Chemical Physics · Cross-listed from cond-mat.str-el
Charge-sensitive vibrational modes in BEDT-TTF salts: Signatures of charge ordering and site charge
Savita Priya, Martin Dressel, Jesse Liebman et al. (+1 more)
BEDT-TTF-based organic conductors host a number of ground states, tuned by electron repulsion from Mott and charge ordered
insulators to
superconductors. Knowing charge distribution on the molecular sites in the insulating state of these materials is a key to understanding the origin of these ground states. We survey and discuss the C=C stretching modes in BEDT-TTF based molecular conductors. These molecular vibrations are extremely crucial in characterization of charge-ordered
insulators, and are recently linked to superconductivity in some compounds. Focusing on the known examples of BEDT-TTF$^{+0.5}$ salts, we analyse the reliability of the C=C stretching modes for the determination of charge ordering and absolute site charge. Considering the charge-ordered states, a prominent shift in frequency of 141 cm$^{-1}$ per elementary charge $e$ for $\nu_{27}(b_{1u})$ and 98 cm$^{-1}$$e$ for $\nu_2$($a_g$) can be clearly realised, however, the distribution resulting from different compounds span over 20 cm$^{-1}$. For nominal BEDT-TTF$^{+0.5}$ compounds, the distribution of the resonance also extends around 20 cm$^{-1}$, yielding an unexpected large uncertainty of $\Delta\rho~\approx~(~\pm~0.045)e$, which is presumably due to the influence of small differences in the structure. This highlights the limitations of charge-frequency relations to detect small deviations in absolute charge values on molecular
lattice sites, and emphasises on the use of the relations to estimate charge-ordering, rather than absolute site charge.
Read the full abstract ▾
All authors Savita Priya, Martin Dressel, Jesse Liebman, Natalia Drichko
Optics
Massively Degenerate Coherent Perfect Absorption in Gradient-Index Fibers
Helmut Hörner, Şahin K. Özdemir and Stefan Rotter
Coherent perfect absorbers (CPAs) have recently attracted considerable
attention due to their ability to enhance light–matter interaction. By exploiting interference, CPAs enable even weakly absorbing materials to achieve complete absorption under appropriate excitation conditions. Generalizing this concept to the simultaneous absorption of arbitrary multimode input states remains challenging, however, since conventional implementations typically operate only for a single or a very small number of input channels. Here, we propose a compact realization of a multimode coherent perfect absorber based on a gradient-index (GRIN) fiber. Using the self-imaging property of the fiber, the bulky free-space architecture of previous approaches is replaced by a monolithic waveguiding platform that supports near-degenerate rephasing of many spatial modes. We show that standard GRIN profiles optimized for minimal intermodal dispersion enable highly efficient absorption of complex multimode fields, with field-of-view reflectivities well below \(1\%\) for realistic parameters. This approach provides a practical and scalable route toward efficient multimode absorption in fiber-based and integrated photonic systems, with potential applications in light harvesting, optical control, and imaging.
Read the full abstract ▾
Biological Physics · Cross-listed from quant-ph
Thermodynamic Networks: Harnessing Non-Equilibrium Steady States for Computation
Patryk Lipka-Bartosik, Gianmichele Blasi, Javier Lalueza Puértolas et al. (+3 more)
We introduce thermodynamic networks, a general framework for autonomous, physics-based computation using non-
equilibrium steady states. These networks are modeled as a collection of finite-size reservoirs that exchange conserved quantities–such as electric charge or molecular number–while relaxing to a non-
equilibrium steady state, which encodes the solution of a computational problem. We identify Negative Differential Conductance (NDC) as the critical physical property governing the computational expressivity of the thermodynamic network. While networks lacking NDC are restricted to computing monotonic functions, the presence of NDC enables universal function approximation. For the training of the network, we use protocols that take advantage of the natural tendency of the system to equilibrate. We illustrate the versatility of our approach via two different platforms:
quantum dot networks and enzymatic reaction networks. Both systems can be engineered to have NDC, enabling high performance in standard
benchmarks, including sine function approximation and MNIST digit classification. Overall, our work establishes a rigorous link between non-
equilibrium steady states and computational expressivity.
Read the full abstract ▾
All authors Patryk Lipka-Bartosik, Gianmichele Blasi, Javier Lalueza Puértolas, Géraldine Haack, Martí Perarnau-Llobet, Nicolas Brunner
Atmospheric and Oceanic Physics
Bridging the climate to energy data gap: simulated annealing for representative climate year selection
Bram van Duinen, Karin van der Wiel, Jean Thorey et al. (+1 more)
Energy system models are increasingly dependent on representative
climate input. Yet, a fundamental mismatch persists between the hundreds of simulated years often used in
climate science and the handful of years that computationally demanding power system models can process. Current practice, including ENTSO-E's European Resource Adequacy Assessment, relies on
climate year selections that have not been validated against explicit representativeness criteria. This risks biased investment decisions and blind spots for plausible weather conditions. This study proposes simulated annealing as an optimisation method for selecting representative subsets of complete
climate years from large
climate ensembles. Representativeness is quantified using the seasonal sliced Wasserstein distance, a metric from optimal transport theory that captures representativeness on marginal distributions, inter-variable correlations, and seasonal structure simultaneously. We evaluate simulated annealing against the alternative methods random search, filtered random search, and K-Medoids clustering across three test cases spanning the Netherlands and Europe, using 180
climate years from the Pan-European
Climate Database as a reference. Simulated annealing consistently produces the most representative subsets and outperforms all compared methods. Simulated annealing achieves an effective sample size four to five times the actual subset size. The resulting subsets are roughly 2.5–3.5 times more representative than current ENTSO-E practice. The method is application-agnostic and its output can serve as a validated
climate data input to any subsequent (energy) impact study.
