arXiv subjects

Olivier Vincent

Publications and source records attributed to Olivier Vincent.

At least 19 recordsLinked to original sources

arXiv2026

White Dwarf Merger Remnants with Cooling Delays on the Q Branch Lack Strong Magnetism

A population of anomalous ultra-massive white dwarfs discovered with Gaia, often referred to as the Q branch, show high (multi-Gyr) cooling delays produced by exotic physical mechanisms. They are believed to be the products of stellar mergers, but the exact origin and formation channel remain unclear. We obtained a spectroscopically complete, volume-limited sample of the Q branch region within 100 pc, and found significant differences in atmospheric composition and rotation rates as a function of tangential velocity. In particular, we discover that stellar remnants with the longest cooling delays do not show strong magnetism nor detectable short-period rotational variability, as opposed to what is generally believed for double-degenerate mergers. This indicates that either these white dwarfs arise from a formation channel with no strong magnetism induced, or that the magnetism produced from the merger dissipates over the cooling delay timescales. Our follow-up photometry has also discovered pulsations in the second and third hydrogen-dominated DAQ white dwarfs, one hotter than 15,500 K, possibly extending the boundaries of the DAV instability strip for white dwarfs with thin hydrogen layers.

astro-ph.SR↗

arXiv2026

Negative Pressure and Cavitation Dynamics in Plant-like Structures

It is well known that a solid (e.g. wood or rubber) can be put under tensile stress by pulling on it. Once a critical stress is overcome, the solid breaks, leaving an empty space. Similarly, due to internal cohesion, a liquid can withstand tension (i.e. negative pressure), up to a critical point where a large bubble spontaneously forms, releasing the tension and leaving a void (the bubble). This process is known as cavitation. While water at negative pressure is metastable, such a state can be long-lived. In fact, water under tension is found routinely in the plant kingdom, as a direct effect of dehydration, e.g. by evaporation. In this chapter, we provide a brief overview of occurrences of water stress and cavitation in plants, then use a simple thermodynamic and fluid mechanical framework to describe the basic physics of water stress and cavitation. We focus specifically on situations close to those in plants, that is water at negative pressure nested within a structure that is solid, but porous and potentially deformable. We also discuss insights from these simple models as well as from experiments with artificial structures mimicking some essential aspects of the structures found within plants.

cond-mat.soft↗

arXiv2026

Coupled imbibition and evaporation of droplets deposited on a nanoporous layer

Liquids in nanoscale hydrophilic pores generate capillary pressures so large that they could theoretically climb kilometers against gravity. However, droplets on thin nanoporous layers form imbibition fronts stopping at millimeters or less due to evaporation competing with capillary flow. Such droplet infiltration dynamics is of growing interest for studying confined fluids and for applications such as water harvesting, printing, chemical delivery, actuation, and sensing. Here, we investigate theoretically and experimentally the spontaneous imbibition and evaporation of sessile droplets into thin mesoporous layers, focusing on their dependence on imposed relative humidity (RH). Theoretically, we provide a unified analytical approach for the dynamics of the wetted annulus ("halo") around the droplet, accounting for arbitrary halo dimensions and confinement-induced thermodynamic shifts (Kelvin effect). Experimentally, we study water droplets on oxidized porous silicon layers (pore diameter 3-4 nm, thickness 5 $μ$m), systematically investigating how halo and droplet dynamics depend on RH. We show that halo formation timescales diverge at a critical RH due to the Kelvin effect, as illustrated by comparing RH-dependent evaporation rates in the halo (confined liquid) and in the droplet (bulk liquid). Our analysis also reveals an apparent divergence of the imbibition coefficient, unexplained by standard capillary models, suggesting a key role for Kelvin-driven vapor transport along the porous surface. The complex couplings revealed by our study call for caution in interpreting halo dynamics data. Our results also highlight RH as a powerful control parameter for tuning droplet imbibition behavior and infiltration patterns.

