. Wesley • arXiv (2023)

Novel Applications (51.8%) Statistics & Trends (47.1%)

Beam Detection Based on Machine Learning Algorithms

Haoyuan Li, Qing Yin • arXiv (2023)

Beam Diagnostics (42.1%) Novel Applications (34.6%)

Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source

Ryan Humble, William Colocho, Finn O'Shea, Daniel Ratner, Eric Darve • arXiv (2023)

Anomaly Detection & Fault Prediction (39.3%) Novel Applications (31.8%)

Variational Autoencoders for Noise Reduction in Industrial LLRF Systems

J. P. Edelen, M. J. Henderson, J. Einstein-Curtis, C. C. Hall, J. A. Diaz Cruz, A. L. Edelen • arXiv (2023)

Novel Applications (54.5%) Statistics & Trends (49.2%)

Machine Learning For Beamline Steering

Isaac Kante • arXiv (2023)

Novel Applications (51.3%) Tools & Libraries (45.9%)

Bayesian Optimization Algorithms for Accelerator Physics

Ryan Roussel, Auralee L. Edelen, Tobias Boltz, Dylan Kennedy, Zhe Zhang, Fuhao Ji, Xiaobiao Huang, Daniel Ratner, Andrea Santamaria Garcia, Chenran Xu, Jan Kaiser, Angel Ferran Pousa, Annika Eichler, Jannis O. Lubsen, Natalie M. Isenberg, Yuan Gao, Nikita Kuklev, Jose Martinez, Brahim Mustapha, Verena Kain, Weijian Lin, Simone Maria Liuzzo, Jason St. John, Matthew J. V. Streeter, Remi Lehe, Willie Neiswanger • arXiv (2024)

Statistics & Trends (50.2%) Novel Applications (49.8%)

Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators

Kishansingh Rajput, Malachi Schram, Willem Blokland, Yasir Alanazi, Pradeep Ramuhalli, Alexander Zhukov, Charles Peters, Ricardo Vilalta • arXiv (2024)

Anomaly Detection & Fault Prediction (48.8%) Novel Applications (37.5%)

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e

Chenwei Xu, Jerry Yao-Chieh Hu, Aakaash Narayanan, Mattson Thieme, Vladimir Nagaslaev, Mark Austin, Jeremy Arnold, Jose Berlioz, Pierrick Hanlet, Aisha Ibrahim, Dennis Nicklaus, Jovan Mitrevski, Jason Michael St. John, Gauri Pradhan, Andrea Saewert, Kiyomi Seiya, Brian Schupbach, Randy Thurman-Keup, Nhan Tran, Rui Shi, Seda Ogrenci, Alexis Maya-Isabelle Shuping, Kyle Hazelwood, Han Liu • arXiv (2023)

Reinforcement Learning & Autonomous Systems (44.5%) Novel Applications (39.6%)

Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations

Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia • arXiv (2024)

Novel Applications (56.6%) Reinforcement Learning & Autonomous Systems (53.0%)

Time-Delayed Koopman Network-Based Model Predictive Control for the FRIB RFQ

Jinyu Wan, Shen Zhao, Wei Chang, Yue Hao • arXiv (2024)

Novel Applications (35.7%) Reinforcement Learning & Autonomous Systems (32.1%)

Machine-learning approach for operating electron beam at KEK $e^-/e^+$ injector Linac

Gaku Mitsuka, Shinnosuke Kato, Naoko Iida, Takuya Natsui, Masanori Satoh • arXiv (2024)

Novel Applications (54.0%) Statistics & Trends (49.0%)

Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology

Zhiyuan Chen, Wei Lu, Radhika Bhong, Yimin Hu, Brian Freeman, Adam Carpenter • arXiv (2024)

Anomaly Detection & Fault Prediction (46.1%) Novel Applications (42.2%)

Optimizing Dynamic Aperture Studies with Active Learning

D. Di Croce, M. Giovannozzi, E. Krymova, T. Pieloni, S. Redaelli, M. Seidel, R. Tomás, F. F. Van der Veken • arXiv (2024)

Novel Applications (53.9%) Statistics & Trends (47.8%)

Leveraging Prior Mean Models for Faster Bayesian Optimization of Particle Accelerators

Tobias Boltz, Jose L. Martinez, Connie Xu, Kathryn R. L. Baker, Zihan Zhu, Jenny Morgan, Ryan Roussel, Daniel Ratner, Brahim Mustapha, Auralee L. Edelen • arXiv (2025)

Novel Applications (50.1%) Statistics & Trends (46.0%)

GAIA: A General AI Assistant for Intelligent Accelerator Operations

Frank Mayet • arXiv (2024)

Tools & Libraries (53.1%) Novel Applications (50.0%)

Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language

Jan Kaiser, Annika Eichler, Anne Lauscher • arXiv (2024)

Tools & Libraries (57.8%) Novel Applications (54.3%)

Accelerator beam phase space tomography using machine learning to account for variations in beamline components

Andrzej Wolski, Diego Botelho, David Dunning, Amelia E. Pollard • arXiv (2024)

Novel Applications (47.4%) Statistics & Trends (42.4%)

Long Short-Term Memory Networks for Anomaly Detection in Magnet Power Supplies of Particle Accelerators

Ihar Lobach, Michael Borland • arXiv (2024)

Novel Applications (42.7%) Statistics & Trends (38.0%)

Accelerator system parameter estimation using variational autoencoded latent regression

Mahindra Rautela, Alan Williams, Alexander Scheinker • arXiv (2024)

Novel Applications (43.6%) Statistics & Trends (41.3%)

Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Abhishek Pathak • arXiv (2024)

Novel Applications (57.2%) Beamline Design & Simulation (50.2%)

Neural Networks for ID Gap Orbit Distortion Compensation in PETRA III

Bianca Veglia, Ilya Agapov, Joachim Keil • arXiv (2024)

Novel Applications (44.1%) Tools & Libraries (33.2%)

A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL

Boukela, Lynda, Eichler, Annika, Branlard, Julien, Jomhari, Nur Zulaiha • arXiv (2024)

Anomaly Detection & Fault Prediction (43.4%) Novel Applications (36.9%)

Beam-based Identification of Magnetic Field Errors in a Synchrotron using Deep Lie Map Networks

Conrad Caliari, Adrian Oeftiger, Oliver Boine-Frankenheim • Phys.Rev.Accel.Beams (2024) • DOI: 10.1103/physrevaccelbeams.28.024601

Novel Applications (43.8%) By Facility Type (37.7%)

Beamline Steering Using Deep Learning Models

Allen, Dexter, Kante, Isaac, Bohler, Dorian • arXiv (2024)

Novel Applications (48.8%) Tools & Libraries (44.4%)

Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation

G. Kane, P. Drobniak, S. Kazamias, V. Kubytskyi, M. Lenivenko, B. Lucas, J. Serhal, K. Cassou, A. Beck, A. Specka, F. Massimo • arXiv (2025)

Novel Applications (47.8%) Beamline Design & Simulation (46.0%) Surrogate Models (50.0%)

Machine Learning for Reducing Noise in RF Control Signals at Industrial Accelerators

M. Henderson, J. P. Edelen, J. Einstein-Curtis, C. C. Hall, J. A. Diaz Cruz, A. L. Edelen • JINST (2024) • DOI: 10.1088/1748-0221/19/11/p11015

Novel Applications (54.3%) Tools & Libraries (46.8%)

Towards Agentic AI on Particle Accelerators

Antonin Sulc, Thorsten Hellert, Raimund Kammering, Hayden Hoschouer, Jason St. John • Journal (2025)

Reinforcement Learning & Autonomous Systems (59.8%) Novel Applications (58.9%)

Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators

Kamaljeet Singh, Kangkan Goswami, Raghunath Sahoo, Sumanta Samal • arXiv (2025)

Novel Applications (38.3%) Beamline Design & Simulation (37.1%)

Data-Driven Discovery of Beam Centroid Dynamics

Pocher, Liam A., Haber, Irving, Antonsen, Thomas M., O'Shea, Patrick G. • arXiv (2024)

Novel Applications (48.3%) Beamline Design & Simulation (45.5%)

Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power

Korten, Till, Rybnikov, Vladimir, Vogt, Mathias, Roensch-Schulenburg, Juliane, Steinbach, Peter, Mirian, Najmeh • Journal (2024)

Novel Applications (33.4%) Tools & Libraries (32.1%)

Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines

Siyu Wang, Shengran Dai, Jianhui Jiang, Shuang Wu, Yufei Peng, Junbin Zhang • arXiv (2024)

Reinforcement Learning & Autonomous Systems (43.1%) Novel Applications (32.3%)

Virtual Pulse Reconstruction Diagnostic for Single-Shot Measurement of Free Electron Laser Radiation Power

Till Korten, Vladimir Rybnikov, Peter Steinbach, Najmeh Mirian • Phys.Rev.Accel.Beams (2024) • DOI: 10.1103/physrevaccelbeams.28.030703

Novel Applications (40.9%) Tools & Libraries (37.9%)

Optimizing Beam-Plasma Interactions Through Jitter Analysis Using Start-to-End Simulations

Hwang, Robin • arXiv (2024)

Novel Applications (54.1%) Beamline Design & Simulation (50.5%)

Inverse Surrogate Model of a Soft X-Ray Spectrometer using Domain Adaptation

Enrico Ahlers, Peter Feuer-Forson, Gregor Hartmann, Rolf Mitzner, Peter Baumgärtel, Jens Viefhaus • arXiv (2025)

Novel Applications (34.4%) Data Management (29.6%) Surrogate Models (50.0%)

Explainable physics-based constraints on reinforcement learning for accelerator controls

Colen, Jonathan, Schram, Malachi, Rajput, Kishansingh, Kasparian, Armen • arXiv (2025)

Reinforcement Learning & Autonomous Systems (60.7%) Novel Applications (49.1%)

Using Convolutional Neural Networks to Accelerate 3D Coherent Synchrotron Radiation Computations

Christopher Leon, Petr M. Anisimov, Nikolai Yampolsky, Alexander Scheinker • arXiv (2025)

Novel Applications (39.8%) By Facility Type (37.1%)

Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach

Anwar Ibrahim, Denis Derkach, Alexey Petrenko, Fedor Ratnikov, Maxim Kaledin • arXiv (2025)

Reinforcement Learning & Autonomous Systems (65.8%) Novel Applications (57.6%)

Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment

A. Sytov, K. Cassou, V. Kubytskyi, M. Lenivenko, A. Huber • arXiv (2025)

Novel Applications (48.7%) Beamline Design & Simulation (46.7%)

AI-Assisted Transport of Radioactive Ion Beams

Sergio Lopez-Caceres, Daniel Santiago-Gonzalez • arXiv (2025)

Novel Applications (43.0%) Statistics & Trends (39.2%)

A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE

Aashwin Mishra, Matt Seaberg, Ryan Roussel, Fred Poitevin, Jana Thayer, Daniel Ratner, Auralee Edelen, Apurva Mehta • arXiv (2025)

Novel Applications (45.5%) Tools & Libraries (41.7%)

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry

Asif Iqbal, John Verboncoeur, Peng Zhang • arXiv (2025)

Novel Applications (38.9%) Statistics & Trends (38.7%) Tools & Libraries (50.0%)

Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach

Anwar Ibrahim, Alexey Petrenko, Maxim Kaledin, Ehab Suleiman, Fedor Ratnikov, Denis Derkach • arXiv (Cornell University) (2025) • DOI: 10.48550/arxiv.2510.26805

Reinforcement Learning & Autonomous Systems (60.6%) Novel Applications (56.0%)

First Experimental Demonstration of Machine Learning-Based Tuning on the PSI Injector 2 Cyclotron

M. Haj Tahar, W. Joho, E. Solodko, M. Bocchio, S. Marquie, M. Busch, A. Barchetti, J. Grillenberger, J. Snuverink, M. Schneider • arXiv (Cornell University) (2025) • DOI: 10.48550/arxiv.2512.03829

Novel Applications (55.9%) Reinforcement Learning & Autonomous Systems (53.1%)

Machine learning-based prediction of magnet errors in storage ring light sources

Xu, Jianhao • arXiv (2025)

Novel Applications (32.0%) Beam Dynamics (27.1%)

Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models

Rodriguez-Llorente, Guillermo, Gallardo, Galo, Navascués, Rodrigo Morant, Petrovsky, Nikita Khvatkin, Sabogal, Anderson, Martín, Roberto Gómez-Espinosa • Journal (2025)

Novel Applications (46.0%) Reinforcement Learning & Autonomous Systems (45.9%) Surrogate Models (50.0%)

U-Net-based surrogate modeling for attosecond X-ray free-electron lasers

Yufei Wei, Bingyang Yan, Chenzhi Xu, Jiawei Yan, Haixiao Deng • arXiv (2026)

Surrogate Models (50.0%) Novel Applications (38.6%)

High-Resolution Image Reconstruction with Unsupervised Learning and Noisy Data Applied to Ion-Beam Dynamics for Particle Accelerators

Francis Osswald, Mohammed Chahbaoui, X. Liang • Journal of Instrumentation (2026)

Novel Applications (42.7%) Statistics & Trends (35.5%)

AI-Ready Control System for the Fermilab Accelerator Complex

Tia Miceli, Erik Gottschalk, Donovan Tooke, Evan Milton, Robert Santucci, Hayden Hoschouer, Michael Balcewicz, Jennifer Case, Abhishek Deshpande, Kit Fieldhouse, Sudeshna Ganguly, Beau Harrison, Aisha Ibrahim, Thomas Kobilarcik, Michael Olander, Abhishek Pathak, Jason St. John, Aaron Sauers • arXiv (2026)

Novel Applications (62.5%) Data Management (59.4%)

Machine learning-based virtual diagnostics of dielectric laser acceleration

Thilo Egenolf, Oliver Boine-Frankenheim • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2603.28549

Surrogate Models (43.9%) Novel Applications (43.3%)

Data-Driven Optimisation of Superconducting Magnets at CEA Paris-Saclay

Damien F. G. Minenna, Guillaume Dilasser, Robin Penavaire, Valerio Calvelli, Thibault de Chabannes, Thibault Lecrevisse, Thomas Achard, Jason Le Coz, Christophe Berriaud, Benoît Bolzon, Antomne Caunes, Phillipe Fazilleau, Hélène Felice, Clément Genot, Antoine Guinet, Nikola Jerance, François-Paul Juster, Thibaut Lemercier, Gilles Lenoir, Clément Lorin, Yann Perron, Camille Pucheu-Plante, Étienne Rochepault, Damien Simon, Francesco Stacchi, Michel Segreti, Vincent Trauchessec, Olivier Tuske, Hajar Zgour • arXiv (2026)

Novel Applications (55.6%) Data Management (47.6%)

Machine-State Embeddings as an Operational Coordinate System for Accelerator Operation

Chris Tennant, Jundong Li, Song Wang • arXiv (2026)

Novel Applications (40.2%) Data Management (39.3%)

Learning Dynamic Aperture from One-turn Maps

Derong Xu • arXiv (2026)

Novel Applications (33.4%) Surrogate Models (29.8%)

Beam-Level Nonlinear Compton Spectra via a Neural Network Surrogate Model

Antonina Timoshenko, Maxim Malakhov, Alexander Fedotov, Sergey Rykovanov • arXiv (2026)

Novel Applications (33.9%) Surrogate Models (50.0%)

Robust Betatron-Tune Measurement from Schottky Spectra: Complementary Classical and Deep-Learning Paradigms

Peihan Sun, Manzhou Zhang, Renxian Yuan, Deming Li, Jian Dong • arXiv (2026)

Beam Diagnostics (32.2%) Novel Applications (30.1%)

Reinforcement Learning applied to Optimization of LHC beams in the CERN Proton Synchrotron

Joel Axel Wulff, Alexandre Lasheen • arXiv (2026)

Reinforcement Learning & Autonomous Systems (45.5%) Novel Applications (41.9%)

Machine Learning Optimization of E-Beam Transport for a Superradiant FEL

Amir Weinberg, Leon Feigin, Ariel Nause, Avraham Gover • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2608.01874

