Physics and Society · Cross-listed from cs.SI
Uncovering higher-order interactions from memory in observed dynamics
Arthur Rondeau, Alain Barrat and Roland Bouffanais
Many complex systems involve group interactions, which can readily be represented by higher-order structures such as hypergraphs. However, when observing a system's dynamical
evolution, establishing whether that
evolution actually results from higher-order interactions, or whether pairwise interactions suffice to explain the data, remains a challenge. Even in the simple case of a temporal sequence of nodes visited during a
diffusion process, such a task is intricate. Indeed, group interactions leave their traces as
memory on the observed nodes, while path dependence in a purely pairwise process can mimic that same footprint without any group structure. To resolve this, we propose an inference pipeline based on a hidden Markov model (HMM) that tests, directly from an observed sequence of nodes carrying no group labels, whether higher-order interactions are required to describe the dynamics. The HMM latent states represent higher-order interactions, seen as hyperedges of an unobserved hypergraph, so that their detection becomes a model selection problem between the HMM class and a class of null models without higher-order interactions. We first validate this approach on synthetic node sequences generated from random walks on graphs and hypergraphs, showing that it recovers both the number and the composition of the underlying hyperedges, while correctly rejecting them when the generating process is pairwise. As a second step, we consider a validation based on empirical data, namely empirical trajectories of ball possession in football matches. This application shows that the pipeline goes beyond providing statistical evidence of the presence of higher-order interactions, and yields interpretable underlying structures: the recovered hyperedges map directly onto recognizable tactical team formations.
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Instrumentation and Detectors
Exploring the proximity effect of thin tungsten-aluminum films for phonon-mediated kinetic inductance detectors
G. Del Castello, M. Mancuso, A. Langenkämper et al. (+18 more)
Lowering the energy threshold of
phonon-mediated kinetic inductance detectors is a key step toward next-generation cryogenic calorimeter arrays with large target mass and high sensitivity. In this work, we demonstrate the feasibility of integrating thin tungsten films, so far used for transition-edge sensors, into the fabrication of Kinetic Inductance Detectors (KIDs). Here we present the results from the characterization of a single KID prototype composed of a tungsten-aluminum bilayer of respectively 15 nm and 35 nm thicknesses. The detector was successfully calibrated, and the superconducting film characteristics were measured by operating the device at different temperatures. We show that, by exploiting the proximity effect of
superconductors, the device combines the relatively high critical temperature of aluminum with the large kinetic inductance contribution of tungsten. The main feature is a high kinetic inductance, which accounts for 36% of the total inductance, combined with a superconducting critical temperature of around 630 mK.
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All authors G. Del Castello, M. Mancuso, A. Langenkämper, A. Bento, C. Bonomo, M. Cappelli, F. Casadei, E. Cipelli, A. Cruciani, D. Delicato, F. Dominsky, M. Folcarelli, D. Hauff, B. Mauri, C. Moore, L. Pesce, F. Petricca, F. Pröbst, D. Quaranta, M. Vignati, M. Zanirato
Plasma Physics · Cross-listed from astro-ph.SR
Flares and flux ropes: an entangled story
Rony Keppens, Yang Guo and Wenzhi Ruan
Solar
flares serve as standard candles for all
magnetic-reconnection-based energy releases, and their consequences into the heliosphere can be modeled from first principles. This review presents the latest developments in modeling solar
flares and their associated flux rope expulsions with state-of-the-art multidimensional magnetohydrodynamic (MHD) descriptions. Observationally established ingredients beyond a single-fluid, thermal
plasma evolution have been incorporated to varying degrees, and we highlight self-consistent efforts realized in "standard
flare" setups.
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Optics
Bias-Modulated Electronically-Tunable Optoelectronic Chromatic Dispersion in Ge PN Photodiodes
Uttama Kumar Saint, Ziv Glasser, Egor Liokumovitch et al. (+3 more)
Optoelectronic Chromatic Dispersion (OED) is a source of significant wavelength-dependent current in photodiodes, offering a pathway to computational spectroscopy in a photodiode-only platform without the need for additional optical or mechanical components. Previous studies have activated OED through light modulation. Here, we present a theoretical and experimental investigation of bias-modulated OED in the C- and L-bands in a Ge PN photodiode, where the applied bias voltage is modulated sinusoidally, and the optical illumination is held constant. The spectral response is due to wavelength-dependent optical absorption together with bias-dependent competition between carrier drift, displacement current, and mobile-charge screening. With the RF phase-shift technique, the maximum measured OED sensitivity is +4.13 deg/nm in the L-band, significantly larger than that of light-modulated OED in Ge. The 15 mdeg phase-noise floor yields a spectral resolution of 3.6 pm. These results establish bm-OED as an electronically reconfigurable dispersive mechanism for applications in chip-scale computational spectrometers, photonic integrated
circuits (PICs), on-chip RF photonics, and wavelength-division multiplexing.
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All authors Uttama Kumar Saint, Ziv Glasser, Egor Liokumovitch, Endalamaw Ewnu Kassa, Asaf Albo, Shmuel Sternklar
Biological Physics
Full Three-dimensional Cell Geometry Matters for Epithelial Fluidization
Toshikaze Chiba, Jan Rozman and Matej Krajnc
Epithelial dynamics are often studied using two-dimensional models that omit
cell height. Here, we investigate how this third dimension influences tissue fluidization driven by
cell self-propulsion. We identify three effects absent from conventional 2D models. First,
cell height controls fluidization through a reentrant transition: squamous and columnar tissues are more resistant to fluidization than cuboidal ones. Second, neighbor exchanges through transient scutoid states substantially enhance
cell motility, compared to classic T1 transitions. Third, self-propelling
cells develop pronounced apicobasal shape asymmetry, with the experimentally less accessible basal surface providing a more accurate indicator of
cells' target shape index and motility.
