Chemical Physics
Two-dimensional IR-Raman spectroscopy of vibrational polaritons: Role of dipole surfaces
Xinwei Ji, Tomislav Begusic and Tao E. Li
Nonlinear spectroscopy provides a unique perspective to understand time-resolved molecular dynamics under vibrational strong coupling (VSC). Herein,
equilibrium-nonequilibrium cavity molecular dynamics simulations are performed to compute the two-dimensional (2D) infrared-infrared-Raman (IIR) spectroscopy of
liquid water under VSC. In conventional computational chemistry practices, accurate molecular spectra are often constructed by using an advanced molecular dipole or polarizability model to post-process molecular dynamics trajectories evolved under a computationally efficient potential. By contrast, this work highlights the necessity of employing a consistent dipole surface model in both CavMD simulations and spectroscopic post-processing. While utilizing inconsistent dipole models only mildly influences the linear polariton spectrum, it severely distorts 2D spectra in wide frequency regions. With a consistent dipole-induced-dipole model, compared to the outside-cavity molecular 2D-IIR spectrum, the cavity 2D-IIR spectrum splits the OH stretch band to a pair of polariton branches along only the IR (not Raman) axis, while fading molecular signals at other frequency regions. This work provides the foundation for employing direct CavMD simulations to construct 2D spectra of realistic molecules under VSC.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Controlled antivortex propagation at bifurcations in reconfigurable NdCo/NiFe racetracks
V.V. Fernandez, A.E. Herguedas-Alonso, C. Fernandez-Gonzalez et al. (+7 more)
The controlled propagation of
spin textures at bifurcations is a critical challenge for racetrack-based logic devices. Here, we investigate the effect of longitudinal and transverse
magnetic fields on the propagation of
magnetic antivortices at bifurcations within the stripe domain pattern of a reconfigurable NdCo/NiFe racetrack in order to control the preferred antivortex trajectory.
Magnetic Transmission X-ray Microscopy experiments were employed to correlate the observed propagation path with the local
magnetic configuration. We demonstrate that Zeeman coupling to the magnetization components at the bifurcation core enables switching of the preferred propagation branch using low-amplitude transverse
magnetic fields, without modifying the global stripe domain configuration that defines the guiding racetrack landscape. In-plane
magnetic anisotropy provides an additional mechanism to break the
symmetry between the upper and lower bifurcation branches by tuning the relative orientation between the stripe domain pattern and the longitudinal
magnetic fields.
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All authors V.V. Fernandez, A.E. Herguedas-Alonso, C. Fernandez-Gonzalez, R. Valcarcel, P. Suarez, A. G. Casero, C. Quiros, A. Sorrentino, A. Hierro-Rodriguez, M. Velez
Plasma Physics
Experimental Evidence for Increased Particle Fluxes Due to a Change in Transport at the Separatrix near Density Limits on Alcator C-Mod
M.A. Miller, J.W. Hughes, T. Eich et al. (+5 more)
Experimental inferences of cross-field particle flux at the separatrix, $\Gamma_{\perp}^\mathrm{sep}$, show rapid growth near H-mode and L-mode density limits at high
magnetic field on Alcator C-Mod. Increases in $\Gamma_{\perp}^\mathrm{sep}$ correlate well with proximity to high density operational boundaries as proposed by the separatrix operational space model. $\Gamma_{\perp}^\mathrm{sep}$ grows as the L-mode density limit and the H-L-mode back transition boundaries are approached, consistent with expectations of
plasma instability-driven
turbulence suggested by theory, confirming the power dependence of density limits. $\Gamma_{\perp}^\mathrm{sep}$ is well-organized by the characteristic wavenumber for resistive ballooning mode
turbulence, $k_\mathrm{RBM}$, from interchange-drift-Alfvén fluid
turbulence theory, with additional dependence on the cylindrical safety factor, $\hat{q}_\mathrm{cyl}$, yielding an empirical limit to
plasma operation of $k_\mathrm{RBM}^{2}\hat{q}_\mathrm{cyl} = 1$. This limit corresponds to the point where the perpendicular heat flux, $Q_{\perp}$, reaches the level of the parallel heat flux, $Q_{\parallel}$, i.e. $Q_{\perp} \approx Q_{\parallel}$, beyond which point thermal
equilibrium is not satisfied, resulting in a fold catastrophe.
