Optimization of microwave emission from laser filamentation with a machine learning algorithm

Englesbe, Alexander; Lin, Jinpu; Nees, John; Lucero, Adrian; Krushelnick, Karl; Schmitt-Sody, Andreas

doi:10.1364/AO.426240

Title: Optimization of microwave emission from laser filamentation with a machine learning algorithm

Journal Article · 14 July 2021 · Applied Optics

DOI: https://doi.org/10.1364/AO.426240 · OSTI ID:2914427

^[1];

^[2]; Nees, John ^[2]; Lucero, Adrian ^[3];

^[2];

^[3]

U.S. Naval Research Laboratory, Washington, DC (United States)
University of Michigan, Ann Arbor, MI (United States)
U.S. Air Force Research Laboratory, NM (United States)

We demonstrate that is it possible to optimize the yield of microwave radiation from plasmas generated by laser filamentation in atmosphere through manipulation of the laser wavefront. A genetic algorithm controls a deformable mirror that reconfigures the wavefront using the microwave waveform amplitude as feedback. Optimization runs performed as a function of air pressure show that the genetic algorithm can double the microwave field strength relative to when the mirror surface is flat. Here, an increase in the volume and brightness of the plasma fluorescence accompanies the increase in microwave radiation, implying an improvement in the laser beam intensity profile through the filamentation region due to the optimized wavefront.

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Research Organization:: University of Michigan, Ann Arbor, MI (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: SC0016804

OSTI ID:: 2914427

Journal Information:: Applied Optics, Journal Name: Applied Optics Journal Issue: 25 Vol. 60; ISSN 2155-3165; ISSN 1559-128X

Publisher:: Optical Society of AmericaCopyright Statement

Country of Publication:: United States

Language:: English

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