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Space Physics · Cross-listed from astro-ph.SR
Tracking a solar energetic eruption and associated energetic particles from Sun to spacecraft
D. E. Morosan, M. Baumgartner-Steinleitner, K. Bhandari et al. (+17 more)
Context. Solar energetic particles (SEPs) in the heliosphere are produced by flaring processes on the Sun or shocks driven by coronal mass ejections (CMEs). Electrons, in particular, are regularly detected remotely as electromagnetic radiation (X-rays or
radio emission), which they generate through various processes, as well as in situ by spacecraft monitoring the Sun and the heliosphere. Aims. We aim to combine remote-sensing and in situ observations of an energetic solar eruption to link energetic processes in the solar corona to energetic particles observed in situ in the heliosphere. This will be done using the tools developed in the Horizon Europe SOLER project. Methods. Here, we investigate a solar energetic eruption at the Sun together with its associated signatures of accelerated electrons observed at
radio and X-ray wavelengths and combine these remote observations to in situ particles observed by spacecraft on 10 March 2024. These observations are combined with a magneto-hydrodynamic (MHD) model of the solar corona in order to make the remote to in situ connections. Results. We observe a high-energy SEP event at multiple spacecraft (>25 MeV
protons and 1 MeV electrons), despite the event being associated with a slow white-light CME. Remote observations of the Sun show that the event was associated with a fast large scale coronal wave (1400 km/s) and also multiple regions where electrons are accelerated around this large scale wave observed as type II
radio bursts. In addition, we observe a strong coronal dimming away from the main flaring region, providing evidence of field lines getting opened to interplanetary space, dislocated from the
flare site.
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All authors D. E. Morosan, M. Baumgartner-Steinleitner, K. Bhandari, N. Dresing, G. Farwa, J. A. J. Mitchell, C. Palmroos, A. Razquin, J. Zhang, C. Briand, K. Dissauer, A. Fedeli, J. Gieseler, E. K. J. Kilpua, S. Normo, J. Pomoell, D. J. Price, A. M. Veronig, A. Warmuth, R. Vainio
Plasma Physics · Cross-listed from astro-ph.SR
Stationary Resistive MHD Solutions Near an Isolated Three-Dimensional Magnetic Null
Kuljeet Singh Saddal, Dieter H. Nickeler, Rodrigo Meneses Pacheco et al. (+1 more)
Magnetic null points define the
magnetic field
topology in stellar coronae, planetary magnetospheres, and astrospheres, and are commonly associated with
magnetic reconnection. We investigate the local structure of stationary three-dimensional (3D) resistive, incompressible magnetohydrodynamic (MHD) flows in the vicinity of an isolated
magnetic null and an isolated velocity stagnation point, assuming that the two nulls are colocated. The governing MHD equations are solved self-consistently using a first-order local expansion of the
magnetic and velocity fields about the null. For any given
magnetic-null configuration, the equations admit a two-parameter family of stationary solutions, comprising multiple solution branches with distinct velocity
topologies. Along these families, the velocity-null polarity can change at bifurcation points where the flow becomes effectively 2.5D. A single
magnetic null can therefore support positive-radial, negative-radial, and spiral stagnation flows, with qualitatively different inflow, outflow, and rotational properties. The associated pressure and resistivity distributions are likewise determined self-consistently and vary across the solution family, demonstrating that they are coupled to the flow
topology rather than prescribed independently. The generic solutions do not require the velocity and
magnetic separatrices to coincide, allowing
plasma to cross the
magnetic fan separatrix and providing a local reconnective flow. These results demonstrate that, in the presence of nonuniform resistivity, the
magnetic topology alone does not uniquely determine the associated stationary flow, pressure, or resistivity structure around a 3D
magnetic null, revealing the nonlinear feedback between all macroscopic variables and resistivity. The constant-resistivity field-aligned and spiral field-crossing solutions identified previously are recovered as special cases.
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All authors Kuljeet Singh Saddal, Dieter H. Nickeler, Rodrigo Meneses Pacheco, Michaela Kraus
Atmospheric and Oceanic Physics
PrecipCorrDiff: A Deep Learning Framework for Generating 0.0625$^\circ$ High-Resolution IMD Gridded Precipitation Data
Bipin Kumar, Rajib Chattopadhyay, Harsh P. Bachhav et al. (+7 more)
High-resolution precipitation
datasets are essential for weather forecasting, hydrological modeling, and
climate risk assessment, yet observation-based gridded products often lack the spatial detail required for regional applications. Here we present PrecipCorrDiff, a
diffusion-based downscaling framework built by modifying the original corrector
diffusion based archetecture that incorporates a customized loss function and a multi-scale residual generation strategy to improve the precipitation especially the representation of extremes. PrecipCorrDiff generates 0.0625{\deg} resolution India Meteorological Department (IMD) precipitation fields from 0.25{\deg} gridded data while preserving precipitation statistics and reconstructing fine-scale spatial variability. Validation against independent IMD station observations, comparing the proposed framework with the SRGAN-based model and the original CorrDiff model, indicates that the proposed framework substantially improves the representation of spatial precipitation patterns, rainfall intensity distributions, and extreme rainfall events, while eliminating the systematic negative bias present in the previously developed SRGAN-based model. Across four climatologically distinct homogeneous Indian monsoon regions, PrecipCorrDiff reproduces observed precipitation characteristics with near-zero spatial bias, demonstrating the potential of
diffusion-based downscaling for generating physically consistent, high-resolution precipitation
datasets for hydrological, meteorological, and
climate applications.
