arXiv subjects

Vincent Dumont

Publications and source records attributed to Vincent Dumont.

At least 19 recordsLinked to original sources

arXiv2026

Determination of the intrinsic mechanical quality factor in high-stress silicon nitride resonators

Recent advances in silicon nitride nanomechanical resonators have pushed mechanical quality factors to ultra-high values by combining stress-induced dissipation dilution with mode-shape engineering. Neither mechanism alters the intrinsic quality factor $Q_{\mathrm{intr}}$. Targeting the intrinsic loss itself therefore remains an untapped route to even higher $Q$. Doing so first requires reliable quantification of $Q_{\mathrm{intr}}$, which has proven challenging. Here we present a robust methodology that quantifies $Q_{\mathrm{intr}}$ by combining automated mode identification with systematic ringdown measurements over a large number of mechanical modes. Applied to high-stress silicon nitride membranes, it reveals a systematic dependence of $Q_{\mathrm{intr}}$ on thickness that cannot be described using established models, particularly in the ultra-thin limit. We account for this trend with a phenomenological model that incorporates a thickness-dependent loss channel. Together, our method and model open a route toward a microscopic understanding of intrinsic dissipation and toward directly mitigating its loss channels.

physics.app-ph↗

arXiv2025

Broadband Thermal Noise Correlations Induced by Measurement Back-Action

Modern mechanical sensors increasingly measure motion with precision sufficient to resolve the fundamental thermal noise floor over a broad band. Compared to traditional sensors -- achieving this limit only near resonance -- this capability provides massive gains in acquisition rates along with access to otherwise obscured transient signals. However, these stronger measurements of motion are naturally accompanied by increased back-action. Here we show how resolving the broadband thermal noise spectrum reveals back-action-induced correlations in the noise from many mechanical modes, even those well-separated in frequency. As a result, the observed spectra can deviate significantly from predictions of the usual single-mode and (uncorrelated) multimode models over the broad band, notably even at the mechanical resonance peaks. This highlights that these effects must be considered in all systems exhibiting measurement back-action, regardless of whether the resonances are spectrally isolated or the readout noise is high enough that the noise peaks appear consistent with simpler models. Additionally, these correlations advantageously allow the thermal noise spectrum to reach a minimum -- equivalent to that of a single mode -- in a band far from the resonance peak, where the mechanical susceptibility is comparatively stable against frequency noise.

physics.optics↗

arXiv2025

A network of parametrically driven silicon nitride mechanical membranes

Networks of nonlinear resonators offer a promising platform for analog computing and the emulation of complex systems. However, realizing such networks remains challenging, as it requires resonators with high quality factors, individual frequency tunability, and strong inter-resonator coupling. In this work, we present a system that meets all these criteria. Our system is based on metallized silicon nitride membranes that are coupled via their common substrate and controlled capacitively via electrodes. We demonstrate individual frequency tuning and strong parametric driving of each membrane. Notably, we tune membrane frequencies through avoided crossings and demonstrate tunability of the coupled membrane's parametric response. This platform provides a scalable and controllable setting for exploring collective phenomena, dynamical phase transitions, nonlinear topology, and analog computing.

cond-mat.mes-hall↗

arXiv2024

Slow and fast topological dynamical phase transitions in a Duffing resonator driven by two detuned tones

The combination of a strong pump and a weak probe has been widely applied to investigate both optical and nanomechanical devices. Such pump-probe measurements allows for the exploration of nonlinear dynamics, driven by the large pump tone, by measuring the system response to a probe tone. In contrast, here we report on the dynamics of a mechanical Duffing resonator driven with a combination of two large tones at different frequencies. Our results indicate the presence of various distinct regimes with very different dynamics. We systematically investigate the impact of the relative strength and detuning between the two drives on the dynamical response. This provides an illustrative example of dynamical phase transitions in out-of-equilibrium systems.

