Chemical Physics
AtomDisc: An Atom-level Tokenizer that Boosts Molecular LLMs and Reveals Structure–Property Associations
Mingxu Zhang, Dazhong Shen and Ying Sun
Advances in large
language models (LLMs) are accelerating discovery in molecular science. However, adapting molecular information to the serialized,
token-based processing of LLMs remains a key challenge. Compared to other representations, molecular graphs explicitly encode atomic connectivity and local topological environments, which are key determinants of atomic behavior and molecular properties. Despite recent efforts to tokenize overall molecular
topology, there still lacks effective fine-grained tokenization of local atomic environments, which are critical for determining sophisticated chemical properties and reactivity. To address these issues, we introduce AtomDisc, a novel framework that quantizes atom-level local environments into structure-aware
tokens embedded directly in LLM's
token space. Our experiments show that AtomDisc, in a data-driven way, can distinguish chemically meaningful structural features that reveal structure-property associations. Equipping LLMs with AtomDisc
tokens injects an interpretable inductive bias that delivers state-of-the-art performance on property prediction and molecular generation. Our methodology and findings can pave the way for constructing more powerful molecular LLMs aimed at mechanistic insight and complex chemical
reasoning.
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Atomic Physics
Micro-Electro-Mechanical System Vapor Cells With Passivated Internal Cavities
Rajesh Pandiyan, Sanyasi Bobbara, Somayeh Mirzaee et al. (+5 more)
Micro-Electro-Mechanical, so called 'MEMs,' vapor
cells are a key component in atom-based
quantum sensors, such as clocks, gyroscopes, electric field sensors and magnetometers. MEMs vapor
cell fabrication for Rydberg atom
radio frequency sensors is particularly demanding. The Rydberg states used for the sensor can shift in a constant electric field which can be generated by the internal surfaces of the vapor
cell cavity. The ratio of the detection wavelength to vapor
cell size can span a large range, meaning that the
radio frequency field-vapor
cell interaction is a critical design consideration. In many
radio frequency sensing cases, there is a desire to minimize the interaction between the vapor
cell and the target
radio frequency field, as well as assure that every vapor
cell behaves uniformly. These criterion favor MEMs vapor
cells with low background electric fields. Known inert, organic coatings cannot survive the bonding temperatures required for conventional anodic bonding of a MEMs vapor
cell. Applying inert, organic coatings to the internal cavities of MEMs vapor
cells is a longstanding challenge. In this paper, we present a low temperature bonding scheme that is compatible with coating the internal cavity of a MEMs vapor
cell with Octadecyltrichlorosilane (CH$_3\,$(CH$_2$)$_{17}\,$SiCl$_3$, OTS). The coating prevents the Cs used in the vapor
cell from sticking to the walls. Spectral linewidths of $\sim300\,$kHz are obtained using Rydberg spectroscopy, with energy shifts corresponding to electric fields $<$10$\,$mV$\,$cm$^{-1}$.
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All authors Rajesh Pandiyan, Sanyasi Bobbara, Somayeh Mirzaee, Su-Peng Yu, Ruoxi Wang, Adam Sibenik, Reza Kohandani, James P. Shaffer
Optics
3D space-variant modal deconvolution with computed point spread functions
Jakub Czuchnowski, Chuan Li, Hongli Ni et al. (+2 more)
Deconvolution is the most widely used aberration correction technique in microscopy, however most techniques assume that the aberrations are the same for each point in the image, which is rarely true. Methods for tracking spatially varying aberrations require burdensome calibration or computation, or require
symmetries in the aberration patterns. Here, we expand on existing modal deconvolution methods to demonstrate 3D fluorescence deconvolution in imaging systems that exhibit no simple
symmetry. Our method is based on a space-variant generalization of Richardson-Lucy deconvolution that makes use of ZEMAX\textsuperscript{\textregistered}-derived point spread functions without the requirement of guide
stars or calibration measurements. We validate the performance of our method by applying it to snapshot multiplane imaging of both bead samples and biological specimens, and show that modal decomposition is a practical solution for deconvolving spatially varying aberrations that do not display clear
symmetries.
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All authors Jakub Czuchnowski, Chuan Li, Hongli Ni, Brandon Weissbourd, Jerome Mertz
Data Analysis and Statistics · Cross-listed from hep-ph
Timothy Legge, Aaron Wang, Jacob Ortiz et al. (+7 more)
Transformer-based models have achieved state-of-the-art performance in jet tagging at the CERN Large
Hadron Collider (LHC), with the Particle
Transformer (ParT) representing a leading example of such models. A striking feature of ParT is its sparse, nearly binary,
attention structure, raising questions about the origin of this behavior and whether it encodes physically meaningful correlations. In this work, we investigate the source of ParT's sparse
attention by comparing models trained on multiple
benchmark datasets and examine the relative contributions of the
attention term and the physics-inspired interaction
matrix before softmax. We find that binary sparsity arises primarily from the
attention mechanism itself, with the interaction
matrix playing a secondary role. Moreove, we show that ParT is able to identify key jet substructure elements, such as
leptons in semileptonic top decays, even without explicit particle identification inputs. These results provide new insight into the interpretability of
transformer-based jet taggers and clarify the conditions under which sparse
attention patterns emerge in ParT.
