Optics
Optomechanical systems with a Fano membrane in the middle
Lei Du, Aymeric Frerejean, Witlef Wieczorek et al. (+2 more)
Conventional membrane-in-the-middle (MIM) optomechanical systems offer limited control over the optical linewidth, which can limit their performance when operating in the unresolved-sideband regime. We investigate cavity optomechanics with a photonic-
crystal Fano membrane placed at the center of a Fabry-Pérot (FP) cavity. In contrast to a conventional dielectric membrane, the photonic-
crystal membrane supports a localized optical resonance, which hybridizes with the cavity field and enables spectral engineering of the relevant optical modes. Besides the usual dispersive optomechanical coupling associated with cavity-length changes, the membrane motion also modifies the Fano-mode resonance and its hybridization with the cavity field. We consider two limits set by the membrane reflectivity: a transparent-membrane regime with a single FP-like mode, and a reflective-membrane regime with two coupled subcavity modes. In the latter case, only the symmetric cavity mode hybridizes with the Fano mode, while the antisymmetric mode remains decoupled. Using
quantum Langevin equations together with a transfer-
matrix description of the optical scattering problem, we show that the Fano-induced hybridization can generate narrow optical normal modes that remain efficiently accessible to the external drive for experimentally realistic parameters. These modes can provide effective sideband resolution and enable ground-state cooling of the membrane motion even when the bare cavity is in the unresolved-sideband regime. Our results establish Fano MIM systems as a promising platform for spectral and optomechanical engineering.
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All authors Lei Du, Aymeric Frerejean, Witlef Wieczorek, Janine Splettstoesser, Juliette Monsel
Instrumentation and Detectors
Performance of a first multi-cell WOM-based liquid scintillator detector as prototype for the SHiP Surrounding Background Tagger
M. Böhles, A. Brignoli, P. Deucher et al. (+20 more)
The Search for Hidden Particles (SHiP) Experiment was approved by CERN in 2024. Feebly-interacting particles that are produced in a
proton Beam Dump Facility (BDF) will decay in the 50m-long Decay Volume of the experiment, which needs to be enveloped by a hermetic veto detector: The Surrounding Background Tagger (SBT). Its technology relies on
liquid scintillator, composed of linear alkylbenzene and 2,5-diphenyloxazole, as active detector material and Wavelength-shifting Optical Module (WOM) tubes collecting the primary scintillation
photons. The
liquid scintillator volume is segmented in large
cells of typically 120cm x 80cm x 20cm that are equipped with two WOMs each. Here, we report on the performance of a full-scale 2x2-
cell prototype detector which was exposed to 5GeV
muons at the CERN PS T9 test beam facility to study the detector response and its time and spatial resolution for minimum ionising particles crossing multiple detector
cells.
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All authors M. Böhles, A. Brignoli, P. Deucher, C. Eckardt, H. Fischer, A. Hollnagel, A. Krolla, H. Lacker, P. Luther, F. Lyons, J. Molins i Bertram, T. Molzberger, A. S. Müller, S. Ochoa Guaman, F. Rehbein, T. J. Rock, M. Schaaf, C. Scharf, M. Schumann, J. M. Webb, J. Wenk, I. Wöstheinrich, M. Wurm
Accelerator Physics
Generalized Theory of Blowout-Regime Plasma Wakefields via a Lagrangian Formulation
Y. Kang, P. Manwani, S. S. Baturin et al. (+3 more)
The blowout regime of
plasma wakefield acceleration is a strongly nonlinear
plasma response in which an intense relativistic beam or
laser pulse expels
plasma electrons forming a nearly uniform ion column. Existing self-consistent theories describe this structure primarily in the axisymmetric limit, reducing the bubble dynamics to a single radial degree of freedom. This restriction is inadequate for flat beams with highly asymmetric transverse emittances, as required in future linear
colliders to control luminosity and beamstrahlung. Using a reduced Lagrangian formulation, we generalize the existing theory beyond axisymmetry and derive a coupled system of equations governing the wake potential driven by an asymmetric beam. This theory self-consistently predicts the focusing fields and their variation along the co-moving coordinate, as well as the accelerating field and its transverse nonuniformity. The resulting theoretical predictions are benchmarked against particle-in-
cell simulations, demonstrating the theory's ability to describe and interpret wakefields driven by nonaxisymmetric sources.