Beamline Design & Simulation (39.6%) Novel Applications (38.1%)

Particle tracking with physics-informed deep learning methods

Matthias Remta, Anja Beck, Shanthalakshmi Kilambi, Thomas Zhang, Francesco Velotti • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2608.14247

Novel Applications (51.3%) Tools & Libraries (45.8%)

Flow-based surrogate models for particle tracking

Matthias Remta, Yann Dutheil, Francesco Velotti • arXiv (2026)

Surrogate Models (50.0%) Novel Applications (39.6%)

On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider

Novel Applications (43.7%) Tools & Libraries (40.2%)

RBF neural net based classifier for the AIRIX accelerator fault diagnosis

J. C. Ribes, G. Delaunay, J. Delvaux, E. Merle, M. Mouillet • arXiv (2000)

Anomaly Detection & Fault Prediction (42.2%) Novel Applications (34.5%)

Machine learning based parametrization of the resolution function for the first experimental area of the n_TOF facility at CERN

Petar Žugec, Marta Sabaté-Gilarte, Michael Bacak, Vasilis Vlachoudis, Adria Casanovas, Francisco García-Infantes • Nuclear Science and Techniques (2025) • DOI: 10.1007/s41365-025-01820-2

Novel Applications (35.7%) Tools & Libraries (28.6%)

Application of quantum machine learning using variational quantum classifier in accelerator physics

Yin, He-Xing, Hu, Zhi-Yuan, Zeng, Huan-Huan, Guan, Jia-Bao, Wang, Ji-ke • Nuclear Science and Techniques (2025) • DOI: 10.1007/s41365-026-02016-y

Novel Applications (41.9%) Statistics & Trends (34.1%)

Machine learning as a service system for particle accelerator and its application in CSNS

Mei, Hao, Zhang, Yuliang, Peng, Na, Cheng, Sinong, He, Yongcheng, Xue, Kangjia, Wang, Lin, Li, Mingtao, Wu, Xuan, Zhu, Peng • Radiation Detection Technology and Methods (2025) • DOI: 10.1007/s41605-025-00527-7

Tools & Libraries (60.6%) Novel Applications (60.5%)

Deep-Learning-Enhanced Beam Diagnostics and High-Resolution Image Denoising for Next-Generation Particle Accelerators

Francis Osswald, M. Chahbaoui, X. Liang • EPJ Research Infrastructures (2026) • DOI: 10.1007/s41781-026-00171-w

Novel Applications (44.4%) Data Management (38.4%)

Automating Accelerator Tuning at GSI/FAIR

Novel Applications (51.6%) Tools & Libraries (48.1%)

Smart prediction of beam availability at synchrotron facilities

Mustafa Alzu’bi, Nailah Al-Madi, Rawan Ghnemat • Discover Artificial Intelligence (2026) • DOI: 10.1007/s44163-026-01817-9

Novel Applications (45.2%) Tools & Libraries (40.5%)

A hybrid monte Carlo-machine learning framework for neutron dose attenuation modeling in layered iron-concrete shields of a 1000 MeV proton accelerator

Demet Sarıyer, Ali Osman GÖKCAN • Annals of Nuclear Energy (2026) • DOI: 10.1016/j.anucene.2026.112572

Novel Applications (39.1%) Beamline Design & Simulation (36.0%) Tools & Libraries (50.0%)

Hybrid machine learning-monte carlo approach for neutron shielding in high-energy proton facilities

Sariyer, Demet • J.Subatomic Part.Cosmol. (2026) • DOI: 10.1016/j.jspc.2026.100309

Novel Applications (38.5%) Statistics & Trends (36.2%)

Method for reconstruction the energy spectrum of a linear electron accelerator using neural networks

A.A. Kim, F.R. Studenikin, V.V. Khankin, S.A. Zolotov, A.P. Lyubomudrov, A.P. Chernyaev • Radiation Physics and Chemistry (2026) • DOI: 10.1016/j.radphyschem.2026.113775

Novel Applications (52.7%) Beamline Design & Simulation (51.7%)

Geoff: The Generic Optimization Framework & Frontend for Particle Accelerator Controls

Madysa, Penelope, Appel, Sabrina, Kain, Verena, Schenk, Michael • SoftwareX (2025) • DOI: 10.1016/j.softx.2025.102335

Tools & Libraries (61.7%) Novel Applications (57.6%)

Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning

Kaiser, Jan, Xu, Chenran, Eichler, Annika, Santamaria Garcia, Andrea, Stein, Oliver, Bründermann, Erik, Kuropka, Willi, Dinter, Hannes, Mayet, Frank, Vinatier, Thomas, Burkart, Florian, Schlarb, Holger • Sci.Rep. (2024) • DOI: 10.1038/s41598-024-66263-y

Reinforcement Learning & Autonomous Systems (58.3%) Novel Applications (48.5%)

Machine Learning for Optimized Polarization at Jefferson Lab

Jeske, Torri, Kasparian, Armen, Lawrence, David, Britton, Thomas, Schram, Malachi, Moran, Patrick, Fanelli, Cristiano, Guo, Jiawei, Jarvis, Naomi, Maxwell, James, Keith, Chris • EPJ Web Conf. (2025) • DOI: 10.1051/epjconf/202533701223

Novel Applications (35.1%) Statistics & Trends (29.7%)

Artificial intelligence for advancing particle accelerators

Ghribi, Adnan, Cassou, Kevin, Dalena, Barbara, Eichler, Annika, Guler, Hayg, Mistry, Andrew K., Oeftiger, Adrian, Shea, Thomas, Valentino, Gianluca, Welsch, Carsten P. • Europhys.News (2025) • DOI: 10.1051/epn/2025106

Novel Applications (68.4%) Tools & Libraries (59.8%)

Machine learning for mapping multipactor susceptibility in RF systems: Capabilities and generalization constraints

Asif Iqbal, John Verboncoeur, Peng Zhang • APL Machine Learning (2026) • DOI: 10.1063/5.0333133

Statistics & Trends (35.8%) Novel Applications (35.0%)

Data-driven beam diagnostics using betatron radiation

Saberi, Hossein, Burvill, Jasper, Siddle, Carys, Xia, Guoxing, Patrick Farmer, John, Pukhov, Alexander • Plasma Phys.Control.Fusion (2026) • DOI: 10.1088/1361-6587/ae3eb3

Novel Applications (50.0%) Statistics & Trends (44.7%)

Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators

Rajput, Kishansingh, Schram, Malachi, Edelen, Auralee, Colen, Jonathan, Kasparian, Armen, Roussel, Ryan, Carpenter, Adam, Zhang, He, Benesch, Jay • Mach.Learn.Sci.Tech. (2024) • DOI: 10.1088/2632-2153/adc221

Reinforcement Learning & Autonomous Systems (48.7%) Novel Applications (44.8%)

Outlook towards deployable continual learning for particle accelerators

Rajput, Kishansingh, Lin, Sen, Edelen, Auralee, Blokland, Willem, Schram, Malachi • Mach.Learn.Sci.Tech. (2025) • DOI: 10.1088/2632-2153/adeb45

Novel Applications (53.0%) Data Management (52.5%)

Explainable physics-based constraints on reinforcement learning for accelerator optimization

Jonathan Colen, Malachi Schram, Kishansingh Rajput, Armen Kasparian • Machine Learning: Science and Technology (2026) • DOI: 10.1088/2632-2153/ae2fa8

Reinforcement Learning & Autonomous Systems (59.8%) Novel Applications (49.6%)

Microsecond-latency feedback at a particle accelerator by online reinforcement learning on hardware

L. Scomparin, Michele Caselle, Andrea Santamaria Garcia, Chenran Xu, Edmund Blomley, Timo Dritschler, Akira Mochihashi, Marcel Schuh, Johannes Leonhard Steinmann, Erik Bründermann, Andreas Kopmann, Jürgen Becker, A.-S. Mueller, Marc Weber • Machine Learning Science and Technology (2026) • DOI: 10.1088/2632-2153/ae5b20

Reinforcement Learning & Autonomous Systems (62.8%) Novel Applications (57.4%)

Machine Learning for Particle Accelerators

Elena Fol, Auralee Edelen • Unknown Venue (2025) • DOI: 10.1093/acrefore/9780190871994.013.138