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All authors M.A. Miller, J.W. Hughes, T. Eich, G.R. Tynan, P. Manz, A.E. Hubbard, B. LaBombard, J. Dunsmore
Accelerator Physics
Generalization of the Froissart-Stora formula to piecewise-linear spin-orbit resonance crossings
Joseph P. Devlin, Georg H. Hoffstaetter and Desmond P. Barber
Spin-polarized beams are important for some
nuclear and high-energy physics experiments, such as those planned for the future Electron-Ion
Collider (EIC). However, maintaining polarization during the acceleration of a charged-particle beam is difficult because the periodic nature of circular accelerators leads to
spin-orbit resonances where the
spin-precession frequency is a sum of integer multiples of the orbital frequencies. Usually, the dominant depolarization mechanisms are first-order
spin-orbit resonances and the depolarization associated with crossing such a resonance can be computed using the Froissart-Stora formula. However, accelerating polarized
hadron beams to high energy requires special magnet structures called Siberian snakes. When these are implemented to maintain a
spin-precession frequency of one-half the revolution frequency, there will be no first-order
spin-orbit resonance crossings. The dominant depolarization mechanisms are then higher-order
spin-orbit resonances. The Froissart-Stora formula can be applied to higher-order resonances when the slope of the amplitude-dependent
spin tune is constant. However, the slope of the amplitude-dependent
spin tune often changes at the moment of resonance crossing. This work introduces a generalization of the Froissart-Stora formula which is applicable when the slope changes in this manner. The applicability of this formula is demonstrated through tracking simulations of a higher-order resonance crossing in both a toy model and the Relativistic Heavy Ion
Collider (RHIC). It is additionally shown that the Froissart-Stora formula is mathematically equivalent to the Landau-Zener formula for the diabatic transition probability in two-level systems with a linearly increasing energy gap and constant coupling. This work therefore also extends the Landau-Zener formula to the case of changing slope.
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Plasma Physics
Multi-GPU Hybrid Particle-in-Cell Monte Carlo Simulations for Exascale Computing Systems
Jeremy J. Williams, Jordy Trilaksono, Stefan Costea et al. (+13 more)
Particle-in-
Cell (PIC) Monte Carlo (MC) simulations are central to
plasma physics but face increasing challenges on heterogeneous HPC systems due to excessive data movement, synchronization overheads, and inefficient utilization of multiple accelerators. In this work, we present a portable, multi-GPU hybrid MPI+OpenMP implementation of BIT1 that enables scalable execution on both Nvidia and AMD accelerators through OpenMP target tasks with explicit dependencies to overlap computation and communication across devices. Portability is achieved through persistent device-resident
memory, an optimized contiguous one-dimensional data layout, and a transition from unified to pinned host
memory to improve large data-transfer efficiency, together with GPU Direct
Memory Access (DMA) and runtime interoperability for direct device-pointer access. Standardized and scalable I/O is provided using openPMD and ADIOS2, supporting high-performance file I/O, in-
memory data streaming, and in-situ analysis and visualization. Performance results on pre-exascale and exascale systems, including Frontier (OLCF-5) for up to 16,000 GPUs, demonstrate significant improvements in run time, scalability, and resource utilization for large-scale PIC MC simulations.
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All authors Jeremy J. Williams, Jordy Trilaksono, Stefan Costea, Yi Ju, Luca Pennati, Jonah Ekelund, David Tskhakaya, Leon Kos, Ales Podolnik, Jakub Hromadka, Allen D. Malony, Sameer Shende, Tilman Dannert, Frank Jenko, Erwin Laure, Stefano Markidis
Fluid Dynamics · Cross-listed from cs.LG
Project and Generate: Divergence-Free Neural Operators for Incompressible Flows
Xigui Li, Hongwei Zhang, Ruoxi Jiang et al. (+6 more)
Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer soft regularization, they provide no structural guarantees, resulting in spurious divergence and long-term collapse. In this work, we introduce a unified framework that enforces the incompressible continuity equation as a hard, intrinsic constraint for both deterministic and generative modeling. First, to project deterministic models onto the divergence-free subspace, we integrate a differentiable spectral Leray projection grounded in the Helmholtz-Hodge decomposition, which restricts the regression hypothesis space to physically admissible velocity fields. Second, to generate physically consistent distributions, we show that simply projecting model outputs is insufficient when the prior is incompatible. To address this, we construct a divergence-free Gaussian reference measure via a curl-based pushforward, ensuring the entire probability flow remains subspace-consistent by construction. Experiments on 2D Navier-Stokes equations demonstrate exact incompressibility up to discretization error and substantially improved stability and physical consistency.
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All authors Xigui Li, Hongwei Zhang, Ruoxi Jiang, Deshu Chen, Chensen Lin, Limei Han, Yuan Qi, Xin Guo, Yuan Cheng
Physics Education
Using Educational Comics in Physics Teaching for Chemistry and Biochemistry Students: Impact on Motivation and Domain-Specific Conceptual Gains
Mauricio Echiburu, Camilo Henriquez, Rodrigo Valdés et al. (+1 more)
This study investigates the impact of educational comics as an active learning strategy in physics workshops for undergraduate students in Chemistry and Pharmacy and Biochemistry during the second semester of 2025. Conceptual understanding was assessed using the Force Concept Inventory (FCI), and student motivation and attitudes toward physics were evaluated through a Likert-type survey administered in pre- and post-test formats. The results show an average normalized gain of g = 0.21 on the FCI, corresponding to a low-to-medium range according to physics education research. A higher gain is observed in items directly related to the intervened content (g = 0.23) compared to non-intervened items (g = 0.19), suggesting that instructional design influences domain-specific conceptual development. At the motivational level, improvements are observed in student interest, self-efficacy, and perceived usefulness of physics, along with a reduction in negative emotional responses toward the subject. These findings indicate that educational comics can serve as an effective pedagogical scaffold, promoting positive learning dispositions and supporting targeted conceptual development in non-physics undergraduate contexts.