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All authors Y. Kang, P. Manwani, S. S. Baturin, G. Andonian, B. Naranjo, J. B. Rosenzweig
Fluid Dynamics · Cross-listed from cond-mat.soft
Effects of long-chain branching, short-chain branching, and polydispersity on pressure sensitive rheology of polymer melts
Lilian Lin, Matthew Joe and Heon E. Park
The rheological behavior of
polymer melts under high pressure is a critical factor in many industrial processes like injection molding and extrusion, yet it is often inadequately characterized. At operating pressures that can exceed 100 MPa, viscosity can increase by orders of magnitude, making atmospheric-pressure data insufficient for accurate process simulation. This pressure induced viscosity increase is highly dependent on molecular architectures of the materials. This study aims to deconstruct the influence of specific structural features such as short-chain branching (SCB), long-chain branching (LCB), and polydispersity on the pressure sensitivity of the viscosity of polyethylene. Utilizing a high-pressure sliding plate rheometer (HPSPR) to ensure accurate measurements under uniform shear and pressure, we characterized four distinct polyethylene melts. All samples, regardless of their structure, exhibited piezorheologically simple behavior, allowing the application of time-pressure superposition over the entire shear rate range. A key finding is that the long-chain branched sample, known from the literature to be thermorheologically complex, was found to be piezorheologically simple. This dichotomy is explained by the different physical mechanisms of temperature and pressure. The pressure sensitivity of the viscosity, quantified by the pressure-viscosity coefficient, was found to be strongly dependent on molecular branching. Both SCB and LCB significantly increase the pressure sensitivity while polydispersity had a negligible effect. These results demonstrate that molecular branches are the dominant structural parameter controlling the rheological response of polyethylene to pressure, providing crucial insights for the development of more accurate predictive models for high-pressure
polymer processing.
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Atomic Physics · Cross-listed from quant-ph
Multiplexed storage and interaction of Rydberg spinwaves via the gradient echo memory protocol
Bartosz Niewelt, Stanisław Kurzyna, Bartosz Kasza et al. (+2 more)
Collective Rydberg excitations offer strong and controllable interactions for
quantum information processing, sensing, and nonlinear
quantum optics, but their integration with temporally or spectrally multiplexed schemes, such as the Gradient Echo
Memory (GEM) protocol, is hindered by rapid motional dephasing caused by the large spinwave wavevector. We demonstrate a new type of multi-
photon addressing and interfacing scheme (with levels following the shape of the letter Ń) that allows us to generate collective Rydberg excitations with near-zero momentum transfer, extending the Rydberg spinwave lifetime almost tenfold. The scheme relies on two additional off-resonant driving fields arranged at a magic angle, forming a closed wavevector loop while remaining compatible with GEM-induced inhomogeneous broadening. This enables storage and manipulation of long-lived Rydberg spinwaves in a multimode
quantum memory. Using microwave coupling between neighboring Rydberg states, we can control the attenuation between stored excitation modes by interaction-induced decay and demonstrate interaction-controlled diffraction of a retrieved optical signal. Our results reestablish compatibility between Rydberg excitations and GEM, providing a route toward multimode
quantum memories with controllable long-range interactions and applications in
quantum networking, sensing, and
quantum information processing.
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All authors Bartosz Niewelt, Stanisław Kurzyna, Bartosz Kasza, Wojciech Wasilewski, Michał Parniak
Fluid Dynamics · Cross-listed from cond-mat.soft
Pulsatile poromechanics in layered soft media controls fluid flow and solute transport: from fundamentals to brain clearance
Matilde Fiori and Sylvie Lorthois
Soft porous media often feature a heterogeneous structure. Notably, biological tissues - such as cartilage and the
brain tissue - consist of two or more layers, with varying mechanical and fluid-flow properties. Despite the ubiquity of periodic loading in these systems, the physical implications of layering on nonlinear poromechanics and solute transport remain poorly understood. Uncovering these coupled mechanisms could clarify the fundamental physics behind pressing topics, such as
brain metabolic clearance. Here, we address this gap using a
bilayer model of a generic soft porous medium. To isolate the specific role of layering, we select combinations of material properties (porosity, permeability, and p-wave modulus) that maintain an identical poroelastic timescale, $T_{\mathrm{PE}}$, across four layered configurations and a reference homogeneous case. We demonstrate that while $T_\mathrm{PE}$ is the key parameter governing the response in a homogeneous medium, the same $T_\mathrm{PE}$ leads to non-trivial localization/propagation patterns for strain, fluid flow and solute transport in a
bilayer medium. These non-intuitive results suggest that layered architectures may provide functional benefits for cellular homeostasis over homogeneous ones. Finally, we show that pathological alterations to the
brain's layered structure significantly disrupt fluid-flow and metabolic waste clearance, offering a possible mechanical explanation for impaired transport in disease.