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All authors Bipin Kumar, Rajib Chattopadhyay, Harsh P. Bachhav, Jannu, Bhupendra Bahadur Singh, Ramesh Ragala, Ananda Kumar Das, Manish Modani, Thara Prabhakaran, Mrutyunjay Mohapatra
Instrumentation and Detectors
Interactive event-driven 4D-STEM with Timepix4 and live analytical ptychography
Arno Annys, Hoelen L. Lalandec Robert and Jo Verbeeck
Four-dimensional scanning transmission electron microscopy provides access to rich diffraction information at every probe position, but the high data rates associated with pixelated detectors remain a major challenge for interactive operation. Event-driven detectors offer an efficient alternative by representing the measurement as a sparse stream of individual incidences, substantially reducing requirements related to data handling and enabling on-the-fly processing. Here, we develop a high-throughput, real-time, 4D-STEM workflow based on the Timepix4 detector and demonstrate its operation at microsecond dwell times and standard beam currents. A common clock shared by the scan unit and detector allows the probe position of each electron event to be determined directly from its time-of-arrival, eliminating the need for inter-stream synchronization and enabling fully concurrent and independent multi-stream processing. The resulting pipeline continuously processes the detector data during acquisition, with the reconstructed images directly available. We demonstrate live virtual annular detector imaging, Real-Time Integration Center of Mass and direct electron ptychography within the guided progressive reconstructive imaging framework. The workflow sustains the high event rates of Timepix4 while maintaining zero latency for live interactive operation, thus extending event-driven 4D-STEM towards beam currents and acquisition conditions beyond those previously demonstrated with Timepix3-based systems. These results demonstrate that the combination of high-bandwidth event-driven detection, hardware-level synchronization and parallel processing can transform 4D-STEM from an offline technique into an interactive imaging modality, in which dose-efficient phase contrast and diffraction-based signals can serve directly as immediate feedback during STEM experiments.
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Optics
Inferring Transition-Dipoles from Two-Dimensional Infrared Spectroscopy with Deep Learning
Hao-Yue Zhang, Rong-Hang Chen and Qing Ai
Two-dimensional infrared (2D IR) spectroscopy is a powerful nonlinear technique for probing vibrational couplings, anharmonicities, coherence, and molecular dynamics. Among the microscopic parameters encoded in 2D IR spectra, transition dipoles are fundamental quantities that characterize molecular structure and govern light-matter interactions. However, reconstructing these quantities from spectroscopic data remains a challenging inverse problem. Here, we introduce a physics-informed convolutional neural network (CNN), Spec2DipoleNet, trained on 2D IR spectra generated via the non-Hermitian
Hamiltonian (NHH) method. This multi-task CNN reconstructs the transition dipole
matrices under physically-motivated masking constraints. On the validation set, the model achieves a root-mean-square error (RMSE) of 0.085, an $R^2$ of 0.97, and a normalized cross-correlation of 0.98. It reliably recovers
matrix elements associated with allowed transitions, while larger deviations are observed for nominally forbidden transitions. Our results suggest that transition dipole operators can be quantitatively inferred from 2D IR spectra, highlighting the potential of physics-informed deep learning for extracting microscopic
quantum parameters from multidimensional spectroscopic data.
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Chemical Physics
High-Harmonic Generation Probes Quantum Nuclear Fluctuations in Liquid Water
George Trenins, Nicolas Tancogne-Dejean, Angana Mondal et al. (+5 more)
Nuclear
quantum effects are essential for an accurate description of the hydrogen-bonded structure of
liquid water, yet their spectroscopic signatures can be subtle and difficult to measure. Moreover, probing these effects in solution on femtosecond timescales (or below) is an open challenge that is pivotal for optical control of hydrogen bonds and ultrafast spectroscopy of
proton transfer. Here, we combine path-integral molecular dynamics with real-time time-dependent density-functional theory to explore high-harmonic generation (HHG) in
liquid water from first principles, rigorously accounting for
quantum nuclear delocalisation. Relative to classical-
nuclei simulations,
quantum sampling suppresses the harmonic yield around certain harmonics by up to an order of magnitude, inducing local structural minima. By analysing the ab initio simulations and reproducing the key effects with a simplified model
Hamiltonian, we trace the physical origins of the interference minima to the broadening of valence-band states induced by hydrogen-bond fluctuations, a mechanism expected to generalise to other hydrogen-bonded structures. Our work shows that nuclear
quantum effects are indispensable for capturing ultrafast electronic dynamics in
liquids, and demonstrates the power of
liquid HHG spectroscopy for attosecond probing of nuclear fluctuations.
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All authors George Trenins, Nicolas Tancogne-Dejean, Angana Mondal, Angel Rubio, Hans Jakob Wörner, Aaron Kelly, Ofer Neufeld, Mariana Rossi
Optics
Preservation of photon statistics upon selective molecular modulation in hollow-core fibers
Arturo Mena, Pau Arcos, María Sánchez-Hernández et al. (+7 more)
Preserving the underlying statistical properties of light during frequency transduction is essential for interfacing spectrally disparate
quantum systems. To do this successfully, the process needs to be highly efficient, broadband and noiseless. Despite significant progress in this field, satisfying all these conditions remains challenging. Recently, molecular modulation-based transduction in hollow-core fibers has emerged as a promising approach, where biphoton correlations were proven to be preserved during frequency up-conversion. However, beyond this previous demonstration of correlation-maintaining conversion, whether molecular modulation can preserve more general statistical properties of light remains experimentally unexplored. Here, we demonstrate the conservation of
photon-number statistics during selective frequency transduction by Raman molecular modulation in a hydrogen-filled hollow-core fiber. As a proof of concept, weak 585-nm signals are selectively converted to 773 nm in the near-infrared and to 471 nm in the blue spectral region with near-unity efficiencies. Phase matching between the generated coherence waves and the transitions of interest is achieved through the effective dispersion of the system, enabling independent selection of the frequency up- and down-conversion processes. The preservation of the
photon-number statistics of the shifted signals, along with the large frequency shifts, broad tunability and compatibility with
quantum-optical fields, provides a versatile fiber-based platform for
quantum frequency transduction. In particular, the demonstrated interfacing between visible and near-infrared wavelengths using low pump energies and low gas pressures offers a route towards connecting spectrally distinct
quantum systems, including
quantum emitters, sensors and atomic
quantum memories, within hybrid
quantum networks.