Novel Applications (70.8%) Statistics & Trends (65.8%)

Symplectic machine learning model for fast simulation of space-charge effects

Wan, Jinyu, Qiang, Ji, Hao, Yue • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/bhpv-bcqk

Novel Applications (44.0%) Tools & Libraries (40.0%)

Reconstructing time-of-flight detector values of angular streaking using machine learning

Meier, David, Viefhaus, Jens, Hartmann, Gregor, Helml, Wolfram, Otto, Thorsten, Sick, Bernhard • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/csvm-858f

Beam Diagnostics (39.2%) Novel Applications (30.4%)

Data-driven gradient optimization for field emission management in a superconducting radio-frequency linac

Goldenberg, Steven, Ahammed, Kawser, Carpenter, Adam, Li, Jiang, Suleiman, Riad, Tennant, Chris • Phys.Rev.Accel.Beams (2024) • DOI: 10.1103/physrevaccelbeams.28.044603

RF Systems (41.3%) Novel Applications (40.0%)

Reinforcement Learning for Charged Particle Beam Control to Minimize Injection Mismatch in Particle Accelerators

Balasooriya, Thilina, Yoo, Shinjae, Schoefer, Vincent, Tseng, Huan-Hsin, Gao, Yuan, Lin, Weijian, Silva, Chanaka De • Journal (2025) • DOI: 10.1109/icassp49660.2025.10889501

Reinforcement Learning & Autonomous Systems (56.9%) Novel Applications (46.9%)

Adaptive Control Meets Deep Learning: EXTREMUM SEEKING IN LATENT SPACE WITH PARTICLE ACCELERATOR APPLICATIONS

Alexander Scheinker • IEEE Control Systems (2026) • DOI: 10.1109/mcs.2025.3646850

Tools & Libraries (34.3%) Novel Applications (33.3%)

Development of an Experimental Test Stand and Machine Learning Surrogate Models for Capturing Complex Responses in Pulsed Septa

Kawa, Krzysztof, Szumlak, Tomasz, Yagci, Omer Yusuf, Balhan, Bruno, Lackner, Friedrich • IEEE Trans.Appl.Supercond. (2026) • DOI: 10.1109/tasc.2026.3660099

Novel Applications (36.1%) Anomaly Detection & Fault Prediction (36.1%) Surrogate Models (50.0%)

Predicting LTS Superconducting NbTi Magnet Quench: A 1D-CNN Model for Real-Time Implementations

Aline Y. Machado, Pedro T. Andrade, Amanda C. Martinez • IEEE Transactions on Applied Superconductivity (2026) • DOI: 10.1109/tasc.2026.3680889

Novel Applications (34.5%) Tools & Libraries (30.9%)

Laser wakefield electron acceleration simulation using physics-informed diffusion probabilistic models

Jech, Matej, Kovalenko, Alexander, Lazzarini, Carlo Maria, Grittani, Gabriele Maria • Proc.SPIE Int.Soc.Opt.Eng. (2025) • DOI: 10.1117/12.3058379

Novel Applications (50.2%) Statistics & Trends (44.4%)

Machine learning application for particle accelerator optimization-a review

Isti Dian Rachmawati, Nazrul Effendy, Taufik Taufik • IAES International Journal of Artificial Intelligence (2025) • DOI: 10.11591/ijai.v14.i4.pp3014-3021

Novel Applications (62.5%) Tools & Libraries (56.9%) Reviews (50.2%)

Applying the machine learning methods to determine the linear optics parameters in the ThomX collector ring

Klekots, D., Bezshyyko, O., Golinka-Bezshyyko, L., Kubytskyi, V., Chaikovska, I. • Nucl.Phys.Atom.Energy (2024) • DOI: 10.15407/jnpae2024.04.394

Novel Applications (45.8%) Statistics & Trends (43.6%)

Automated beam tuning of the TRIUMF ISAC facility

Shelbaya, Olivier, Jung, Paul, Kester, Oliver, Hassan, Omar • JACoW (2025) • DOI: 10.18429/jacow-hiat2025-tux02

Novel Applications (48.7%) Beamline Design & Simulation (44.4%)

Towards continual machine learning for particle accelerators

Rajput, Kishansingh, Schram, Malachi, Blokland, Willem, Zhukov, Alexander, Lin, Sen • JACoW (2026) • DOI: 10.18429/jacow-ibic2025-tuai02

Novel Applications (53.4%) Tools & Libraries (50.1%)

Beam diagnostics, data acquisition system, and applications of machine learning at the KEK e-/e+ Linac

Miyahara, Fusashi, Kinoshita, Kaito, Kurata, Masakazu, Iwasaki, Masako, Natsui, Takuya, Okayasu, Yuichi, Satake, Itsuka, Satoh, Masanori, Uemura, Kosuke, Wang, Di • JACoW (2026) • DOI: 10.18429/jacow-ibic2025-tubc02

Novel Applications (45.5%) Statistics & Trends (43.7%)

Long short-term memory of recurrent neural network to the analysis of temperature rise on the production target in Hadron Experimental Facility of J-PARC

Agari Keizo, Sato Yoshinori, Toyoda Akihisa, Morino Yuhei, Akiyama Hironobu • JACOW (2025) • DOI: 10.18429/jacow-icalepcs2025-wepd077

Novel Applications (35.7%) Statistics & Trends (34.2%)

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Roussel, Ryan, Kennedy,Dylan, Edelen, Auralee, Kuklev, Nikita, Miskovich,Sara, Zhang Zhe • JACOW (2025) • DOI: 10.18429/jacow-icalepcs2025-wesv001

Tools & Libraries (59.3%) Novel Applications (54.5%)

Modelization of an Injector With Machine Learning

Mathieu Debongnie, Maud Baylac, Frédéric Bouly, Nicolas Chauvin, Angélique Gatera, Tomas Junquera, Didier Uriot • 10th International Particle Accelerator Conference (2019) • DOI: 10.18429/jacow-ipac2019-wepts006

Novel Applications (50.6%) Beamline Design & Simulation (50.2%)

A Machine Learning Technique for Dynamic Aperture Computation

Barbara Dalena, Mehdi Ben Ghali • 12th International Particle Accelerator Conference (2021) • DOI: 10.18429/jacow-ipac2021-thpab201

Novel Applications (45.2%) Statistics & Trends (40.0%)

Surrogate Model for Linear Accelerator: A fast Neural Network approximation of ThomX's simulator

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag • IPAC 2023 - 14th International Particle Accelerator Conference (2023) • DOI: 10.18429/jacow-ipac2023-thpl039

Novel Applications (57.0%) Beamline Design & Simulation (56.9%) Surrogate Models (56.7%)

Updates to Xopt for online accelerator optimization and control

Roussel, Ryan, Kennedy, Dylan, Boltz, Tobias, Baker, Kathryn, Mayes, Christopher, Edelen, Auralee • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-thpg85

Tools & Libraries (58.5%) Novel Applications (55.3%)

High fidelity numerical modelling and condition monitoring applied to septum magnets at CERN

Kawa, Krzysztof, Szumlak, Tomasz, Kokkinos, Charilaos, Lackner, Friedrich • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-thpr29

Beamline Design & Simulation (41.8%) Novel Applications (41.2%)

Machine learning-based extraction of longitudinal beam parameters in the LHC

Iliakis, Konstantinos, Karlsen-Bæck, Birk Emil, Trad, Georges, Timko, Helga, Zampetakis, Michail, Argyropoulos, Theodoros • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-tups56

Novel Applications (44.7%) Beamline Design & Simulation (43.6%)

The reinforcement learning for autonomous accelerators collaboration

Santamaria Garcia, Andrea, Eichler, Annika, Xu, Chenran, Kaiser, Jan, Scomparin, Luca, Schenk, Michael, Pochaba, Sabrina, Hirlaender, Simon • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-tups62

Reinforcement Learning & Autonomous Systems (67.0%) Novel Applications (55.8%)

Breaking new ground in data-intensive science: first insights from the LIV.INNO center for doctoral training

Welsch, Carsten • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-tups74

Novel Applications (57.9%) Statistics & Trends (54.3%)