cond-mat.mes-hall↗

arXiv2024

An optically defined phononic crystal defect

We demonstrate a mechanical crystal with an optically programmable defect mode. By applying an optical spring to a single unit cell of a phononic crystal membrane, we smoothly transfer a single mechanical mode into the bandgap, thereby localizing its spatial profile from one spanning the entire crystal to one confined within a few unit cells. This localization is evidenced by an enhanced mechanical frequency shift commensurate with a 37-fold reduction in the mode's participating mass. Our results lay groundwork for a new class of optomechanical systems that control mechanical mode profile and participating mass.

physics.optics↗

arXiv2024

Topological classification of driven-dissipative nonlinear systems

In topology, one averages over local geometrical details to reveal robust global features. This approach proves crucial for understanding quantized bulk transport and exotic boundary effects of linear wave propagation in (meta-)materials. Moving beyond linear Hamiltonian systems, the study of topology in physics strives to characterize open (non-Hermitian) and interacting systems. Here, we establish a framework for the topological classification of driven-dissipative nonlinear systems. Specifically, we define a graph index for the Floquet semiclassical equations of motion describing such systems. The graph index builds upon topological vector analysis theory and combines knowledge of the particle-hole nature of fluctuations around each out-of-equilibrium stationary state. To test this approach, we divulge the topological invariants arising in a micro-electromechanical nonlinear resonator subject to forcing and a time-modulated potential. Our framework classifies the complete phase diagram of the system and reveals the topological origin of driven-dissipative phase transitions, as well as that of under- to over-damped responses. Furthermore, we predict topological phase transitions between symmetry-broken phases that pertain to population inversion transitions. This rich manifesting phenomenology reveals the pervasive link between topology and nonlinear dynamics, with broad implications for all fields of science.

cond-mat.mes-hall↗

arXiv2024

Enhancing Membrane-Based Scanning Force Microscopy Through an Optical Cavity

The new generation of strained silicon nitride resonators harbors great promise for scanning force microscopy, especially when combined with the extensive toolbox of cavity optomechanics. However, accessing a mechanical resonator inside an optical cavity with a scanning tip is challenging. Here, we experimentally demonstrate a cavity-based scanning force microscope based on a silicon nitride membrane sensor. We overcome geometric constraints by making use of the extended nature of the mechanical resonator normal modes, which allows us to spatially separate the scanning and readout sites of the membrane. Our microscope is geared towards low-temperature applications in the zeptonewton regime, such as nanoscale nuclear spin detection and imaging.

physics.optics↗

arXiv2024

Hamiltonian reconstruction via ringdown dynamics

Many experimental techniques aim at determining the Hamiltonian of a given system. The Hamiltonian describes the system's evolution in the absence of dissipation, and is often central to control or interpret an experiment. Here, we theoretically propose and experimentally demonstrate a method for Hamiltonian reconstruction from measurements over a large area of phase space, overcoming the main limitation of previous techniques. A crucial ingredient for our method is the presence of dissipation, which enables sampling of the Hamiltonian through ringdown-type measurements. We apply the method to a driven-dissipative system -- a parametric oscillator -- observed in a rotating frame, and reconstruct the (quasi-)Hamiltonian of the system. Furthermore, we demonstrate that our method provides direct experimental access to the so-called symplectic norm of the stationary states of the system, which is tied to the particle- or hole-like nature of excitations of these states. In this way, we establish a method to unveil qualitative differences between the fluctuations around stabilized minima and maxima of the nonlinear out-of-equilibrium stationary states. Our method constitutes a versatile approach to characterize a wide class of driven-dissipative systems.

cond-mat.mes-hall↗

arXiv2023

High-Power Quantum-Limited Photodiode and Classical Laser Noise Squashing

To benefit high-power interferometry and the creation of low-noise light sources, we develop a simple lead-compensated photodetector enabling quantum-limited readout from 0.3 mW to 10 mW and 10 k$Ω$ transimpedance gain from 85 Hz - 35 MHz. Feeding the detector output back to an intensity modulator, we squash the classical amplitude noise of a commercial 1550 nm fiber laser to the shot noise limit over a bandwidth of 700 Hz - 200 kHz, observing no degradation to its (nominally ~100 Hz) linewidth.

