arXiv subjects

Christian Pedersen

Publications and source records attributed to Christian Pedersen.

At least 19 recordsLinked to original sources

arXiv2025

Surface mobility of a glass-forming polymer in an ionic liquid

The free surface of glassy polymers exhibits enhanced segmental dynamics compared to the bulk, forming a liquid-like layer that lowers the glass transition temperature (Tg) in nanometersized polymer samples. Recent studies have shown that immersing polymers in ionic liquids can suppress this enhanced surface dynamics. To investigate how ionic liquids influence polymer dynamics near the ionic-liquid-polymer interface, we measure the surface leveling of nanometersized stepped polystyrene films immersed in ionic liquids, and compared the results to the case of films in vacuum. Our results reveal that ionic liquids significantly slow the leveling process both above and below Tg. However, our results indicate that the liquid-like surface layer below Tg does exist in ionic liquids. Numerical solutions of the thin-film equation, incorporating appropriate boundary conditions, show that the surface mobility of PS films in ionic liquids can match that of PS films in vacuum. Thus, while ionic liquids alter the polymer flow process, they do not eliminate the dynamical heterogeneity inherent to glassy polymers.

cond-mat.soft↗

arXiv2024

Multi-scale decomposition of sea surface height snapshots using machine learning

Knowledge of ocean circulation is important for understanding and predicting weather and climate, and managing the blue economy. This circulation can be estimated through Sea Surface Height (SSH) observations, but requires decomposing the SSH into contributions from balanced and unbalanced motions (BMs and UBMs). This decomposition is particularly pertinent for the novel SWOT satellite, which measures SSH at an unprecedented spatial resolution. Specifically, the requirement, and the goal of this work, is to decompose instantaneous SSH into BMs and UBMs. While a few studies using deep learning (DL) approaches have shown promise in framing this decomposition as an image-to-image translation task, these models struggle to work well across a wide range of spatial scales and require extensive training data, which is scarce in this domain. These challenges are not unique to our task, and pervade many problems requiring multi-scale fidelity. We show that these challenges can be addressed by using zero-phase component analysis (ZCA) whitening and data augmentation; making this a viable option for SSH decomposition across scales.

physics.ao-ph↗

arXiv2023

Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures

In Elmarakeby et al., "Biologically informed deep neural network for prostate cancer discovery", a feedforward neural network with biologically informed, sparse connections (P-NET) was presented to model the state of prostate cancer. We verified the reproducibility of the study conducted by Elmarakeby et al., using both their original codebase, and our own re-implementation using more up-to-date libraries. We quantified the contribution of network sparsification by Reactome biological pathways, and confirmed its importance to P-NET's superior performance. Furthermore, we explored alternative neural architectures and approaches to incorporating biological information into the networks. We experimented with three types of graph neural networks on the same training data, and investigated the clinical prediction agreement between different models. Our analyses demonstrated that deep neural networks with distinct architectures make incorrect predictions for individual patient that are persistent across different initializations of a specific neural architecture. This suggests that different neural architectures are sensitive to different aspects of the data, an important yet under-explored challenge for clinical prediction tasks.

cs.LG↗

arXiv2023

Coalescence of elastic blisters filled with a viscous fluid

Pockets of viscous fluid coalescing beneath an elastic plate are encountered in a wide range of natural phenomena and engineering processes, spanning across scales. As the pockets merge, a bridge is formed with a height increasing as the plate relaxes. We study the spatiotemporal dynamics of such an elasto-hydrodynamic coalescence process by combining experiments, lubrication theory and numerical simulations. The bridge height exhibits an exponential growth with time, which corresponds to a self-similar solution of the bending-driven thin-film equation. We address this unique self-similarity and the self-similar shape of the bridge, both of which are corroborated in numerical simulations and experiments.

physics.flu-dyn↗

arXiv2023

Static wetting of a barrel-shaped droplet on a soft-layer-coated fiber

A droplet can deform a soft substrate due to capillary forces when they are in contact. We study the static deformation of a soft solid layer coated on a rigid cylindrical fiber when an axisymmetric barrel-shaped droplet is embracing it. We find that the elastic deformation increases with decreasing rigid fiber radius. Significant disparities of deformation between the solid-liquid side and the solid-gas side are found when their solid surface tensions are different. When the coated layer is soft enough and the rigid fiber radius is less than the thickness of the coated layer, pronounced displacement oscillations are observed. Such slow decay of deformation with distances from the contact line position suggests a possible long-range interaction between droplets on a soft-layer-coated fiber.