Machine learning approach to MDI optimization for 3 TeV c.o.m. Muon Collider

Castelli, Luca • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-mopm092

Novel Applications (47.5%) Beamline Design & Simulation (45.1%)

Uncertainty-Quantified Machine Model Construction Using Physics-Informed Gaussian Processes and Bayesian Optimization

Isensee,Victoria, Oeftiger, Adrian, Boine-Frankenheim, Oliver • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thpm017

Beamline Design & Simulation (39.7%) Novel Applications (39.4%)

Beam Energy Forecasting using Machine Learning at the CLEAR accelerator

Gilardi, Antonio, Malyzhenkov, Alexander, Petersson,Alfred, Mostacci Andrea, Pollastro, Andrea, Aksoy, Avni, Filippetto, Daniele, Gamba,Davide, Granados, Eduardo, Tangari,Giacomo, Sjobak, Kyrre, Bonnard,Ladislas, Wroe,Laurence, Carranza-García, Manuel, Franek Ondrej, Korysko,Pierre, Corsini Roberto, Rieker,Vilde, Farabolini, Wilfrid, Mazzoni, Stefano • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thpm031

Novel Applications (53.0%) Statistics & Trends (46.2%)

Advancing accelerator science through data-intensive research and training

Welsch, Carsten • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thps110

Novel Applications (63.2%) Data Management (57.5%)

Machine learning for calibration drift forecasting in superconducting RF cavities

Sun Yue, Bellandi Andrea, Eichler, Annika, Richter,Bozo, Diomede,Marco, Schmidt, Christian, Schlarb Holger, Branlard, Julien, Herrmann,Max • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thps135

Novel Applications (40.6%) RF Systems (39.8%)

Advances in machine learning inference of dynamic aperture evaluation for the LHC

Montanari,Carlo Emilio, Di Croce, Davide, Van der Veken,Frederik, Giovannozzi, Massimo, Appleby, Robert, Redaelli, Stefano, Pieloni, Tatiana • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-wepm062

Novel Applications (45.4%) Data Management (42.4%)

Advancements in backwards differentiable beam dynamics simulations for accelerator design, model calibration, and machine learning

Roussel, Ryan, Edelen, Auralee, Gonzalez-Aguilera, Juan Pablo, Lehe, Remi, Huebl, Axel, Kaiser, Jan, Santamaria Garcia, Andrea, Xu, Chenran, Eichler, Annika, Charleux, Grégoire • JACoW (2024) • DOI: 10.18429/jacow-linac2024-thpb068

Beamline Design & Simulation (54.2%) Novel Applications (53.2%)

Beam emittance and Twiss parameters from pepper-pot images using physically informed neural nets

Knight, Ian, Mustapha, Brahim • JACoW (2024) • DOI: 10.18429/jacow-linac2024-tuaa005

Novel Applications (38.2%) Beam Dynamics (34.6%)

Machine learning-based non-destructive measurement of bunch length at FRIB

Wan, Jinyu, Plastun, Alexander, Ostroumov, Peter • JACoW (2024) • DOI: 10.18429/jacow-linac2024-tupb006

Beam Diagnostics (47.2%) Novel Applications (37.4%)

Advanced algorithms for linear accelerator design and operation

Ong, Ysabella Kassandra, Bellan, Luca, Pisent, Andrea, Comunian, Michele, Fagotti, Enrico, Bortolato, Damiano, Montis, Maurizio, Giacchini, Mauro, Carletto, Osvaldo • JACoW (2024) • DOI: 10.18429/jacow-linac2024-tupb075

Novel Applications (58.4%) Statistics & Trends (55.5%)

Adaptive machine learning with hard physics constraints and generative diffusion for 6D phase space diagnostics

Scheinker, Alexander • JACoW (2024) • DOI: 10.18429/jacow-linac2024-wexa005

Novel Applications (52.6%) Tools & Libraries (45.2%)

Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems

Rautela, Mahindra, Scheinker, Alexander • arXiv (2026) • DOI: 10.18429/jacow-napac2025-mop002

Novel Applications (43.0%) Tools & Libraries (36.8%)

A self-supervised transformer for RF cavity signal denoising

Rajesh, Vikshar, Edelen, Auralee, Hall, Christopher, O'Shea, Finn, Edelen, Jonathan • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop006

Novel Applications (35.8%) Reinforcement Learning & Autonomous Systems (30.9%)

Automated RF Phase Adjustment for Beam Stabilization in the Fermilab Linac

Chichili, R.R., Sulskis, J.A., Sharankova, R., Vamanan, B., Ravi, S. • Journal (2025) • DOI: 10.18429/jacow-napac2025-mop011

Novel Applications (37.6%) Statistics & Trends (31.8%)

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

Novel Applications (37.3%) Optimization & Control (36.1%)

Machine learning assisted Bayesian calibration of accelerator digital twin from orbit response data

Lin, Weijian, Kelly, Christopher, Hoffstaetter, Georg, Brown, Kevin, Urban, Nathan • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop056

Surrogate Models (56.8%) Novel Applications (43.3%)

Machine learning at the Spallation Neutron Source accelerator and target

Armen Kasparian, Willem Blokland, Alexander Zhukov, Anant Ray, Drew Winder, Kishansingh Rajput, Malachi Schram • JACOW (2026) • DOI: 10.18429/jacow-napac2025-mop057

Novel Applications (54.4%) Data Management (49.5%)

Machine learning-enhanced infrared imaging for temperature anomaly detection in power supplies

Mohsen, Osama, Lobach, Ihar, Borland, Michael, Sun, Yine • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop059

Anomaly Detection & Fault Prediction (45.9%) Novel Applications (37.0%)

Integrating simulation and machine learning for Proton Storage Ring beam analysis

Leon, Christopher, Huang, En-Chuan, Scheinker, Alexander, Anisimov, Petr M. • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop065

Beam Diagnostics (47.7%) Novel Applications (43.7%)

Machine learning-driven computations of 3D Coherent Synchrotron Radiation

Leon, Christopher, Scheinker, Alexander, Yampolsky, Nikolai, Anisimov, Petr M. • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop097

Novel Applications (41.5%) By Facility Type (37.1%)

Online optimizations of NSLS-II Linac and Linac-to-Booster beam lines using machine learning methods

Song, Minghao, Wang, Guimei, Hidaka, Yoshiteru, Yang, Xi • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop103

Novel Applications (44.7%) Beamline Design & Simulation (42.6%)

Digital Twin Framework for PIP-II Linac: AI-Driven Multi-Scale Modeling from Ion Source to 800 MeV

Pathak, Abhishek, Hanlet, Pierrick, Miceli, Tia • Journal (2025) • DOI: 10.18429/jacow-napac2025-thp028

Surrogate Models (49.9%) Novel Applications (49.5%) Tools & Libraries (50.0%)

JuTrack, a Julia-based auto-differentiable accelerator simulation code for advanced dynamics, scientific machine learning and optimization

Wan, Jinyu, Alamprese, Helena, Ratcliff, Christian, Qiang, Ji, Hao, Yue • JACoW (2026) • DOI: 10.18429/jacow-napac2025-tup056

Beamline Design & Simulation (55.3%) Novel Applications (53.8%)

Towards differentiable beam dynamics modeling in BLAST/ImpactX

Huebl, Axel, Mitchell, Chad, Lehe, Remi, Charleux, Grégoire, Myers, Andrew, Zhang, Weiqun, Qiang, Ji, Vay, Jean-Luc, Kaiser, Jan, Hespe, Christian, Gonzalez-Aguilera, Juan Pablo, Xu, Chenran, Santamaria Garcia, Andrea, Roussel, Ryan, Edelen, Auralee, Moses, William Steven • JACoW (2026) • DOI: 10.18429/jacow-napac2025-tup101

Novel Applications (46.0%) Tools & Libraries (46.0%)

Safe extremum seeking for real-time adaptive accelerator control

Williams, Alan, Scheinker, Alexander • JACoW (2026) • DOI: 10.18429/jacow-napac2025-weyn02

Novel Applications (55.4%) Reinforcement Learning & Autonomous Systems (49.0%)