cond-mat.soft↗

arXiv2025

Salt crystallization and deliquescence triggered by humidity cycles in nanopores

We study the response of materials with nanoscale pores containing sodium chloride solutions, to cycles of relative humidity (RH). Compared to pure fluids, we show that these sorption isotherms display much wider hysteresis, with a shape determined by salt crystallization and deliquescence rather than capillary condensation and Kelvin evaporation. Both deliquescence and crystallization are significantly shifted compared to the bulk and occur at unusually low RH. We systematically analyze the effect of pore size and salt amount, and rationalize our findings using confined thermodynamics, osmotic effects and classical nucleation theory.

cond-mat.soft↗

arXiv2025

Neural Posterior Estimation for White Dwarf Spectroscopic Characterization

White dwarf spectroscopic characterization is entering a big data era, with the number of spectroscopically characterized white dwarfs expected to grow from $\sim$100,000 to over 300,000 in upcoming years. Traditional methods like least-squares fitting and Markov Chain Monte Carlo have become computationally prohibitive for large-scale analysis, requiring minutes to days per star. Furthermore, these methods impose fundamental limitations on model complexity by requiring explicit likelihood functions, typically restricting them to Gaussian assumptions. We present neural posterior estimation (NPE), a simulation-based inference technique that directly approximates posterior distributions through neural networks trained on simulated spectra. Our approach provides accurate parameter inference in milliseconds per star after upfront training costs, enabling statistical tests of the procedure's reliability. We demonstrate NPE's effectiveness on DA, DB, and carbon-atmosphere white dwarfs, validating its calibration with simulation-based calibration and tests of accuracy with random points. Application to SDSS data shows excellent agreement with previous studies, recovering parameters from previous work within 6.8% for effective temperature and 2.1% for surface gravity, on average. We also apply our technique on WD 1153+012, a hot DQ star with a carbon-oxygen-hydrogen atmosphere, using high-resolution spectroscopy. This methodology combines computational efficiency with the flexibility to model complex atmospheres, making it ideal for upcoming surveys. Our approach also integrates spectroscopic and photometric constraints through an iterative procedure, providing comprehensive characterization of white dwarfs.

astro-ph.SR↗

arXiv2025

An All-sky Survey of White Dwarf Merger Remnants: Far-UV is the Key

The majority of merging white dwarfs leave behind a white dwarf remnant. Hot/warm DQ white dwarfs with carbon-rich atmospheres have high masses and unusual kinematics. All evidence points to a merger origin. Here, we demonstrate that far-UV + optical photometry provides an efficient way to identify these merger remnants. We take advantage of this photometric selection to identify 167 candidates in the GALEX All-Sky Imaging Survey footprint, and provide follow-up spectroscopy. Out of the 140 with spectral classifications, we identify 75 warm DQ white dwarfs with $T_{\rm eff}>10,000$ K, nearly tripling the number of such objects known. Our sample includes 13 DAQ white dwarfs with spectra dominated by hydrogen and (weaker) carbon lines. Ten of these are new discoveries, including the hottest DAQ known to date with $T_{\rm eff}\approx23,000$ K and $M=1.31~M_{\odot}$. We provide a model atmosphere analysis of all warm DQ white dwarfs found, and present their temperature and mass distributions. The sample mean and standard deviation are $T_{\rm eff} = 14,560 \pm 1970$ K and $M=1.11 \pm 0.09~M_{\odot}$. Warm DQs are roughly twice as massive as the classical DQs found at cooler temperatures. All warm DQs are found on or near the crystallization sequence. Even though their estimated cooling ages are of order 1 Gyr, their kinematics indicate an origin in the thick disk or halo. Hence, they are likely stuck on the crystallization sequence for $\sim$10 Gyr due to significant cooling delays from distillation of neutron-rich impurities. Future all-sky far-UV surveys like UVEX have the potential to significantly expand this sample.

astro-ph.SR↗

arXiv2025

Synthetic Spectroscopy for White Dwarf Classification: Addressing Label Uncertainty and Class Imbalance