cond-mat.soft↗

arXiv2023

Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation

Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated to improve the accuracy of coarse resolution simulations. However, when trained in a purely offline mode, integrating ML models into the numerical scheme can lead to instabilities. In the context of a 2D, quasi-geostrophic turbulent system, we demonstrate that including an additional network in the loss function, which emulates the state of the system into the future, produces offline-trained ML models that capture important subgrid processes, with improved stability properties.

physics.flu-dyn↗

arXiv2023

Learnable wavelet neural networks for cosmological inference

Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astrophysical effects extremely well. However, CNNs require large amounts of training data, which is potentially problematic in the domain of expensive cosmological simulations, and it is difficult to interpret the network. In this work we apply the learnable scattering transform, a kind of convolutional neural network that uses trainable wavelets as filters, to the problem of cosmological inference and marginalisation over astrophysical effects. We present two models based on the scattering transform, one constructed for performance, and one constructed for interpretability, and perform a comparison with a CNN. We find that scattering architectures are able to outperform a CNN, significantly in the case of small training data samples. Additionally we present a lightweight scattering network that is highly interpretable.

astro-ph.IM↗

arXiv2023

Instability and rupture of sheared viscous liquid nanofilms

Liquid nanofilms are ubiquitous in nature and technology, and their equilibrium and out-of-equilibrium dynamics are key to a multitude of phenomena and processes. We numerically study the evolution and rupture of viscous nanometric films, incorporating the effects of surface tension, van der waals forces, thermal fluctuations and viscous shear. We show that thermal fluctuations create perturbations that can trigger film rupture, but they do not significantly affect the growth rate of the perturbations. The film rupture time can be predicted from a linear stability analysis of the governing thin film equation, by considering the most unstable wavelength and the thermal roughness. Furthermore, applying a sufficiently large unidirectional shear can stabilise large perturbations, creating a finite-amplitude travelling wave instead of film rupture. In contrast, in three dimensions, unidirectional shear does not inhibit rupture, as perturbations are not suppressed in the direction perpendicular to the applied shear. However, if the direction of shear varies in time, the growth of large perturbations is prevented in all directions, and rupture can hence be impeded.

cond-mat.soft↗

arXiv2023

A neural network emulator for the Lyman-$\alpha$ 1D flux power spectrum

The Lyman-$\alpha$ forest offers a unique avenue for studying the distribution of matter in the high redshift universe and extracting precise constraints on the nature of dark matter, neutrino masses, and other $\Lambda$CDM extensions. However, interpreting this observable requires accurate modelling of the thermal and ionisation state of the intergalactic medium, and therefore resorting to computationally expensive hydrodynamical simulations. In this work, we build a neural network that serves as a surrogate model for rapid predictions of the one-dimensional \lya flux power spectrum ($P_{\rm 1D}$), thereby making Bayesian inference feasible for this observable. Our emulation technique is based on modelling $P_{\rm 1D}$ as a function of the slope and amplitude of the linear matter power spectrum rather than as a function of cosmological parameters. We show that our emulator achieves sub-percent precision across the full range of scales ($k_{\parallel }=0.1$ to 4Mpc$^{-1}$) and redshifts ($z=2$ to 4.5) considered, and also for three $\Lambda$CDM extensions not included in the training set: massive neutrinos, running of the spectral index, and curvature. Furthermore, we show that it performs at the 1% level for ionisation and thermal histories not present in the training set and performs at the percent level when emulating down to $k_{\parallel}$=8Mpc$^{-1}$. These results affirm the efficacy of our emulation strategy in providing accurate predictions even for cosmologies and reionisation histories that were not explicitly incorporated during the training phase, and we expect it to play a critical role in the cosmological analysis of the DESI survey.

astro-ph.CO↗

arXiv2023

Equivalent circuit and continuum modeling of the impedance of electrolyte-filled pores