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All authors Mauricio Echiburu, Camilo Henriquez, Rodrigo Valdés, Cristobal Ríos
Atmospheric and Oceanic Physics
The forgotten role of wave dynamics in modulating the low cloud response to warm pool warming
Cristian Proistosescu, Pappu Paul, Nicholas J. Lutsko et al. (+2 more)
The Pattern Effect describes the dependence of top-of-atmosphere radiation anomalies on changes in the pattern of sea surface temperatures. The emerging consensus in the field explains the impact of Pacific warm pool temperature on radiation using Convective Quasi-
Equilibrium Weak Temperature Gradient (QE-WTG) theory: warm pool warming leads to increase in free-tropospheric temperatures across the tropics, a strengthening of inversion, increased cloud cover in the East Pacific low cloud decks, and negative radiative anomalies. Here we call on overlooked past results and new simulations from the Energy Exascale Earth System model to show that Rossby waves dominate the low-cloud response over the subtropical East Pacific low cloud decks, leading to decrease cloud cover in the low cloud decks. While the global radiative response is negative and consistent with QE-WTG, it is dominated by the response of the deep tropics, rather than the subtropical low cloud decks.
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All authors Cristian Proistosescu, Pappu Paul, Nicholas J. Lutsko, Andrew I.L. Williams, Malte F. Stuecker
Medical Physics
Robust synchrotron-based deep learning algorithm for intracochlear segmentation in clinical scans: development and international validation
Ashley Micuda, Daniel Newsted, Nastaran Shakourifar et al. (+9 more)
Clinical imaging is routinely used for cochlear implant
surgical planning yet lacks the resolution and contrast necessary to visualize the fine intracochlear structures critical for individualized intervention. To address this limitation, an ensemble deep learning model was developed to automatically segment cochlear micro-anatomy from standard clinical scans. The model was trained and validated using an independent internal
dataset comprised of paired synchrotron and clinical scans of the same cochlea across various acquisition protocols. Performance was evaluated quantitatively on an unseen internal test
dataset and a multi-institutional external test
dataset. The deep learning model achieved accurate
segmentation of intracochlear anatomy across all tested modalities, outperformed all previously published models, and demonstrated strong viability on the multi-institutional external
dataset. Furthermore, anatomical measurements on the automatic
segmentations closely matched those obtained from high-resolution ground truth
segmentations, confirming reliable estimation of clinically relevant metrics. By bridging the gap between high-resolution imaging and routine clinical imaging, this work provides a practical solution for patient-specific cochlear implant
surgical planning and postoperative assessment, advancing the goals of atraumatic insertions and more effective hearing restoration.
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All authors Ashley Micuda, Daniel Newsted, Nastaran Shakourifar, Sachin Pandey, Asma Alahmadi, Kevin D. Brown, Abdulrahman Hagr, Jacob B. Hunter, Joachim Müller, Kristen Rak, Hanif M. Ladak, Sumit K. Agrawal
Data Analysis and Statistics · Cross-listed from astro-ph.IM
Short-Term Turbulence Prediction for Seeing Using Machine Learning
Mary Joe Medlej, Rahul Srinivasan, Simon Prunet et al. (+2 more)
Optical
turbulence, driven by fluctuations of the atmospheric refractive index, poses a significant challenge to ground-based optical systems, as it distorts the propagation of light. This degradation affects both astronomical observations and free-space optical communications. While adaptive optics systems correct
turbulence effects in real-time, their reactive nature limits their effectiveness under rapidly changing conditions, underscoring the need for predictive solutions. In this study, we address the problem of short-term
turbulence forecasting by leveraging machine learning models to predict the atmospheric seeing parameter up to two hours in advance. We compare statistical and deep learning approaches, with a particular focus on probabilistic models that not only produce accurate forecasts but also quantify predictive uncertainty, crucial for robust decision-making in dynamic environments. Our evaluation includes Gaussian processes (GPs) for statistical modeling, recurrent neural networks (RNNs) and long short-term
memory networks (LSTMs) as deterministic baselines, and our novel implementation of a normalizing flow for time series (FloTS) as a flexible probabilistic deep learning method. All models are trained exclusively on historical seeing data, allowing for a fair performance comparison. We show that FloTS achieves the best overall balance between predictive accuracy and well-calibrated uncertainty.