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All authors Timothy Legge, Aaron Wang, Jacob Ortiz, Victor Limouzi, Zihan Zhao, Abhijith Gandrakota, Elham E. Khoda, Jennifer Ngadiuba, Javier Duarte, Richard Cavanaugh
Atmospheric and Oceanic Physics
Tropopause Detection and Analysis in the Mid Latitude Region
Mohammed El Abdioui
This study presents an advanced framework for tropopause detection and analysis using ERA5 reanalysis data, with particular application to extreme meteorological events affecting Morocco and Southern Europe. The research implements and compares multiple detection methodologies, including classical approaches (thermal/WMO and dynamical/1.5 PVU criteria) alongside novel hybrid techniques combining stability and humidity parameters originally developed by ECMWF. Through systematic validation against January 2010 monthly means and a detailed case study of the October 2024 DANA (Depression Aislada en Niveles Altos) event, this work demonstrates the superior performance of hybrid approaches in capturing tropopause dynamics. The development of an automated Python-based comparison tool enabled a quantitative evaluation against Water Vapor 6.2 {\mu}m satellite imagery, revealing that the hybrid method achieved the highest structural similarity (mean SSIM: 0.5327) and the most consistent performance across the studied meteorological conditions. The findings highlight the critical role of accurate tropopause detection in improving
forecasting capabilities for extreme weather events, particularly in topographically complex regions like Southeast Morocco. This research contributes both methodological advancements in tropopause identification and practical insights for operational meteorology, providing a foundation for enhanced early warning systems and improved understanding of atmospheric processes governing high-impact weather phenomena.
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Applied Physics
Nanoscale Magnonic Neurons
K. G. Fripp, A. V. Shytov and V. V. Kruglyak
We use micromagnetic simulations to demonstrate
neuron functionality of two-dimensional (2D)
chiral magnonic resonators. Our design exploits nonlinear resonant scattering of
spin waves propagating in a YIG medium from an edge mode of a permalloy nano-element. The reduced frequency and volume of the edge mode facilitate matching it to the YIG modes and give rise to their wide-angle scattering. As the amplitudes of the incident
spin waves increase, the edge mode exhibits a positive nonlinear frequency shift. This shift leads to a complex frequency-dependent nonlinear variation of the amplitude and phase of
spin waves scattered in different directions. We show that the scattered waves are strong enough to activate secondary
neurons. This provides the connectivity required for combining our proposed
neurons into 2D magnonic neural networks.
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Geophysics · Cross-listed from cs.LG
Hybrid Context-Fusion Attention (CFA) U-Net and Clustering for Robust Seismic Horizon Interpretation
Jose Luis Lima de Jesus Silva, Joao Pedro Gomes, Paulo Roberto de Melo Barros Junior et al. (+2 more)
Interpreting seismic horizons is a critical task for characterizing subsurface structures in hydrocarbon exploration. Recent advances in deep learning, particularly U-Net-based architectures, have significantly improved automated horizon tracking. However, challenges remain in accurately segmenting complex geological features and interpolating horizons from sparse annotations. To address these issues, a hybrid framework is presented that integrates advanced U-Net variants with spatial clustering to enhance horizon continuity and
geometric fidelity. The core contribution is the Context Fusion
Attention (CFA) U-Net, a novel architecture that fuses spatial and Sobel-derived
geometric features within
attention gates to improve both precision and surface completeness. The performance of five architectures, the U-Net (Standard and compressed), U-Net++,
Attention U-Net, and CFA U-Net, was systematically evaluated across various data sparsity regimes (10-, 20-, and 40-line spacing). This approach outperformed existing baselines, achieving state-of-the-art results on the Mexilhao field (Santos Basin, Brazil)
dataset with a validation IoU of 0.881 and MAE of 2.49ms, and excellent surface coverage of 97.6% on the F3 Block of the North Sea
dataset under sparse conditions. The framework further refines merged horizon predictions (inline and cross-line) using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to produce geologically plausible surfaces. The results demonstrate the advantages of hybrid methodologies and
attention-based architectures enhanced with
geometric context, providing a robust and generalizable solution for seismic interpretation in structurally complex and data-scarce environments.