Read the full abstract ▾
All authors Bram van Duinen, Karin van der Wiel, Jean Thorey, Laurens Stoop
Physics and Society
TeraGram: A Structured Longitudinal Dataset of the Telegram Messenger
Anastasia Golovin, Sebastian B. Mohr, Arne I. Gottwald et al. (+5 more)
Here we present a massive longitudinal
dataset of public Telegram content, comprising over 5.9 billion messages dating from 2015 to 2025, collected from 712 thousand channels and groups, enriched with metadata on forwards, reactions, and polls. The
dataset spans multiple
languages including Russian and Farsi, representing countries where Telegram shows mainstream adoption, as well as Western
languages where Telegram is used in specific sub-communities. The
dataset has several advantages. First, when restricted by
language, it provides a versatile example of an
algorithm-free platform, contrary to many other social media platforms that are strongly influenced by opaque content-curation
algorithms. Second, it enables comparative studies across different
languages, communities, and user bases under identical platform affordances. The
dataset thus offers a foundation for studying engagement patterns, network
evolution, and community formation in the absence of algorithmic curation.
Read the full abstract ▾
All authors Anastasia Golovin, Sebastian B. Mohr, Arne I. Gottwald, Ulrik Hvid, Srushhti Trivedi, Joao Pinheiro Neto, Andreas C. Schneider, Viola Priesemann
Optics
Intra-Gauge Rotated Vector Sum (IG-RVS) for Rayleigh Fading Mitigation in Coherent {\phi}-OTDR Systems
Igor Koltchanov and André Richter
We propose Intra-Gauge Rotated Vector Sum (IG-RVS), a DSP-based fading mitigation method for coherent ${\varphi}$-OTDR. IG-RVS exploits spatial diversity within the gauge length by phase-aligning and coherently summing neighboring bins, thereby suppressing Rayleigh fading while preserving spatial resolution.
Read the full abstract ▾
Biological Physics
Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
Marco Rando, Robin A. Heinonen, Yujia Qi et al. (+1 more)
Finding an odor source in a turbulent flow requires effectively leveraging the history of olfactory observations into a robust
navigation strategy. In this work, we use tabular Q-learning to train an olfactory search
agent with a minimal
memory of past observations: only a running clock since the last whiff. This
agent learns an interpretable strategy to recover the plume which combines well-known behaviors observed in insects: surging, casting, and a return downwind. While achieving good performance on data from direct numerical simulations of
turbulence, the
agent is limited by an inability to adapt its strategy to the local intermittency level; we show that providing more flexibility improves robustness.
Read the full abstract ▾
All authors Marco Rando, Robin A. Heinonen, Yujia Qi, Agnese Seminara
Plasma Physics · Cross-listed from cs.RO
Dynamic Plasma Shape Control with Arbitrary Sensor Subsets
D. Sorokin, M. Stokolesov, A. Granovskiy et al. (+7 more)
Plasma shape control in tokamaks requires a real-time controller that tracks dynamically changing shape targets while tolerating diagnostic failures. Classical approaches decompose the problem into
equilibrium reconstruction followed by a linear controller, and assume a fixed, fully operational sensor set. We present a reinforcement learning
agent that addresses both limitations simultaneously. The
agent is trained in NSFsim, a high-fidelity tokamak simulator configured for DIII-D, on a curated
dataset of 120 experimental
plasma shapes. The shape targets are resampled as random step changes every 0.25 s, exposing the
agent to diverse transitions across the full shape envelope. At test time the
agent zero-shot tracks dynamic shape sequences; on a held-out static configuration in simulation it achieves a mean shape error of 2.01 cm, and dynamic trajectory following is demonstrated qualitatively in simulation and on the physical device. Diagnostic dropout randomly masks 30% of
magnetic sensors per episode, yielding a single policy robust to arbitrary sensor subsets without backup controllers or mode-switching logic. An asymmetric actor-critic architecture with privileged
equilibrium information improves value estimation under partial observability; an auxiliary shape reconstruction head on the actor enables end-to-end shape reconstruction from raw diagnostics and serves as an interpretability tool for policy analysis. The policy transfers to experimental DIII-D shots, where it directly commands the coil actuators on two dynamic shape maneuvers, and to the independent GSevolve simulator.