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All authors Mary Joe Medlej, Rahul Srinivasan, Simon Prunet, Aziz Ziad, Christophe Giordano
Plasma Physics
Study of Low-Frequency Core-Edge Coupling in a Tokamak: II. Spatial Channeling & Focusing In Antenna-Driven MHD
Andreas Bierwage, Wonjun Lee, Young-chul Ghim et al. (+7 more)
Motivated by evidence for core-edge coupling in the form of double-peaked fishbone-like low-frequency modes ($\lesssim 20\,{\rm kHz}$) in KSTAR, which exhibit synchronized Alfvénic activity both in the central core and near the
plasma edge [1], we study the nonlocal response of a tokamak
plasma in a visco-resistive full MHD simulation model using the code MEGA. The waves are driven by an internal "
antenna" that is localized both radially and azimuthally in the poloidal $(R,z)$ plane and has a sinusoidal form $\exp(in\zeta - i\omega t)$ with
Fourier mode number $n=\pm 1$ in the toroidal angle $\zeta$ and fixed angular frequency $\omega$ in time $t$. By flattening the safety factor profile $q(r)$ at suitable locations in the minor radius $r$, we created plateaus in the low-frequency Alfvén continua that act as wave "receivers". First, we confirm that such continuum plateaus respond with a coherent quasi-mode even when the driving
antenna is located at a distant radius. Second, by varying the
antenna location, we confirm the expectation of inward drive being more efficient than outward drive, which we attribute to volumetric focusing. Third, we find that the central core also responds well at frequencies below the central Alfvénic continuum plateau, which could facilitate chirping. Our results show that a core-localized low-frequency response does not necessarily require core-localized drive nor an exactly matching continuum, but may be driven from the edge and sub-resonantly. It remains to be seen to what extent the examined effects play a role in double-peaked fishbone-like activity. Other possible contributing mechanisms are discussed to motivate further study. Our analyses also elucidate the mode structure formation process, from transients to quasi- or eigenmodes, here in the realm of MHD, and to be followed by a verification study against kinetic models.
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All authors Andreas Bierwage, Wonjun Lee, Young-chul Ghim, Panith Adulsiriswad, Nobuyuki Aiba, Seungmin Bong, Gyungjin Choi, Matteo Falessi, Philipp W. Lauber, Masatoshi Yagi
Fluid Dynamics
Geometric Memory Generates Irreversible Transport in Time-Periodic Irrotational Flows
Mounir Kassmi
Irreversible transport is generally attributed to vorticity, nonlinear forcing, or explicit
symmetry breaking. We show that it can arise even in strictly time-periodic and locally irrotational flows through a purely
geometric mechanism. By reconstructing the velocity gradient through causal self-transport over a finite
memory time, deformation acquires the structure of a
geometric connection whose holonomy generates a finite Lagrangian drift over one forcing cycle. The resulting contribution admits a closed-form, parameter-free expression. A quantitative consistency analysis using independently published experimental measurements shows that the predicted scaling and magnitude agree with observations without fitting or normalization. These results identify
geometric memory as a minimal and generic source of irreversible transport.
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Computational Physics · Cross-listed from cs.LG
Learning Response-Statistic Shifts and Parametric Roll Episodes from Wave–Vessel Time Series via LSTM Functional Models
Jose del Aguila Ferrandis
Parametric roll is a rare but high-consequence instability that can trigger abrupt regime changes in ship response, including pronounced shifts in roll statistics and tail risk. This paper develops a data-driven surrogate that learns the nonlinear, causal functional mapping from incident wave–motion time series to vessel motions, and demonstrates that the surrogate reproduces both (i) parametric roll episodes and (ii) the associated statistical shifts in the response. Crucially, the learning framework is data-source agnostic: the paired wave–motion time series can be obtained from controlled experiments (e.g., towing-tank or basin tests with wave probes and motion tracking) when a hull exists, or from high-fidelity simulations during design when experiments are not yet available. To provide a controlled severe-sea demonstration, we generate training data with a URANS numerical wave tank, using long-crested irregular seas synthesized from a modified Pierson–Moskowitz spectrum. The demonstration
dataset comprises 49 random-phase realizations for each of three sea states, simulated at a fixed forward speed selected to yield encounter conditions under which parametric-roll episodes can occur. A stacked LSTM surrogate is trained on wave-elevation time series and evaluated on held-out realizations using time-domain accuracy and distributional fidelity metrics. In the most severe case, the model tracks the onset and growth of large-amplitude roll consistent with parametric excitation, and captures the corresponding changes in roll probability density functions (PDFs). We further compare loss-function choices (MSE, relative-
entropy-based objectives, and amplitude-weighted variants) and show how they trade average error for improved tail fidelity relevant to operability and risk assessment.
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Medical Physics
Reconstructing effective ultrasound transducer models via distributed source inversion
Tim Bürchner, Simon Schmid, Ernst Rank et al. (+2 more)
Accurate modeling of ultrasound wave propagation is essential for high-fidelity simulation and imaging in ultrasonic testing. A primary challenge lies in characterizing the excitation source, particularly for transducers with large apertures relative to the acoustic wavelengths. In such cases, non-uniform excitation and spatial interference significantly affect the resulting radiation patterns. This paper proposes a distributed source inversion strategy to reconstruct an effective spatio-temporal transducer model that reproduces experimentally measured wavefields. The reconstructed source model captures aperture-dependent phase and amplitude variations without the need for detailed knowledge of the transducer structure. The approach is validated using directivity measurements on an aluminum half-cylinder, where simulations incorporating the reconstructed source model show close agreement with experimental directivity patterns and waveform shapes. Finally, synthetic studies on reverse time migration and full-waveform inversion demonstrate that accurate transducer modeling is critical for the success of simulation-based imaging and inversion workflows and significantly improves reconstruction quality.