LinacNet: A Composable Particle-Cloud Surrogate Model for Linear Accelerators, and Its Path to Larger Machines

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag • Research Square (2026) • DOI: 10.21203/rs.3.rs-10486818/v1

Novel Applications (47.6%) Tools & Libraries (45.1%) Surrogate Models (50.0%)

Longitudinal Phase Space Tomography via Machine Learning at the Los Alamos Neutron Science Center

Christopher Leon, Alexander Scheinker, Petr M. Anisimov • Research Square (2026) • DOI: 10.21203/rs.3.rs-10697074/v1

Novel Applications (36.0%) Beam Diagnostics (35.9%)

Continual Learning for Particle Accelerators

Nuclear Physics (NP) USDOE Office of Science (SC), M. Schram, Thomas Jefferson National Accelerator Facility (TJNAF), Sen Lin, Kishansingh Rajput, Alexander Zhukov, Willem Blokland • Unknown Venue (2026) • DOI: 10.2172/3012392

Novel Applications (53.4%) Data Management (51.7%)

A Hybrid Monte Carlo–Machine Learning Framework for High-Energy Neutron Shielding Using Boron-Enhanced Concrete

Demet Sariyer, Elif Yıldırım • International Journal of Computational and Experimental Science and Engineering (2026) • DOI: 10.22399/ijcesen.4827

Novel Applications (33.5%) Statistics & Trends (32.8%) Tools & Libraries (50.0%)

Machine learning

Snuverink, Jochem • CERN Yellow Rep.School Proc. (2024) • DOI: 10.23730/cyrsp-2024-003.2131

Novel Applications (70.3%) Tools & Libraries (60.4%)

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

Hal Ferguson • ODU Digital Commons (Old Dominion University) (2026) • DOI: 10.25777/2pjk-8q69

Anomaly Detection & Fault Prediction (36.9%) Novel Applications (30.3%)

Deep Lie Map Networks:A novel Approach to Infer Nonlinear Synchrotron Optics from Beam Oscillations

Caliari, Conrad • Thesis (2025) • DOI: 10.26083/tuprints-00029370

Novel Applications (38.3%) Beam Dynamics (36.6%)

Rapid Secondary Neutron Dose Estimation in Proton Accelerators Using FLUKA and Machine Learning

Demet Sariyer, Elif Yıldırım • Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi (2026) • DOI: 10.29233/sdufeffd.1783848

Novel Applications (39.5%) Statistics & Trends (37.2%)

Machine Learning-Driven Beam Tuning Using Adaptive Region Bayesian Optimization at INFN-LNL

Novel Applications (46.2%) Optimization & Control (41.2%)

Assessing the Performance of Deep Learning Predictions for Dynamic Aperture of a Hadron Circular Particle Accelerator

Di Croce, Davide, Giovannozzi, Massimo, Montanari, Carlo Emilio, Pieloni, Tatiana, Redaelli, Stefano, Van der Veken, Frederik F. • Instruments (2024) • DOI: 10.3390/instruments8040050

Novel Applications (49.4%) Tools & Libraries (40.9%)

Simulated six-dimensional beam-dynamics dataset for machine-learning surrogate modelling

Santiago Martinez Saenz • Zenodo (CERN European Organization for Nuclear Research) (2026) • DOI: 10.5281/zenodo.21644177

Surrogate Models (50.0%) Novel Applications (39.9%)

Gaussian Process Regression and Bayesian Optimization for a 40-90 MeV Laser-Plasma Injector for the cSTART Storage Ring

David Squires, Elias Sailer, Joseph Natal, Alexander Saw, Nathan Ray, M. Fuchs, Thiemo Schmelzer • KITopen (2026) • DOI: 10.5445/ir/1000196455

Beamline Design & Simulation (43.0%) Novel Applications (37.0%)

Machine Learning Applications for Improving Accelerator Operations

Lin, Lucy • Thesis (2024) • DOI: 10.7298/2e46-7a49

Novel Applications (62.6%) Statistics & Trends (57.3%)

Machine Learning for superconducting magnets application

Stabilini, Elisa • Thesis (2025)

Novel Applications (39.6%) RF Systems (34.0%)

Machine learning-based particle accelerator modeling

Emmanuel Goutierre • Laboratoire Interdisciplinaire des Sciences du Numérique (2024)

Novel Applications (52.4%) Surrogate Models (49.2%)

Third-integer Resonant Extraction Regulation System for Mu2e

Narayanan, Aakaash • InspireHEP (2024)

Novel Applications (43.8%) Tools & Libraries (36.4%)

Machine Learning approach to classifying quench antenna signals

Plebani, Alberto, Barzi, Emanuela, Teyber, Reed • Thesis (2025)

Novel Applications (36.0%) By Facility Type (33.0%)

Physics-aware modelling of an accelerated particle cloud

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag • MLPS 2023 - Machine Learning and the Physical Sciences Workshop 23023 - At the 37th conference on Neural Information Processing Systems (NeurIPS) (2023)

Novel Applications (49.8%) Tools & Libraries (47.8%)

Machine learning for slow-extraction uniformity at the Fermilab Delivery Ring

Prescott, Matthew J. • InspireHEP (2026)

Data Management (32.1%) Novel Applications (31.1%)

Optimization

▼

Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities

Jorge Alberto Diaz Cruz, Sandra Biedron, Manel Martinez-Ramon, Salvador Sosa, Reza Pirayesh • arXiv (2019)

Optimization & Control (40.3%) RF Systems (36.9%)

Machine learning for design optimization of storage ring nonlinear dynamics

Faya Wang, Minghao Song, Auralee Edelen, Xiaobiao Huang • arXiv (2019)

Optimization & Control (43.7%) Novel Applications (31.2%)

Online tuning and light source control using a physics-informed Gaussian process Adi

A. Hanuka, J. Duris, J. Shtalenkova, D. Kennedy, A. Edelen, D. Ratner, X. Huang • arXiv (2019)

Optimization & Control (46.2%) Operations & Control (42.3%)

Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Reinforcement Learning & Autonomous Systems (57.9%) Optimization & Control (46.4%)

Application of ensemble machine learning algorithms and filtering techniques in slow orbit feedback systems of electron storage rings

Fan, Jiaqi, Liu, Weibin, Wang, Jiuqing, Wei, Yanru, Wei, Yuanyuan, Ji, Daheng • Phys.Rev.Accel.Beams (2025) • DOI: 10.1103/physrevaccelbeams.28.042801

Optimization & Control (36.3%) Reinforcement Learning & Autonomous Systems (35.4%)

Fast Adaptive Neural Control of Resonant Extraction at Fermilab

Novel Applications (37.3%) Optimization & Control (36.1%)

Machine Learning-Driven Beam Tuning Using Adaptive Region Bayesian Optimization at INFN-LNL

Novel Applications (46.2%) Optimization & Control (41.2%)

Simulations

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Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators

Andrei Ivanov, Ilya Agapov • arXiv (2020)

Novel Applications (49.3%) Beamline Design & Simulation (43.2%)

Surrogate Modeling of the CLIC Final-Focus System using Artificial Neural Networks

J. Ogren, C. Gohil, D. Schulte • arXiv (2021)

Surrogate Models (50.0%) Beamline Design & Simulation (38.0%)

Improving Surrogate Model Accuracy for the LCLS-II Injector Frontend Using Convolutional Neural Networks and Transfer Learning

Lipi Gupta, Auralee Edelen, Nicole Neveu, Aashwin Mishra, Christopher Mayes, Young-Kee Kim • arXiv (2021)

Novel Applications (50.9%) Beamline Design & Simulation (46.4%) Surrogate Models (50.0%)

SPIRAL2 Cryomodules Models: a Gateway to Process Control and Machine Learning

Adrien Vassal, Adnan Ghribi, François Millet, François Bonne, Patrick Bonnay, Pierre-Emmanuel Bernaudin • arXiv (2021)

Beamline Design & Simulation (42.1%) Novel Applications (41.7%)

Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks

Friedrich Bethke, Richard Pausch, Patrick Stiller, Alexander Debus, Michael Bussmann, Nico Hoffmann • arXiv (2021)

Surrogate Models (51.3%) Novel Applications (42.4%)