Batteries, supercapacitors, and several other electrochemical devices charge by accumulating ions in the pores of electrolyte-immersed porous electrodes. The charging of such devices has long been interpreted using equivalent circuits and the partial differential equations these give rise to. Here, we discuss the validity of the transmission line (TL) circuit and equation for modeling a single electrolyte-filled pore in contact with a reservoir of resistance $R_{r}$. The textbook derivation of the pore-reservoir impedance $R_r+Z_p$ from the TL equation does not correctly account for ionic current conservation at the pore-reservoir interface. However, correcting this shortcoming leads to the same impedance. We also show that the pore impedance $Z_p$ can be derived directly from the TL circuit, bypassing the TL equation completely. The TL circuit assumes equipotential lines in an electrolyte-filled pore to be straight, which is not the case near the pore entrance and end. To determine the importance of these regions, we numerically simulated the charging of pores of different lengths $\ell_p$ and radii $\varrho_p$ through the Poisson-Nernst-Planck equations. We find that pores with aspect ratios beyond $\ell_p/\varrho_p\gtrapprox5$ have impedances in good agreement with $Z_p$.

cond-mat.soft↗

arXiv2022

Surface and Bulk Relaxation of Vapour-Deposited Polystyrene Glasses

We have studied the liquid-like response of the surface of vapour-deposited glassy films of polystyrene to the introduction of gold nanoparticles on the surface. The build-up of polymer material was measured as a function of time and temperature for both as-deposited films, as well as films that have been rejuvenated to become normal glasses cooled from the equilibrium liquid. The temporal evolution of the surface profile is well described by the characteristic power law of capillary-driven surface flows. In all cases, the surface evolution of the as-deposited films and the rejuvenated films are enhanced compared to bulk and are not easily distinguishable from each other. The temperature dependence of the measured relaxation times determined from the surface evolution is found to be quantitatively comparable to similar studies for high molecular weight spincast polystyrene. Comparisons to numerical solutions of the glassy thin film equation provide quantitative estimates of the surface mobility. For temperatures sufficiently close to the glass-transition temperature, particle embedding is also measured and used as a probe of bulk dynamics, and in particular bulk viscosity.

cond-mat.mtrl-sci↗

arXiv2022

Compressing the cosmological information in one-dimensional correlations of the Lyman-$\alpha$ forest

Observations of the Lyman-$\alpha$ (Ly$\alpha$) forest from spectroscopic surveys such as BOSS/eBOSS, or the ongoing DESI, offer a unique window to study the growth of structure on megaparsec scales. Interpretation of these measurements is a complicated task, requiring hydrodynamical simulations to model and marginalise over the thermal and ionisation state of the intergalactic medium. This complexity has limited the use of Ly$\alpha$ clustering measurements in joint cosmological analyses. In this work we show that the cosmological information content of the 1D power spectrum ($P_\mathrm{1D}$) of the Ly$\alpha$ forest can be compressed into a simple two-parameter likelihood without any significant loss of constraining power. We simulate $P_\mathrm{1D}$ measurements from DESI using hydrodynamical simulations and show that the compressed likelihood is model independent and lossless, recovering unbiased results even in the presence of massive neutrinos or running of the primordial power spectrum.

astro-ph.CO↗

arXiv2022

Near-critical spreading of droplets

We study the spreading of droplets in a near-critical phase-separated liquid mixture, using a combination of experiments, lubrication theory and finite-element numerical simulations. The classical Tanner's law describing the spreading of viscous droplets is robustly verified when the critical temperature is neared. Furthermore, the microscopic cut-off length scale emerging in this law is obtained as a single free parameter for each given temperature. In total-wetting conditions, this length is interpreted as the thickness of the thin precursor film present ahead of the apparent contact line. The collapse of the different evolutions onto a single Tanner-like master curve demonstrates the universality of viscous spreading before entering in the fluctuation-dominated regime. Finally, our results reveal a counter-intuitive and sharp thinning of the precursor film when approaching the critical temperature, which is attributed to the vanishing spreading parameter at the critical point.

cond-mat.soft↗

arXiv2022

Non-linearities in the Lyman-$\alpha$ forest and in its cross-correlation with dark matter halos

Three-dimensional correlations of the Lyman-$\alpha$ (Ly$\alpha$) forest and cross correlations between the Ly$\alpha$ forest and quasars have been measured on large scales, allowing a precise measurement of the baryon acoustic oscillation (BAO) feature at redshifts $z>2$. These 3D correlations are often modelled using linear perturbation theory, but full-shape analyses to extract cosmological information beyond BAO will require more realistic models capable of describing non-linearities present at smaller scales. We present a measurement of the Ly$\alpha$ forest flux power spectrum from large hydrodynamic simulations -- the Sherwood simulations -- and compare it to different models describing the small-scale deviations from linear theory. We confirm that the model presented in Arinyo-i-Prats et al. (2015) fits the measured 3D power up to $k=10\, h\rm{Mpc^{-1}}$ with an accuracy better than 5%, and show that the same model can also describe the 1D correlations with similar precision. We also present, for the first time, an equivalent study for the cross-power spectrum of halos with the Ly$\alpha$ forest, and we discuss different challenges we face when modelling the cross-power spectrum beyond linear scales. We make all our measured power spectra public in \url{https://github.com/andreufont/sherwood_p3d}. This study is a step towards joint analyses of 1D and 3D flux correlations, and towards using the quasar-Ly$\alpha$ cross-correlation beyond BAO analyses.

