activity
20122024
most citedPhase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations

41 citations · 194 across the 21 of their papers we have counts for

collaborators

21 papers

physics.acc-ph2024★ 1 cited

Experimental demonstration of cascaded round-to-flat and flat-to-round beam transformations

Seongyeol Kim, Philippe Piot, Gonxiaohui Chen +5

Magnetized beams beam with significant canonical angular momentum are critical to electron cooling of hadron beams such as contemplated in next-generation hadron and electron-ion c…

physics.acc-ph2024

Efficient 6-dimensional phase space reconstruction from experimental measurements using generative machine learning

Ryan Roussel, Juan Pablo Gonzalez-Aguilera, Auralee Edelen +5

Next-generation accelerator concepts which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distribution…

physics.acc-ph2024★ 9 cited

Four-Dimensional Phase-Space Reconstruction of Flat and Magnetized Beams Using Neural Networks and Differentiable Simulations

Seongyeol Kim, Juan Pablo Gonzalez-Aguilera, Philippe Piot +9

Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams with large transverse-emit…

physics.acc-ph2023

Demonstration of Autonomous Emittance Characterization at the Argonne Wakefield Accelerator

Ryan Roussel, Auralee Edelen, Dylan Kennedy +3

Transverse beam emittance plays a key role in the performance of high brightness accelerators. Characterizing beam emittance is often done using a quadrupole scan, which fits beam…

physics.acc-ph2022★ 7 cited

Beam shaping using an ultra-high vacuum multileaf collimator and emittance exchange beamline

N. Majernik, G. Andonian, W. Lynn +9

We report the development of a multileaf collimator (MLC) for charged particle beams, based on independently actuated tungsten strips which can selectively scatter unwanted particl…

physics.acc-ph2022★ 41 cited

Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations

Ryan Roussel, Auralee Edelen, Christopher Mayes +5

Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional rec…