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Details of Grant 

EPSRC Reference: EP/Y001737/1
Title: Automated multi-dimensional mapping of dynamic laser-liquid interactions
Principal Investigator: Palmer, Dr CAJ
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Central Laser Facility SLAC National Accelerator Laboratory
Department: Sch of Mathematics and Physics
Organisation: Queen's University of Belfast
Scheme: Standard Research - NR1
Starts: 01 February 2024 Ends: 31 January 2026 Value (£): 150,053
EPSRC Research Topic Classifications:
Light-Matter Interactions
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
24 May 2023 ECR International Collaboration Grants Panel 3 Announced
Summary on Grant Application Form
High-intensity laser interactions with matter produce extreme environments with very high temperatures and densities such that the electrons within the atoms of the material no longer remain bound to the atomic nuclei and the material becomes a plasma. These interactions can create conditions for studying astrophysical phenomena, including supernova shocks and solar flares, as well as supporting very high electric fields that can be used to accelerate charged particles over distances 100s to 1000s times shorter than the limits of radio-frequency accelerator technology. These compact accelerators have been shown to generate ion beams with highly desirable properties for key applications in materials testing, radiobiology, and inertial fusion energy. So far, full exploration and exploitation of these interactions has been hampered by the difficulty in reproducing their complex behaviour in numerical and computational models and by the limited data available which is caused by the low repetition rate of the high-energy pulsed laser (typically <<0.002 Hz - a shot every 10 mins) used to create the plasma and drive particle acceleration. This is particularly the case in the study of fragile ultra-thin opaque targets where the absorption of energy from the laser causes the target to heat and expand leading to the target becoming transparent as the density falls. When this occurs the laser can propagate through the target and the transfer of laser energy to the plasma is no-longer localised at the target surface. This interaction is of significant interest as it is here that the highest energy laser-accelerated protons have been recorded.

A new generation of multi-Hz high-energy laser-technology is facilitating orders of magnitude increase in data acquisition rate. In order to exploit these new lasers, it is also necessary to test target technology that can provide fresh ultra-thin foils with high positional stability at multi-Hz repetition rate. In addition, despite the enormous increase in data acquisition-rate the dependence of the interaction dynamics on a large number of variables (e.g. laser energy, laser spatial and temporal energy distribution, target density profile) means that `grid-scanning' each parameter is not an efficient method to map their interdependence. By incorporating machine learning tools the high data rate enabled by the lasers and target can be used to intelligently sample the parameter space to model the interaction and quantify the stability of these novel accelerators.

The proposed collaboration will address this challenge by coupling a liquid sheet target, developed at the US SLAC National Accelerator Laboratory (SLAC), with a new computer-guided approach to laser-plasma experiments, pioneered by researchers at Queen's University Belfast (QUB). The development of this novel experimental platform will enable deeper understanding of the key energy transfer pathways between laser and plasma and their dependence on experimental variables. The research will directly impact on plasma modelling, advanced accelerator research, plasma astrophysics, inertial confinement fusion, materials testing and FLASH radiobiology. The research outputs will feed into EPSRC 2022-2025 strategic priorities on the physical and mathematical sciences powerhouse, frontiers in engineering and artificial intelligence up-skilling through the research themes: AI and Data Science for Engineering, Health and Government by exploiting AI for experimental science; Energy through inertial confinement fusion; Plasma and lasers by developing crucial technology to facilitate deeper understanding and broader exploitation of novel radiation sources; and research infrastructure by enhancing the capabilities of high-intensity laser facilities.
Key Findings
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Date Materialised
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Further Information:  
Organisation Website: http://www.qub.ac.uk