Classification of laser beam profiles using machine learning at the ELI-NP high power laser system
V. Gaciu,
I. Dăncuş,
B. Diaconescu,
D. G. Ghiţă,
E. Sluşanschi,
C. M. Ticoş
Affiliations
V. Gaciu
Extreme Light Infrastructure – Nuclear Physics (ELI-NP), “Horia Hulubei” National Institute for Physics and Nuclear Engineering, Măgurele, Ilfov 077125, Romania
I. Dăncuş
Extreme Light Infrastructure – Nuclear Physics (ELI-NP), “Horia Hulubei” National Institute for Physics and Nuclear Engineering, Măgurele, Ilfov 077125, Romania
B. Diaconescu
Extreme Light Infrastructure – Nuclear Physics (ELI-NP), “Horia Hulubei” National Institute for Physics and Nuclear Engineering, Măgurele, Ilfov 077125, Romania
D. G. Ghiţă
Extreme Light Infrastructure – Nuclear Physics (ELI-NP), “Horia Hulubei” National Institute for Physics and Nuclear Engineering, Măgurele, Ilfov 077125, Romania
E. Sluşanschi
Engineering and Applications of Lasers and Accelerators Doctoral School (SDIALA), National University of Science and Technology Politehnica Bucureşti, Bucharest RO-060042, Romania
C. M. Ticoş
Extreme Light Infrastructure – Nuclear Physics (ELI-NP), “Horia Hulubei” National Institute for Physics and Nuclear Engineering, Măgurele, Ilfov 077125, Romania
The high power laser system at Extreme Light Infrastructure—Nuclear Physics has demonstrated 10 PW power shot capability. It can also deliver beams with powers of 1 PW and 100 TW in several different experimental areas that carry out dedicated sets of experiments. An array of diagnostics is deployed to characterize the laser beam spatial profiles and to monitor their evolution during the amplification stages. Some of the essential near-field and far-field profiles acquired with CCD cameras are monitored constantly on a large screen television for visual observation and for decision making concerning the control and tuning of the laser beams. Here, we present results on the beam profile classification obtained from datasets with over 14 600 near-field and far-field images acquired during two days of laser operation at 1 PW and 100 TW. We utilize supervised and unsupervised machine learning models based on trained neural networks and an autoencoder. These results constitute an early demonstration of machine learning being used as a tool in the laser system data classification.