Read the full abstract ▾
All authors D. Sorokin, M. Stokolesov, A. Granovskiy, I. Prokofyev, E. Adishchev, M. Nurgaliev, E. Khayrutdinov, G. Subbotin, R. Clark, D. Orlov
Chemical Physics
Data-driven complete basis set limit estimates from a minimal auxiliary basis
Nicolas Grimblat, Gabriel Klassen and Guido Falk von Rudorff
Quantum chemistry calculations are often performed using atom-centered basis sets which are chosen to balance accuracy and cost. While they are systematically improvable, the total energy converges slowly with basis set size towards the complete basis set (CBS) limit. Common extrapolation methods require several intermediate-quality calculations to afford an estimate of the CBS energy. We propose combining a pairwise interaction model with a minimal complementary auxiliary basis set (CABS) baseline to estimate the CBS energy from a single
quantum chemistry calculation in a minimal basis set via Kernel-Ridge-Regression (KRR), which is more efficient than both direct and $\Delta$-machine learning. We show that KRR on standard molecular representations can be improved by approximating atom-wise local kernels using Chebyshev
polynomials which allows us to train KRR models efficiently on moderate compute resources, further enabling a data-driven approach towards CBS combining physical baselines capturing leading order effects with data-efficient machine learning models.
Read the full abstract ▾
Biological Physics · Cross-listed from cond-mat.soft
Actin cross-linking organizes basal body patterning through anomalous diffusion transitions
Raghavan Thiagarajan, Younes Farhangi Barooji, Poul-Martin Bendix et al. (+2 more)
Subcellular
protein complexes and organelles exhibit diverse dynamic behaviors that reflect the mechanical constraints and organization of the intracellular environment. Although some structures follow classical Brownian motion, many display anomalous dynamics. The transitions between these regimes are increasingly recognized as critical for subcellular organization, yet how they influence pattern formation remains unclear. Here, we investigate the spatial arrangement of cilia on the apical surface of multiciliated
cells (MCCs) in developing Xenopus laevis embryos, where coordinated ciliary beating depends on the precise organization of hundreds of centriole-derived basal bodies (BBs). Using quantitative confocal, high-resolution and high-speed TIRF imaging together with theoretical modeling, we show that BB trajectories undergo time-resolved transitions between diffusive and anomalous motion, with distinct regimes that correlate with apical surface expansion. During the early stages, actin remodeling facilitates the dispersal of BBs by providing a permissive, low-confinement environment. As development progresses, the actin network becomes increasingly cross-linked that constrains BB movement and promotes uniform spacing across the apical domain. Disruption of $\alpha$-actinin-1, a major actin cross-linking
protein, impairs the integrity of the apical actin meshwork, weakens BB confinement, and disrupts regular spatial patterning, ultimately compromising the arrangement of BBs required for proper cilia
alignment. Together, we show that progressive apical actin cross-linking coordinates BB positioning and regulates their dynamic state, guiding the shift from diffusive to confined motion. This transition in dynamics enables the emergence of a uniform BB pattern, which in turn ensures the aligned deployment of motile cilia necessary for effective directional fluid flow.
Read the full abstract ▾
All authors Raghavan Thiagarajan, Younes Farhangi Barooji, Poul-Martin Bendix, Mandar M. Inamdar, Jakub Sedzinski
Optics
Optimized near-field optical response via adaptive tip illumination
Tao Chen, Wei Wang, Ziyang Gan et al. (+7 more)
The performance of tip-enhanced optical microscopy is often limited by inefficient coupling of the excitation field to the plasmonic tip apex, as well as by thermal drift and optical aberrations. Here, we demonstrate that adaptive wavefront shaping based on Zernike mode provides a practical approach to achieving robust near-field optimisation at the tip apex. Using a sequential feedback
algorithm, initially using the near-field signal, we narrow the illumination point-spread function and suppress sidelobes. This demonstrates that Zernike-mode control can be used for both aberration correction and field engineering. In tip-enhanced Raman measurements of a Janus MoSSe monolayer, conventional near-field optimisation increases the signal intensity by around 1.4 fold. A second optimisation step based directly on the Raman-band intensity yields a further 5 to 15 fold enhancement, depending on the specific tips used. These results establish a systematic, optics-based strategy for optimising tip fields, providing a transferable framework for improving tip-enhanced and related near-field spectroscopies.
