Results 11 to 20 of about 2,772,396 (335)

Neural Networks for Modeling and Control of Particle Accelerators [PDF]

, 2016
We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural ...
Auralee Edelen, S.G. Biedroń, B. E. Chase, D. Edstrom, S.V. Milton, Pierpaolo Stabile   +5 more
openalex   +3 more sources

The supernova remnant SN 1006 as a Galactic particle accelerator. [PDF]

Nat Commun, 2022
The origin of cosmic rays is a pivotal open issue of high-energy astrophysics. Supernova remnants are strong candidates to be the Galactic factory of cosmic rays, their blast waves being powerful particle accelerators.
Giuffrida R, Miceli M, Caprioli D, Decourchelle A, Vink J, Orlando S, Bocchino F, Greco E, Peres G.   +8 more
europepmc   +3 more sources

Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning. [PDF]

Sci Rep
Online tuning of particle accelerators is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods like Bayesian ...
Kaiser J, Xu C, Eichler A, Santamaria Garcia A, Stein O, Bründermann E, Kuropka W, Dinter H, Mayet F, Vinatier T, Burkart F, Schlarb H.   +11 more
europepmc   +2 more sources

Large language models for human-machine collaborative particle accelerator tuning through natural language. [PDF]

Sci Adv
Autonomous tuning of particle accelerators is an active and challenging research field with the goal of enabling advanced accelerator technologies and cutting-edge high-impact applications, such as physics discovery, cancer research, and material ...
Kaiser J, Lauscher A, Eichler A.
europepmc   +2 more sources

Differentiable Preisach Modeling for Characterization and Optimization of Particle Accelerator Systems with Hysteresis. [PDF]

Physical Review Letters, 2022
Future improvements in particle accelerator performance are predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and material components of accelerators are often neglected in online accelerator
R. Roussel, A. Edelen, D. Ratner, K. Dubey, J. P. Gonzalez-Aguilera, Y. K. Kim, N. Kuklev   +6 more
semanticscholar   +1 more source

Stable and Scalable Multistage Terahertz-Driven Particle Accelerator. [PDF]

Physical Review Letters, 2021
Particle accelerators that use electromagnetic fields to increase a charged particle's energy have greatly advanced the development of science and industry since invention.
Heng Tang, Lingrong Zhao, P. Zhu, Xiaoqun Zou, Jia Qi, Ya Cheng, J. Qiu, Xianggang Hu, Wei Song, D. Xiang, Jie Zhang   +10 more
semanticscholar   +1 more source

European Strategy for Particle Physics -- Accelerator R&D Roadmap [PDF]

, 2022
The 2020 update of the European Strategy for Particle Physics emphasised the importance of an intensified and well-coordinated programme of accelerator R&D, supporting the design and delivery of future particle accelerators in a timely, affordable and ...
C. Adolphsen, D. Angal-Kalinin, T. Arndt, M. Arnold, R. Assmann, B. Auchmann, K. Aulenbacher, A. Ballarino, B. Baudouy, P. Baudrenghien, M. Benedikt, S. Bentvelsen, A. Blondel, A. Bogacz, F. Bossi, L. Bottura, S. Bousson, O. Bruning, R. Brinkmann, M. Bruker, O. Brunner, P. Burrows, G. Burt, S. Calatroni, K. Cassou, A. Castilla, N. Catalan-Lasheras, E. Cenni, A. Chanc'e, N. Colino, S. Corde, L. Corner, B. Cros, A. Cross, J. Delahaye, G. Devanz, A. Etienvre, P. Evtushenko, A. Faus-Golfe, P. Fazilleau, M. Ferrario, A. Gallo, L. Garc'ia-Tabar'es, C. Geddes, F. Gerigk, F. Gianotti, S. Gilardoni, A. Grudiev, E. Gschwendtner, G. Hoffstaetter, M. Hogan, S. Hooker, A. Hutton, R. Ischebeck, K. Jakobs, P. Janot, E. Jensen, J. Kuhn, W. Kaabi, D. Kayran, M. Klein, J. Knobloch, M. Koratzinos, B. Kuske, M. Lamont, A. Latina, P. Lebrun, W. Leemans, D. Li, K. Long, D. Longuevergne, R. Losito, W. Lu, D. Lucchesi, O. Lundh, E. M'etral, F. Marhauser, S. Michizono, B. Militsyn, J. Mnich, E. Montesinos, N. Mounet, P. Muggli, P. Musumeci, S. Nagaitsev, T. Nakada, A. Neumann, D. Newbold, P. Nghiem, M. Noe, K. Oide, J. Osterhoff, M. Palmer, N. Pastrone, N. Pietralla, S. Prestemon, E. Previtali, T. Proslier, L. Quettier, T. Raubenheimer, B. Rimmer, L. Rivkin, E. Rochepault, C. Rogers, G. Rosaz, T. Roser, L. Rossi, R. Ruber, D. Schulte, M. Seidel, C. Senatore, B. Shepherd, J. Shi, N. Shipman, A. Specka, S. Stapnes, A. Stocchi, D. Stratakis, I. Syratchev, O. Tanaka, Sami G. Tantawi, C. Tennant, E. Tsesmelis, P. V'edrine, C. Vaccarezza, A. Valente, J. Vieira, N. Vinokurov, H. Weise, M. Wenskat, P. Williams, M. Wing, A. Yamamoto, Y. Yamamoto, K. Yokoya, F. N. Laboratory, Stanford, California, Usa, Stfc Daresbury Laboratory, UK., Kititep, Karlsruhe, H Germany, T. U. Darmstadt, Desy, Hamburg, Lnfinfn, Frascati, Italy, Psi, Villigen, Switzerland., U. Mainz, Cern, Geneva, Cea, Saclay, France, Nikhef, Amsterdam, Netherlands., University Paris-Sorbonne, U. Geneva, J. Lab, Virgínia, IJCLab, Orsay, J. A. Institute, O. University, L. University, Ciemat, Madrid, Spain., I. Paris, Palaiseau, U. Liverpool, LPGP-CNRS-Universit'e Paris Saclay, U. Strathclyde, H. Rossendorf, Lbnl, Berkeley, Cornell University, Ithaca, New York., Bnl, Upton, U. Freiburg, Helmholtz-Zentrum Berlin, U. Siegen, Mit, Cambridge, Massachusetts, U. Hamburg, Stfc Rutherford Appleton Laboratory, Harwell Campus, I. -. London, Tsinghua University, Beijing, China., Infn, Padova, U. Padova, Lund University, Sweden., Kek, Tsukuba, Japan., M. Physics, Munich, Ucla, L. Angeles, Fermilab, Batavia, Illinois., Epfl, Lausanne, Torino, Lngsinfn, L’Aquila, Stanford University, Lasainfn, Milano, Uppsala University, Astec, Daresbury, Llr, Ist, Lisbon., Portugal, Binp, Novosibirsk, Russia., Ucl, London, United Kingdom.   +235 more
semanticscholar   +1 more source

Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks [PDF]

Inf., 2021
Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the ...
Renato Bellotti, R. Boiger, A. Adelmann
semanticscholar   +1 more source

Extremum Seeking-Based Control System for Particle Accelerator Beam Loss Minimization

IEEE Transactions on Control Systems Technology, 2022
Particle accelerators throughout the world vary widely in terms of age and availability of advanced noninvasive diagnostics that provide varying levels of detail about the accelerated beams.
A. Scheinker, En-Chuan Huang, C. Taylor
semanticscholar   +1 more source

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Inf., 2021
Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models.
A. Scheinker
semanticscholar   +1 more source