Single-Photon Level Dispersive Fourier Transform: Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Lynn Sader
  • Surajit Bose
  • Anahita Khodadad Kashi
  • Yassin Boussafa
  • Raktim Haldar
  • Romain Dauliat
  • Philippe Roy
  • Marc Fabert
  • Alessandro Tonello
  • Vincent Couderc
  • Michael Kues
  • Benjamin Wetzel
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Details

OriginalspracheEnglisch
Seiten (von - bis)3915-3928
Seitenumfang14
FachzeitschriftACS PHOTONICS
Jahrgang10
Ausgabenummer11
Frühes Online-Datum25 Okt. 2023
PublikationsstatusVeröffentlicht - 15 Nov. 2023

Abstract

Dispersive Fourier transform is a characterization technique that allows directly extracting an optical spectrum from a time domain signal, thus providing access to real-time characterization of the signal spectrum. However, these techniques suffer from sensitivity and dynamic range limitations, hampering their use for special applications in, e.g., high-contrast characterizations and sensing. Here, we report on a novel approach to dispersive Fourier transform-based characterization using single-photon detectors. In particular, we experimentally develop this approach by leveraging mutual information analysis for signal processing and hold a performance comparison with standard dispersive Fourier transform detection and statistical tools. We apply the comparison to the analysis of noise-driven nonlinear dynamics arising from well-known modulation instability processes. We demonstrate that with this dispersive Fourier transform approach, mutual information metrics allow for successfully gaining insight into the fluctuations associated with modulation instability-induced spectral broadening, providing qualitatively similar signatures compared to ultrafast photodetector-based dispersive Fourier transform but with improved signal quality and spectral resolution (down to 53 pm). The technique presents an intrinsically unlimited dynamic range and is extremely sensitive, with a sensitivity reaching below the femtowatt (typically 4 orders of magnitude better than ultrafast dispersive Fourier transform detection). We show that this method can not only be implemented to gain insight into noise-driven (spontaneous) frequency conversion processes but also be leveraged to characterize incoherent dynamics seeded by weak coherent optical fields.

ASJC Scopus Sachgebiete

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Single-Photon Level Dispersive Fourier Transform: Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics. / Sader, Lynn; Bose, Surajit; Kashi, Anahita Khodadad et al.
in: ACS PHOTONICS, Jahrgang 10, Nr. 11, 15.11.2023, S. 3915-3928.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Sader, L, Bose, S, Kashi, AK, Boussafa, Y, Haldar, R, Dauliat, R, Roy, P, Fabert, M, Tonello, A, Couderc, V, Kues, M & Wetzel, B 2023, 'Single-Photon Level Dispersive Fourier Transform: Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics', ACS PHOTONICS, Jg. 10, Nr. 11, S. 3915-3928. https://doi.org/10.1021/acsphotonics.3c00711
Sader, L., Bose, S., Kashi, A. K., Boussafa, Y., Haldar, R., Dauliat, R., Roy, P., Fabert, M., Tonello, A., Couderc, V., Kues, M., & Wetzel, B. (2023). Single-Photon Level Dispersive Fourier Transform: Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics. ACS PHOTONICS, 10(11), 3915-3928. https://doi.org/10.1021/acsphotonics.3c00711
Sader L, Bose S, Kashi AK, Boussafa Y, Haldar R, Dauliat R et al. Single-Photon Level Dispersive Fourier Transform: Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics. ACS PHOTONICS. 2023 Nov 15;10(11):3915-3928. Epub 2023 Okt 25. doi: 10.1021/acsphotonics.3c00711
Sader, Lynn ; Bose, Surajit ; Kashi, Anahita Khodadad et al. / Single-Photon Level Dispersive Fourier Transform : Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics. in: ACS PHOTONICS. 2023 ; Jahrgang 10, Nr. 11. S. 3915-3928.
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title = "Single-Photon Level Dispersive Fourier Transform: Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics",
abstract = "Dispersive Fourier transform is a characterization technique that allows directly extracting an optical spectrum from a time domain signal, thus providing access to real-time characterization of the signal spectrum. However, these techniques suffer from sensitivity and dynamic range limitations, hampering their use for special applications in, e.g., high-contrast characterizations and sensing. Here, we report on a novel approach to dispersive Fourier transform-based characterization using single-photon detectors. In particular, we experimentally develop this approach by leveraging mutual information analysis for signal processing and hold a performance comparison with standard dispersive Fourier transform detection and statistical tools. We apply the comparison to the analysis of noise-driven nonlinear dynamics arising from well-known modulation instability processes. We demonstrate that with this dispersive Fourier transform approach, mutual information metrics allow for successfully gaining insight into the fluctuations associated with modulation instability-induced spectral broadening, providing qualitatively similar signatures compared to ultrafast photodetector-based dispersive Fourier transform but with improved signal quality and spectral resolution (down to 53 pm). The technique presents an intrinsically unlimited dynamic range and is extremely sensitive, with a sensitivity reaching below the femtowatt (typically 4 orders of magnitude better than ultrafast dispersive Fourier transform detection). We show that this method can not only be implemented to gain insight into noise-driven (spontaneous) frequency conversion processes but also be leveraged to characterize incoherent dynamics seeded by weak coherent optical fields.",
keywords = "fiber optics, modulation instability, nonlinear photonics, real-time characterization techniques, spectral correlation",
author = "Lynn Sader and Surajit Bose and Kashi, {Anahita Khodadad} and Yassin Boussafa and Raktim Haldar and Romain Dauliat and Philippe Roy and Marc Fabert and Alessandro Tonello and Vincent Couderc and Michael Kues and Benjamin Wetzel",
note = "Funding Information: This work has received funding from the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement No. 950618 (STREAMLINE project) and No. 947603 (QFreC project), from the French Agence Nationale de la Recherche (ANR) through the OPTIMAL project (ANR-20-CE30-0004), from the German Federal Ministry of Education and Research within the project PQuMAL and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany{\textquoteright}s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). L.S., Y.B., and B.W. further acknowledge the support of the Conseil R{\'e}gional Nouvelle-Aquitaine (SCIR & SPINAL projects). R.H. acknowledges the financial support provided by the Alexander von Humboldt Stiftung to conduct the research. ",
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Download