Read the full abstract ▾
All authors Tao Chen, Wei Wang, Ziyang Gan, Daniel Repp, Jinxin Zhan, Antony George, Henrik Schneidewind, Ulf Peschel, Andrey Turchanin, Volker Deckert
Applied Physics · Cross-listed from cond-mat.mes-hall
Stable magnetic nanodomains engineered via Ga+-ion irradiation for deterministic sequential switching
Gijs W.A. Simons, Rik F.J. van Haren and Bert Koopmans
Precise control of
magnetic domain formation at the nanoscale remains constrained by
stochastic defect-mediated and unstable pinning, limiting scalability and reproducibility in spintronic architectures. Here we demonstrate that spatially engineered anisotropy gradients provide a deterministic alternative. Using focused Ga+-ion irradiation, we pattern
magnetic energy landscapes containing nanoscale "anisotropy wells" that confine
magnetic domain walls and enable bidirectional sequential switching without reliance on difficult-to-control material disorder. An analytical framework describing domain-wall energetics in graded anisotropy profiles yields predictive design rules for depinning and stability, which are supported by micromagnetic simulations and experiments. We realize programmable multi-domain configurations in continuous
ferromagnetic films and demonstrate robust, reproducible switching of 750 nm regions, while first results for 100 nm are shown, approaching the theoretical limit set by the domain-wall width. By replacing unstable pinning with engineered energy landscapes, this anisotropy landscape establishes a scalable materials strategy for deterministic
magnetic-state programming and opens a pathway toward dense, energy-efficient spintronic and reconfigurable
magnetic nanodevices.
Read the full abstract ▾
Computational Physics · Cross-listed from math.DS
Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems
Yeang Makara, Yusuke Tanaka, Takashi Matsubara et al. (+1 more)
The modeling and simulation of infinite-dimensional
Hamiltonian systems are central problems in mathematical physics and engineering, however they pose significant computational and structural challenges for standard data-driven architectures. In this work, we introduce the
Symplectic Neural Operator, a neural operator architecture designed to preserve the
symplectic structure intrinsic to
Hamiltonian PDEs. We provide a theoretical characterization of their symplecticity and establish a rigorous long-term stability result based on the combination of
symplectic structure preservation and learning accuracy. Numerical experiments on canonical
Hamiltonian PDEs corroborate this theoretical result and show that SNOs exhibit improved energy behavior compared with non-structure-preserving neural operators.
Read the full abstract ▾
All authors Yeang Makara, Yusuke Tanaka, Takashi Matsubara, Takaharu Yaguchi
Fluid Dynamics
Coarse-grained local available potential energy
Jacob O. Wenegrat, Tomas Chor and Roy Barkan
The available potential energy (APE) of a fluid can be defined locally in space, providing useful insights into both the energetics and dynamics of stratified flows ranging from three-dimensional
turbulence to planetary scale circulations. Here we develop a framework for considering the multi-scale
evolution of the local APE using a spatial filtering, or coarse-graining, approach. A scale decomposition valid generally for filters with both horizontal and vertical support is proposed, and shown to admit a construction in terms of a series of band-passed energies. The physical interpretation of the decomposition is discussed, and
evolution equations for the APE at scales larger, and smaller, than the filtering scale are derived — including the cross-scale APE flux term. These results can be paired with existing frameworks for coarse-grained kinetic energy, offering the potential for examining a complete energy cycle that accounts for conversions between both spatial scales and energy reservoirs. An illustrative example of the application of this approach to a simulation of three-dimensional Kelvin-Helmholtz instability is provided, including an exact formulation of the scale-dependent instability energy budgets, and the distribution of terms such as cross-scale transfers and APE dissipation in physical space.
Read the full abstract ▾
Optics · Cross-listed from quant-ph
Gauge-Engineered Tunable Mode Selection in Non-Hermitian Directed-Graph Networks
Wenwen Liu and Zhang Shuang
Programmable mode selection is essential for integrated photonic networks, yet selectively isolating individual modes without delicate balancing of gain and loss remains challenging. Here we introduce a gauge-engineering method in non-Hermitian directed-graph networks that support geometry-protected pure decay modes:eigenstates exhibiting smooth exponential amplitude decay along directed paths. In fully connected configurations, a single dominant mode emerges naturally, separated from the remaining modes by a large, tunable energy gap. By introducing synthetic gauge fields through phase-compensated non-reciprocal hopping, any desired pure decay mode can be promoted to the dominant position while its amplitude profile is preserved, which can be naturally interpreted by spectral graph theory. The approach further extends to simultaneous selection of paired modes in half-connected graphs and customizable multi-mode distributions in higher dimensions via orthogonal folding. Our method enables robust, loss/gain-free control over mode profiles, advancing applications in single-mode
lasers, sensors, and
quantum processing.
Read the full abstract ▾
Fluid Dynamics
Bounce or coalescence : a physical learning frame
J. H. Xu and Z. L. Wang
In this study, we develop an interface-contact simulation framework based on physical criteria and machine-learning-assisted classification to describe coalescence and bouncing within a unified formulation. The framework realizes interfacial coalescence and bouncing through the fusion and generation of multiple volume-of-fluid fields. When adjacent interfaces are predicted to coalesce, multiple VOF fields are collapsed into a single VoF field. When approaching interfaces are predicted to bounce, a single VOF field is regenerated into multiple VOF fields, allowing the interfaces to continue evolving independently. With this treatment, the difficulties associated with topological transition, regime-map identification, increasing computational demand, and
stochastic behavior during interfacial approach are separated from the interface-tracking procedure. These decisions are instead assigned to a physics-guided machine-learning model with strong adaptability. This strategy avoids the direct resolution of an ultrathin gas film and reduces the dependence on empirical molecular-force parameters. Simulations of droplet–droplet collisions show that the proposed framework can reproduce both coalescence and bouncing over different impact conditions. By further introducing a drainage-time criterion, the framework is extended to the simulation of droplet impact on a
liquid surface. For this problem, the numerical results agree well with both previous experimental observations and the present experiments. Moreover, the framework captures the complete sequence of bouncing followed by subsequent coalescence within a single simulation, These results demonstrate that the proposed framework has strong adaptability for interfacial contact problems and provides a unified modeling route for droplet coalescence, bouncing.
