arXiv subjects

Mario Chemnitz

Publications and source records attributed to Mario Chemnitz.

10 recordsLinked to original sources

arXiv2026

Statistical Methods for Determining Turbulence in Supercontinuum Generation

Distinguishing coherent, turbulent, and chaotic operating regimes in supercontinuum generation is important for understanding nonlinear optical dynamics and optimizing broadband light sources. Experimentally identifying the onset of turbulence remains challenging because the most common metric, first-order coherence, requires access to the complex optical field and cannot be directly obtained from intensity-only measurements. In this work, we investigate whether experimentally accessible statistical observables can identify turbulence in supercontinuum generation. We compare wavelength-integrated variance and kurtosis with simulation-based first-order coherence over a chirp-controlled pulse-duration sweep implemented through additional $\beta_2$ dispersion. The study combines generalized nonlinear Schr\"odinger equation simulations with shot-to-shot dispersive Fourier transform measurements validated against optical spectrum analyzer spectra. Statistical intensity distributions were analyzed using histograms, complementary cumulative distribution functions, and kurtosis measurements across the generated supercontinuum bandwidth. Simulations and experiments both revealed heavy-tailed intensity statistics in the intermediate pulse-duration regime associated with reduced spectral coherence. The integrated kurtosis reached a maximum near 600 fs in simulations and near 700 fs in experiments, while the integrated variance within the first 20 dB spectral range decreased with increasing pulse duration. The agreement between simulations and experiments demonstrates that variance- and kurtosis-based observables can serve as experimentally accessible indicators of turbulence in supercontinuum generation. These results show that intensity-only statistical measurements can distinguish coherent and incoherent operating regimes without requiring direct field-resolved coherence measurements.

physics.optics↗

arXiv2026

Effective Training Principles of Physical Reservoirs

Reservoir computers benefit from the inherent complexity of optical phenomena, which provide rich, often nonlinear dynamics. However, training directly on the reservoir's output renders the system prone to overfitting and computationally inefficient during the training phase. In this work, we investigate strategies to mitigate overfitting and reduce computational overhead through output pruning and regularization. We compare loss-minimizing search methods (Equal Search and Branch and Bound) against an output-oriented statistical filtering approach (Variance Filter) and random pruning, highlighting advantages and disadvantages of each approach and the overall importance of informed reservoir output sampling, particularly for a shrinking latent space. We further demonstrate that enforcing readout selection across the full output spectrum improves performance, especially for non-iterative methods. Additionally, we examine L1 and L2 regularization techniques (LASSO and ridge regression), both of which significantly enhance performance on highly nonlinear tasks such as the Spiral Benchmark. While our methods are of general use, results are obtained from and discussed exemplarily for a nonlinear fiber-optical extreme learning machine. Overall, this study provides a deep analysis of the reservoirs' hidden-layer filtering mechanisms and the output-layer training, enabling optimized performance in physical reservoir computing systems.

physics.optics↗

arXiv2026

Giant Brillouin gain in frozen CS2 capillaries

Stimulated Brillouin-Mandelstam scattering offers exceptional capabilities for photonic signal processing, but current platforms demand performance trade-offs between long interaction lengths, high gain, low optical losses, and practical implementation. Here, we demonstrate a novel platform based on the reversible freezing of a carbon disulfide filled liquid-core optical fiber. This approach delivers a giant in-fiber Brillouin gain of 434 W-1m-1 with a linewidth of 24 MHz, while maintaining low propagation losses in a fully spliced architecture and providing the potential for meter-scale interaction lengths. Leveraging this gain, as a proof of principle, we realize an optoacoustic memory operating at sub-nanojoule pulse energies - more than two orders of magnitude lower than state-of-the-art implementations. This power reduction is universal for Brillouin-based fiber applications in general and will enable low-power photonic signal processing and neuromorphic computing, efficient microwave photonics and sensing, as well as in-fiber quantum optomechanics-based technologies.

physics.optics↗

arXiv2026

Kerr-induced In-line Interferometry for Ultra-sensitive Phase-Contrast Imaging

Measuring the phase of light is fundamental to optical imaging, sensing, and signal processing applications. Conventional optical phase measurements rely on multipath configurations, bulky interferometric setups, and computationally intensive data pipelines, limiting scalability, robustness, and practicality. We introduce a technique that allows for reference-free in-line phase retrieval of abrupt phase transitions in optical pulses directly from spectral measurements. Theory, simulations, and experiments concurrently explain the effect as a result of a Kerr-mediated interference between a projected linear wave component and the high-intensity residual of the phase-altered pulse. Utilizing this phenomenon, we demonstrate algorithm-free phase measurements of up to {\pi}/385 sensitivity and shot-to-shot signal prominence at 13 dB above noise at 80 MHz rates and 50 pJ pulse energies. This approach offers new paths toward the use of femtosecond pulses as broadband data carriers for optical communications, information processing, and direct high-throughput phase imaging.