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

Daniel Koser, Loyd Waites, Daniel Winklehner, Matthias Frey, Andreas Adelmann, Janet Conrad • arXiv (2021)

Surrogate Models (51.1%) Beamline Design & Simulation (50.3%)

Physics-informed neural network method for modelling beam-wall interactions

Kazuhiro Fujita • arXiv (2022)

Beamline Design & Simulation (52.5%) Novel Applications (49.2%)

Differentiable Preisach Modeling for Characterization and Optimization of Accelerator Systems with Hysteresis

R. Roussel, A. Edelen, D. Ratner, K. Dubey, J. P. Gonzalez-Aguilera, Y. K. Kim, N. Kuklev • arXiv (2022)

Novel Applications (52.2%) Beamline Design & Simulation (51.3%)

Neural Network Solver for Coherent Synchrotron Radiation Wakefield Calculations in Accelerator-based Charged Particle Beams

Auralee Edelen, Christopher Mayes • arXiv (2022)

Novel Applications (52.4%) Beamline Design & Simulation (47.3%)

Predicting Beam Transmission Using 2-Dimensional Phase Space Projections Of Hadron Accelerators

Anthony Tran, Yue Hao, Brahim Mustapha, Jose L. Martinex Marin • arXiv (2022)

Beamline Design & Simulation (40.5%) Novel Applications (37.7%)

Uncertainty Aware ML-based surrogate models for particle accelerators: A Study at the Fermilab Booster Accelerator Complex

Malachi Schram, Kishansingh Rajput, Karthik Somayaji Peng Li, Jason St. John, Himanshu Sharma • arXiv (2022)

Novel Applications (43.2%) Statistics & Trends (43.1%) Surrogate Models (50.0%)

Neural Network Prior Mean for Particle Accelerator Injector Tuning

Connie Xu, Ryan Roussel, Auralee Edelen • arXiv (2022)

Novel Applications (47.5%) Surrogate Models (47.5%)

Applications of Differentiable Physics Simulations in Particle Accelerator Modeling

Ryan Roussel, Auralee Edelen • arXiv (2022)

Beamline Design & Simulation (57.4%) Novel Applications (54.4%)

Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows

Anna Willmann, Jurjen Couperus Cabadağ, Yen-Yu Chang, Richard Pausch, Amin Ghaith, Alexander Debus, Arie Irman, Michael Bussmann, Ulrich Schramm, Nico Hoffmann • arXiv (2023)

Beam Dynamics (32.2%) Surrogate Models (31.8%)

From Compact Plasma Particle Sources to Advanced Accelerators with Modeling at Exascale

Axel Huebl, Remi Lehe, Edoardo Zoni, Olga Shapoval, Ryan T. Sandberg, Marco Garten, Arianna Formenti, Revathi Jambunathan, Prabhat Kumar, Kevin Gott, Andrew Myers, Weiqun Zhang, Ann Almgren, Chad E. Mitchell, Ji Qiang, David Grote, Alexander Sinn, Severin Diederichs, Maxence Thevenet, Luca Fedeli, Thomas Clark, Neil Zaim, Henri Vincenti, Jean-Luc Vay • 20th Advanced Accelerator Concepts Workshop (AAC’22) (2023)

Beamline Design & Simulation (53.2%) Surrogate Models (52.7%)

Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Abhishek Pathak • arXiv (2024)

Novel Applications (57.2%) Beamline Design & Simulation (50.2%)

Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation

G. Kane, P. Drobniak, S. Kazamias, V. Kubytskyi, M. Lenivenko, B. Lucas, J. Serhal, K. Cassou, A. Beck, A. Specka, F. Massimo • arXiv (2025)

Novel Applications (47.8%) Beamline Design & Simulation (46.0%) Surrogate Models (50.0%)

Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators

Kamaljeet Singh, Kangkan Goswami, Raghunath Sahoo, Sumanta Samal • arXiv (2025)

Novel Applications (38.3%) Beamline Design & Simulation (37.1%)

Data-Driven Discovery of Beam Centroid Dynamics

Pocher, Liam A., Haber, Irving, Antonsen, Thomas M., O'Shea, Patrick G. • arXiv (2024)

Novel Applications (48.3%) Beamline Design & Simulation (45.5%)

Optimizing Beam-Plasma Interactions Through Jitter Analysis Using Start-to-End Simulations

Hwang, Robin • arXiv (2024)

Novel Applications (54.1%) Beamline Design & Simulation (50.5%)

Inverse Surrogate Model of a Soft X-Ray Spectrometer using Domain Adaptation

Enrico Ahlers, Peter Feuer-Forson, Gregor Hartmann, Rolf Mitzner, Peter Baumgärtel, Jens Viefhaus • arXiv (2025)

Novel Applications (34.4%) Data Management (29.6%) Surrogate Models (50.0%)

Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment

A. Sytov, K. Cassou, V. Kubytskyi, M. Lenivenko, A. Huber • arXiv (2025)

Novel Applications (48.7%) Beamline Design & Simulation (46.7%)

Acceleration of Multi-Scale LTS Magnet Simulations with Neural Network Surrogate Models

Louis Denis, Julien Dular, Vincent Nuttens, Mariusz Wozniak, Benoît Vanderheyden, Christophe Geuzaine • arXiv (2025)

Surrogate Models (50.0%)

Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models

Rodriguez-Llorente, Guillermo, Gallardo, Galo, Navascués, Rodrigo Morant, Petrovsky, Nikita Khvatkin, Sabogal, Anderson, Martín, Roberto Gómez-Espinosa • Journal (2025)

Novel Applications (46.0%) Reinforcement Learning & Autonomous Systems (45.9%) Surrogate Models (50.0%)

U-Net-based surrogate modeling for attosecond X-ray free-electron lasers

Yufei Wei, Bingyang Yan, Chenzhi Xu, Jiawei Yan, Haixiao Deng • arXiv (2026)

Surrogate Models (50.0%) Novel Applications (38.6%)

Machine learning-based virtual diagnostics of dielectric laser acceleration

Thilo Egenolf, Oliver Boine-Frankenheim • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2603.28549

Surrogate Models (43.9%) Novel Applications (43.3%)

Closing the Loop: Deploying Auto-Generating Digital Twins for Particle Accelerators

A. D. Brynes, M. King, K. R. L. Baker, R. Banerjee, R. Clarke, D. J. Dunning, J. K. Jones, M. Leputa, A. E. Pollard, M. Romanovschi, M. Shaw, N. Ziyan • arXiv (2026)

Surrogate Models (67.2%) Beamline Design & Simulation (57.9%)

Learning Dynamic Aperture from One-turn Maps

Derong Xu • arXiv (2026)

Novel Applications (33.4%) Surrogate Models (29.8%)

Beam-Level Nonlinear Compton Spectra via a Neural Network Surrogate Model

Antonina Timoshenko, Maxim Malakhov, Alexander Fedotov, Sergey Rykovanov • arXiv (2026)

Novel Applications (33.9%) Surrogate Models (50.0%)

Report on the Advanced Linear Collider Study Group (ALEGRO) Workshop 2026

L. Verra, P. Muggli, B. Cross, E. Adli, R. Babjak, T. Barklow, F. Bencivenga, C. Benedetti, C. Benedetti, M. Buscher, S. S. Bulanov, A. Caldwell, G. Chen, O. Chubenko, R. D'Arcy, S. Diederichs, K. Downham, J. Farmer, M. Ferrario, A. Ferran Pousa, A. Formenti, M. Fuchs, S. Gessner, L. Giannessi, Z. Gong, J. Grames, T. Grismayer, H. Jaworska, L. Ji, V. Kain, S. Karkare, A. Knetsch, C. Lindstrom, N. Lopes, N. Lopes, F. Massimo, C. Masciovecchio, P. Oliveira, T. Opferkuch, J. Osterhoff, J. Osterhoff, S. Pagan Griso, G. Penco, J. Power, A. Pukhov, T. P. Rakitzis, A. Rastogi, L. Reichwein, C. B. Schroeder, L. O. Silva, A. Sinn, D. Sofikitis, D. Storey, M. Thévenet, M. Trovò, M. Turner, M. Vranic, F. Willeke, T. Wilson, M. Wing, W. Zhang, Chuan Zheng • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2607.09457

