Automated control and optimisation of laser-driven ion acceleration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F23%3A00366267" target="_blank" >RIV/68407700:21340/23:00366267 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1017/hpl.2023.23" target="_blank" >https://doi.org/10.1017/hpl.2023.23</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1017/hpl.2023.23" target="_blank" >10.1017/hpl.2023.23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated control and optimisation of laser-driven ion acceleration

Popis výsledku v původním jazyce

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimisation of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimisation. Here, an automated, HRR-compatible system produced high fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimisation of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually-optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimisation of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources. 2023 Cambridge University Press. All rights reserved.

Název v anglickém jazyce

Automated control and optimisation of laser-driven ion acceleration

Popis výsledku anglicky

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimisation of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimisation. Here, an automated, HRR-compatible system produced high fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimisation of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually-optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimisation of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources. 2023 Cambridge University Press. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10306 - Optics (including laser optics and quantum optics)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

High Power Laser Science and Engineering

ISSN

2095-4719

e-ISSN

2052-3289

Svazek periodika

11

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

000988440000001

EID výsledku v databázi Scopus

2-s2.0-85151549946