physics.optics↗

arXiv2022

A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography

Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an object at different rotations relative to the beam. By imaging 2-dimensional projections, a 3-dimensional object can be reconstructed through a computational algorithm. Imaging at a greater number of rotation angles allows for improved reconstruction. However, taking more measurements increases the x-ray dose and may cause sample damage. Deep neural networks have been used to transform sparse 2-D projection measurements to a 3-D reconstruction by training on a dataset of known similar objects. However, obtaining high-quality object reconstructions for the training dataset requires high x-ray dose measurements that can destroy or alter the specimen before imaging is complete. This becomes a chicken-and-egg problem: high-quality reconstructions cannot be generated without deep learning, and the deep neural network cannot be learned without the reconstructions. This work develops and validates a self-supervised probabilistic deep learning technique, the physics-informed variational autoencoder, to solve this problem. A dataset consisting solely of sparse projection measurements from each object is used to jointly reconstruct all objects of the set. This approach has the potential to allow visualization of fragile samples with x-ray computed tomography. We release our code for reproducing our results at: https://github.com/vganapati/CT_PVAE .

cs.CV↗

arXiv2022

Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations

The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating events in High Energy Physics (HEP), including simulating detector responses and physics events. However, training GANs is notoriously hard and optimizing their hyperparameters even more so. It normally requires many trial-and-error training attempts to force a stable training and reach a reasonable fidelity. Significant tuning work has to be done to achieve the accuracy required by physics analyses. This work uses the physics-agnostic and high-performance-computer-friendly hyperparameter optimization tool HYPPO to optimize and examine the sensitivities of the hyperparameters of a GAN for two independent HEP datasets. This work provides the first insights into efficiently tuning GANs for Large Hadron Collider data. We show that given proper hyperparameter tuning, we can find GANs that provide high-quality approximations of the desired quantities. We also provide guidelines for how to go about GAN architecture tuning using the analysis tools in HYPPO.

hep-ex↗

arXiv2022

Asymmetry-Based Quantum Backaction Suppression in Quadratic Optomechanics

As the field of optomechanics advances, quadratic dispersive coupling (QDC) promise an increasingly feasible class of qualitatively new functionality. However, the leading QDC geometries also generate linear dissipative coupling, and an associated quantum radiation force noise that is detrimental to QDC applications. Here, we propose a simple modification that dramatically reduces this noise without altering the QDC strength. We identify optimal regimes of operation, and discuss advantages within the examples of optical levitation and nondestructive phonon measurement.

quant-ph↗

arXiv2022

Do cities have a unique magnetic pulse?

We present a comparative analysis of urban magnetic fields between two American cities: Berkeley (California) and Brooklyn Borough of New York City (New York). Our analysis uses data taken over a four-week period during which magnetic field data were continuously recorded using a fluxgate magnetometer of 70 pT/$\sqrt{\mathrm{Hz}}$ sensitivity. We identified significant differences in the magnetic signatures. In particular, we noticed that Berkeley reaches a near-zero magnetic field activity at night whereas magnetic activity in Brooklyn continues during nighttime. We also present auxiliary measurements acquired using magnetoresistive vector magnetometers (VMR), with sensitivity of 300 pT/$\sqrt{\mathrm{Hz}}$, and demonstrate how cross-correlation, and frequency-domain analysis, combined with data filtering can be used to extract urban magnetometry signals and study local anthropogenic activities. Finally, we discuss the potential of using magnetometer networks to characterize the global magnetic field of cities and give directions for future development.

eess.SP↗

arXiv2021

Search for topological defect dark matter with a global network of optical magnetometers

