arXiv subjects

Goëry Genty

Publications and source records attributed to Goëry Genty.

At least 19 recordsLinked to original sources

arXiv2026

Mid- and long-wavelength infrared computational ghost spectroscopy

Spectral-domain ghost imaging enables high-resolution spectroscopy with a single-pixel detector. The technique does not rely on spectrally resolved detectors, which makes it inherently robust against turbulence and particularly adapted to weak-light conditions. These features are very attractive for spectral imaging in the mid-infrared region which hosts numerous molecular absorption features but lacks highly sensitive detectors. The implementation of spectral ghost imaging in the mid-infrared has however been limited by the absence of suitable light sources and detectors capable of generating and measuring spectral fluctuations in real time. Here, we demonstrate spectral-domain computational ghost imaging in the mid-infrared based on a nonlinear frequency downconversion scheme. Pre-programmed spectral patterns imposed on broadband light at 1.5 mm using a programmable spectral filter are transferred into the mid-infrared through difference-frequency generation in a nonlinear crystal. This enables computational ghost spectroscopy with a spectral resolution of 0.62 cm-1 using a single-pixel mid-infrared detector. The method is flexible, broadly applicable and, as proof of concept, we demonstrate ghost spectroscopy in mid-wavelength infrared and long-wavelength infrared bands using the nonlinear frequency conversion in chirped-poling lithium niobate and ZnGeP2 crystals, respectively. Our approach provides a new avenue for mid-infrared spectroscopy, remote sensing and spectral imaging.

physics.optics↗

arXiv2026

Power law scaling for classification accuracy in physical neural networks

Physical neural networks (PNNs) harness the intrinsic complexity of physical systems to perform neural computation, potentially at speeds and energy efficiencies inaccessible to conventional digital hardware. Yet, a principled framework for quantifying and predicting their computing accuracy across diverse substrates has remained elusive. Here we introduce the Hotelling Trace Criterion (HTC), a task-conditioned measure of PNN- state separability that can be evaluated without training. We demonstrate that it predicts PNN classification performance with high fidelity across highly nonlinear optical fibres, vertical-cavity surface-emitting lasers, and coupled nonlinear oscillator networks, for benchmark tasks of different difficulty. Classification loss follows a power law in HTC, with Pearson correlation coefficients exceeding 0.99 for MNIST and $\approx$0.97 for Fashion-MNIST, noteworthy experimental and simulated data from physically distinct systems collapse onto a single scaling curve determined by the task rather than the substrate. Applying HTC layer-by-layer during training further reveals that gradient-based optimisation distributes representational capacity unevenly across PNN layers, providing a quantitative diagnostic of training and architecture efficiency invisible to standard loss monitoring. Crucially, once the scaling exponent is established from a small number of trained calibration systems, all further performance predictions require no training since performance can be derived from the much more efficient HTC measurement. These results establish HTC as a substrate-agnostic figure of merit for comparing and scaling PNNs, advancing the field further towards a complete theory connecting fundamental hardware parameters to task performance through universal scaling laws.

cs.ET↗

arXiv2025

Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine

We report a generalized nonlinear Schr\"odinger equation simulation model of an extreme learning machine (ELM) based on optical fiber propagation. Using the MNIST handwritten digit dataset as a benchmark, we study how accuracy depends on propagation dynamics, as well as parameters governing spectral encoding, readout, and noise. For this dataset and with quantum noise limited input, test accuracies of : over 91% and 93% are found for propagation in the anomalous and normal dispersion regimes respectively. Our results also suggest that quantum noise on the input pulses introduces an intrinsic penalty to ELM performance.

physics.optics↗

arXiv2025

Principles and Metrics of Extreme Learning Machines Using a Highly Nonlinear Fiber