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All authors Arturo Mena, Pau Arcos, María Sánchez-Hernández, Benoit Debord, Frédéric Gérôme, Fetah Benabid, Nicolas Y. Joly, Oskar Arrizabalaga, Joseba Zubia, David Novoa
Instrumentation and Detectors · Cross-listed from hep-ex
MoEDAL-MAPP: the first dedicated search experiment at the LHC
John R. Ellis, Nick E. Mavromatos, Vasiliki A. Mitsou et al. (+1 more)
This special issue brings together a collection of original research articles and review papers addressing topics directly related to the MoEDAL-MAPP experiment at the Large
Hadron Collider (LHC). The experiment is designed to search for (meta-)stable, anomalously ionising particles predicted by theoretical extensions of the Standard Model. Several contributions explore the theoretical and phenomenological aspects of models of particular relevance to the experiment, including scenarios involving
magnetic monopoles, highly electrically charged particles, and millicharged particles. Other contributions focus on experimental developments, highlighting novel detection techniques and technological advances aimed at identifying these unconventional signatures and probing new phenomena beyond the Standard Model.
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All authors John R. Ellis, Nick E. Mavromatos, Vasiliki A. Mitsou, James L. Pinfold
Space Physics
The Gannon Storm: Citizen science observations during the geomagnetic superstorm of 10 May 2024
Maxime Grandin, Emma Bruus, Vincent E. Ledvina et al. (+12 more)
The 10 May 2024 geomagnetic storm, referred to as the Gannon Storm in this paper, was one of the most extreme to have occurred in over 20 years. In the era of smartphones and social media, millions of people from all around the world were alerted to the possibility of exceptional auroral displays. Hence, many people not only witnessed but also photographed the aurora during this event. These citizen science observations, although not from scientific instruments operated by observatories or research groups, can prove invaluable in obtaining data to characterise this extraordinary event. In particular, many observers saw and photographed the aurora at mid-latitudes, where ground-based instruments targeting auroral studies are sparse or absent. We created an online survey and circulated it within networks of aurora photographers to collect observations of the aurora and disruptions in technological systems that were experienced during this superstorm. We obtained 696 citizen science reports from over 30 countries, containing information such as the time and location of aurora sightings, observed colours and auroral forms during the geomagnetic storm. We supplemented the obtained
dataset with 186 auroral observations logged in the Skywarden catalogue by citizen scientists. The main findings enabled by the data collected through these reports are that the aurora was widely seen from locations at geomagnetic latitudes ranging between 30{\deg} and 60{\deg}, with a few reports from even lower latitudes. This was significantly further equatorward than predicted by auroral oval models. The reported auroral emission colours, predominantly red and pink and intense enough to reach naked-eye visibility, suggest that the auroral electron precipitation contained large fluxes of low-energy (< 1 keV) particles. (...)
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All authors Maxime Grandin, Emma Bruus, Vincent E. Ledvina, Noora Partamies, Mathieu Barthelemy, Carlos Martinis, Rowan Dayton-Oxland, Bea Gallardo-Lacourt, Yukitoshi Nishimura, Katie Herlingshaw, Neethal Thomas, Eero Karvinen, Donna Lach, Marjan Spijkers, Calle Bergstrand
Biological Physics · Cross-listed from q-bio.QM
Maximum likelihood, neural networks and posterior mean for inferring fractional Brownian motion parameters
Daniel S. Han, Vaishnavi Ananthanarayanan and Matthew J. Simpson
Fractional Brownian motion (fBM) is a model of anomalous
diffusion parameterised by a Hurst exponent $H \in (0,1)$ and a generalised
diffusion coefficient $D_H > 0$. We jointly estimate $H$ and $D_H$ using maximum likelihood (MLE) from density measurements with Poisson counting noise and single-particle trajectory with Gaussian localisation error. We show that one density measurement in time cannot infer both $H$ and $D_H$ and that several time points are needed, which matters most for inference precision. For trajectories, we show that a neural network trained with a squared error loss approximates the posterior mean of $H$, which can be computed exactly at negligible cost. Paired with the profile-likelihood estimate of $D_H$, the posterior mean has the lowest root mean squared error in both $H$ and $D_H$, except near $H=0$ and $H=1$ where MLE estimates $H$ better. In vesicle trajectories from two
cell types, $H$ and $D_H$ form a continuum rather than distinct modes of motion. The posterior probability that each window within a track is subdiffusive or superdiffusive shows that $26\%$ of MRC5 fibroblast and $21\%$ of HeLa vesicle trajectories contain both subdiffusive and superdiffusive windows, against $2\%$ and $7\%$ expected from estimation uncertainty alone. Intracellular cargoes therefore switch between subdiffusion and superdiffusion over time rather than forming populations with fixed $H$.