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All authors Jose Luis Lima de Jesus Silva, Joao Pedro Gomes, Paulo Roberto de Melo Barros Junior, Vitor Hugo Serravalle Reis Rodrigues, Alexsandro Guerra Cerqueira
Optics · Cross-listed from nlin.PS
Quasi-confined modes produced by the Lugiato-Lefever model with a localized pump and the pseudo-Raman term
Evgeny M. Gromov and Boris A. Malomed
We introduce an extended nonlinear Lugiato-Lefever equation (LLE) with the pseudo-stimulated-Raman-scattering (pseudo-SRS) cubic term, linear damping/gain, and spatial inhomogeneous (weakly or strongly localized) pump. The LLE is derived, in the extended adiabatic approximation, from the underlying Zakharov system (ZS), which includes a viscosity term, acting on its low-frequency (LF) component, and the pump supporting the high-frequency (HF) one. Dynamics of quasi-
solitons in the model is addressed by means of analytical and numerical methods. The sech-based approximation for the quasi-
soliton predicts it as a stable fixed point (FP) of the system of
evolution equations for the moments of the system moments (the HF norm, wave momentum, and center-of-mass coordinate). The attraction basin of the FP is identified too. The prediction is corroborated by direct simulations of the full LLE. A quasi-singular mode, supported by the delta-functional pump, is briefly considered too.
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Instrumentation and Detectors
Cross-Geometry Transfer Learning in Fast Electromagnetic Shower Simulation
Frank Gaede, Gregor Kasieczka and Lorenzo Valente
Accurate particle shower simulation remains a critical computational bottleneck for high-energy physics. Traditional Monte Carlo methods, such as Geant4, are computationally prohibitive, while existing machine learning surrogates are tied to specific detector geometries and require complete retraining for each design change or alternative detector. We present a transfer learning methodology for generative calorimeter simulation models that enables adaptation across diverse geometries with high data efficiency. Using point cloud representations and pre-training on the International Large Detector, our approach handles new configurations without re-voxelizing showers for each geometry. On the CaloChallenge
dataset, transfer learning with only 100 target-domain samples achieves a $51\%$ improvement on the
geometric mean of Wasserstein distance over training from scratch. Parameter-efficient fine-tuning with bias-only adaptation achieves competitive performance while updating only $17\%$ of model parameters. Our analysis provides insight into adaptation mechanisms for particle shower development, establishing a baseline for future progress of point cloud approaches in calorimeter simulation.
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Biological Physics · Cross-listed from cond-mat.dis-nn
Tuning Universality in Deep Neural Networks
Arsham Ghavasieh
Deep neural networks (DNNs) exhibit crackling-like avalanches whose origin lacks a mechanistic explanation. Here, I derive a
stochastic theory of deep information propagation (DIP) by incorporating Central Limit
Theorem (CLT)-level fluctuations. Four effective couplings $(r, h, D_1, D_2)$ characterize the dynamics, yielding a Landau description of the static exponents and a Directed Percolation (DP) structure of activity cascades. Tuning the couplings selects between avalanche dynamics generated by a Brownian Motion (BM) in a logarithmic trap and an absorbed free BM, each corresponding to a distinct universality classes. Numerical simulations confirm the theory and demonstrate that activation function design controls the collective dynamics in random DNNs.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Singularly isostatic and geometrically unstable rigidity of metal-organic frameworks
Christopher M. Owen and Michael J. Lawler
Metal-organic frameworks (MOFs) combine high porosity with structural fragility, raising important questions about their mechanical stability. We develop a
rigidity-based framework in which spring networks parameterized by UFF4MOF are used to construct
rigidity and dynamical
matrices. Large-scale analysis of 5,682 MOFs from the CoRE 2019 database shows that most frameworks are formally over-constrained yet cluster sharply near the isostatic threshold, revealing accidental
geometric modes and placing many MOFs near mechanical instability. In the representative case of UiO-66, we show that auxiliary long-range constraints introduced by tuning the neighbor cutoff lift these modes into soft, flat, finite-frequency bands. The results show that
rigidity-
matrix analysis can rapidly identify MOFs likely to remain mechanically stable. This near-criticality mirrors behavior known from topological mechanics and points to a deeper design principle in porous
crystals.