With the imminent data releases from next-generation spectroscopic surveys, hundreds of thousands of white dwarf spectra are expected to become available within the next few years, increasing the data volume by an order of magnitude. This surge in data has created a pressing need for automated tools to efficiently analyze and classify these spectra. Although machine learning algorithms have recently been applied to classify large spectroscopic datasets, they remain constrained by the limited availability of training data. The Sloan Digital Sky Survey (SDSS) serves as the current standard training set, as it provides the largest collection of labeled spectra; however, it faces challenges related to severe class imbalance and uncertain label consistency across different surveys. In this work, we address these limitations by training histogram gradient-boosted classifiers on a synthetic SDSS dataset to identify six ubiquitous chemical signatures in the atmosphere of white dwarfs, and test them on 14,246 objects with SNR$>$10 SDSS spectroscopy. We show our approach not only surpasses human expert performance, but also enables subtype classification and effectively resolves label transferability issues. The methodology developed here is adaptable to any spectroscopic survey, providing a critical tool for the astronomical community.

astro-ph.SR↗

arXiv2024

Tunable transport in bi-disperse porous materials with vascular structure

We study transport in synthetic, bi-disperse porous structures, with arrays of microchannels interconnected by a nanoporous layer. These structures are inspired by the xylem tissue in vascular plants, in which sap water travels from the roots to the leaves to maintain hydration and carry micronutrients. We experimentally evaluate transport in three conditions: high pressure-driven flow, spontaneous imbibition, and transpiration-driven flow. The latter case resembles the situation in a living plant, where bulk liquid water is transported upwards in a metastable state (negative pressure), driven by evaporation in the leaves; here we report stable, transpiration-driven flows down to $\sim -15$ MPa of driving force. By varying the shape of the microchannels, we show that we can tune the rate of these transport processes in a predictable manner, using a simple analytical (effective medium) approach and numerical simulations of the flow field in the bi-disperse media. We also show that the spontaneous imbibition behavior of a single structure - with fixed geometry - can behave very differently depending on its preparation (filled with air, vs. evacuated), because of a dramatic change in the conductance of vapor in the microchannels; this change offers a second way to tune the rate of transport in bi-disperse, xylem-like structures, by switching between air-filled and evacuated states.

cond-mat.soft↗

arXiv2023

Discovery of a Magnetic Double-Faced DBA White Dwarf

We report the discovery of spectroscopic variations in the magnetic DBA white dwarf SDSS J091016.43+210554.2. Follow-up time-resolved spectroscopy at the Apache Point Observatory (APO) and the MMT show significant variations in the H absorption lines over a rotation period of 7.7 or 11.3 h. Unlike recent targets that show similar discrepancies in their H and He line profiles, such as GD 323 and Janus (ZTF J203349.8+322901.1), SDSS J091016.43+210554.2 is confirmed to be magnetic, with a field strength derived from Zeeman-split H and He lines of B ~ 0.5 MG. Model fits using a H and He atmosphere with a constant abundance ratio across the surface fail to match our time-resolved spectra. On the other hand, we obtain excellent fits using magnetic atmosphere models with varying H/He surface abundance ratios. We use the oblique rotator model to fit the system geometry. The observed spectroscopic variations can be explained by a magnetic inhomogeneous atmosphere where the magnetic axis is offset from the rotation axis by beta = 52 degrees, and the inclination angle between the line of sight and the rotation axis is i = 13 - 16 degrees. This magnetic white dwarf offers a unique opportunity to study the effect of the magnetic field on surface abundances. We propose a model where H is brought to the surface from the deep interior more efficiently along the magnetic field lines, thus producing H polar caps