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Atmospheric and Oceanic Physics
Generative modelling for multivariate spatio-temporal downscaling of monthly climate fields
Yu Huang, Shangshang Yang, Sebastian Bathiany et al. (+3 more)
Inferring
climate fields at higher spatio-temporal resolutions from coarse representations is essential for accessing socioeconomic and environmental impacts. The downscaling of modelled
climate fields faces a trade-off between data storage constraints, simulation efficiency, and resolution requirements. While many methods for spatial downscaling have been proposed, joint spatio-temporal downscaling has received comparable little
attention. When deriving, for example, daily fields from
climate datasets that are available only as monthly averages at coarser spatial resolution, it is highly challenging to faithfully reproduce extreme event statistics and spatiotemporal consistency. To address this task, we introduce a generative machine learning approach, using a
diffusion model to jointly downscale monthly temperature and precipitation fields into temporally consistent daily fields at higher spatial resolution. We demonstrate that our approach can generate high-dimensional multivariate
climate fields at daily temporal and 0.25{\deg} spatial resolution from monthly input fields at 1{\deg} spatial resolution, exhibiting coherent temporal dynamics and physically plausible spatial structures. In particular, by incorporating a conditional guidance technique, we show that a pre-trained
diffusion model can be directly leveraged to efficiently generate daily high-resolution fields conditioned on monthly inputs, without requiring additional fine-tuning or retraining. Our probabilistic approach thus provides an efficient and generalizable framework for spatio-temporal
climate downscaling, enabling flexible
climate fields generation for
climate impact assessments and digital Earth simulator constructions.
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All authors Yu Huang, Shangshang Yang, Sebastian Bathiany, Michael Aich, Philipp Hess, Niklas Boers
Applied Physics · Cross-listed from cond-mat.mes-hall
Magnetic field induced modulation of heat transport in CrPS$_4$ probed by TaIrTe$_4$ transverse Seebeck thermometry
Krishnaraajan Sundararajan, Sergio Alvarruiz, Cédric A. Cordero-Silis et al. (+5 more)
Understanding and controlling heat flow in low-dimensional
magnetic materials is central to thermoelectrics and
spin caloritronics. One of the challenges is the absence of sensitive probes of small, field-dependent modulations in thermal transport. Here we demonstrate transverse Seebeck thermometry using exfoliated TaIrTe$_4$ as a probe of thermal conductivity modulated by an external
magnetic field on a van der Waals magnet, CrPS$_4$. With a temperature gradient oriented at $45^\circ$ to its principal crystallographic axes, TaIrTe$_4$ exhibits a transverse thermopower exceeding $200~\mathrm{\mu V\,K^{-1}}$ for 900 nm wide and 30 $\mu$m long electrodes, enabling precise detection of variations in the local temperature gradients. Exploiting this with CrPS$_4$ in a nonlocal geometry, we resolve distinct field dependent thermal conductivity changes associated with both the
spin-flip and
spin-flop
magnetic transitions which we attribute to variations in the
magnon$-$
phonon coupling in CrPS$_4$. At 5 K, the transverse Seebeck response is modulated by more than 8$\%$ by an external
magnetic field, demonstrating strong
magnetic control of heat transport in CrPS$_4$. This all van der Waals platform offers high sensitivity to modulations of thermal conductivity and a broad adaptability to a wide range of exfoliated
magnetic systems.
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All authors Krishnaraajan Sundararajan, Sergio Alvarruiz, Cédric A. Cordero-Silis, Majid Ahmadi, Marcos H.D. Guimarães, Jagoda S\lawińska, Joseph Barker, Bart J. van Wees
Computational Physics
Online reduced-order modeling from primal solver trajectories for PDE-constrained optimization
Zhe Lu, Wenbo Cao and Weiwei Zhang
Repeated high-fidelity state and gradient evaluations make large-scale PDE-constrained optimization computationally expensive. Reduced-order modeling provides a natural way to reduce the cost of these repeated high-dimensional computations. However, existing reduced-order modeling approaches often rely on converged high-fidelity states for model construction or updating, while their reduced spaces may lose accuracy as the design moves beyond the region represented by the available data. To address these limitations, we present Reduced-order Trajectory-based Optimization (RETO), an online framework that constructs local reduced-order models (ROMs) directly from unconverged primal solver trajectories. Each local ROM is built from snapshots collected during a finite number of high-fidelity primal iterations, without requiring full-order convergence, and supports multiple low-cost state evaluations, adjoint-based gradient evaluations, and design updates. New solver trajectories are generated at updated design points to reconstruct the local ROM and continue the optimization. Across three distinct PDE-constrained optimization problems, RETO achieves solution quality comparable to that of full-order optimization, with speedups of approximately 8 to 22 times in cases without discontinuities in the state fields. Even in the transonic inverse-design case involving moving shocks, RETO retains an approximately threefold speedup. The transonic results further show that the online local ROM progressively captures evolving shock-related flow features as new primal solver trajectories are incorporated. These results demonstrate the value of unconverged primal solver trajectories as an online information source for reduced-order optimization, allowing the reduced basis to evolve with the design rather than requiring a basis that covers the entire design space in advance.
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Chemical Physics
Density and excess molar enthalpy of 2-butanol + glyme liquid mixtures. Application of the DISQUAC, UNIFAC (Dortmund) and ERAS models to 2-alkanol + linear ether systems
Fatemeh Pazoki, João Victor Alves-Laurentino, Susana Villa et al. (+4 more)
Densities and excess molar volumes ($V_{\text{m}}^{\text{E}}$) at (293.15 to 303.15) K and excess molar enthalpies ($H_{\text{m}}^{\text{E}}$) at 298.15 K are reported for the 2-butanol + 2,5-dioxahexane, or + 2,5,8-trioxanonane, or + 2,5,8,11-tetraoxadodecane, or + 2,5,8,11,14-pentaoxapentadecane systems at 95 kPa. Densities were measured using a densitometer Anton Paar DSA 5000, and excess enthalpies by means of a micro-calorimeter Tian-Calvet. $H_{\text{m}}^{\text{E}}$ and $V_{\text{m}}^{\text{E}}$ data are positive, indicating that interactions between like molecules are dominant and that the main contribution to $V_{\text{m}}^{\text{E}}$ is the interactional one. Our enthalpic data together with data from the literature have been used to evaluate the enthalpy of alcohol-glyme interactions, which are ranged between ($-$31 and $-$22) kJ mol$^{-1}$. From the comparison with $H_{\text{m}}^{\text{E}}$ results for glyme + heptane systems, and from the position of the investigated mixtures in the diagram $G_{\text{m}}^{\text{E}}$ (excess molar Gibbs energy) vs. $H_{\text{m}}^{\text{E}}$, it is shown that interactions are of dispersive type. 2-Alkanol + dipropyl ether (DPE), or + glyme systems have also been studied using the DISQUAC, UNIFAC (Dortmund) and ERAS models. According to ERAS results, physical interactions in glyme systems are dominant, while association effects are predominant in mixtures with DPE. The DISQUAC interaction parameters suggest that interactions related to the OH/O contacts are essentially dispersive. UNIFAC fails when representing thermodynamic properties of the studied solutions.