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Physics Education
High schoolers excel at Oxford quantum course using pictorial mathematics
Bob Coecke, Aleks Kissinger, Stefano Gogioso et al. (+12 more)
We are at the dawn of the second
quantum revolution, where our ability to create and control individual
quantum systems is poised to drive transformative advancements in basic science, computation, and everyday life. However,
quantum theory has long been conceived as notoriously hard to learn, creating a significant barrier to workforce development, informed decision-making by stakeholders and policymakers, and broader public understanding. This paper is concerned with
Quantum Picturalism, a novel visual mathematical
language for
quantum physics. Originally developed over two decades ago to explore the foundational structure of
quantum theory, this rigorous diagrammatic framework has since been adopted in both academia and industry as a powerful tool for
quantum computing research and software development. Here, we demonstrate its potential as a transformative educational methodology. We report the findings from a pilot study involving 54 UK high school students, randomly selected from a pool of 734 volunteers across the UK. Despite the absence of advanced mathematical prerequisites, these students demonstrated a strong conceptual grasp of key
quantum principles and operations. On an assessment comprising university graduate-level exam questions, participants achieved an 82% pass rate, with 48% obtaining a distinction-level grade. These results pave the way for making
quantum more inclusive, lowering traditional cognitive and demographic barriers to
quantum learning. This approach has the potential to broaden participation in the field and provide a promising new entry point for stakeholders, future experts, and the general public.
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All authors Bob Coecke, Aleks Kissinger, Stefano Gogioso, Selma Dündar-Coecke, Caterina Puca, Lia Yeh, Muhammad Hamza Waseem, Emmanuel M. Pothos, Sieglinde Pfaendler, Vincent Wang-Mascianica, Thomas Cervoni, Ferdi Tomassini, Vincent Anandraj, Peter Sigrist, Ilyas Khan
Physics and Society · Cross-listed from q-bio.NC
An Operational Quantum Information Framework for Experimental Studies on Color Perception
Roberto Leporini, Edoardo Provenzi and Michel Berthier
Starting from the foundational axiomatization of the perceptual color space initiated by Schrödinger in 1920 and eventually refined by Resnikoff in 1974, Berthier, Provenzi and their collaborators have recently proposed a reformulation of perceptual color attributes within the framework of
quantum information. Their work is based on the Jordan
algebra formalism of
quantum theories and, more specifically, on a
quantum system described by a
spin factor over the field of real numbers. This theoretical framework is not that of ordinary
quantum mechanics, mainly because it requires dealing with rebits, whereas the latter uses
qubits. The aim of this paper is to show that this difference in no way hinders the implementation of experimental protocols for testing the validity of the predictions of the color perception model. In particular, we show how to compute the
quantum information based perceptual attributes of perceived colors in terms of
qubit density
matrices.
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Atomic Physics
Arbitrary control of the temporal waveform of photons during spontaneous emission
Carl Thomas, Rebecca Munk and Boris Blinov
Control of the temporal waveform of
photons produced during spontaneous emission from single
quantum emitters provides a crucial tool in the establishment of hybrid
quantum systems, optimization of
quantum state transfer protocols and mitigation of effects due interferometric instability for network architectures based on flying
qubits. We describe a method to generate
photons of any temporal waveform from emitters of any excited state lifetime, limited only by the timing resolution of control hardware. We show how the temporal waveform of
photons can be controlled by deterministically varying the population of an excited state which undergoes spontaneous emission. Our broadly applicable approach has only two requirements for a candidate
quantum emitter: modulation of the (1) amplitude and (2) relative phase of a field coupling a ground state to the excited
manifold. We detail how to identify optimal excitation pulses by employing variational
algorithms to feed back on atomic populations. Additionally, we develop
Quantum Monte Carlo based tools to determine
photon-number statistics and establish techniques to identify optimal excitation strengths and post-selection thresholds for
photon generation protocols. We situate our work in the context of other prior research on bespoke single
photon sources and networking including post-emission pulse shaping, temporal gating and cavity-based methods. In comparison, our free-space process has greater flexibility in producing any waveform, requires less infrastructure, and can be readily applied across a wide range of
quantum emitters. We discuss the applications and limits of this technique, including how increasing
photon emission probabilities affects achievable temporal-mode overlap fidelities between emitted and target
photon waveforms.
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Atmospheric and Oceanic Physics
Near-inertial waves enhance vertical transport at ocean fronts
Nihar Paul and Amala Mahadevan
The interactions between near-inertial waves (NIWs) and submesoscale currents in the surface ocean are challenging to deconvolve due to their overlapping temporal and spatial scales. The frequency of NIW is modulated by the relative vorticity, $\zeta$, of submesoscale currents, which varies between positive and negative $\zeta$ of $O(f)$ on spatial scales of 1 – 10~$km$, particularly across fronts where the horizontal buoyancy gradient, $\nabla_H b$, is intensified. The effective NIW frequency $f_{\scriptstyle{eff}} = f + \zeta/2$ can therefore also vary by $O(f)$ on these scales, causing the waves to be out of phase. This generates periodic convergence and divergence in the surface layer, particularly at fronts. The resulting vertical motion, known as inertial pumping, is traditionally considered to be reversible. However, the strong vertical shear of the horizontal velocity at fronts, $v_z \sim |\nabla_H b|/f$, implies that not all of the water that is pumped downward will return. We examine the effect of this asymmetry on the vertical transport of tracers with an ambient vertical gradient, analogous to biogeochemical tracers, such as oxygen and dissolved organic carbon. Using numerical simulations of an unstable front forced by NIW, we demonstrate that inertial pumping can lead to net vertical tracer transport. Spectral analysis of the vertical tracer flux given by the covariance between tracer and vertical velocity anomalies reveals that the interaction of strong NIW with submesoscale currents enhances the vertical exchange at the front on both the sub-inertial and inertial time scales.