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Fluid Dynamics
Modeling and Stabilization of Transport-Dominated Flows
Christopher Agesen, Sam Bradford, Abhijnan Dikshit et al. (+7 more)
This report explores and compares numerical stabilization methods for transport-dominated flows arising in atmospheric modeling. The Streamline-Upwind (SU) and Streamline-Upwind Petrov-Galerkin (SUPG) stabilization formulations are implemented in Julia's ClimaCore.jl package and Python's Firedrake package for a test problem with slotted-cylinder initial conditions. These methods are compared for their ability to mitigate spurious oscillations while preserving sharp features against existing hyperdiffusion and quasi-monotone limiter methods, alongside various combinations. For this
benchmark, quasi-monotone limiters were most effective at minimizing un-physical extrema, at the expense of diffusing the overall structure. The SUPG method was not found to improve upon the no stabilization case, for this test case in the computed error metrics. However, it performs best at preserving sharp feature and overall structure, among tested stabilized runs. Theoretical properties of SUPG are analyzed, and an asymptotics-based SUPG
algorithm is proposed as future work.
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All authors Christopher Agesen, Sam Bradford, Abhijnan Dikshit, Anshi Gupta, Bhavana Morankar, Jack Murphy, Nanda N. Raghunathan, Karnav Raval, Lane H. Rogers, Valeria Barra
Atomic Physics
Long-range magnetic interaction within quantum electrodynamics formalism
D. Solovyev, T. Zalialiutdinov, A. Anikin et al. (+6 more)
Within the framework of
quantum electrodynamics, the interaction between two atoms at large distances is analyzed. Using the S-
matrix formalism, an expression for the
magnetic interaction potential is derived, which agrees with the well-known result of classical electrodynamics. However,
quantum electrodynamics goes beyond this conventional result and allows one to treat a wide range of problems related to the structure of atomic energy levels. In particular, it is shown that the asymptotic behavior of the interaction potential can deviate from the classical prediction, depending on the atomic states involved. As an example, dispersion coefficients are calculated for the s-states of hydrogen atoms, where the long-range potential reduces to a
spin-
spin interaction. The results obtained open up the possibility of a straightforward comparative analysis of long-range interaction potentials between atoms of matter and antimatter. The applicability of this approach is demonstrated for the hydrogen-antihydrogen system.
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All authors D. Solovyev, T. Zalialiutdinov, A. Anikin, A. Bobylev, P. Kvasov, J. J. Lopez-Rodriguez, A. Moshkin, D. Zinenko, D. Glazov
Fluid Dynamics
PRIMS: Physics-guided Representation for Fluid Identification in Multimodal Sensing
Hai-Long Nguyen, Trung Thanh Nguyen, Lars Holm et al. (+2 more)
Accurate on-device fluid identification is essential for microfluidic applications, yet maintaining reliability under varying flow, pressure, and temperature remains a key challenge. Existing learning-based methods often treat sensor signals as domain-agnostic features, neglecting the underlying physical relationships that govern fluid behavior, thereby limiting generalization and interpretability. To address this, we propose PRIMS, a physics-aware
multimodal Transformer that integrates physical knowledge into representation learning and
attention mechanisms through three dedicated modules: (1) Physics-based
Token Vectorization transforms raw Coriolis and pressure sensor signals into physically meaningful
token embeddings; (2) Physical Component Synthesizer models viscosity-related dependencies among flow, pressure, and density; and (3) Physics-guided Fusion captures cross-physical correlations through
attention-based integration. By
embedding these physics-based relationships directly into the model architecture, PRIMS bridges analytical fluid mechanics and deep learning, enabling interpretable, data-efficient, and resilient fluid classification. Evaluations on a five-fluid
benchmark under dynamic flow, pressure, and temperature conditions show that PRIMS achieves 98.92% average F1-score with only 0.46 million parameters, a 14 times reduction compared to state-of-the-art
Transformer-based methods. PRIMS also consistently outperforms prior SOTA models under out-of-distribution shifts to unseen temperature ranges and unseen flow-rate ranges, indicating strong robustness to operating conditions not observed during training. These findings suggest that designing architectures that explicitly mirror governing physical relationships can make them learn transferable, environment-independent representations, improving real-world reliability for microfluidic sensing.