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All authors Fatemeh Pazoki, João Victor Alves-Laurentino, Susana Villa, Daniel Lozano-Martín, Fernando Hevia, Luis Felipe Sanz, Juan Antonio González
Optics
Computational compressive imaging reconstruction with a simulated-annealing-based Ising solver
Ze Zheng, Xiaoyan Wu, Hang Xu et al. (+3 more)
Computational compressive imaging, such as single-pixel imaging and lensless imaging, is a promising imaging technique, enabling the reconstruction of objects from under-sampled measurements through compressed sensing
algorithms or deep learning methods. However, the demand for electronic computational resources grows rapidly when recovering high-resolution and multi-dimensional images and training neural networks with numerous parameters, which has become a bottleneck for the widespread adoption of this technique. Here, we propose a physics-driven reconstruction framework that formulates imaging inverse problems as Ising models and directly recovers target objects through the self-
evolution of non-von Neumann Ising machines toward the ground state of the
Hamiltonian. We employ a simulated-annealing-based Ising solver to demonstrate this proof-of-concept by reconstructing objects in both single-pixel imaging and a lensless diffuser camera at sampling rates ranging from 1% to 25%. By further incorporating a foreground-sparsity constraint and a smoothness constraint, the reconstruction quality of our method is comparable to or even better than that of mainstream
algorithms. This approach has the potential to substantially reduce the electronic computational burden of inverse reconstruction, paving the way for real-time, low-power computational imaging.
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All authors Ze Zheng, Xiaoyan Wu, Hang Xu, Jingzheng Huang, Tailong Xiao, Guihua Zeng
Atmospheric and Oceanic Physics
Unmasking Compensating Errors in Numerical Weather Models: A Process-Additive Neural Network Approach
Yuiko Ichikawa, Edward Groot and Jack Atkinson
This work introduces a Process Additive Neural Network (PANN) framework to disentangle compensating biases in atmospheric models, where errors in different physical processes can offset each other and obscure their origins. Using a single-column model, each PANN branch represents the tendency error associated with a separate physical process, and the summed branch outputs approximate the total error relative to a reference tendency. Once trained successfully, the branch outputs are expected to provide process-level error attributions and, when averaged, estimates of process-level bias. Initial experiments focus on cloud-related processes in OpenIFS SCM case studies, where cloud and convection tendencies are strongly coupled and compensation is difficult to diagnose using standard approaches. The aim is not to replace human interpretation of physical processes, but to extend it to regimes where model complexity, process interaction, and compensating errors make direct diagnosis difficult. By providing a physically structured form of machine-learning interpretability, PANN can help connect complex model behaviour with process-level explanations. This work supports both improved understanding of compensating errors and the development of more physically consistent weather and
climate models.
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Optics · Cross-listed from quant-ph
Quantum-enhanced phase interrogation of a fiber-optic strain sensor
Romain Dalidet, Marialuisa Capezzuto, Hugo Degert et al. (+7 more)
Fiber-optic sensors enable high-sensitivity measurements of various physical parameters. Here, we demonstrate a
quantum-enhanced interrogation of an all-fiber-optic Fabry-Perot resonator, employing entangled two-
photon interference to probe directly strain-induced optical phase shifts. The resonator is interrogated by a narrow-linewidth
laser that is frequency-locked to its resonance mode, thereby exploiting the cavity's inherent sensitivity to variations in the fiber length. To access the phase fluctuations transferred to the interrogation field due to external perturbations on the sensor, we implement an in-fiber Mach-Zehnder interferometer readout. This configuration allows the sensor to operate both with classical coherent light and with entangled
photon pairs generated via spontaneous parametric down-conversion. Harnessing two-
photon entangled states corresponding to an N = 2 NOON state, we experimentally demonstrate a reduction of the measurement noise below the standard
quantum limit. These results provide clear evidence of the
quantum advantage in enhancing fiber-optic sensors under practical constraints on optical power.
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All authors Romain Dalidet, Marialuisa Capezzuto, Hugo Degert, Davide D'Ambrosio, Saverio Avino, Anthony Martin, Sebastien Tanzilli, Laurent Labonte, Paolo De Natale, Gianluca Gagliardi
Fluid Dynamics
Response of Confined Mesoscale Flames to Acoustic Forcing
Akhil Aravind, Balasundaram Mohan and Saptarshi Basu
Mesoscale combustion plays a vital role in applications ranging from portable power generation to micro-propulsion systems. This study investigates the response of mesoscale flames to upstream acoustic forcing across three characteristic flame regimes: Flames with Repetitive Extinction and Ignition (FREI), Stable Flames (SF), and Propagating Flames (PF). Acoustic forcing is applied over a broad range of pressure intensities at the first harmonic of the combustor tube. At low forcing intensities, all the flame regimes exhibit oscillations at the forcing frequency without qualitative changes in their respective flame behaviours. However, as the acoustic pressure increases, all flame regimes transition to a common unsteady regime characterised by periodic ignition-extinction cycles (resembling FREI) alongside strong oscillations at the forcing frequency. This response is identified as the oscillating FREI (O-FREI) regime, in which increasing forcing intensity leads to a reduction in the mean heat release rate, an amplification of oscillations at the forcing frequency, and a drop in the Strouhal number associated with the ignition-extinction cycle. The response characteristics across all flame regimes are examined in detail and are further complemented using reduced-order mathematical models.