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Atmospheric and Oceanic Physics
A retrospective on the 2025 Atlantic hurricane season
Charles W. Powell
Tropical cyclone activity was intermittent during the 2025 Atlantic season, with extended quiet periods. Cumulative activity was near-average relative to 1991-present, despite warm sea-surface temperatures and La Nina conditions. We compare drivers of activity in 2025 with climatology, using reanalysis data to examine variability in environmental conditions, wave activity, and circulation patterns. During the early and peak season, high pressure, a strong upper-level trough and cool equatorial sea-surface temperatures led to weak seed disturbances and unfavourable development conditions. Stronger disturbances coinciding with Kelvin wave activity and favourable MJO conditions led to a more active late season.
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Atmospheric and Oceanic Physics
Epistemic and Aleatoric Uncertainty Quantification in Weather and Climate Models
Laura A. Mansfield and Hannah M. Christensen
Representing and quantifying uncertainty in physical parameterisations is a central challenge in weather and
climate modelling, and approaches are often developed separately for different timescales. Here, we introduce a unified framework for analysing uncertainty in parameterisations across weather and
climate regimes. Using the Lorenz 1996 system as a testbed for simplified chaotic dynamics, we quantify uncertainties in a subgrid-scale parameterisation using a
Bayesian Neural Network (BNN). This allows us to disentangle aleatoric uncertainty, arising from internal variability in the training data, and epistemic uncertainties, arising from poorly constrained parameters during training. At runtime, we sample uncertainties in line with
stochastic approaches in weather models and perturbed-parameter methods in
climate models. On weather timescales, aleatoric uncertainty dominates, underscoring the value of
stochastic parameterisations. On longer,
climate timescales and under changing forcings, accounting for both types of uncertainty is necessary for well-calibrated ensembles, with epistemic uncertainty widening the range of explored
climate states, and aleatoric uncertainty promoting transitions between them. Constraining parameter uncertainty with short simulations reduces epistemic uncertainty and improves long-term model behaviour under perturbed forcings. This framework links concepts from machine learning with traditional uncertainty quantification in Earth system modelling, offering a pathway toward seamless treatment of uncertainty in weather and
climate prediction.
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Optics
Interrogation trajectory optimisation for Fabry-Perot based photoacoustic tomography
Jakub Czuchnowski and Robert Prevedel
Fabry-Pérot based photoacoustic
tomography (FP-PAT) is a promising all-optical imaging modality for a wide range of preclinical and clinical applications. However, there exist several challenges in routinely applying FP-PAT in time-critical experiments. Among those, the need for spectral tuning of the
laser between each scan position can severely limit the effective imaging speed. Here, we present an interrogation trajectory optimization approach which allows to increase the overall speed in a way that is independent of the type of interrogation
laser used as well as the FP quality. Our approach provides a way to tackle speed degradation caused by hardware limitations and simplify the use of FP-PAT systems.
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Biological Physics · Cross-listed from cond-mat.soft
Non-reciprocal interactions between condensates in chemically active mixtures
Jacopo Romano, Martin Kjøllesdal Johnsrud, Benoît Mahault et al. (+1 more)
We study the behaviour of catalytically active droplets in multi-component conserved mixtures affected by noise. Working in the thin interface limit, we analytically determine the state diagram of the system, characterized by multiple dynamical regimes, and verify our findings using numerical simulations. In particular, we show the emergence of a non-reciprocal, chemically-mediated interaction between the droplets, which leads to the formation of (meta-)stable clusters of droplets of different species. We find that the clusters can display self-propulsion in a large part of the parameter space, including regions where the non-reciprocal interactions between the droplets are purely attractive. This surprising feature arises from the non-local nature of the chemical interactions, and points to locality violations as a general mechanism for energy dissipation and emergence of out-of-
equilibrium steady states in active matter.
