Recent progress in fiber-based supercontinuum sources

Sylvestre, Thibaut; Genier, Étienne; Ghosh, Amar N.; Bowen, Patrick D.; Genty, Goëry; Trolès, Johann; Mussot, Arnaud; Peacock, Anna; Klimczak, Mariusz; Heidt, Alexander M.; Travers, John C.; Bang, Ole; Dudley, John M.

doi:10.1117/12.2622303

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For completeness, we also conducted similar optimization considering all-normal dispersion (ANDi) fiber propagation, a regime where spectral broadening is well-known for its inherent stability (Dudley et al, 2006;Sylvestre et al, 2021).…”

Section: All-normal Dispersion Supercontinuum Generation For Multi-ph...mentioning

confidence: 99%

“…The first two approaches, however, do not exhibit proper methods for "on the fly" control properties of SC generation without significantly modifying the setup. In contrast, controlling nonlinear propagation by changing the input pulse laser parameters (i.e., pulse duration, peak power, chirp) has been used as a convenient way to optimize SC generation for practical applications, in particular, due to the strong dependence and sensitivity of spectral broadening dynamics on the input laser source parameters (Veljković et al, 2019;Sylvestre et al, 2021). Interestingly, such input laser parameters may be automatically modified via active systems (such as amplifiers, programmable filters, modulators, polarization controllers) enabling the implementation of optimization ions algorithms and thus allowing for live and autonomous adjustment of the desired SC properties.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing supercontinuum spectro-temporal properties by leveraging machine learning towards multi-photon microscopy

et al. 2022

View full text Add to dashboard Cite

Multi-photon microscopy has played a significant role in biological imaging since it allows to observe living tissues with improved penetration depth and excellent sectioning effect. Multi-photon microscopy relies on multi-photon absorption, enabling the use of different imaging modalities that strongly depends on the properties of the sample structure, the selected fluorophore and the excitation laser. However, versatile and tunable laser excitation for multi-photon absorption is still a challenge, limited by e.g. the narrow bandwidth of typical laser gain medium or by the tunability of wavelength conversion offered by optical parametric oscillators or amplifiers. As an alternative, supercontinuum generation can provide broadband excitations spanning from the ultra-violet to far infrared domains and integrating numerous fluorophore absorption peaks, in turn enabling different imaging modalities or potential multiplexed spectroscopy. Here, we report on the use of machine learning to optimize the spectro-temporal properties of supercontinuum generation in order to selectively enhance multi-photon excitation signals compatible with a variety of fluorophores (or modalities) for multi-photon microscopy. Specifically, we numerically explore how the use of reconfigurable (femtosecond) pulse patterns can be readily exploited to control the nonlinear propagation dynamics and associated spectral broadening occurring in a highly-nonlinear fiber. In this framework, we show that the use of multiple pulses to seed optical fiber propagation can trigger a variety of nonlinear interactions and complex propagation scenarios. This approach, exploiting the temporal dimension as an extended degree of freedom, is used to maximize typical multi-photon excitations at selected wavelengths, here obtained in a versatile and reconfigurable manner suitable for imaging applications. We expect these results to pave the way towards on-demand and real time supercontinuum shaping, with further multi-photon microscopy improvements in terms of spatial 3D resolution, optical toxicity, and wavelength selectivity.

show abstract

Section: All-normal Dispersion Supercontinuum Generation For Multi-ph...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%