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Fluid Dynamics · Cross-listed from cond-mat.soft
Active Sculpting: morphodynamics of internally stirred active vesicles
Devadevan M M and Brato Chakrabarti
Active flows are often confined by soft, deformable boundaries that they can push against, reshape, and in turn get reorganized by. Motivated by this interplay, we combine theory and numerical simulations to analyze the morphodynamics of an elastic vesicle enclosing a dilute suspension of extensile active particles. We model the two-dimensional vesicle as a weakly extensible elastic ring with bending rigidity, immersed in a Stokesian fluid, and describe the confined active suspension using a kinetic theory. The vesicle and the suspension are coupled hydrodynamically, through the kinematic and dynamic boundary conditions at the interface, and energetically, through a single
alignment interaction that simultaneously aligns active particles with the membrane and the membrane with the particles. We show that this interaction reduces to a weak-anchoring boundary condition familiar from passive
liquid crystals and derive a closed-form quiescent
equilibrium state in which the nematic order in the suspension organizes into a
vortex with a +1 defect core. A stability analysis of this state predicts an ordered hierarchy of activity thresholds at which successive shape modes of the vesicle are destabilized, as well as a purely passive, anchoring-driven instability of floppy vesicles. Nonlinear simulations, performed with a volume-conserving immersed boundary method, reveal that each threshold crossing gives rise to a distinct emergent state: a static spindle with boundary-pinned defects, a rotor that tank treads against its own rotation, a fluctuating spinner, and a turbulent state in which the vesicle acts as a self-stirring compartment. Beyond demonstrating how boundary deformability sculpts, and is sculpted by, confined active matter, our results open the door to study hydrodynamic interactions between such active vesicles and to use membrane fluctuations as a probe of activity of suspensions.
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Fluid Dynamics
Two-Way Coupled Dynamics of Quantized Vortex Bundle Formation in Spatially Periodic Normal-Fluid Turbulence
Hiromichi Kobayashi, Makoto Tsubota and Rio Yokota
Using fully coupled two-way simulations combining the
vortex filament model with the Navier–Stokes equations, we discover that quantized
vortices in superfluid $^4$He are systematically rolled up into coherent bundles within normal-fluid
vortex tubes. Although co-rotating quantized
vortices do not aggregate spontaneously, mutual friction with the normal fluid actively drives their bundling, directly inheriting the spatial hierarchy of classical
turbulence. Mutual friction excites high-wavenumber normal-fluid
vortex tubes adjacent to quantized
vortices, while the superfluid forms a nested structure of thick low-wavenumber outer tubes and fine high-wavenumber inner tubes around the
vortex lines. A distinct time lag between superfluid kinetic energy and dissipation fuels the energy cascade, giving rise to Kolmogorov's $k^{-5/3}$ spectrum. Crucially, bundle lifetimes are governed by the ratio of the local coupling time scale $t_\mathrm{cl}$ to the Kolmogorov dissipation time scale $\tau_\eta$ ($t_\mathrm{cl}/\tau_\eta < 20$), leading to enhanced thermal stability at higher temperatures. These findings establish a unified mechanism for two-fluid multiscale
vortex organization.
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Optics
Two-Photon Spatial Multiplexing: Image Encoding in Gaussian Noncircularity of Light
Wanting Hou, Zhiyuan Ye, Hong-Chao Liu et al. (+2 more)
The search for new degrees of freedom (DoF) of light is central to advancing optical information encoding and multiplexing. Gaussian statistics are ubiquitous in nature, and here we unlock an unexpected statistical DoF in the joint noncircularity of Gaussian light. For a pair of Gaussian light fields, the covariance- and pseudocovariance-dominated two types of correlations dictate the propagation rules and the image-encoding responses, mapping optical information into coordinate-difference and coordinate-sum momentum spaces, respectively. A hybrid random-phase grating array enables the experimental preparation of noncircular Gaussian light with programmable coexistence, yielding X-shaped intensity correlations while subtly reducing the degree of second-order coherence. Two independently programmed images coexist in the same beams at identical wavelength and polarization, yet are selectively decoded through two-
photon correlation geometry without physical filters. Remarkably, both images remain recoverable even when structured stray light is as intense as the signal. These results unlock a hidden statistical dimension of optical information encoding, transforming Gaussian fluctuations into a dual-channel information carrier and joint noncircularity into a programmable optical DoF.