Read the full abstract ▾
Physics and Society
Reconstructing temporal multi-relational firm networks at scale using large language models. The case of the semiconductor industry
Seyda Köse, Christian Diem, Elma Dervic et al. (+5 more)
The
semiconductor industry is foundational to modern technology, yet its complex global multi-relational firm network remains poorly understood, posing challenges to scientists, firms, and policymakers. Traditional analysis relies on proprietary databases that are often expensive, incomplete, and slowly updated, limiting their ability to capture rapidly evolving dependencies. Here, we demonstrate that a novel, generalizable methodology combining Large
Language Models (LLMs) with open web data can reconstruct this network and its structural dynamics at scale. We identify and classify supply-chain, partnership, and ownership links from 170 million
semiconductor firm webpages, yielding a temporal network of over 1,300 linked firms. We validate link-extraction quality (Precision: 0.884; F1-score: 0.784), network overlap and complementarity with a proprietary database, and consistency with aggregate economic data. Our network reveals a temporary 9% decline in edges during the 2022 chip shortage, rapid increases in the centrality of AI supply-chain bottleneck firms such as NVIDIA, and geographic realignment of interfirm relations amid geopolitical
turbulence. This generalizable framework overcomes barriers to transparency and provides essential, up-to-date maps for assessing resilience and informing policy across strategically relevant sectors.
Read the full abstract ▾
All authors Seyda Köse, Christian Diem, Elma Dervic, Klaus Friesenbichler, Georg Heiler, Jan Hurt, Hernan Picatto, Peter Klimek
Atomic Physics
Fluorescence and Relaxation Dynamics of Cesium in Argon Matrices: Multiple Trapping Sites and Host-Guest Interactions
S. Lahs, H. Dinesan, S. Mahapatra et al. (+6 more)
We investigate the fluorescence and relaxation dynamics of Cs atoms embedded in a cryogenic argon
matrix using spectroscopy measurements combined with diatomic-in-molecule (DIM) simulations. The data reveal complex emission spectra, large Stokes shifts, and slow relaxation effects, indicating strong host-guest interactions and substantial
lattice reorganization. Although the spectra are superimposed on a broad background, possibly due to low-
symmetry, defect-related, or grain-boundary trapping sites, the main spectral structure is consistent with two dominant trapping environments that give rise to two triplet absorption features with distinct fluorescence behavior of the doublet and singlet components. Polarization measurements further suggest that these sites may differ in
symmetry, although a unique structural assignment remains difficult
Read the full abstract ▾
All authors S. Lahs, H. Dinesan, S. Mahapatra, W. Chin, C. Crepin, L. Dontot, J. Douady, B. Gervais, D. Comparat
Biological Physics · Cross-listed from q-bio.CB
How nature discovers rare Turing islands: exploration by common limit cycles
Seyoon Kim, Antonio Matas-Gil and Robert G. Endres
Turing patterns are a cornerstone of biological self-organization, yet their emergence typically requires finely tuned parameters occupying narrow regions of high-dimensional space. This poses a fundamental challenge: how can evolving biological systems reliably find and exploit such rare conditions? In this work, we propose that common biochemical limit cycles, such as those arising from genetic feedback loops, can act as natural explorers of Turing space. By coupling a reaction-
diffusion system to an orbit that modulates some of its parameters, we show that the system can dynamically sweep through Turing-permissive regimes and generate transient spatial patterns. We use an
entropy-based measure in
Fourier space to quantify pattern formation and demonstrate how cycles enhance the detectability and robustness of Turing islands. We further explore how coupling to positional gradients increases reproducibility, suggesting a route from oscillatory dynamics to stable developmental programs. Our results highlight a powerful mechanism by which nature might bootstrap complex spatial structure from simple temporal motifs.
Read the full abstract ▾
Chemical Physics · Cross-listed from cond-mat.soft
Markov State Model for the forced unfolding of a small peptide
Marco Oestereich, Jürgen Gauss and Gregor Diezemann
In typical single-molecule force spectroscopy experiments the mechanical unfolding of molecular complexes or biomolecules is studied applying a force ramp to one end of the system while the other end is kept fixed in space. The computational counterpart of this type of experiments can routinely be performed using molecular dynamics simulations with atomistic resolution. However, due to the large difference in time scales often coarse graining procedures are applied in the simulations. Most of the applied techniques do not allow to follow the atomistic details of the relevant conformational transitions due to the structural simplifications used to speed up the simulations. Here, we apply an earlier developed dynamic coarse graining technique based on Markov state modeling to a model peptidic system that does not unfold in a simple two-state manner. Using the donor-acceptor distances of the helical hydrogen bonds as collective variables and performing a dimension reduction technique allows us to construct a Markov model of the unfolding process that correctly represents the microscopic behavior of the system. The chosen example shows that the method can be used to mimick the mechanical unfolding process of systems for which the end-to-end distance does not provide a sufficient order parameter and that do not unfold in a simple cooperative manner.