Optical computing offers potential for ultra high-speed and low latency computation by leveraging the intrinsic properties of light. Here, we explore the use of highly nonlinear optical fibers (HNLFs) as platforms for optical computing based on the concept of Extreme Learning Machines. Task-independent evaluations are introduced to the field for the first time and focus on the fundamental metrics of effective dimensionality and consistency, which we experimentally characterize for different nonlinear and dispersive conditions. We show that input power and fiber characteristics significantly influence the dimensionality of the computational system, with longer fibers and higher dispersion producing up to 100 principal components (PCs) at input power levels of 30 mW, where the PC correspond to the linearly independent dimensions of the system. The spectral distribution of the PC's eigenvectors reveals that the high-dimensional dynamics facilitating computing through dimensionality expansion are located within 40~nm of the pump wavelength at 1560~nm, providing general insight for computing with nonlinear Schr\"odinger equation systems. Task-dependent results demonstrate the effectiveness of HNLFs in classifying MNIST dataset images. Using input data compression through PC analysis, we inject MNIST images of various input dimensionality into the system and study the impact of input power upon classification accuracy. At optimized power levels we achieve a classification test accuracy of 88\%, significantly surpassing the baseline of 83.7\% from linear systems. Noteworthy, we find that best performance is not obtained at maximal input power, i.e. maximal system dimensionality, but at more than one order of magnitude lower. The same is confirmed regarding the MNIST image's compression, where accuracy is substantially improved when strongly compressing the image to less than 50 PCs.

physics.optics↗

arXiv2024

Supercontinuum generation in a graded-index multimode tellurite fiber

We report a near two-octave spanning supercontinuum (SC) from 790 nm to 2900 nm in a nanostructured tellurite graded-index multimode fiber with a nanostructured core. We study the SC dynamics in different dispersion regimes and observe near-single mode spatial intensity distribution at high input energy values. Numerical simulations of the (3+1)D generalized nonlinear Schrödinger equation are in good agreement with our experiments. Our results open a new avenue for the generation of high-power mid-infrared SC sources in soft glass fibers.

physics.optics↗

arXiv2024

Mid-infrared computational temporal ghost imaging

Ghost imaging in the time domain allows for reconstructing fast temporal objects using a slow photodetector. The technique involves correlating random or pre-programmed probing temporal intensity patterns with the integrated signal measured after modulation by the temporal object. However, the implementation of temporal ghost imaging necessitates ultrafast detectors or modulators for measuring or pre-programming the probing intensity patterns, which is not universally available in all spectral regions especially in the mid-infrared range. Here, we demonstrate a frequency downconversion temporal ghost imaging scheme that enables to extend the operation regime to arbitrary wavelengths regions where fast modulators and detectors are not available. The approach modulates a signal with temporal intensity patterns in the near-infrared and transfers the patterns to an idler via difference-frequency generation at the wavelength of the temporal object to be retrieved. As a proof-of-concept, we demonstrate temporal ghost imaging in the mid-infrared. The scheme is flexible and introduces new possibilities for scan-free pump-probe imaging and the study of ultrafast dynamics in spectral regions where ultrafast modulation or detection is challenging such as the mid-infrared and THz regions.

physics.optics↗

arXiv2023

Analysis of interaction dynamics and rogue wave localization in modulation instability using data-driven dominant balance

We analyze the dynamics of modulation instability in optical fiber (or any other nonlinear Schrödinger equation system) using the machine-learning technique of data-driven dominant balance. We aim to automate the identification of which particular physical processes drive propagation in different regimes, a task usually performed using intuition and comparison with asymptotic limits. We first apply the method to interpret known analytic results describing Akhmediev breather, Kuznetsov-Ma, and Peregrine soliton (rogue wave) structures, and show how we can automatically distinguish regions of dominant nonlinear propagation from regions where nonlinearity and dispersion combine to drive the observed spatio-temporal localization. Using numerical simulations, we then apply the technique to the more complex case of noise-driven spontaneous modulation instability, and show that we can readily isolate different regimes of dominant physical interactions, even within the dynamics of chaotic propagation.

nlin.PS↗

arXiv2023

Tailored supercontinuum generation using genetic algorithmoptimized Fourier domain pulse shaping

We report the generation of spectrally-tailored supercontinuum using Fourier-domain pulse shaping of femtosecond pulses injected into a highly nonlinear fiber controlled by a genetic algorithm. User-selectable spectral enhancement is demonstrated over the 1550-2000~nm wavelength range, with the ability to both select a target central wavelength and a target bandwidth in the range 1--5~nm. The spectral enhancement factor relative to unshaped input pulses is typically $\sim$5--20 in the range 1550--1800~nm and increases for longer wavelengths, exceeding a factor of 160 around 2000~nm. We also demonstrate results where the genetic algorithm is applied to the enhancement of up to four wavelengths simultaneously.

physics.optics↗

arXiv2022

Sensitive multi-species photoacoustic gas detection based on mid-infrared supercontinuum source and miniature multipass cell

