We report an experimental demonstration of soliton self-frequency shift in a nitrogen-filled multipass cell. The use of a molecular gas combined with the flexible geometry of the multipass platform enables efficient wavelength conversion from 1030 nm to 1110 nm. By employing 24-fs input pulses with energies up to 150 mu J, we generate soliton pulses with energies up to 55 mu J and durations below 50 fs, corresponding to peak powers as high as 0.5 GW. Experimental results are supported by numerical simulations, which emphasize how a suitable combination of dispersion, input pulse energy and duration, and gas pressure enables a wavelength-tunable, high-power, and high-energy source. This approach offers enhanced scalability in terms of average power and pulse energy compared to previously studied systems based on waveguides. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

High-energy soliton frequency shifting in a nitrogen-filled multipass cell

Cichelli, Giovanni;Laporta, Paolo;Coluccelli, Nicola;
2025-01-01

Abstract

We report an experimental demonstration of soliton self-frequency shift in a nitrogen-filled multipass cell. The use of a molecular gas combined with the flexible geometry of the multipass platform enables efficient wavelength conversion from 1030 nm to 1110 nm. By employing 24-fs input pulses with energies up to 150 mu J, we generate soliton pulses with energies up to 55 mu J and durations below 50 fs, corresponding to peak powers as high as 0.5 GW. Experimental results are supported by numerical simulations, which emphasize how a suitable combination of dispersion, input pulse energy and duration, and gas pressure enables a wavelength-tunable, high-power, and high-energy source. This approach offers enhanced scalability in terms of average power and pulse energy compared to previously studied systems based on waveguides. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
2025
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1299006
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