astro-ph.CO↗

arXiv2021

Towards Machine Learning-Based Meta-Studies: Applications to Cosmological Parameters

We develop a new model for automatic extraction of reported measurement values from the astrophysical literature, utilising modern Natural Language Processing techniques. We use this model to extract measurements present in the abstracts of the approximately 248,000 astrophysics articles from the arXiv repository, yielding a database containing over 231,000 astrophysical numerical measurements. Furthermore, we present an online interface (Numerical Atlas) to allow users to query and explore this database, based on parameter names and symbolic representations, and download the resulting datasets for their own research uses. To illustrate potential use cases we then collect values for nine different cosmological parameters using this tool. From these results we can clearly observe the historical trends in the reported values of these quantities over the past two decades, and see the impacts of landmark publications on our understanding of cosmology.

astro-ph.IM↗

arXiv2021

High resolution mid-infrared optical coherence tomography with kHz line rate

We report on Mid-infrared (MIR) OCT at 4 $\mu$m based on collinear sum-frequency upconversion and promote the A-scan scan rate to 3 kHz. We demonstrate the increased imaging speed for two spectral realizations, one providing an axial resolution of 8.6 $\mu$m, and one providing a record axial resolution of 5.8 $\mu$m. Image performance is evaluated by sub-surface micro-mapping of a plastic glove and real-time monitoring of CO$_2$ in parallel with OCT imaging.

physics.optics↗

arXiv2021

Nanobubble-induced flow of immersed glassy polymer films

We study the free-surface deformation dynamics of an immersed glassy thin polymer film supported on a substrate, induced by an air nanobubble at the free surface.We combine analytical and numerical treatments of the glassy thin film equation, resulting from the lubrication approximation applied to the surface mobile layer of the glassy film, under the driving of an axisymmetric step function in the pressure term accounting for the nanobubble's Laplace pressure. Using the method of Green's functions, we derive a general solution for the film profile. We show that the lateral extent of the surface perturbation follows an asymptotic viscocapillary power-law behaviour in time, and that the film's central height decays logarithmically in time in this regime. This process eventually leads to film rupture and dewetting at finite time, for which we provide an analytical prediction exhibiting explicitly the dependencies in surface mobility, film thickness and bubble size, among others. Finally, using finite-element numerical integration, we discuss how non-linear effects induced by the curvature and film profile can affect the evolution.

cond-mat.mtrl-sci↗

arXiv2020

An emulator for the Lyman-$\alpha$ forest in beyond-$\Lambda$CDM cosmologies

Interpreting observations of the Lyman-$\alpha$ forest flux power spectrum requires interpolation between a small number of expensive simulations. We present a Gaussian process emulator modelling the 1D flux power spectrum as a function of the amplitude and slope of the small-scale linear matter power spectrum, and the state of the intergalactic medium at the epoch of interest ($2 < z < 4$). This parameterisation enables the prediction of the flux power spectrum in extended cosmological models that are not explicitly included in the training set, eliminating the need to construct bespoke emulators for a number of extensions to $\Lambda$CDM. Our emulator is appropriate for cosmologies in which the linear matter power spectrum is described to percent level accuracy by just an amplitude and slope across the epoch of interest, and in the regime probed by eBOSS/DESI data. We demonstrate this for massive neutrino cosmologies, where the emulator is able to predict the flux power spectrum in a $\Sigma m_\nu=0.3$ eV neutrino cosmology to sub-percent accuracy, without including massive neutrinos in the training simulations. Further parameters would be required to describe models with sharp features in the linear power, such as warm or light axion dark matter. This work will facilitate the combination of upcoming DESI data with observations of the cosmic microwave background, to obtain constraints on neutrino mass and other extensions to $\Lambda$CDM cosmology.

astro-ph.CO↗