We report multipass broadband photoacoustic spectroscopy of trace gases in the mid-infrared. The measurement principle of the sensor relies on supercontinuum-based Fourier transform photoacoustic spectroscopy (FT-PAS), in which a scanning interferometer modulates the intensity of a mid-infrared supercontinuum light source and a cantilever microphone is employed for sensitive photoacoustic detection. With a custom-built external Herriott cell, the supercontinuum beam propagates ten times through a miniature and acoustically non-resonant gas cell. The performance of the FT-PAS system is demonstrated by measuring the fundamental C-H stretch bands of various hydrocarbons. A noise equivalent detection limit of 11 ppb is obtained for methane (40 s averaging time, 15 $μ$W/cm$^{-1}$ incident power spectral density, 4 cm$^{-1}$ resolution), which is an improvement by a factor of 12 compared to the best previous FT-PAS systems. High linearity and good stability of the sensor provide reliable identification of individual species from a gas mixture with strong spectral overlap, laying the foundation for sensitive and selective multi-species detection in small sample volumes.

physics.ins-det↗

arXiv2021

A feed-forward neural network as a nonlinear dynamics integrator for supercontinuum generation

The nonlinear propagation of ultrashort pulses in optical fiber depends sensitively on both input pulse and fiber parameters. As a result, optimizing propagation for specific applications generally requires time-consuming simulations based on sequential integration of the generalized nonlinear Schrödinger equation (GNLSE). Here, we train a feed-forward neural network to learn the differential propagation dynamics of the GNLSE, allowing emulation of direct numerical integration of fiber propagation, and particularly the highly complex case of supercontinuum generation. Comparison with a recurrent neural network shows that the feed-forward approach yields faster training and computation, and reduced memory requirements. The approach is generic and can be extended to other physical systems.

physics.comp-ph↗

arXiv2021

Two octave supercontinuum generation in a non-silica graded-index multimode fiber

The generation of a two-octave supercontinuum from the visible to mid-infrared (700 - 2800 nm) in a non-silica graded-index multimode fiber is reported. The fiber design is based on a nanostructured core comprised of two types of drawn lead-bismuth-gallate glass rods with different refractive indices. This structure yields an effective parabolic index profile, an extended transmission window, and ten times increased nonlinearity when compared to silica fibers. Using femtosecond pulse pumping at wavelengths in both normal and anomalous dispersion regimes, a detailed study is carried out into the supercontinuum generating mechanisms and instabilities seeded by periodic self imaging. Significantly, suitable injection conditions in the high power regime are found to result in the output beam profile showing clear signatures of beam self-cleaning from nonlinear mode mixing. Experimental observations are interpreted using spatio-temporal 3+1D numerical simulations of the generalized nonlinear Schrödinger equation, and simulated spectra are in excellent agreement with experiment over the full two-octave spectral bandwidth. These results demonstrate a new pathway towards the generation of bright, ultrabroadband light sources in the mid-infrared.

physics.optics↗

arXiv2020

Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network

The propagation of ultrashort pulses in optical fibre displays complex nonlinear dynamics that find important applications in fields such as high power pulse compression and broadband supercontinuum generation. Such nonlinear evolution however, depends sensitively on both the input pulse and fibre characteristics, and optimizing propagation for application purposes requires extensive numerical simulations based on generalizations of a nonlinear Schrödinger-type equation. This is computationally-demanding and creates a severe bottleneck in using numerical techniques to design and optimize experiments in real-time. Here, we present a solution to this problem using a machine-learning based paradigm to predict complex nonlinear propagation in optical fibres with a recurrent neural network, bypassing the need for direct numerical solution of a governing propagation model. Specifically, we show how a recurrent neural network with long short-term memory accurately predicts the temporal and spectral evolution of higher-order soliton compression and supercontinuum generation, solely from a given transform-limited input pulse intensity profile. Comparison with experiments for the case of soliton compression shows remarkable agreement in both temporal and spectral domains. In optics, our results apply readily to the optimization of pulse compression and broadband light sources, and more generally in physics, they open up new perspectives for studies in all nonlinear Schrödinger-type systems in studies of Bose-Einstein condensates, plasma physics, and hydrodynamics.