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All authors Hai-Long Nguyen, Trung Thanh Nguyen, Lars Holm, Dennis Alveringh, Duc Viet Le
Computational Physics
Hyperspatial Sampling: Circumventing Free-Energy Barriers via Replica Exchange with Extra Dimensions
Henrik Christiansen, Matheus Ferraz, Takashi Maruyama et al. (+1 more)
Simulating systems with rugged free-energy landscapes remains a central challenge in computational physics and chemistry. We introduce hyperspatial replica exchange (HS-REX), an enhanced sampling method in which the physical system is artificially extended by additional spatial dimensions. In higher dimensions, free-energy barriers can be circumvented through paths that are geometrically inaccessible in the original space. Restricting the penalty to only solute atoms dramatically reduces the number of replicas required for solvated systems compared to standard temperature replica exchange, a feature especially relevant for biological applications. As proof of concept, we demonstrate the method on a double-well model system and on alanine dipeptide in explicit water as
benchmark system. In the latter case, HS-REX achieves enhanced conformational sampling of not only the slow backbone dihedral angles, but also both
chiral configurations of the molecule, which are sterically inaccessible to standard sampling in three dimensions. This demonstrates enhanced ergodic sampling over conventional temperature replica exchange.
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All authors Henrik Christiansen, Matheus Ferraz, Takashi Maruyama, Francesco Alesiani
Instrumentation and Detectors
A Kalman Filter Based Approach to NV Diamond Data Fusion For Improved Temperature Sensing
Shraddha Rajpal, Qiaochu Guo, Brendon A. McCullian et al. (+2 more)
Nitrogen-vacancy (NV) centers in diamond have been demonstrated to enable highly sensitive temperature measurements using multiple modalities. Standalone optically detected
magnetic resonance (ODMR) provides robust temperature estimates, albeit with high latency, whereas all-optical measurements provide millisecond resolution but suffer from poorer long-term accuracy. In this work, we demonstrate a hot-start Kalman filtering approach that fuses the two modalities, leading to a 57% improvement in accuracy. The fused estimate achieves higher long-term accuracy with lower latency, demonstrating a viable route toward implementing self-correcting, high-precision NV-diamond temperature sensing schemes.
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All authors Shraddha Rajpal, Qiaochu Guo, Brendon A. McCullian, Tyrus Berry, Zeeshan Ahmed
Accelerator Physics
Electromagnetic Simulations
Thomas Flisgen
This contribution reviews fundamental concepts of electromagnetic simulations for RF structures used in particle accelerators. The necessity of numerical methods for electromagnetic simulations is discussed, and the fundamental steps involved in performing such simulations are presented. Spatial discretization is reviewed in a very general way to obtain semi-discrete equations. These semi-discrete equations are employed to revisit time-domain and frequency-domain approaches. Special
attention is devoted to the computation of network
matrices (e.g. scattering
matrices) of structures with large quality factors as this topic is highly relevant in the context of
radio-frequency for accelerators. This leads to a very short recapitulation of model-order reduction methods. The various sources of error inherent in electromagnetic simulations are examined. Finally, recommendations for the careful validation and assessment of simulation results are presented.
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Fluid Dynamics
Neural Differential Equations for Oscillatory Flows in Aeroelasticity Applied to Transonic Buffet
Michael Candon, Pier Marzocca and Earl Dowell
Self-excited aerodynamic flows arise across a broad range of systems and can drive nonlinear fluid-structure interactions and aeroelastic instabilities that are challenging and computationally expensive to predict. This paper presents a physics-guided neural differential equation (DE) reduced order model (ROM) combining a nonlinear fluid oscillator, a finite-
memory multi-input Volterra series, and a compact neural network correction. The multi-input aerodynamic formulation is generalized to m structural modes, capturing direct and nonlinear cross-modal coupling. The model is identified from a single prescribed-motion CFD simulation with simultaneous excitation of all retained structural modes, and is then coupled with the structural equations of motion for efficient aeroelastic prediction. Applied to transonic buffet over the ONERA OAT15A airfoil, the time-marching ROM predicts aeroelastic stability, frequency lock-in, and limit cycle amplitudes in good agreement with full-order reference solutions. The ROM is used to provide substantial new insight into buffet-induced aeroelastic instabilities involving more than one structural mode.