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All authors Tim Bürchner, Simon Schmid, Ernst Rank, Stefan Kollmannsberger, Andreas Fichtner
Physics Education
A visual introduction to curved geometry for physicists
Karol Urbański
This article provides a gentle, visual introduction to the basic concepts of differential geometry appropriate for students familiar with special relativity. Visual methods are used to explain basics of differential geometry and build intuition for all types of Riemannian and Lorentzian
manifolds of constant
curvature. A visual derivation of the Thomas precession is given, showcasing the utility of differential geometry while also pointing a spotlight at certain intricacies of Minkowski space crucial from a pedagogical perspective. In addition, a straightforward method to generate some Carter-Penrose diagrams – suitable for students with no differential geometry knowledge – is presented, and a new method of indicating distortion on spacetime diagrams is shown.
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Space Physics · Cross-listed from astro-ph.EP
Simulation of laser travel-time on Mercury for BELA
Jean Barron, Frédéric Schmidt, François Andrieu et al. (+3 more)
Recent
laser altimeters are able to not only measure the ranging distance between the spacecraft and the surface but also the full time-of-flight of the
photons or pulse shape. This new capabilities allows to measure the intra-footprint properties: surface slope distribution and surface microtexture. Here we simulate and discuss for the first time the effect of surface microtexture, especially for ice covered surface with longer penetration depth. Using the WARPE simulation software, two kind of microtextures are simulated: compact slab and granular.
Laser pulse shape for an ideal instrument is simulated using physical properties such as the grain size, material composition, thickness, compacity (filling factor, porosity) rather than radiative properties. The effects of these parameters on the pulse shape are discussed as well in the range that could be possibly be observed with actual BELA measurement. Finally, examples of WARPE's simulated pulse shapes are used as input in the precise simulation chain of the BELA measurement output, to further assess the capability to detect variation in surface microtexture.
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All authors Jean Barron, Frédéric Schmidt, François Andrieu, Gaku Nishiyama, Alexander Stark, Hauke Hussmann
Classical Physics
Criterion for the Thermal Radiation Spectrum in Classical Physics
Timothy H. Boyer
Two criteria for the spectra of relativistic waves are proposed. Zero-point radiation provides the identity representation of the conformal group in Minkowski spacetime. Thermal radiation provides the irreducible representation of the conformal group in Minkowski spacetime which involves exactly one scaling parameter (the temperature) which is also time-stationary in a Rindler frame. Zero-point radiation is the limit of thermal radiation as the temperature goes to zero. Crucially, both zero-point radiation and thermal radiation take basically the same functional form in a Rindler frame. For relativistic scalar waves, a full derivation of the Planck spectrum including zero-point radiation is obtained with the classical theory.
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Physics and Society
Opinion-Driven Vaccination and Epidemic Dynamics on Heterogeneous Networks
Anika Roy, Ujjwal Shekhar, Subrata Ghosh et al. (+2 more)
Vaccination campaigns play a pivotal role in controlling infectious diseases. Their success, however, depends not only on vaccine efficacy and availability but also significantly on public opinion and the willingness of individuals to vaccinate. This paper investigates a coupled opinion-epidemic model on heterogeneous networks, where individual opinions influence vaccination probability, and opinions themselves evolve through a combination of peer interaction and local risk perception derived from observed infection rates.
Embedding the coupled dynamics in scale-free networks, particularly barabasi-Albert structures, allows us to examine the role of network heterogeneity beyond homogeneous-mixing assumptions. Using Monte Carlo simulations and a semi-analytical microscopic Markov-chain approach, we derive and numerically validate analytical expressions for the critical infection threshold and stable vaccinated population where risk perception dominated peer influence. Our results show that stronger local risk perception enhances pro-vaccination opinions and suppresses infection, while dominant peer influence can increase long-term infection levels. These findings underscore the importance of accounting for social behavior and network structure when designing effective vaccination and epidemic control strategies.
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All authors Anika Roy, Ujjwal Shekhar, Subrata Ghosh, Tomasz Kapitaniak, Chittaranjan Hens
Medical Physics · Cross-listed from cond-mat.mtrl-sci
Plasmonic Mediated Atomically Engineered 2D Aluminium Quasicrystals for Dopamine Biosensing
Saswata Goswami, Guilherme S. L. Fabris, Diganta Mondal et al. (+7 more)
Dopamine levels are linked to neurological illnesses like Parkinson's and Alzheimer's. Thus, reliable and sensitive detection of dopamine is crucial for early diagnosis and surveillance of neurodegenerative diseases. Non-noble-metal-based nanomaterials are ideal for light-mediated sensing of organic molecules. Among these, 2D quasicrystal structures consisting of five elements, namely Al70Co10Fe5Ni10Cu5, provide active sites due to their high surface-to-volume ratio, making them excellent for organic chemical sensing. Here, we propose a simple, label-free, spatial self-phase-modulation (SSPM)-based sensing method in
liquid form. SSPM-based time
evolution of the diffraction pattern for varied mixing levels of a 1100 ppb dopamine solution shows a shift in the active 2D Al QC solution. The 1100 ppb solution shows a distinct value, indicating a change in the nonlinear refractive index. Time-
evolution analysis is used to calculate sensitivities to changes in the nonlinear refractive index and time constant. The SPR-activated 2D Al QC nanostructure is used to demonstrate dopamine sensing and to perform qualitative and quantitative evaluations. The SSPM-based sensing has been further compared with other optical-based sensing methods such as Raman spectroscopy, UV-Vis spectroscopy, and FTIR spectroscopy. The experimental observations are also explained using DFT-based simulations. The current SSPM method can be used for rapid, large-scale medical diagnostics.