astro-ph.SR↗

arXiv2023

Data-Driven Selection and Spectral Classification of White Dwarf Stars

The next generation of spectroscopic surveys is expected to provide spectra for hundreds of thousands of white dwarf (WD) candidates in the upcoming years. Currently, spectroscopic classification of white dwarfs is mostly done by visual inspection, requiring substantial amounts of expert attention. We propose a data-driven pipeline for fast, automatic selection and spectroscopic classification of WD candidates, trained using spectroscopically confirmed objects with available Gaia astrometry, photometry, and Sloan Digital Sky Survey (SDSS) spectra with signal-to-noise ratios $\geq9$. The pipeline selects WD candidates with improved accuracy and completeness over existing algorithms, classifies their primary spectroscopic type with $\gtrsim 90\%$ accuracy, and spectroscopically detects main sequence companions with similar performance. We apply our pipeline to the Gaia Data Release 3 cross-matched with the SDSS Data Release 17 (DR17), identifying 424 096 high-confidence WD candidates and providing the first catalogue of automated and quantifiable classification for 36 523 WD spectra. Both the catalogue and pipeline are made available online. Such a tool will prove particularly useful for the undergoing SDSS-V survey, allowing for rapid classification of thousands of spectra at every data release.

astro-ph.SR↗

arXiv2021

Impact of individual rater style on deep learning uncertainty in medical imaging segmentation

While multiple studies have explored the relation between inter-rater variability and deep learning model uncertainty in medical segmentation tasks, little is known about the impact of individual rater style. This study quantifies rater style in the form of bias and consistency and explores their impacts when used to train deep learning models. Two multi-rater public datasets were used, consisting of brain multiple sclerosis lesion and spinal cord grey matter segmentation. On both datasets, results show a correlation ($R^2 = 0.60$ and $0.93$) between rater bias and deep learning uncertainty. The impact of label fusion between raters' annotations on this relationship is also explored, and we show that multi-center consensuses are more effective than single-center consensuses to reduce uncertainty, since rater style is mostly center-specific.

cs.CV↗

arXiv2021

Benefits of Linear Conditioning with Metadata for Image Segmentation

Medical images are often accompanied by metadata describing the image (vendor, acquisition parameters) and the patient (disease type or severity, demographics, genomics). This metadata is usually disregarded by image segmentation methods. In this work, we adapt a linear conditioning method called FiLM (Feature-wise Linear Modulation) for image segmentation tasks. This FiLM adaptation enables integrating metadata into segmentation models for better performance. We observed an average Dice score increase of 5.1% on spinal cord tumor segmentation when incorporating the tumor type with FiLM. The metadata modulates the segmentation process through low-cost affine transformations applied on feature maps which can be included in any neural network's architecture. Additionally, we assess the relevance of segmentation FiLM layers for tackling common challenges in medical imaging: multi-class training with missing segmentations, model adaptation to multiple tasks, and training with a limited or unbalanced number of annotated data. Our results demonstrated the following benefits of FiLM for segmentation: FiLMed U-Net was robust to missing labels and reached higher Dice scores with few labels (up to 16.7%) compared to single-task U-Net. The code is open-source and available at www.ivadomed.org.

cs.CV↗

arXiv2020

ivadomed: A Medical Imaging Deep Learning Toolbox

ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data. The package includes APIs, command-line tools, documentation, and tutorials. ivadomed also includes pre-trained models such as spinal tumor segmentation and vertebral labeling. Original features of ivadomed include a data loader that can parse image metadata (e.g., acquisition parameters, image contrast, resolution) and subject metadata (e.g., pathology, age, sex) for custom data splitting or extra information during training and evaluation. Any dataset following the Brain Imaging Data Structure (BIDS) convention will be compatible with ivadomed without the need to manually organize the data, which is typically a tedious task. Beyond the traditional deep learning methods, ivadomed features cutting-edge architectures, such as FiLM and HeMis, as well as various uncertainty estimation methods (aleatoric and epistemic), and losses adapted to imbalanced classes and non-binary predictions. Each step is conveniently configurable via a single file. At the same time, the code is highly modular to allow addition/modification of an architecture or pre/post-processing steps. Example applications of ivadomed include MRI object detection, segmentation, and labeling of anatomical and pathological structures. Overall, ivadomed enables easy and quick exploration of the latest advances in deep learning for medical imaging applications. ivadomed's main project page is available at https://ivadomed.org.