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Optics · Cross-listed from quant-ph
Metrology of quantum imaging schemes
Emma Brambila and Giacomo Sorelli
We compare the performance of
quantum imaging schemes based on spatially correlated
photon pairs by formulating them as
quantum multiparameter estimation problems, in which the object is characterized by transmission coefficients associated with different spatial modes. Our work focuses on standard
quantum imaging techniques such as ghost imaging, two-
photon imaging, and imaging with undetected
photons. Specifically, we compute the
quantum Fisher information
matrices and show that they are saturated by Fisher information
matrices corresponding to measurements in the object-mode basis, which generalize the common detection schemes employed in each imaging configuration. We find that ghost imaging and two-
photon imaging generally provide higher precision for transmission estimation than imaging with undetected
photons, but the latter is the only one that naturally does not couple transmission estimation across different spatial modes. These results identify which imaging protocols are best suited for specific tasks and provide practical guidelines for the design and optimization of
quantum sensing technologies based on spatial correlations.
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Medical Physics · Cross-listed from cs.LG
IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data
Masashi Sode and Gianmarco Pinton
The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem. Learned solvers are fast yet label hungry. Simulated sound-speed labels are expensive, while abundant real channel data is unlabeled. We propose IQ-JEPA to exploit both data types. An encoder is pretrained without labels to predict the latent representation of masked in-phase and quadrature (IQ) regions from visible context, then fine-tuned on simulated maps. Sound speed appears in the IQ signal as a phase difference, invariant to the constant phase offset. The encoder is a Hermitian
vision transformer that operates on the complex signal directly. Its
attention is equivariant to that phase and its conjugate-product feed-forward is invariant to it, so the encoder reads a quantity analogous to the one classical coherence methods use. On 79,293 Fullwave 2.5 simulations at 2.5 MHz, pretraining on the 63,435 unlabeled acquisitions reaches 15.60 m/s at 10,000 labels. This is a roughly threefold gain in label efficiency over supervised training, growing to over fourfold at 1,000 labels. It is about 2.2x below an InversionNet baseline, and 8.71 m/s at full labels. The gain still grows with more unlabeled pretraining data. Our comparisons point to self-supervision as the dominant factor. The same encoder transfers. Its frozen features expose sound speed and attenuation, and cross-distribution pretraining between layered and abdominal phantoms costs little accuracy. We see this as a first step toward a foundation model for quantitative ultrasound.
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Plasma Physics
First Observation of Fishbone-Driven Zonal Flows with Fine Reversed Structure in Tokamak Plasmas
Liutian Gao, Yuehao Ma, Huishan Cai et al. (+12 more)
We present the first direct experimental observation of fishbone-driven zonal flows in the core of the EAST tokamak. In contrast to the global pattern predicted by previous models and simulations based on the energetic-particle-expulsion mechanism, the observed flows exhibit a fine-scale, radially reversed structure inside the q = 1 rational surface. The flow rises faster and saturates earlier than the fishbone within a single burst, indicating that a beat-driven nonlinear process dominates the early stage rather than the energetic-particle-expulsion mechanism. Global nonlinear gyrokinetic simulations quantitatively reproduce the observed radial profile and reveal that this structure arises from the cancellation of comparable but opposite contributions from thermal ions and electrons. This cancellation mechanism is not captured in previous theoretical frameworks. These findings establish that the fishbone can generate sheared flows with a distinct radial
topology, offering a promising pathway for regulating
turbulence and improving core confinement.
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All authors Liutian Gao, Yuehao Ma, Huishan Cai, Adi Liu, Bin Zhang, Ming Xu, Haiqing Liu, Liqing Xu, Chu Zhou, Feifei Long, Mingyuan Wang, Xiaoming Zhong, Jinlin Xie, Ge Zhuang, the EAST team
Optics
Fluctuational Quantum Electrodynamics of Dispersive Time-Varying Media
Jaime E. Sustaeta-Osuna, Thomas F. Allard, Francisco J. García-Vidal et al. (+1 more)
We present the theoretical framework of fluctuational
quantum electrodynamics in frequency-dispersive and dissipative time-varying media. Our theory accounts for dispersion and losses in the temporal modulation, which is treated in an exact manner, without relying on perturbative methods. Thus, our work constitutes the first consistent quantization of the electromagnetic field in time-modulated material bodies. We derive a
Fermi Golden Rule for time-varying media and use it to define the local density of states for these time-dependent systems, which includes both loss and gain contributions. Additionally, we prove the equivalence between the
quantum Fermi Golden Rule and the power emitted by a classical harmonic point dipole. Moreover, we show that neglecting the dispersive and dissipative nature of the time modulation leads to erroneous predictions for both slow and fast modulations. Furthermore, we analyze the thermal radiation emitted by a time-varying material body, revealing new features in the enhancement of thermal emission in time-varying media. Finally, we study the dynamical Casimir effect, showing how the time modulation amplifies
vacuum fluctuations and generates entangled pairs of polaritons exhibiting non-local spatial correlations.