Read the full abstract ▾
Physics and Society
Event-based spatiotemporal networks for modelling emergent phenomena in complex systems
Matthijs Romeijnders, Michiel van Boven, Francesco Corman et al. (+3 more)
Complex systems display emergent phenomena that vary significantly across spatial and temporal scales. These variations originate from fine-grained system processes, yet arriving at macroscopic dynamics from micro-level data – particularly when large, high-resolution
datasets are available – remains a persistent challenge. Here we develop event-based spatiotemporal networks, a computational modelling framework that encodes system processes as discrete events anchored in space and time. Event-based spatiotemporal networks offer a unified, flexible and efficient approach to generate emergent behaviour in complex systems across space and time from these events. We demonstrate the effectiveness of event-based spatiotemporal networks through two illustrative real-world applications. First, following a local outbreak of a novel respiratory pathogen in the Netherlands, spatiotemporal networks enable fine-grained tracking of transmission routes and infection patterns through space and time. Second, we use spatiotemporal networks to model propagation of delays in a public transportation system (S-bahn) around Zürich, Switzerland. We also discuss broader uses of event-based spatiotemporal networks in fields like developmental biology and community ecology, where focusing on events rather than static system states can improve data analysis, simulation, and collection strategies.
Read the full abstract ▾
All authors Matthijs Romeijnders, Michiel van Boven, Francesco Corman, Carl D. Modes, Phillip Staniczencko, Debabrata Panja
Optics · Cross-listed from quant-ph
Quantum compressed sensing
Jianyong Hu, Wei Li, Shuxiao Wu et al. (+12 more)
How many measurements are fundamentally required to capture a signal. Shannon's information theory established the bedrock of this question in 1948, the Nyquist Shannon
theorem set the first answer, and compressed sensing (CS) rewrote it in 2006 by reducing the required measurement number to M = O(Klog(N/K)) for a K sparse signal. Here, we propose
quantum compressed sensing (QCS), a paradigm that reframes signal acquisition as a
unitary quantum evolution. By encoding high dimensional signal information into a single
quantum probe state, then introducing domain-
alignment evolution,a physically realizable
unitary transformation that maps the sparse basis directly onto the measurement basis. QCS executes the support-set search at the
quantum level without consuming measurement trials. The logarithmic penalty vanishes, compressing the required measurement number from the classical bound to M =O(K) and reducing reconstruction from ill posed optimization to linear estimation. We experimentally validate QCS using frequency and time domain sparse signals, confirming that the measurement number scales linearly with sparsity and decouples entirely from the signal dimension. Our work provides a physical pathway toward ultimate information acquisition efficiency, with broad implications for sensing, imaging, and communication.
Read the full abstract ▾
All authors Jianyong Hu, Wei Li, Shuxiao Wu, Liwen Zhang, Yongchuang Sun, Jiazhao Tian, Guosheng Feng, Zhixing Qiao, Jianqiang Liu, Changgang Yang, Ruiyun Chen, Chengbing Qin, Guofeng Zhang, Liantuan Xiao, Suotang Jia
Computational Physics
Spatiotemporal decoupled physics-informed Stone-Weierstrass neural operator for long-time prediction of time-dependent parametric PDEs
Shan Ding, Yongfu Tian, Lang Qin et al. (+3 more)
Driven by rapid advances in artificial intelligence and modern GPU computing capabilities, deep learning methods based on the optimization paradigm have provided new pathways to solve spatiotemporal physical problems, whose mathematical core lies in solving partial differential equations (PDEs). As an emerging class of function-space learning methods, neural operators (NOs) have exhibited great potential in efficient PDE solving. However, existing mainstream neural operator frameworks suffer from critical bottlenecks when modeling time-dependent PDEs over long time horizons, including accuracy degradation, insufficient stability, high training costs, and excessive
memory consumption, which severely limit their practical deployment. To address these challenges in long-time prediction with neural operators, we propose a novel spatiotemporally decoupled physics-informed neural operator architecture, termed the physics-informed Stone-Weierstrass neural operator (PI-SWNO). The design is theoretically grounded in the decoupling paradigm combining time-invariant spatial basis functions with time-varying
evolution coefficients, as well as the Stone-Weierstrass approximation
theorem. By encoding spatial and temporal information via two separate subnetworks, the framework structurally mitigates the accumulation of errors over extended time intervals. Furthermore, we introduce a time-marching batch-wise sampling strategy to resolve the
memory bottleneck of full-range modeling over extended time spans, ensuring continuity and convergence of full-time-domain solutions.
