Projects per year
Abstract
Laser driven proton beams driven by the Target Normal Sheath Acceleration (TNSA) mechanism exhibit large divergence and a broad energy distribution with low particle number at high energy. Such undesirable characteristics of the beam can be controlled and optimised by employing a recently developed helical coil technique, which exploits the transient self-charging of solid targets irradiated by intense laser pulses. Highly chromatic focusing of the broadband proton beams was achieved by employing this technique at the TARANIS laser system, where the selected energy slice was tuned by varying the pitch of the coil. Using a longer coil of larger pitch, a quasi-collimated, narrow energy band proton beam of ~10^7 particles at 10 MeV was achieved, through a combination of focussing, energy selection and in-situ post-acceleration. This technique may provide a platform for the next generation of compact, all-optical ion accelerators.
Original language | English |
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Article number | C06025 |
Number of pages | 9 |
Journal | Journal of Instrumentation |
Volume | 12 |
DOIs | |
Publication status | Published - 22 Jun 2017 |
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Dive into the research topics of 'Optimisation of laser driven proton beams by an innovative target scheme'. Together they form a unique fingerprint.Projects
- 2 Finished
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R1323CPP: Novel Techniques for control & Optimisation of Laser driven ion beams
01/08/2012 → 30/06/2015
Project: Research
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R1304CPP: Advanced laser-ion acceleration strategies towards next generation healthcare
Borghesi, M., Kar, S., Prise, K. & Zepf, M.
01/08/2012 → 20/01/2020
Project: Research
Datasets
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Optimisation of laser driven proton beams by an innovative target scheme
Ahmed, H. (Creator), Kar, S. (Contributor), Cantono, G. (Contributor), Doria, D. (Contributor), Giesecke, A. L. (Contributor), Gwynne, D. (Contributor), Lewis, C. L. S. (Contributor), Macchi, A. (Contributor), Nersisyan, G. (Contributor), Naughton, K. (Contributor), Willi, O. (Contributor) & Borghesi, M. (Contributor), Queen's University Belfast, 2017
DOI: 10.17034/f54913dc-9ead-44c7-896d-d96bdae23690
Dataset
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