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All authors Jacopo Romano, Martin Kjøllesdal Johnsrud, Benoît Mahault, Ramin Golestanian
Fluid Dynamics
Bubble curtains in a lock-exchange flow: the importance of transient dynamics in the curtain-driven regime
Shravan K.R. Raaghav, Ronald J.A. Driessen, Tom S.D. O'Mahoney et al. (+3 more)
Bubble curtains are line bubble plumes that are used to mitigate saltwater intrusion in ship locks. When the lock gate that separates saline seawater from fresh river water is opened, a lock-exchange flow develops. Installing a bubble curtain at the gate location disrupts this flow and reduces saltwater infiltration. For real-world applications, it is important to quantify how effective the bubble curtain is as a function of the key governing parameters. To this end, we performed multiphase large-eddy simulations that faithfully reproduce earlier experimental results including the two distinct operating regimes: the breakthrough regime and the curtain-driven regime. This paper focuses on the curtain-driven regime and seeks to clarify how the effectiveness of bubble curtains evolves over time. The detailed spatial and temporal data from the simulations, together with the ability to systematically vary the governing parameters, enabled us to overcome several limitations inherent in previous experiments. Furthermore, the simulations were used to obtain parameter values to build a semi-analytical model. Both the simulations and the semi-analytical model successfully capture and elucidate the time
evolution of the density field and of the bubble curtain's effectiveness. The findings highlight that the time elapsed since the gate opening and the transient dynamics play a crucial role in determining the performance of bubble curtains for mitigation of salt intrusion.
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All authors Shravan K.R. Raaghav, Ronald J.A. Driessen, Tom S.D. O'Mahoney, Rob E. Uittenbogaard, Herman J.H. Clercx, Matias Duran-Matute
Optics · Cross-listed from quant-ph
Persistence of Quantum Triality Relations in Open Qubit and Qutrit Systems
Pratidhwani Swain, Ramita Sarkar, Sukanta K. Tripathy et al. (+1 more)
We examine the complementarity among coherence (visibility), predictability, and
entanglement for
qubit and qutrit systems subjected to noisy
quantum channels. Using the system-path
entanglement framework, analytical expressions for all three quantities are derived for two- and three-slit interferometric setups. The study first establishes the validity of the triality relation in ideal conditions and then investigates its behavior under amplitude and phase damping. We find that amplitude damping redistributes coherence and population imbalance without violating complementarity, while phase damping reduces coherence but leaves predictability unchanged. These results demonstrate that the complementarity relation remains preserved even in open
quantum systems, highlighting its robustness against decoherence and providing a unified analytical understanding of noisy
quantum interferometry in low-dimensional systems.
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All authors Pratidhwani Swain, Ramita Sarkar, Sukanta K. Tripathy, Prasanta K. Panigrahi
Atomic Physics · Cross-listed from quant-ph
Hilbert space fragmentation in driven-dephasing Rydberg atom array
Tianyi Yan, Chun Hei Leung and Weibin Li
We investigate the onset and mechanism of
Hilbert space fragmentation (HSF) in a chain of strongly interacting Rydberg atoms subject to local dephasing. It is found that the emergence of multiple long-lived metastable states is fundamentally tied to HSF of the driven-dephasing Rydberg atom system. We demonstrate that the manifesting HSF is captured by a dephasing PXP model that supports multiple degenerate zero modes. These modes form disconnected, block-diagonal subspaces of maximally mixed states, which consist of many-body
spin states sharing the same
symmetry. A key result is the identification of the underlying
symmetry in the HSF, where conserved quantities in each subspace are defined by the consecutive double excitation addressing operator. Moreover, we show explicitly that the number of the fragmented
Hilbert space grows exponentially with the chain length, following a modified Fibonacci sequence. Our work provides insights into many-body dynamics under dynamical constraints and opens avenues for controlling and manipulating HSF in Rydberg atom systems.
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Optics
Analytical Fresnel Treatment of Double-Slit Diffraction with Multiple Coherent Waves
J. Sumaya-Martinez, MA Ortiz-Ferreyro and O. Rojas-Hernandez
We present an analytical and numerical investigation of double-slit diffraction under coherent illumination by three plane waves: one normally incident and two symmetrically angled at plus/minus theta. By imposing an edge-zero condition on the incident field, we derive compact closed-form Fresnel expressions written solely in terms of standard Fresnel integrals. The framework generalizes straightforwardly to an arbitrary number of incident plane-wave components, enabling intuitive control of the transmitted angular spectrum through interference engineered at the aperture. Numerical simulations confirm the accuracy of the closed-form model and characterize the influence of slit geometry, wavelength, partial coherence, and Gaussian beam width. We also discuss experimental feasibility and highlight potential applications in apodization, structured illumination, beam shaping, and multiplexed sensing. Overall, the results show that multi-wave coherent illumination provides a simple and tunable route to tailoring diffraction patterns and generating propagation-robust field profiles.