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All authors Jaime E. Sustaeta-Osuna, Thomas F. Allard, Francisco J. García-Vidal, Paloma A. Huidobro
Chemical Physics
Uncertainty Quantification for Free Energy Calculations by Generalized Hierarchical Bayesian Inference
Martin Skorna, Adam Gottfried, Zuzana Janackova et al. (+3 more)
Free energy calculations are routinely used to study molecular processes inaccessible to unbiased molecular dynamics, but their utility ultimately depends on knowing when and how much their predictions can be trusted. Uncertainty estimation is therefore essential for distinguishing genuine physical features of a free energy profile from artifacts arising from limited simulation data or inadequate sampling. Gaussian processes have emerged as a powerful framework for reconstructing free energy profiles together with predictive uncertainties. However, existing implementations typically condition on fixed hyperparameters and observation noise, preventing predictive uncertainties from adapting to the information content of the simulation data. Here, we develop a generalized hierarchical Gaussian process framework that accounts for these neglected sources of uncertainty. Applications to umbrella sampling and extended Lagrangian metadynamics of peptide-lipid membrane interactions demonstrate that the resulting uncertainty estimates track reconstruction errors across a wide range of sampling and data conditions.
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All authors Martin Skorna, Adam Gottfried, Zuzana Janackova, Katarina Baxova, Pavel Jungwirth, Brennon L. Shanks
Chemical Physics
Hidden excimer formation in the gas-phase photodynamics of a BN-doped phenanthrene
Jonas Fackelmayer, Michael Bühler, Michael Müller et al. (+3 more)
Replacing CC units by isoelectronic BN motifs provides a powerful strategy to tune the electronic structure and excited-state chemistry of polycyclic aromatic hydrocarbons (PAHs). Here, we combine multiphoton ionization spectroscopy, time-resolved photoelectron imaging, ion velocity-map imaging, and
quantum-chemical calculations to disentangle the monomer and dimer photophysics of 4a,4b-azaboraphenanthrene. The monomer exhibits a structured S$_1 \leftarrow$ S$_0$ spectrum with an origin at $22880 \pm 15\,\mathrm{cm}^{-1}$, corresponding to $2.837 \,\mathrm{eV}$, and pronounced activity in low-wavenumber deformation modes. Photoelectron spectroscopy yields an adiabatic ionization energy of $7.18 \pm 0.02\,\mathrm{eV}$. While the structured spectrum, high fluorescence
quantum yield, small computed geometry changes, and weak
spin-orbit couplings all point to a long-lived monomer S$_1$ state, time-resolved photoelectron images reveal an additional picosecond component. Ion imaging shows that this component originates from dissociative ionization of the molecular dimer, which projects dimer excited-state dynamics into the monomer mass channel. Computations identify the initially excited dimer state as a bright H-aggregate-like
exciton, followed by ultrafast S$_2 \rightarrow$ S$_1$ internal conversion and subsequent structural relaxation toward an excimeric S$_1$ minimum. The experimentally observed $\approx 15\,\mathrm{ps}$ time constant is therefore assigned to excimer formation in the neutral dimer.
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All authors Jonas Fackelmayer, Michael Bühler, Michael Müller, Holger Helten, Ingo Fischer, Merle I. S. Röhr
Accelerator Physics
Robustness of Off-Axis Electron Vortices in Nonuniform Magnetic Fields
Hui-Dong Huang, Qi Meng, Zhi-Bin Wang et al. (+3 more)
Rotational
symmetry protects the topological charge of on-axis electron
vortices but not of off-axis
vortices. We identify an additional SU(1,1) dynamical invariant that guarantees conservation of their intrinsic orbital angular momentum within the near-axis approximation. First-principles simulations of an off-axis electron
vortex traversing a Glaser lens confirm this prediction, establishing a robust transport mechanism in axisymmetric nonuniform
magnetic fields.