By Facility Type (53.2%) Beamline Design & Simulation (50.5%)

Machine Learning Optimization of E-Beam Transport for a Superradiant FEL

Amir Weinberg, Leon Feigin, Ariel Nause, Avraham Gover • arXiv (Cornell University) (2026) • DOI: 10.48550/arxiv.2608.01874

Beamline Design & Simulation (39.6%) Novel Applications (38.1%)

Bayesian Optimization of Molybdenum-99 Production by Laser Wakefield Acceleration Using Coupled PIC and Monte Carlo Simulations

Bruno Silveira Nunes, Nilson Dias Vieira Junior, Mirko Salomón Alva Sánchez, Alexandre Bonatto, Ricardo Elgul Samad • arXiv (2026)

Beamline Design & Simulation (32.9%) Surrogate Models (32.1%)

Flow-based surrogate models for particle tracking

Matthias Remta, Yann Dutheil, Francesco Velotti • arXiv (2026)

Surrogate Models (50.0%) Novel Applications (39.6%)

A hybrid monte Carlo-machine learning framework for neutron dose attenuation modeling in layered iron-concrete shields of a 1000 MeV proton accelerator

Demet Sarıyer, Ali Osman GÖKCAN • Annals of Nuclear Energy (2026) • DOI: 10.1016/j.anucene.2026.112572

Novel Applications (39.1%) Beamline Design & Simulation (36.0%) Tools & Libraries (50.0%)

Method for reconstruction the energy spectrum of a linear electron accelerator using neural networks

A.A. Kim, F.R. Studenikin, V.V. Khankin, S.A. Zolotov, A.P. Lyubomudrov, A.P. Chernyaev • Radiation Physics and Chemistry (2026) • DOI: 10.1016/j.radphyschem.2026.113775

Novel Applications (52.7%) Beamline Design & Simulation (51.7%)

Using a neural network model to guide protection heater design in Nb$_{3}$Sn accelerator magnets

Bakrani Balani, Shahriar, Salmi, Tiina • Supercond.Sci.Technol. (2025) • DOI: 10.1088/1361-6668/addaed

Beamline Design & Simulation (32.7%) Statistics & Trends (25.2%)

Development of an Experimental Test Stand and Machine Learning Surrogate Models for Capturing Complex Responses in Pulsed Septa

Kawa, Krzysztof, Szumlak, Tomasz, Yagci, Omer Yusuf, Balhan, Bruno, Lackner, Friedrich • IEEE Trans.Appl.Supercond. (2026) • DOI: 10.1109/tasc.2026.3660099

Novel Applications (36.1%) Anomaly Detection & Fault Prediction (36.1%) Surrogate Models (50.0%)

femto-PIXAR: a self-supervised neural network method for reconstructing femtosecond X-ray free electron laser pulses

Goetzke, Gesa, Plumley, Rajan, Hartmann, Gregor, Maxwell, Tim, Decker, Franz-Josef, Lutman, Alberto, Dunne, Mike, Ratner, Daniel, Turner, Joshua J. • Opt.Express (2025) • DOI: 10.1364/oe.562798

By Facility Type (28.8%) Surrogate Models (26.4%)

Automated beam tuning of the TRIUMF ISAC facility

Shelbaya, Olivier, Jung, Paul, Kester, Oliver, Hassan, Omar • JACoW (2025) • DOI: 10.18429/jacow-hiat2025-tux02

Novel Applications (48.7%) Beamline Design & Simulation (44.4%)

Modelization of an Injector With Machine Learning

Novel Applications (50.6%) Beamline Design & Simulation (50.2%)

Surrogate Model for Linear Accelerator: A fast Neural Network approximation of ThomX's simulator

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag • IPAC 2023 - 14th International Particle Accelerator Conference (2023) • DOI: 10.18429/jacow-ipac2023-thpl039

Novel Applications (57.0%) Beamline Design & Simulation (56.9%) Surrogate Models (56.7%)

High fidelity numerical modelling and condition monitoring applied to septum magnets at CERN

Kawa, Krzysztof, Szumlak, Tomasz, Kokkinos, Charilaos, Lackner, Friedrich • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-thpr29

Beamline Design & Simulation (41.8%) Novel Applications (41.2%)

Machine learning-based extraction of longitudinal beam parameters in the LHC

Iliakis, Konstantinos, Karlsen-Bæck, Birk Emil, Trad, Georges, Timko, Helga, Zampetakis, Michail, Argyropoulos, Theodoros • JACoW (2024) • DOI: 10.18429/jacow-ipac2024-tups56

Novel Applications (44.7%) Beamline Design & Simulation (43.6%)

Machine learning approach to MDI optimization for 3 TeV c.o.m. Muon Collider

Castelli, Luca • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-mopm092

Novel Applications (47.5%) Beamline Design & Simulation (45.1%)

Uncertainty-Quantified Machine Model Construction Using Physics-Informed Gaussian Processes and Bayesian Optimization

Isensee,Victoria, Oeftiger, Adrian, Boine-Frankenheim, Oliver • JACOW (2025) • DOI: 10.18429/jacow-ipac2025-thpm017

Beamline Design & Simulation (39.7%) Novel Applications (39.4%)

Advancements in backwards differentiable beam dynamics simulations for accelerator design, model calibration, and machine learning

Beamline Design & Simulation (54.2%) Novel Applications (53.2%)

Machine learning assisted Bayesian calibration of accelerator digital twin from orbit response data

Lin, Weijian, Kelly, Christopher, Hoffstaetter, Georg, Brown, Kevin, Urban, Nathan • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop056

Surrogate Models (56.8%) Novel Applications (43.3%)

Online optimizations of NSLS-II Linac and Linac-to-Booster beam lines using machine learning methods

Song, Minghao, Wang, Guimei, Hidaka, Yoshiteru, Yang, Xi • JACoW (2026) • DOI: 10.18429/jacow-napac2025-mop103

Novel Applications (44.7%) Beamline Design & Simulation (42.6%)

Digital Twin Framework for PIP-II Linac: AI-Driven Multi-Scale Modeling from Ion Source to 800 MeV

Pathak, Abhishek, Hanlet, Pierrick, Miceli, Tia • Journal (2025) • DOI: 10.18429/jacow-napac2025-thp028

Surrogate Models (49.9%) Novel Applications (49.5%) Tools & Libraries (50.0%)

JuTrack, a Julia-based auto-differentiable accelerator simulation code for advanced dynamics, scientific machine learning and optimization

Wan, Jinyu, Alamprese, Helena, Ratcliff, Christian, Qiang, Ji, Hao, Yue • JACoW (2026) • DOI: 10.18429/jacow-napac2025-tup056

Beamline Design & Simulation (55.3%) Novel Applications (53.8%)

LinacNet: A Composable Particle-Cloud Surrogate Model for Linear Accelerators, and Its Path to Larger Machines

Emmanuel Goutierre, Christelle Bruni, Johanne Cohen, Hayg Guler, Michèle Sebag • Research Square (2026) • DOI: 10.21203/rs.3.rs-10486818/v1

Novel Applications (47.6%) Tools & Libraries (45.1%) Surrogate Models (50.0%)

Simulated six-dimensional beam-dynamics dataset for machine-learning surrogate modelling

Santiago Martinez Saenz • Zenodo (CERN European Organization for Nuclear Research) (2026) • DOI: 10.5281/zenodo.21644177

Surrogate Models (50.0%) Novel Applications (39.9%)

Gaussian Process Regression and Bayesian Optimization for a 40-90 MeV Laser-Plasma Injector for the cSTART Storage Ring

David Squires, Elias Sailer, Joseph Natal, Alexander Saw, Nathan Ray, M. Fuchs, Thiemo Schmelzer • KITopen (2026) • DOI: 10.5445/ir/1000196455

Beamline Design & Simulation (43.0%) Novel Applications (37.0%)

Machine learning-based particle accelerator modeling

Emmanuel Goutierre • Laboratoire Interdisciplinaire des Sciences du Numérique (2024)

Novel Applications (52.4%) Surrogate Models (49.2%)

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