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Physics and Society
Multilayer network science: theory, methods, and applications
Alberto Aleta, Andreia Sofia Teixeira, Guilherme Ferraz de Arruda et al. (+14 more)
Multilayer network science has emerged as a central framework for analysing interconnected and interdependent complex systems. Its relevance has grown substantially with the increasing availability of rich, heterogeneous data, which makes it possible to uncover and exploit the inherently multilayered organisation of many real-world networks. In this review, we summarise recent developments in the field. On the theoretical and methodological front, we outline core concepts and survey advances in community detection, dynamical processes, temporal networks, higher-order interactions, and machine-learning-based approaches. On the application side, we discuss progress across diverse domains, including interdependent infrastructures, spreading dynamics, computational social science, economic and financial systems, ecological and
climate networks, science-of-science studies, network medicine, and network neuroscience. We conclude with a forward-looking perspective, emphasizing the need for standardised
datasets and software, deeper integration of temporal and higher-order structures, and a transition toward genuinely predictive models of complex systems.
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All authors Alberto Aleta, Andreia Sofia Teixeira, Guilherme Ferraz de Arruda, Andrea Baronchelli, Alain Barrat, János Kertész, Albert Díaz-Guilera, Oriol Artime, Michele Starnini, Giovanni Petri, Márton Karsai, Siddharth Patwardhan, Kathryn Coronges, Ann McCranie, Alessandro Vespignani, Yamir Moreno, Santo Fortunato
Optics · Cross-listed from cond-mat.mes-hall
Response regimes in on-chip THz spectroscopy
Gunda Kipp, Marios H. Michael, Alexander M. Potts et al. (+6 more)
On-chip THz spectroscopy enables quantitative measurements of the optical conductivity of sub-wavelength 2D materials by tightly confining THz fields in metallic transmission line structures interfaced to the material. However, because the probed structures are smaller than the THz wavelength, finite-size and environmental effects can strongly influence the measured response. Here, we identify the conditions under which a metallic sample exhibits a genuine Drude response and when finite-size and environmental effects must be considered. We further introduce and characterize an additional regime, the Phantom-Drude response, which mimics Drude behavior but instead originates from the superposition of multiple finite-momentum plasmonic resonances. If unrecognized, this regime can lead to misinterpretation of intrinsic material properties. We systematically show how the Phantom-Drude response can emerge and demonstrate its sensitivity to sample dimensions, transmission line geometry, material shape, and gate properties, providing practical guidelines to avoid this regime in future on-chip THz measurements.
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All authors Gunda Kipp, Marios H. Michael, Alexander M. Potts, Dorothee Herrmann, Toru Matsuyama, Guido Meier, Matthew W. Day, Hope M. Bretscher, James W. McIver
Optics
Optical bistability of continuous-wave and multi-pulse phase transition within EDFL via low threshold saturable absorber
Hsuan-Sen Wang, Wen-Hsuan Kuan, Ahmed F. M. El-Mahdy et al. (+4 more)
We demonstrate optical bistability in an erbium-doped fiber
laser (EDFL) using a low saturation intensity covalent organic framework (COF) saturable absorber (SA). The COF-SA satisfies the free-energy criterion for bistability, enabling switching among non-lasing, continuous-wave, and mode-locking states. Two optical bistability regions are observed, including a distinctive direct mode-locking to non-lasing transition during pump down-sweep, absent in previous EDFL studies. Stable
soliton-like pulses and stepwise pulse-number hysteresis further indicate first-order phase-transition-like dynamics. These results show that low-saturation intensity SAs offer a compact route to bistability-assisted pulse formation in fiber
lasers.
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All authors Hsuan-Sen Wang, Wen-Hsuan Kuan, Ahmed F. M. El-Mahdy, Gong-Ru Lin, Kuei-Huei Lin, Shiao-Wei Kuo, Chao-Kuei Lee
Fluid Dynamics
Bursting bubbles in Herschel-Bulkley fluids: dynamics and jetting transitions
A. H. Ghaemi, Z. Yang, A. Huang et al. (+3 more)
When a bubble rises to a free surface, its bursting dynamics in Newtonian fluids are governed by the interplay between viscous, capillary, and gravitational forces. In this work, we extend this classical problem to Herschel-Bulkley fluids, elucidating the role of viscoplasticity and non-Newtonian rheology in bubble bursting. Using direct numerical simulations validated against experiments, we systematically explore the influence of the key governing dimensionless parameters, such as the Bond number, the Ohnesorge number, the shear-dependent behavior and the plastocapillary number, each varied over several orders of magnitude. Our results reveal that viscoplasticity strongly controls the
evolution and interaction of capillary waves within the cavity formed upon bubble rupture. Shear-thinning and shear-thickening effects are significant only for moderate Ohnesorge numbers, while at large Ohnesorge values the free surface dynamics converge to a non-flat
equilibrium shape once the internal stresses fall below the yield stress. These findings provide new insights into the coupled effects of viscosity,
gravity, yield stress, and shear-dependent rheology in multiphase flows, with broad implications for natural and industrial processes involving gas-
liquid interfaces.