TY - JOUR

T1 - Single-Photon Level Dispersive Fourier Transform

T2 - Ultrasensitive Characterization of Noise-Driven Nonlinear Dynamics

AU - Sader, Lynn

AU - Bose, Surajit

AU - Kashi, Anahita Khodadad

AU - Boussafa, Yassin

AU - Haldar, Raktim

AU - Dauliat, Romain

AU - Roy, Philippe

AU - Fabert, Marc

AU - Tonello, Alessandro

AU - Couderc, Vincent

AU - Kues, Michael

AU - Wetzel, Benjamin

N1 - Funding Information: This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 950618 (STREAMLINE project) and No. 947603 (QFreC project), from the French Agence Nationale de la Recherche (ANR) through the OPTIMAL project (ANR-20-CE30-0004), from the German Federal Ministry of Education and Research within the project PQuMAL and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). L.S., Y.B., and B.W. further acknowledge the support of the Conseil Régional Nouvelle-Aquitaine (SCIR & SPINAL projects). R.H. acknowledges the financial support provided by the Alexander von Humboldt Stiftung to conduct the research.

PY - 2023/11/15

Y1 - 2023/11/15

N2 - Dispersive Fourier transform is a characterization technique that allows directly extracting an optical spectrum from a time domain signal, thus providing access to real-time characterization of the signal spectrum. However, these techniques suffer from sensitivity and dynamic range limitations, hampering their use for special applications in, e.g., high-contrast characterizations and sensing. Here, we report on a novel approach to dispersive Fourier transform-based characterization using single-photon detectors. In particular, we experimentally develop this approach by leveraging mutual information analysis for signal processing and hold a performance comparison with standard dispersive Fourier transform detection and statistical tools. We apply the comparison to the analysis of noise-driven nonlinear dynamics arising from well-known modulation instability processes. We demonstrate that with this dispersive Fourier transform approach, mutual information metrics allow for successfully gaining insight into the fluctuations associated with modulation instability-induced spectral broadening, providing qualitatively similar signatures compared to ultrafast photodetector-based dispersive Fourier transform but with improved signal quality and spectral resolution (down to 53 pm). The technique presents an intrinsically unlimited dynamic range and is extremely sensitive, with a sensitivity reaching below the femtowatt (typically 4 orders of magnitude better than ultrafast dispersive Fourier transform detection). We show that this method can not only be implemented to gain insight into noise-driven (spontaneous) frequency conversion processes but also be leveraged to characterize incoherent dynamics seeded by weak coherent optical fields.

AB - Dispersive Fourier transform is a characterization technique that allows directly extracting an optical spectrum from a time domain signal, thus providing access to real-time characterization of the signal spectrum. However, these techniques suffer from sensitivity and dynamic range limitations, hampering their use for special applications in, e.g., high-contrast characterizations and sensing. Here, we report on a novel approach to dispersive Fourier transform-based characterization using single-photon detectors. In particular, we experimentally develop this approach by leveraging mutual information analysis for signal processing and hold a performance comparison with standard dispersive Fourier transform detection and statistical tools. We apply the comparison to the analysis of noise-driven nonlinear dynamics arising from well-known modulation instability processes. We demonstrate that with this dispersive Fourier transform approach, mutual information metrics allow for successfully gaining insight into the fluctuations associated with modulation instability-induced spectral broadening, providing qualitatively similar signatures compared to ultrafast photodetector-based dispersive Fourier transform but with improved signal quality and spectral resolution (down to 53 pm). The technique presents an intrinsically unlimited dynamic range and is extremely sensitive, with a sensitivity reaching below the femtowatt (typically 4 orders of magnitude better than ultrafast dispersive Fourier transform detection). We show that this method can not only be implemented to gain insight into noise-driven (spontaneous) frequency conversion processes but also be leveraged to characterize incoherent dynamics seeded by weak coherent optical fields.

KW - fiber optics

KW - modulation instability

KW - nonlinear photonics

KW - real-time characterization techniques

KW - spectral correlation

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