TOF analysis of ions accelerated at high repetition rate from laser-induced plasma

0565841 - FZÚ 2023 RIV CH eng J - Článek v odborném periodiku
Russell, E. - Istokskaia, Valeria - Giuffrida, Lorenzo - Levy, Yoann - Huynh, Jaroslav - Cimrman, Martin - Smrž, Martin - Margarone, Daniele
TOF analysis of ions accelerated at high repetition rate from laser-induced plasma.
Applied Sciences-Basel. Roč. 12, č. 24 (2022), č. článku 13021. E-ISSN 2076-3417
Grant CEP: GA MŠMT EF16_019/0000789; GA MŠMT EF15_003/0000445; GA MŠMT EF15_006/0000674
GRANT EU: European Commission(XE) 871161 - IMPULSE; European Commission(XE) 871124 - LASERLAB-EUROPE; European Commission(XE) 739573 - HiLASE CoE
Grant ostatní: OP VVV - ADONIS(XE) CZ.02.1.01/0.0/0.0/16_019/0000789; OP VVV - BIATRI(XE) CZ.02.1.01/0.0/0.0/15_003/0000445; OP VVV - HiLASE-CoE(XE) CZ.02.1.01/0.0/0.0/15_006/0000674
Institucionální podpora: RVO:68378271
Klíčová slova: time of flight ion spectroscopy * laser induced plasma * data processing
Obor OECD: Optics (including laser optics and quantum optics)
Impakt faktor: 2.7, rok: 2022
Způsob publikování: Open access

The generation,detection,and quantification of high-energy proton spectra that are produced from laser-target interaction methodologies is a field of increasingly growing popularity over the last 20 years.Generation methods such as target normal sheath acceleration or similar allow for collimated laminar ion beams to be produced in a compact environment through the use of short-burst terawatt lasers and are a growing field of investment.This project details and refinement of a python-based code to analyze time-of-flight ion spectroscopy data,with the intent to pinpoint the maximum proton energy within the incident beam to as reliable and accurate a value as possible within a feasible processing time.TOF data for 2.2×1016 W/cm2 intensity laser shots incident on a 2mm Cu target that were gathered from the PERLA 1kHz laser at the HiLASE center were used as training and testing data with the implementation of basic machine learning techniques to train these methods to the data being used.
Trvalý link: https://hdl.handle.net/11104/0337937