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All authors Saswata Goswami, Guilherme S. L. Fabris, Diganta Mondal, Raphael B. de Oliveira, Anyesha Chakraborty, Thakur Prasad Yadav, Nilay Krishna Mukhopadhyay, Samit K. Ray, Douglas S. Galvão, Chandra Sekhar Tiwary
Optics
Analysis and control of Raman phonon dynamics for enhanced optical frequency conversion
Yi-Hao Chen and Frank Wise
Raman
phonons are quantized molecular motions that arise from the inelastic scattering of light and mediate a wide range of spectroscopic and nonlinear optical phenomena. These can play a major role in frequency-conversion processes, but commonly-used theoretical treatments based on the Raman gain spectrum largely neglect the
phonons and their dynamical interaction with the field. In this work, we clarify the physical role of Raman
phonons within a recently-developed time-domain framework based on the Raman-induced index modulation, and show that
phonons correspond to the oscillatory component of the Raman-induced index modulation. The analysis further reveals a linear
phonon-mediated interaction embedded within Raman scattering, in which optical fields couple through wave-vector matching with existing
phonons. This mechanism underlies, but has been neglected in, coherent Stokes and anti-Stokes scattering, as well as molecular modulation. Building on this insight, we introduce a
phonon-controlled approach that enables efficient conversion into a selected Stokes order by tuning the wave-vector-matching relation between the driven
phonons and the targeted Raman process, and we confirm the approach by numerical simulations that consider realistic Raman dynamics. These results provide a clearer physical interpretation of Raman
phonons and their dynamics, and offer new strategies for controlling Raman interactions.
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Physics and Society
On a Co-evolving Opinion-Leadership Model in Social Networks
Martina Alutto, Lorenzo Zino, Karl H. Johansson et al. (+1 more)
Leadership in social groups is often a dynamic characteristic that emerges from interactions and opinion exchange. Empirical evidence suggests that individuals with strong opinions tend to gain influence, at the same time maintaining
alignment with the social context is crucial for sustained leadership. Motivated by the social psychology literature that supports these empirical observations, we propose a novel dynamical system in which opinions and leadership co-evolve within a social network. Our model extends the Friedkin-Johnsen framework by making susceptibility to peer influence time-dependent, turning it into the leadership variable. Leadership strengthens when an
agent holds strong yet socially aligned opinions, and declines when such
alignment is lost, capturing the trade-off between conviction and social acceptance. After illustrating the emergent behavior of this complex system, we formally analyze the coupled dynamics, establishing sufficient conditions for convergence to a non-trivial
equilibrium, and examining two time-scale separation regimes reflecting scenarios where opinion and leadership evolve at different speeds.
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All authors Martina Alutto, Lorenzo Zino, Karl H. Johansson, Angela Fontan
Optics · Cross-listed from quant-ph
Fluctuation-induced symmetry breaking in high harmonic generation for bicircular quantum light
Philipp Stammer, Camilo Granados and Javier Rivera-Dean
Symmetries are ubiquitous in physics and play a pivotal role in light-matter interactions, where they determine the selection rules governing allowed atomic transitions and define the associated conserved quantities. For the up-conversion process of high harmonic generation, the
symmetries of the driving field determine the allowed frequencies and the polarization properties of the resulting harmonics. As a consequence, it is possible to establish classical selection rules when the process is driven by coherent radiation. In this work, we show that fluctuation-induced
symmetry breaking in the driving field leads to the appearance of otherwise forbidden harmonics. This is achieved by considering bicircular
quantum light, and demonstrate that the enhanced
quantum fluctuations due to squeezing in the driving field break the classical selection rules. To this end, we develop a
quantum optical description of the dynamical
symmetries in the process of high harmonic generation, revealing corrections to the classical selection rules. Moreover, we show that the new harmonics show squeezing-like signatures in their
photon statistics, allowing them to be clearly distinguished from classical thermal fluctuations.
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Atmospheric and Oceanic Physics
Sub-seasonal Modulation and Predictability of Indian monsoon hourly Rainfall Extremes
Bijit Kumar Banerjee, Devabrat Sharma, Mahen Konwar et al. (+3 more)
Hourly rainfall extremes cause some of the most destructive weather disasters, yet numerical weather prediction models still struggle to forecast them, and a physical basis for their predictability remains unclear. Here, we identify a trivariate clustering of hourly rainfall extremes with surface temperature, phases of the Monsoon Intraseasonal Oscillation (MISO), and precipitable water vapor, establishing a physical foundation for the medium range predictability of these events. This clustering arises from multiscale interactions in which extremes organize into storm systems embedded within mesoscale convective clusters and synoptic low-pressure systems during active MISO phases. We develop an
algorithm to identify, track, and monitor these storm systems. Although rapid error growth limits the prediction of isolated hourly extremes, our results provide basis for a physics informed training of deep learning, data driven models to forecast organized clusters of hourly rainfall extremes more than a week in advance, offering substantial potential to reduce losses from extreme rainfall.