physics.optics↗

arXiv2020

Machine learning analysis of rogue solitons in supercontinuum generation

Supercontinuum generation is a highly nonlinear process that exhibits unstable and chaotic characteristics when developing from long pump pulses injected into the anomalous dispersion regime of an optical fiber. A particular feature associated with this regime is the long-tailed "rogue wave"-like statistics of the spectral intensity on the long wavelength edge of the supercontinuum, linked to the generation of a small number of "rogue solitons" with extreme red-shifts. Here, we apply machine learning to analyze the characteristics of these solitons at the edge of the supercontinuum spectrum, and show how supervised learning can train a neural network to predict the peak power, duration, and temporal delay of these solitons from only the supercontinuum spectral intensity without phase information. The network accurately predicts soliton characteristics for a wide range of scenarios, from the onset of spectral broadening dominated by pure modulation instability to near octave-spanning supercontinuum with distinct rogue solitons.

physics.optics↗

arXiv2020

Low-noise octave-spanning mid-infrared supercontinuum generation in a multimode chalcogenide fiber

We demonstrate the generation of a low-noise, octave-spanning mid-infrared supercontinuum from 1700 to 4800 nm by injecting femtosecond pulses into the normal dispersion regime of a multimode step-index chalcogenide fiber with 100 $μ$m core diameter. We conduct a systematic study of the intensity noise across the supercontinuum spectrum and show that the initial fluctuations of the pump laser are at most amplified by a factor of three. We also perform a comparison with the noise characteristics of an octave-spanning supercontinuum generated in the anomalous dispersion regime of a multimode fluoride fiber with similar core size and show that the all-normal dispersion supercontinuum in the multimode chalcogenide fiber has superior noise characteristics. Our results open up novel perspective for many practical applications such as long-distance remote sensing where high power and low noise are paramount.

physics.optics↗

arXiv2019

Interferometric Autocorrelation Measurements of Supercontinuum based on Two-Photon Absorption

We report on interferometric autocorrelation measurements of broadband supercontinuum light in the anomalous dispersion regime using two-photon absorption in a GaP photodetector. The method is simple, low-cost, and provides a direct measure of the second-order coherence properties, including quantitative information on the coherence time and average duration of the supercontinuum pulses as well as on the presence of temporally coherent sub-structures. We report measurements in regimes where the supercontinuum is coherent and incoherent. In the former case, the interferometric measurements are similar to what is observed for mode-locked laser pulses while in the latter case the interferometric measurements and coherence properties are shown to have characteristics similar to that of a stationary chaotic light source.

physics.optics↗

arXiv2018

Ghost optical coherence tomography

We demonstrate experimentally ghost optical coherence tomography using a broadband incoherent supercontinuum light source with shot-to-shot random spectral fluctuations. The technique is based on ghost imaging in the spectral domain where the object is the spectral interference pattern generated from an optical coherence tomography interferometer in which a physical sample is placed. The image of the sample is obtained from the Fourier transform of the correlation between the spectrally-resolved intensity fluctuations of the supercontinuum and the integrated signal measured at the output of the interferometer. The results are in excellent agreement with measurements obtained from a conventional optical coherence tomography system.

physics.optics↗

arXiv2018

Broadband cantilever-enhanced photoacoustic spectroscopy in the mid-IR using supercontinuum

We demonstrate cantilever-enhanced photoacoustic spectroscopy in the mid-infrared using a supercontinuum source. The approach is broadband, compact, and allows for higher photoacoustic signal intensity and enhanced signal-to-noise ratio as compared to systems employing conventional back body radiation sources. Using this technique, we perform spectroscopic measurements of the full ro-vibrational band structure of water vapor at 1900 nm and methane at 3300 nm with relative signal enhancement factors of 70 and 19, respectively, when compared to measurements that use a black body radiation source. Our results offer novel perspective for photoacoustic detection opening the door to compact and sensitive broadband analyzers in the mid-infrared spectral region.

physics.ins-det↗

arXiv2018

Broadband continuous spectral ghost imaging for high resolution spectroscopy

Ghost imaging is an unconventional imaging technique that generates high resolution images by correlating the intensity of two light beams, neither of which independently contains useful information about the shape of the object. Ghost imaging has great potential to provide robust imaging solutions in the presence of severe environmental perturbations, and has been demonstrated both in the spatial and temporal domains . Here, we exploit recent progress in ultrafast real-time measurement techniques to demonstrate ghost imaging in the frequency domain using a continuous spectrum from an incoherent supercontinuum (SC) light source. We demonstrate the particular application of this ghost imaging technique to broadband spectroscopic measurements of methane absorption, and our results offer novel perspectives for remote sensing in low light conditions, or in spectral regions where sensitive detectors are lacking.

physics.optics↗