physics.optics↗

arXiv2025

Stimulated Emission from 2D CdSe/CdS Nanoplatelets Integrated in a Liquid-Core Fiber

Colloidal nanocrystals are unique optical gain materials due to their high intrinsic absorption, excellent quantum yield, and tunable emission. However, integration of colloidal nanocrystal solutions into photonic systems for lasing applications is challenging since high concentration levels are required for optical amplification. Here, we address this challenge by integrating colloidally dispersed core/crown CdSe/CdS 2D nanoplatelets in liquid-core optical fibers as a scalable platform. The platform allowed achieving sufficiently effective gain for amplified spontaneous emission at a threshold as low as 1.8 kW/cm2 under quasi-CW pumping, even at a con-centration two orders of magnitude lower than the minimal concentration considered to be required for gain in conventional colloidal quantum dots. We show that the low-loss optical waveguiding of the fiber is crucial for efficient stimulated emission, rendering liquid-core fibers as a promising and unique platform to realize lasers based on colloidally dispersed nanocrystals.

physics.optics↗

arXiv2025

Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies

Bringing together researchers, funders, industry partners, and publishers from 14 countries across 5 continents, we advance the debate around open-science, assessment and learning. We introduce an integrative "open knowledge system" framework linking knowledge production, validation, assessment, and reuse into one ecosystem view, and translate it into actionable recommendations for each stakeholder. Shifting focus to modular, machine-readable knowledge objects, these recommendations are intended to help diagnose misaligned incentives and guide reforms that properly value all scientific contributions.

cs.SI↗

arXiv2025

Nonlinear Inference Capacity of Fiber-Optical Extreme Learning Machines

The intrinsic complexity of nonlinear optical phenomena offers a fundamentally new resource to analog brain-inspired computing, with the potential to address the pressing energy requirements of artificial intelligence. We introduce and investigate the concept of nonlinear inference capacity in optical neuromorphic computing in highly nonlinear fiber-based optical Extreme Learning Machines. We demonstrate that this capacity scales with nonlinearity to the point where it surpasses the performance of a deep neural network model with five hidden layers on a scalable nonlinear classification benchmark. By comparing normal and anomalous dispersion fibers under various operating conditions and against digital classifiers, we observe a direct correlation between the system's nonlinear dynamics and its classification performance. Our findings suggest that image recognition tasks, such as MNIST, are incomplete in showcasing deep computing capabilities in analog hardware. Our approach provides a framework for evaluating and comparing computational capabilities, particularly their ability to emulate deep networks, across different physical and digital platforms, paving the way for a more generalized set of benchmarks for unconventional, physics-inspired computing architectures.

physics.optics↗

arXiv2025

Roadmap on Neuromorphic Photonics

This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

cs.ET↗

arXiv2023

Microfluidic Filling and Spectroscopy of Colloidal CdSe/CdS Nanoplatelets in Liquid Core Fibers

Colloidal 2D semiconductor nanoplatelets are highly efficient light emitters, which exhibit large absorption and emission cross sections, and constitute promising laser gain media. However, if dispersed in solutions, such nanoplatelets lack a suitable optical platform for scalable and application-oriented integration into optical setups such as lasers. Here, we demonstrate the first successful integration of solution-processed 2D CdSe/CdS Core/Crown nanoplatelets in m-scale liquid core optical fibers. We compare the nanoplatelets' spectroscopic properties before and after filling them into the fibers and find that spontaneous emission is shifted and broadened. We even observe a first evidence of stimulated emission at high excitation energies. In conclusion, liquid core fibers constitute a novel and scalable platform for optical integration of nanoplatelets for applications as novel, highly reconfigurable laser gain medium.

physics.optics↗

arXiv2022

Exploring extreme thermodynamics in nanoliter volumes through stimulated Brillouin-Mandelstam scattering

Examining the physical properties of materials - particularly of toxic liquids - under a wide range of thermodynamic states is a challenging problem due to the extreme conditions the material has to be exposed to. Such temperature and pressure regimes, which result in a change of refractive index and sound velocity can be accessed by optoacoustic interactions such as Brillouin-Mandelstam scattering. Here we experimentally demonstrate Brillouin-Mandelstam measurements of nanoliter volumes of liquids in extreme thermodynamic regimes. We use a fully-sealed liquid-core optical fiber containing carbon disulfide; within this waveguide, which exhibits tight optoacoustic confinement and a high Brillouin gain of (32.2 $\pm$ 0.8) 1/(Wm), we are able to conduct spatially resolved measurements of the Brillouin frequency shift. Knowledge of the local Brillouin response enables us to control the temperature and pressure independently over a wide range. We observe and measure the material properties of the liquid core at very large positive pressures (above 1000 bar), substantial negative pressures (below -300 bar) and we explore the isobaric and isochoric regimes. The extensive thermodynamic control allows the tunability of the Brillouin frequency shift of more than 40% using only minute volumes of liquid. This work opens the way for future studies of liquids under a variety of conventionally hard-to-reach conditions.

physics.optics↗