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All authors Bijit Kumar Banerjee, Devabrat Sharma, Mahen Konwar, Simanta Das, Utpal Sarma, B. N. Goswami
Optics
Shape-Dependent, Deep-Learning-Assisted Metamaterial Solid Immersion Lens (mSIL) Super-Resolution Imaging
Baidong Wu, Fiza Khan, Lingya Yu et al. (+1 more)
We present the first systematic comparison of three TiO2 metamaterial solid immersion lens geometries - sub-hemispherical, super-hemispherical, and full-spherical - for label-free super-resolution imaging. Using SEM, we characterised both the cap profiles and the nanoparticle-fluid immersion at the lens-sample interface, revealing that super-hemispherical lenses achieve the deepest immersion and closest contact with sample features. Imaging experiments under wide-field and
laser confocal microscopes show that this enhanced immersion drives superior resolution and contrast. In addition, we introduce a deep learning approach based on a SinCUT image translation model to establish a cross-modal mapping between SEM morphology and optical imaging response, enabling virtual optical predictions and providing a first step toward a digital twin representation of mSIL imaging behaviour. Electromagnetic simulations further confirm a direct correlation between immersion depth and far-field main lobe intensity. Our findings demonstrate that careful control of lens shape and nanoparticle-fluid penetration, together with data-driven modelling, is essential to maximise super-resolution performance in TiO2 mSILs.
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All authors Baidong Wu, Fiza Khan, Lingya Yu, Zengbo Wang
Computational Physics · Cross-listed from hep-th
soliton_solver: A GPU-based finite-difference PDE solver for topological solitons in two-dimensional non-linear field theories
Paul Leask
This paper introduces
soliton_solver, an open-source GPU-accelerated software package for the simulation and real-time visualization of topological
solitons in two-dimensional non-linear field theories. The software is structured around a theory-agnostic numerical core implemented using Numba CUDA kernels, while individual physical models are introduced through modular theory components. This separation enables a single computational framework to be applied across a broad class of systems, from nanoscale
magnetic spin textures in condensed matter physics to
cosmic strings spanning
galaxies in high energy physics. The numerical backend provides finite-difference discretization, energy minimization, and GPU-resident evaluation of observables. A CUDA–PyOpenGL rendering pipeline allows direct visualization of evolving field configurations without staging full arrays through host
memory. The package is distributed in Python via PyPI and supports both reproducible batch simulations and interactive exploration of metastable configurations,
soliton interactions, and model-dependent initial states. We describe the software architecture, numerical workflow, and extensibility model, and we present representative example applications. We also outline how additional theories can be incorporated with minimal modification of the shared numerical infrastructure.
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Fluid Dynamics
Evaporative cooling and deposition patterns of evaporating $Al_2O_3$ nanofluid droplets
S.K. Saroj and P. K. Panigrahi
The present study examines evaporative cooling and the resulting deposition patterns of a sessile $Al_2O_3$-based nanofluid droplet on a hydrophobic glass substrate at different temperatures. Evaporation predominantly occurs in the pinned contact line mode for both heated and non-heated cases, with only slight recession observed without heating. The droplet height and contact angle decrease linearly with time, and scaling relations are proposed to describe the
evolution of droplet geometry and volume. A non-dimensional parameter, $\Pi_{rel}$, is introduced to characterize transitions in deposition patterns. For $\Pi_{rel} \leq 1$ ($T_s \leq 26^\circ$C), interconnected irregular polygonal network structures form at the periphery, which are rarely reported in evaporating droplets. With increasing substrate temperature, this structure is suppressed, giving rise to a classical coffee-ring pattern for $1 < \Pi_{rel} \leq 10$. At higher temperatures ($T_s > 40^\circ$C), dual-ring formation along with central particle deposition is observed for $\Pi_{rel} > 10$. The interfacial temperature is higher near the contact line and decreases toward the apex, and a universal scaling for the temperature profile is proposed. Internal flow velocity increases with substrate temperature, exhibiting asymmetric multi-
vortex structures. Evaporative cooling intensifies with heating, enhancing evaporation flux and capillary flow. Appropriate scaling relations for evaporation flux and capillary velocity are established. Overall, the dynamics are governed by thermocapillary (Marangoni) flow induced by evaporative cooling, which enhances internal circulation and governs nanoparticle deposition morphology.