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All authors Hui-Dong Huang, Qi Meng, Zhi-Bin Wang, Liang Lu, Jian Chen, Li-Ping Zou
Fluid Dynamics
Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows
Harish Ramachandran, Björn Kimpel, Thomas Paula et al. (+3 more)
Compressible multiphase flows involving shocks and material interfaces arise in applications such as bubble collapse and droplet breakup, where strong nonlinear interactions produce complex interface deformation, mixing, and multiscale dynamics. Developing reliable machine learning surrogates for these flows remains challenging due to the simultaneous presence of compressibility, sharp discontinuities, and multiphase effects. In this work, we introduce the first large-scale
benchmark specifically designed for shock-driven compressible multiphase flows, comprising 3 TB of high-fidelity 2D and 3D
datasets featuring shock-induced bubble collapse and droplet breakup. We evaluate diverse surrogate model families on our benchmarking framework: Neptuna, including convolutional, spectral,
transformer-based, and pre-trained PDE foundation models. Beyond standard MSE training, we investigate composite losses that combine MSE with Sobolev, interface-aware, and structure-aware terms, along with adaptive loss balancing using SoftAdapt and GradNorm. Evaluation includes pointwise, spectral, feature-focused, structural, and physics-informed metrics. Results show that no single model performs best across all
datasets and metrics, while composite losses significantly improve interface preservation and spectral fidelity. Among adaptive weighting strategies, SoftAdapt provides the most consistent improvements with almost no overhead compared to MSE-only training. The benchmarking repository and the
dataset are made available at \url{https://tumaer.github.io/Neptuna/}
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All authors Harish Ramachandran, Björn Kimpel, Thomas Paula, Josef Winter, Steffen Schmidt, Nikolaus Adams
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Unbiased Diffusion Monte Carlo for non local operators
Carlos Rodriguez Perez, Valerio Olevano, Francesco Sottile et al. (+1 more)
We propose a new mathematically exact method for computing unbiased
Diffusion Monte Carlo (DMC) estimates of non-local operators. We demonstrate that the current state-of- the-art technique, Forward Walking, is only exact for local quantities and fails to yield unbiased results for the non-local components of reduced density
matrices (RDMs). Our method significantly outperforms Forward Walking, as shown in two systems: in the symmetric Hubbard dimer it yields a pure 1RDM; while in the Helium atom it will give an unbiased 1RDM in the limits of zero time step and infinite walkers.
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All authors Carlos Rodriguez Perez, Valerio Olevano, Francesco Sottile, Vitaly Gorelov
Chemical Physics
Chiral vibrational modes and vibrational circular dichroism
Xuecheng Tao, Clàudia Climent, Ethan Abraham et al. (+2 more)
The recent interest in
chiral phonons in a variety of physical phenomena and their hypothesized role in the
chiral-induced
spin selectivity effect [Phys. Rev. Research, 5, L022039 (2023)] call for further investigation into the chirality of molecular vibrations. Although molecular chirality has conventionally been quantified using structural properties, recent work has highlighted the role of dynamical response properties as chirality metrics. In this work, we examine an inter-atom helicity pseudoscalar as a complementary measure of vibrational chirality, associated with the vibrational circular dichroism (VCD) intensity in the fixed partial charge (FPC) approximation. This pseudoscalar is translationally and rotationally invariant, can distinguish between opposite enantiomers, and unlike an atomic pseudoscalar measure considered in our earlier work [Phys. Rev. Lett., 133, 268001 (2024)] does not rely on a predefined
symmetry axis. For a twisted ethane model as well as several small molecules, this pseudoscalar correlates well with structural descriptors based on the continuous chirality measure. Overall, our results support response-based metrics as a physically meaningful and practically useful characterization of vibrational chirality. Importantly, while the FPC-based VCD estimate provides a useful quantifier of vibrational chirality, we show that it is a rather poor predictor of the actual molecular VCD response because the latter is strongly influenced by the (vibrational configuration-dependent) molecular electronic response.
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All authors Xuecheng Tao, Clàudia Climent, Ethan Abraham, Jichen Feng, Abraham Nitzan
Plasma Physics
Full 3D Maxwell solver for Inertial Electrostatic Confinement devices
Victor-Otto de Haan
Results of a full 3D electromagnetic
plasma fusion simulator are presented. It will enable investigations into the influence of structured electromagnetic potentials, anomalous
magnetic moments, or charge cluster structures on the fusion yield. The simulator calculates fusion yields similar to those found in real world fusors. In addition, the ion energy and density are accurately simulated. Given that the ion properties arise from a full Maxwell solver there is ample evidence that this part of the simulation is accurate. Concluded with a recommendation for continued research.