Read the full abstract ▾
All authors Shan Ding, Yongfu Tian, Lang Qin, Hongxiang Ma, Guofeng Su, Rui Yang
Instrumentation and Detectors
R&D of cosmic ray detection module with liquid scintillator and wavelength shift fiber
Jun Zou, Xiangdong Sheng, Zhimin Wang et al. (+12 more)
For
neutrino physics and rare event searches, background related to
cosmic muons poses a notable challenge, and must be identified and rejected. It is also a challenge to control the cost with good performance for a large array of
cosmic ray detection. We proposed a
cosmic ray detection module with
liquid scintillator and wavelength-shifting fibers for its reasonable cost and performances. The results from the measurements of a prototype with
Muon indicate that the detector's photoelectron response is good. % comparing to the expectation. The outcomes of this study hold significant potential for applications in
cosmic ray observation experiments and underground rare-event detection, providing a viable option for future large-scale observatories. This work highlights the feasibility of
liquid scintillator-based detectors in addressing current and emerging challenges in particle physics and astrophysics.
Read the full abstract ▾
All authors Jun Zou, Xiangdong Sheng, Zhimin Wang, Fengjiao Luo, Bo Zheng, Cunfeng Feng, Chao Hou, Guang Luo, Sibo Wang, Peisheng Niu, Fang Liu, Yichen Zheng, Dong Liu, Ziqi Huang, Shulong Ji
Optics
Nonlocal Optical Response and Surface Susceptibilities: A Systematic Derivation via Spatial Moment Expansion
Frédéric Zolla
We present a systematic theory connecting the nonlocal response kernel of a homogeneous medium to its effective surface susceptibilities for an arbitrary curved interface. Starting from the most general tensorial nonlocal constitutive relation and combining a spatial moment expansion with a distributional thin-layer limit, we show that the full complexity of the interfacial response condenses, at leading order, into a single scalar: the surface susceptibility $\chi^s$, equal for the tangential and normal components of the electric field. These quantities provide a constructive generalization of the Feibelman $d$-parameters to interfaces of arbitrary
curvature, and the
curvature corrections, proportional to the
geometric invariants $H$ (mean
curvature) and $K$ (Gaussian
curvature), are derived explicitly. The formalism is illustrated on a comprehensive set of analytically tractable cases (planar, spherical, cylindrical, and ellipsoidal interfaces) for several kernel choices (Gaussian, Yukawa, tensorial Lorentz). Generalized Maxwell boundary conditions are established and compared with the classical Fresnel results.
Read the full abstract ▾
Optics
Ultralong pump-probe movies of magnon and phonon dynamics from ultrafast generation to microsecond relaxation
Riku Shibata, Shun Fujii and Shinichi Watanabe
The long lifetimes of
magnons and
phonons make them attractive for information-processing devices, highlighting the importance of visualizing their spatiotemporal dynamics from generation through relaxation. Ultrafast pump-probe spectroscopy is a powerful tool for investigating their early-stage dynamics after impulsive excitation; however, their long-lived nature makes it challenging to comprehensively track their
evolution across all relevant time scales while maintaining sufficient temporal resolution. Here, we demonstrate spatiotemporal tracking of
magnon and
phonon dynamics over more than seven orders of magnitude in time, from 500 femtoseconds to 20 microseconds, using $4 \times 10^7$ sampled time points enabled by the highly precise time base of optical frequency combs. The resulting spatiotemporal movie, consisting of $4.5 \times 10^{5}$ frames, captures their generation, coherent motion, propagation, and relaxation, providing a powerful platform for exploring their full dynamical
evolution.
Read the full abstract ▾
Atmospheric and Oceanic Physics
Multi-stability of Atlantic and Pacific overturning: The role of Freshwater Forcing Asymmetries and the Hydrological Cycle
Elian Vanderborght, Oliver Mehling and Henk A. Dijkstra
A defining feature of the present-day global overturning circulation (GOC) is the absence of deep water formation in the Pacific, in contrast to the Atlantic. This asymmetry, associated with higher surface salinities in the North Atlantic, is reflected in the Atlantic Meridional Overturning Circulation (AMOC) and the lack of a Pacific overturning (PMOC). A commonly cited explanation is the asymmetry in surface freshwater fluxes, with the Pacific receiving more freshwater per unit area than the Atlantic. Here, we develop a two-basin conceptual ocean model, consisting of a wide and a narrow basin. The model admits three states: sinking confined to the narrow basin, sinking confined to the wide basin, and sinking in both basins. We analyze the (co-)existence of these states as a function of freshwater asymmetry and hydrological cycle strength, defined as the longitudinally symmetric freshwater flux. For a weak hydrological cycle, representative of warm Pliocene-like
climate conditions, sinking occurs in both basins, with
symmetry breaking only when one basin is sufficiently more evaporative. For intermediate conditions, representative of the present-day
climate, the basin with slightly stronger evaporation tends to host sinking, with a stronger preference in the narrow basin. For a strong hydrological cycle, single-basin sinking states are preferred, although a large interbasin freshwater asymmetry is required to uniquely localize sinking. These results provide insight into GOC sinking configurations under past, present, and potential future climates, and show good agreement with a three-dimensional global circulation model.