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Fluid Dynamics
aPriori: a Python package to process direct numerical simulations
Lorenzo Piu, Heinz Pitsch and Alessandro Parente
In the field of computational fluid dynamics, direct numerical simulations generate highly detailed data for the analysis of turbulent flows by resolving all relevant physical scales. Yet their large size, complexity, and heterogeneity make systematic post-processing and data reuse increasingly challenging. Despite the growing availability of high-fidelity simulations through public repositories, extracting meaningful physical insight often requires substantial technical effort, specialized workflows, and access to high-performance computing resources. In this article we introduce aPriori, an open-source Python package developed to address these limitations by providing a dedicated,
memory-efficient, and user-oriented framework for the analysis of direct numerical simulation data. The software enables streamlined handling of three-dimensional fields, including filtering, scale separation, gradient evaluation, thermochemical analysis, and visualization, using concise and reproducible scripts. Its pointer-based data management strategy allows very large
datasets to be processed on standard workstations without excessive
memory usage, significantly lowering the barrier to advanced analysis. Beyond basic post-processing, aPriori supports workflows central to modern
turbulence and combustion research, such as a priori model assessment, data-driven closure development, and detailed chemical analyses that include computational singular perturbation. By unifying these capabilities within a coherent and extensible software architecture, aPriori enhances productivity, promotes reproducibility, and facilitates broader and more effective use of high-fidelity simulation data within the computational fluid dynamics community.
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Computational Physics
Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations
Ian Störmer and Julija Zavadlav
Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often difficult to determine experimentally. Molecular dynamics can bridge this gap, but only if the interatomic model is both accurate and computationally efficient. Here, we develop a Machine Learning Potential (MLP) for aluminum and demonstrate its near ab initio fidelity when trained with the sequential-refinement workflow that fine-tunes the model on low-energy structures. The favorable scaling of the model enables nanosecond simulations involving millions of atoms, thereby overcoming finite-size effects in simulations of polycrystalline solidification and subsequent mechanical testing. Comparison with classical potentials and recent MLP models, including a general-purpose model, shows that inaccuracies in stacking-fault energetics and
diffusion can lead to qualitatively incorrect solidified grain structures and post-solidification mechanical behavior. Since our framework is based on an equivariant graph neural network, it allows for straightforward extensions to multi-component systems, providing valuable guidance for the future design and fine-tuning of both specialized and universal MLPs in computational mechanics simulations.
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Geophysics
Cemented fibers as a testbed for distributed acoustic sensing (DAS)
Thomas Forbriger, Felix Münch, Laura Hillmann et al. (+6 more)
A rigid connection between the optical fiber and the rock makes amplitudes of 'fiber strain' measured with Distributed Acoustic Sensing (DAS) equal to 'rock strain'. We demonstrate this by running four interrogator units (IU) on a DAS testbed with single-fiber patch cables being cemented into a groove in the concrete floor of Black Forest Observatory (BFO). The recorded signals are compared with the recordings of a calibrated Invar wire strain meter array that has been continuously in operation for the last decades. This way we measure 'strain transfer rate' (ratio of 'fiber strain' over 'rock strain') at frequencies below 0.2 Hz. Waveform similarity for strong earthquake signals is high with typical values of the normalized correlation coefficient greater than 0.95. The 'strain transfer rate' is close to 1 for all four IUs, while it was significantly less in a previous study with DAS cables unreeled on the floor and loaded down by sand and sandbags, only. At frequencies up to 14 Hz we make an intercomparison of IUs, showing no significant variation with frequency. The scatter of 'strain transfer rate' in between channels which are spatially near to each other in the same fiber route is about $\pm$10 % in most cases. The variation of median values in between different IUs and earthquakes is less than 5 %. By subtracting the common mode
laser noise, which is coherent along the fiber route, we lower the background signal level to an rms-amplitude of 100 pstrain at 0.1 Hz and 5 pstrain at 1 Hz in a bandwidth of 1/6 decade for the best cases. This allows the detection of the marine microseisms during times of moderate amplitude level.
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All authors Thomas Forbriger, Felix Münch, Laura Hillmann, Verónica Rodríguez Tribaldos, Rudolf Widmer-Schnidrig, Han Xiao, Andreas Rietbrock, Angelo Strollo, Philippe Jousset
Optics · Cross-listed from quant-ph
Efficient photon-pair emission from a nanostructured resonator and its theoretical description
Michael Poloczek, Alberto Paniate, Attilio Zilli et al. (+12 more)
Spontaneous parametric down-conversion (SPDC) in subwavelength nanostructures is a promising source of
quantum light, owing to its multifunctionality and ability to generate complex
quantum states. Nevertheless, the mechanisms governing
photon-pair generation in such systems remain only partially understood. In particular, experimental investigations of key emission properties in individual resonators, such as directionality and spectral distribution, are still lacking, and predictive theoretical frameworks have not yet been experimentally validated. Here, we report the first measurement of the directional and spectral distributions of
photon pairs generated via SPDC in a nanostructured resonator. Both distributions exhibit resonant behaviour, which we describe using an extended quasi-normal-mode theory. This comparison is enabled by
photon-pair count rates of up to 0.45 Hz/mW – to our knowledge, the highest reported for a nanostructured resonator. Our results provide new physical insight into nanoscale SPDC and represent an important step toward designing of efficient miniaturized
quantum light sources.
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All authors Michael Poloczek, Alberto Paniate, Attilio Zilli, Vitaliy Sultanov, Yigong Luan, Tomàs Santiago-Cruz, Luca Carletti, Marco Finazzi, Marco Genovese, Ivano Ruo-Berchera, Marzia Ferrera, Andrea Toma, Francesco Monticone, Michele Celebrano, Maria Chekhova