Ultralight bosons such as axion-like particles are viable candidates for dark matter. They can form stable, macroscopic field configurations in the form of topological defects that could concentrate the dark matter density into many distinct, compact spatial regions that are small compared to the galaxy but much larger than the Earth. Here, we report the results of a search for transient signals from axion-like particle domain walls with the Global Network of Optical Magnetometers for Exotic physics searches (GNOME). We search the data, consisting of correlated measurements from optical atomic magnetometers located in laboratories all over the world, for patterns of signals propagating through the network consistent with domain walls. The analysis of data from a continuous month-long operation of the GNOME finds no statistically significant signals, thus placing experimental constraints on such dark matter scenarios.

astro-ph.CO↗

arXiv2021

Cavity-enhanced absorption sensing with robust sideband locking

We present a simple, continuous, cavity-enhanced optical absorption measurement technique based on high-bandwidth Pound-Drever-Hall (PDH) sideband locking. The technique provides a resonant amplitude quadrature readout that can be mapped onto the cavity's internal loss rate, and is naturally compatible with weak probe beams. With a proof-of-concept 5-cm-long Fabry-Perot cavity, we measure an absorption sensitivity of $6.6 \times 10^{-11} ~\text{cm}^{-1}/\sqrt{\text{Hz}}$ at 100 kHz (roughly the cavity bandwidth) and $\sim 10^{-10}~\text{cm}^{-1}/\sqrt{\text{Hz}}$ from 30 kHz to 1 MHz, with $38~μ$W collected from the cavity's circulating power.

physics.optics↗

arXiv2021

HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization

We present a new software, HYPPO, that enables the automatic tuning of hyperparameters of various deep learning (DL) models. Unlike other hyperparameter optimization (HPO) methods, HYPPO uses adaptive surrogate models and directly accounts for uncertainty in model predictions to find accurate and reliable models that make robust predictions. Using asynchronous nested parallelism, we are able to significantly alleviate the computational burden of training complex architectures and quantifying the uncertainty. HYPPO is implemented in Python and can be used with both TensorFlow and PyTorch libraries. We demonstrate various software features on time-series prediction and image classification problems as well as a scientific application in computed tomography image reconstruction. Finally, we show that (1) we can reduce by an order of magnitude the number of evaluations necessary to find the most optimal region in the hyperparameter space and (2) we can reduce by two orders of magnitude the throughput for such HPO process to complete.

cs.LG↗

arXiv2021

Deep Learning for Surface Wave Identification in Distributed Acoustic Sensing Data

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. Distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of these data at very high spatial and temporal resolution for tens of kilometers. One major challenge when utilizing the DAS technology is the large volume of data that is produced, thereby presenting a significant Big Data challenge to find regions of useful energy. In this work, we present a highly scalable and efficient approach to process real, complex DAS data by integrating physics knowledge acquired during a data exploration phase followed by deep supervised learning to identify "useful" coherent surface waves generated by anthropogenic activity, a class of seismic waves that is abundant on these recordings and is useful for geophysical imaging. Data exploration and training were done on 130~Gigabytes (GB) of DAS measurements. Using parallel computing, we were able to do inference on an additional 170~GB of data (or the equivalent of 10 days' worth of recordings) in less than 30 minutes. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.

eess.SP↗

arXiv2020

Deep Learning on Real Geophysical Data: A Case Study for Distributed Acoustic Sensing Research

Deep Learning approaches for real, large, and complex scientific data sets can be very challenging to design. In this work, we present a complete search for a finely-tuned and efficiently scaled deep learning classifier to identify usable energy from seismic data acquired using Distributed Acoustic Sensing (DAS). While using only a subset of labeled images during training, we were able to identify suitable models that can be accurately generalized to unknown signal patterns. We show that by using 16 times more GPUs, we can increase the training speed by more than two orders of magnitude on a 50,000-image data set.

cs.LG↗