Read the full abstract ▾
Applied Physics · Cross-listed from hep-ex
Locating nuclear-powered submarines with antineutrinos
Sven-Patrik Hallsjö
Nuclear-powered submarines are difficult to track with conventional methods in congested waterways. We revisit antineutrino-based detection as a barrier concept, analogous to a
neutrino-enabled SOSUS-style fence in strategic straits. Using analytic scaling relations and numerical estimates, we show that detectability depends primarily on closest approach, detector depth, and deployed mass. For representative assumptions, a 20\,kt detector in the Strait of Gibraltar reaches a local
benchmark score $Z_A\simeq2.54$ for an assumed 100\,MW thermal-power sensitivity-study case in a conservative worst-case transit (with Poisson operating point $(P_\mathrm{FA},P_\mathrm{det})\simeq(5.5\times10^{-3},0.51)$ at threshold $k=2$), while a three-detector line raises the mapped score to $Z_A\simeq4.66$. For broad ocean passages such as GIUK, required detector counts are substantially larger; in the baseline maximum passing distance $\mathrm{PDD}_{\max}=5$\,km geometry, about 80 detectors yield only $Z_A\sim1.6$. The paper outlines detector technology choices, statistical assumptions, and deployment constraints for a first-generation feasibility assessment.
Read the full abstract ▾
Optics
Chiral-Mode Control around a Hermitian Diabolic Point in Discrete Non-Hermitian Coupled Resonators
Kota Yagi, Takahiro Uemura, Yuto Moritake et al. (+2 more)
Motivated by the prospect of
chiral-mode control in compact photonic systems, we analyze discrete coupled single-mode resonators. Using the minimal three-resonator model, we show that an infinitesimal complex onsite perturbation near a Hermitian diabolic point (DP) induces
chiral-mode selection, governed by what we term an asymptotic exceptional point (AEP). Here, an AEP denotes a Hermitian DP equipped with a non-Hermitian perturbation that induces an asymptotically defective effective
Hamiltonian. The eigenvectors coalesce in the asymptotic limit toward the DP, although the
Hamiltonian at the point itself remains diagonalizable. Operationally, this AEP response realizes chirality switching from an achiral state to a
chiral state. The associated
eigenvalue response exhibits the anomalous fractional-power scaling ${\Delta}{\lambda} \propto {\varepsilon}^{3/2}$, distinct from the square-root response of an ordinary exceptional point (EP). We further show that, in a broader two-parameter perturbation space, ordinary EPs lie on exceptional-line branches that meet at the AEP. A finitebias control sweep crosses these branches at an EP pair, enabling chirality reversal between opposite
chiral states. The central message is therefore that the AEP organizes two related routes for chirality switching: direct switching from an achiral state to a
chiral state via the AEP, and switching between opposite
chiral states via an EP pair in the vicinity of the AEP. Within a finite-resolution averaging model, these two operating points exhibit different practical performance characteristics, and under sufficiently high control resolution, the AEP operating point can become more favorable than the EP-pair operating point, suggesting a route toward compact and low-energy
chiral photonic devices.
Read the full abstract ▾
All authors Kota Yagi, Takahiro Uemura, Yuto Moritake, Adam Mock, Masaya Notomi
Optics
About Time: Observation of Time-Reflection at Optical Frequencies
Ohad Segal, Noa Konforty, Oded Schiller et al. (+9 more)
Time-reflection occurs when a wave is propagating in a medium undergoing a large and abrupt change in its properties: the original wave splits into a time-refracted wave and a time-reflected wave, each displaying different features. The time-refracted wave continues along its original course but experiences a frequency shift, whereas the time-reflected wave is propagating backwards in space with a reversed phase, also with a shifted frequency. These phenomena are fundamental to any wave system, but the most interesting are electromagnetic (EM) waves, specifically at optical frequencies, where they can couple to light-matter interactions. However, time-reflection of EM waves was thus far observed only at RF frequencies, never at optical frequencies. This is because time-reflection requires an order-unity variation of the refractive index occurring faster than a single wave cycle, and conventional optical nonlinearities are either too weak or too slow by orders of magnitude. Here, we present the first observation of time-reflection at optical frequencies. We induce an order-unity refractive-index change with sub-cycle duration, observe the time-reflection, and study its fundamental properties. These results provide an experimental pathway to experimenting with time-interfaces, generating photonic time-
crystals and exploring new regimes of light-matter interaction in time-varying media.
Read the full abstract ▾
All authors Ohad Segal, Noa Konforty, Oded Schiller, Maxwell J. Tolchin, Tony Yasniger, Michael Birk, Alexander Palatnik, Soham Saha, Pavel Sidorenko, Jon-Paul Maria, Yonatan Plotnik, Mordechai Segev