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All authors Wanting Hou, Zhiyuan Ye, Hong-Chao Liu, Jun Xiong, Kaige Wang
Optics
Femtosecond all-optical polarization manipulation of harmonic generation in an anisotropic strongly coupled plasmon-epsilon-near-zero metasurface
Yingying Zhang, Jinhai Si, Xun Hou et al. (+1 more)
Ultrafast manipulation of light polarization plays a key role in modern high-speed photonics and optoelectronic operations. However, most current polarization-active modulation schemes are restricted by a limited operating wavelength range (visible to infrared), narrow-band resonant fundamental wavelength working condition, and relatively long modulation time (> 1 picosecond). Here, by taking advantage of the transient anisotropic nonlinearity of optical strong coupling between the epsilon-near-zero mode of indium tin oxide (ITO) film and polarization-selective plasmonic resonance, we achieve femtosecond polarization manipulation of broadband light that spans ultraviolet (UV) to visible region by controlling harmonic generation of the signal light under resonant pumping. We demonstrate polarization elliptic rotation of 23.3{\deg} at 407 nm for third-harmonic generation (THG) and 55.0{\deg} at 610 nm for second-harmonic generation (SHG) simultaneously within ~600 fs. In addition, the strongly coupled system can also serve as a universal femtosecond polarization modulation source, imprinting a 32.9{\deg} polarization rotation in the UV region by converting the modulation information of the fundamental signal light to that of THG light via a uniform ITO film in a cascaded configuration. Our study establishes a new paradigm for UV-based, ultrahigh-speed broadband polarization photonics with direct implications for high-speed optical information processing and programmable nonlinear photonic devices.
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All authors Yingying Zhang, Jinhai Si, Xun Hou, Kuidong Wang
Optics
A compact six-beam 3D magneto-optical trap based on multifunctional metasurface chips
Chen Qing, Huijie Sun, Yize Liu et al. (+5 more)
Magneto-optical traps (MOTs), indispensable to cold-atom-based
quantum computing and
quantum precision measurement, are increasingly desired in compact and stable form. Particularly, the six-beam 3D-MOT, distinguished by symmetric geometry and superior trapping performance, finds particularly broad applications. Conventional implementations, however, rely on complex and bulky free-space optics, hindering miniaturization, practical assembly, and long-term stability. Here, we demonstrate a compact six-beam 3D-MOT system that employs two multifunctional optical metasurface chips to implement the optical-field functionalities required for cooling and trapping atoms. By combining two metasurface chips and a
vacuum cell, a photonic-integrated
cell module (PICM) is implemented as the core assembly and the volume is only" 73*73*107 mm^3" . In success, 1.3 * 10^3 Rb atoms are trapped and cooled to the temperature of ~183 {\mu}K. It should be noticed that the
laser beam is guided to the metasurface through optical fiber, thus such fully fiber-coupled architecture enables straightforward and stable operations. We believe that this work would pave a practical route toward compact and portable 3D-MOT.
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All authors Chen Qing, Huijie Sun, Yize Liu, Yuxuan Liao, Yongzhuo Li, Dengke Zhang, Xue Feng, Yidong Huang
Computational Physics
GeONet: Geometry-Encoded Operator Network for Predicting Flow in Heterogeneous Porous Media
Md Lal Mamud, Maruti K. Mudunuru, Yucheng Fu et al. (+13 more)
Rapid prediction of pressure-driven flow in microfluidic porous media is important for experimental design, tracer-test interpretation, and digital-twin development for subsurface energy and critical mineral applications. High-fidelity simulations resolve geometry-controlled flow but are computationally expensive across large geometry ensembles. Here, we introduce GeONet, a Geometry-Encoded Operator Network that predicts steady-state pressure fields by jointly representing material properties, spatial coordinates, and solid–void geometry. GeONet extends the Branch–Trunk architecture with a dedicated Leaf Network encoding the binary domain mask through multiscale features. The Branch Network represents porosity and permeability, the Trunk Network combines coordinates with local
geometric descriptors and random
Fourier features, and a gated DeepMixer integrates these representations to predict pressure. GeONet was trained and evaluated using 5,000 PFLOTRAN simulations of heterogeneous Reduced Complexity Platform domains, comprising nearly 30 TB of data. Comparisons with pore-scale Stokes solutions across three geometries yielded Pearson correlations of 0.987–0.991 and $R^2$ values of 0.974–0.982, supporting PFLOTRAN as reference data. Under matched partitions and two-stage hyperparameter optimization, GeONet substantially outperformed a boundary-condition-aware DeepONet baseline. Across three held-out domains, GeONet achieved NRMSE of 0.0381–0.0500, MAPE of 1.76–2.59%, and $R^2$ of 0.9673–0.9822, versus 0.1483–0.2040, 7.81–10.45%, and 0.5658–0.7126 for DeepONet, respectively. On three additional unseen geometries, GeONet achieved NRMSE of 0.0448–0.0676 and $R^2$ of 0.9511–0.9770. These results demonstrate improved accuracy, pressure-range preservation, and cross-geometry generalization through explicit geometry encoding.
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All authors Md Lal Mamud, Maruti K. Mudunuru, Yucheng Fu, Aramy Troung, Satish Karra, Ivani Patel, Arunima Bhattacharjee, Tamas Varga, Pratanu Roy, Michael Penwarden, Rajesh K. Singh, Arjun Chakrawal, Kalyana B. Nakshatrala, Christof Meile, Erin Rooney, Glenn Hammond
Biological Physics · Cross-listed from q-bio.NC
How Long Is the Coast of a Neuron? Resolution-Dependent Interaction Order and Computational Capacity
Nima Dehghani
Mandlebrot's coastline paradox reframes measurement as a joint property of an object and its ruler. Here we ask how observational resolution shapes the apparent structure of neural computation. We introduce a scale-indexed decomposition that separates the predictive information captured by direct dependencies from the residual outside that model family. Exact constructions show that projection can convert higher-order dependence into pairwise structure. Projection can increase the direct fraction of surviving information even as total predictive information decreases. A schematic branch-to-soma cascade grounds these effects in local coincidence detection, saturating integration and somatic mixing. Resolving organized nonlinear subunits improves approximation and raises effective representational rank. In a nested hierarchy, these gains occur without increasing information about the target. This distinction separates information availability from access by a restricted readout. A point-
neuron or direct population model therefore establishes an effective description at a specified scale. It does not uniquely identify the finer computation that generated the observed relation. Which resolution is adequate depends on the observable and perturbation of interest.
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