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Multistability manipulation by reinforcement learning algorithm inside mode-locked fiber laser.
- Source :
- Nanophotonics (21928606); Jul2024, Vol. 13 Issue 16, p2891-2901, 11p
- Publication Year :
- 2024
-
Abstract
- Fiber mode-locked lasers are nonlinear optical systems that provide ultrashort pulses at high repetition rates. However, adjusting the cavity parameters is often a challenging task due to the intrinsic multistability of a laser system. Depending on the adjustment of the cavity parameters, the optical output may vary significantly, including Q-switching, single and multipulse, and harmonic mode-locked regimes. In this study, we demonstrate an experimental implementation of the Soft Actor–Critic algorithm for generating a harmonic mode-locked regime inside a state-of-the-art fiber laser with an ion-gated nanotube saturable absorber. The algorithm employs nontrivial strategies to achieve a guaranteed harmonic mode-locked regime with the highest order by effectively managing the pumping power of a laser system and the nonlinear transmission of a nanotube absorber. Our results demonstrate a robust and feasible machine-learning–based approach toward an automatic system for adjusting nonlinear optical systems with the presence of multistability phenomena. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21928606
- Volume :
- 13
- Issue :
- 16
- Database :
- Complementary Index
- Journal :
- Nanophotonics (21928606)
- Publication Type :
- Academic Journal
- Accession number :
- 178426124
- Full Text :
- https://doi.org/10.1515/nanoph-2023-0792