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All authors A. H. Ghaemi, Z. Yang, A. Huang, V. Sanjay, J. Feng, C. R. Constante-Amores
Biological Physics · Cross-listed from q-bio.SC
A theory for coexistence and selection of branched actin networks in a shared and finite pool of monomers
Valentin Wössner, Falko Ziebert and Ulrich S. Schwarz
Cellular actin structures are continuously turned over while keeping similar sizes. Since they all compete for a shared pool of actin monomers, the question arises how they can coexist in these dynamic steady states. Recently, the coexistence of branched actin networks with different densities growing in a shared and finite pool of purified
proteins has been demonstrated in a biomimetic bead assay. However, theoretical work in the context of organelle size regulation has mainly been focused on linear architectures, such as single filaments and bundles, and thus is not able to explain this observation. Here we show theoretically that the local depletion of actin monomers caused by the growth of a branched network naturally gives rise to a negative feedback loop between network density and growth rate, and that this competition is captured by one central ordinary differential equation. A comprehensive bifurcation analysis shows that the theory leads to well-defined steady states even in the case of multiple networks sharing the same pool of monomers, without any need for specific molecular processes. Under increasing competition strength, coexistence is replaced by selection. We also show that our theory is in excellent agreement with spatiotemporal simulations, implemented in a finite element framework, and that local depletion even occurs in the presence of a large pool of non-polymerizable actin. In summary, our work suggests that local monomer depletion is the decisive and universal factor controlling growth of branched actin networks.
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Physics and Society
Who will import hydrogen in 2050? Global assessment with China and US case studies
Veronika Brooks, Joshua Fragoso García, Lin Zheng et al. (+4 more)
This study assesses the global hydrogen import potential in 2050 by looking at the renewable hydrogen production potential in prospective import-oriented countries. Renewable energy potentials calculated with a GIS based model and 2050 primary energy consumption projections are used to identify candidate importers by comparing it with expected demand. Two approaches are applied: (1) a meta-analysis of literature on hydrogen production potential and demand for the identified countries, and (2) detailed regional analyses for the United States and China. The results suggest limited prospects for a fully global hydrogen
market, although certain countries, such as Germany, Italy, and the Netherlands, are likely to remain net importers. By contrast, many Asian countries have enough renewable resources to decarbonise their energy systems. China and the United States may follow divergent pathways: while the United States is well placed to meet its overall demand, using its own resources, it can still benefit from regional connections with neighbouring countries. China is facing strong demand in its eastern regions, so it may source cost-effective renewable hydrogen from foreign countries instead of relying solely on domestic production. Overall, the findings suggest that hydrogen trade will likely remain regionally concentrated, shaped by the interplay of renewable resource distribution, energy demand, other available hydrogen production pathways, and infrastructure constraints.
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All authors Veronika Brooks, Joshua Fragoso García, Lin Zheng, Viktor Paul Müller, Christoph Nolden, Dominik Möst, Martin Wietschel
Medical Physics
Optimization and application of ultra-high field preclinical high-resolution and 3D 1H-MRSI using compressed sensing
Brayan Alves, Thanh Phong Lê, Gianna Nossa et al. (+7 more)
Proton magnetic resonance spectroscopic imaging (1H-MRSI) at ultra-high field has seen an increase in usage in the preclinical field. Challenges related to long acquisition time and low concentration of
brain metabolites in the rodent
brain have led to the development and application of acceleration schemes for 3D-1H-MRSI, such the undersampling technique Compressed Sensing (CS). This present study aims to explore the CS tool in the context of preclinical in vivo application in order to achieve high-resolution MRSI acquisition in both 2D with an in-plane increase and 3D/multi-slice acquisition with through-plane. The parameters are explored to achieve the highest acceleration possible as a way to make 3D as time efficient as possible. Results of the parameter study showed that an acceleration factor (AF) of 4 was possible with the right sampling size of the core at the center of the k-space. With this specific set, higher
matrix size resulting in sub 1 {\mu}L nominal voxel size was explored with 2D-FID-MRSI and 9 supplementary phase-encoding/slices were added to achieve 3D-FID-MRSI. The spectral quality and the metabolic maps were accurate enough in the comparison with the non-accelerated 2D-FID-MRSI, within the slice of interest. Issues related with the point spread function (PSF) were noted throughout the different usage of CS. Our work presents a robust and effective protocol to achieve 3D-1H-MRSI using CS in order to reach an acquisition time below the 30 minutes bar, with minimal technical limitations and high-quality acquisition.
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All authors Brayan Alves, Thanh Phong Lê, Gianna Nossa, Tan Toi Phan, Alessio Siviglia, Bernard Lanz, Wolfgang Bogner, Antoine Klauser, Bernhard Strasser, Cristina Cudalbu