eess.IV↗

arXiv2020

Searching for ZZ Ceti White Dwarfs in the Gaia Survey

The {\it Gaia} satellite recently released parallax measurements for $\sim$260,000 high-confidence white dwarf candidates, allowing for precise measurements of their physical parameters. By combining these parallaxes with Pan-STARRS and $u$-band photometry, we measured the effective temperature and stellar mass for all white dwarfs in the Northern Hemisphere within 100 parsecs of the Sun, and identified a sample of ZZ Ceti white dwarf candidates within the so-called instability strip. We acquired high-speed photometric observations for 90 candidates using the PESTO camera attached to the 1.6-m telescope at the Mont-Mégantic Observatory. We report the discovery of 38 new ZZ Ceti stars, including two very rare ultra-massive pulsators. We also identified 5 possibly variable stars within the strip, in addition to 47 objects that do not appear to show any photometric variability. However, several of those could be variable with an amplitude below our detection threshold, or could be located outside the instability strip due to errors in their photometric parameters. In the light of our results, we explore the trends of the dominant period and amplitude in the $M - T_{\rm eff}$ plane, and briefly discuss the question of the purity of the ZZ Ceti instability strip (i.e. a region devoid of non-variable stars).

astro-ph.SR↗

arXiv2020

Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?

Despite recent improvements in medical image segmentation, the ability to generalize across imaging contrasts remains an open issue. To tackle this challenge, we implement Feature-wise Linear Modulation (FiLM) to leverage physics knowledge within the segmentation model and learn the characteristics of each contrast. Interestingly, a well-optimised U-Net reached the same performance as our FiLMed-Unet on a multi-contrast dataset (0.72 of Dice score), which suggests that there is a bottleneck in spinal MS lesion segmentation different from the generalization across varying contrasts. This bottleneck likely stems from inter-rater variability, which is estimated at 0.61 of Dice score in our dataset.

eess.IV↗

arXiv2016

Imbibition triggered by capillary condensation in nanopores

We study the spatio-temporal dynamics of water uptake by capillary condensation from unsaturated vapor in mesoporous silicon layers (pore radius $r_\mathrm{p} \simeq 2$ nm), taking advantage of the local changes in optical reflectance as a function of water saturation. Our experiments elucidate two qualitatively different regimes as a function of the imposed external vapor pressure: for low saturations, equilibration occurs via a diffusion-like process; for high saturations, an imbibition-like wetting front results in fast equilibration towards a fully saturated sample. We show that the imbibition dynamics can be described by a modified Lucas-Washburn equation that takes into account the liquid stresses implied by Kelvin equation.

cond-mat.soft↗

arXiv2015

Capillarity-Driven Flows at the Continuum Limit

We experimentally investigate the dynamics of capillary-driven flows at the nanoscale, using an original platform that combines nanoscale pores and microfluidic features. Our results show a coherent picture across multiple experiments including imbibition, poroelastic transient flows, and a drying-based method that we introduce. In particular, we exploit extreme drying stresses - up to 100 MPa of tension - to drive nanoflows and provide quantitative tests of continuum theories of fluid mechanics and thermodynamics (e.g. Kelvin-Laplace equation) across an unprecedented range. We isolate the breakdown of continuum as a negative slip length of molecular dimension.

cond-mat.soft↗

arXiv2014

Drying by Cavitation and Poroelastic Relaxations in Porous Media with Macroscopic Pores Connected by Nanoscale Throats

We investigate the drying dynamics of porous media with two pore diameters separated by several orders of magnitude. Nanometer-sized pores at the edge of our samples prevent air entry, while drying proceeds by heterogeneous nucleation of vapor bubbles (cavitation) in the liquid in micrometer-sized voids within the sample. We show that the dynamics of cavitation and drying are set by the interplay of the deterministic poroelastic mass transport in the porous medium and the stochastic nucleation process. Spatio-temporal patterns emerge in this unusual reaction-diffusion system, with temporal oscillations in the drying rate and variable roughness of the drying front.

cond-mat.soft↗