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Optics
Counterdirectional Exciton and Trion Motion in Applied Electric Field
Daniel Vaz, Yuanjun Guan, Qiaochu Wan et al. (+13 more)
Charged excitonic complexes are central to the optoelectronic and many-body properties of
semiconductors, yet their real-space transport dynamics remain largely unexplored. Here, we report the direct optical observation of trion motion under an applied electric field. The trions exhibit electrically driven drift with velocities approaching {$10^5~\mathrm{m/s}$}. Unexpectedly, the trion flow induces a pronounced back-action on coexisting neutral
excitons, driving them in the opposite direction and giving rise to counterpropagating
exciton-trion transport. Our results reveal an interaction-driven nonequilibrium transport regime of mixed excitonic fluids and establish a direct route for imaging the dynamics of more complex charged quasiparticles, including doubly charged
excitons.
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All authors Daniel Vaz, Yuanjun Guan, Qiaochu Wan, Bobby Bobby, Anshul Ramavath, Brandon Vargo, Juntong Ye, Jonathan Beaumariage, Om Patel, Kenji Watanabe, Takashi Taniguchi, Xiong Feng, James Hone, Nathan Youngblood, Zheng Sun, David W. Snoke
Fluid Dynamics
Critical assessment of RANS Models for Turbulent Heat Transfer in Low-Prandtl-Number Forced Convection
Luca Marocco, Jonathan Schmitt, Jonathan Neuhauser et al. (+2 more)
RANS modeling of turbulent heat transfer in
liquid metals remains challenging because the very low Prandtl number weakens the similarity between momentum and thermal transport. Several thermal
turbulence closures for forced convection in
liquid metals are assessed using OpenFOAM v2212. The combinations include the $k$–$\omega$ SST model with the Kays correlation for $\mathrm{Pr}_t$, the four-equation $k$–$\epsilon$–$k_\theta$–$\epsilon_\theta$ model, the logarithmic $k$–$\Omega$–$k_\theta$–$\Omega_\theta$ model, and two algebraic heat-flux formulations for $\overline{u_i'\theta'}$ coupled with either a low-Reynolds $k$–$\epsilon$ model or an
elliptic blending Reynolds-stress model (EBRSM). They are evaluated in channel flow, pipe flow, and heated backward-facing step flow against DNS data and results from the original publications, focusing on reproducibility, robustness, and accuracy. Only a limited subset of models proves reliable for low-$\mathrm{Pr}$ flows. The $k$–$\omega$ SST model with the Kays correlation gives the most robust performance and accurate temperature predictions in all cases. The $k$–$\epsilon$–$k_\theta$–$\epsilon_\theta$ model shows good reproducibility and satisfactory agreement with reference data, remaining the most consistent multi-equation alternative. The logarithmic four-equation model exhibits reduced numerical robustness, while the algebraic heat-flux model coupled with the $k$–$\epsilon$ closure fails to reproduce published thermal results despite correct prediction of the momentum field. The EBRSM-based algebraic heat-flux formulation reproduces selected reference results but suffers from significant robustness limitations. The study establishes a unified formulation of the examined closures, correcting inconsistencies in their published forms, and verifies their reproducibility and robustness.
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All authors Luca Marocco, Jonathan Schmitt, Jonathan Neuhauser, Bettina Frohnapfel, Davide Gatti
Atomic Physics · Cross-listed from cond-mat.quant-gas
Ashton S. Bradley, Tyler W. Neely, Xiaoquan Yu et al. (+1 more)
We review
turbulence in ultracold
quantum gases, using the scalar contact-interaction Bose-Einstein condensate as the reference system for quantized circulation, compressibility,
vortices, sound, and cascades. We focus on the quantitative diagnostics that connect helium and classical phenomenology to microscopic wave-function dynamics: incompressible and compressible kinetic-energy spectra, wave-occupation spectra, spectral fluxes,
vortex-resolved correlations, and velocity statistics. These diagnostics distinguish
equilibrium vortex organization, decaying turbulent relaxation, forced cascade dynamics, and weak-wave
turbulence, and show why power laws alone are insufficient evidence for a cascade. We survey experiments on two-dimensional Onsager clustering, three-dimensional
vortex-line
turbulence, box-trap wave cascades, engineered dissipation, and turbulent equations of state. We close by briefly placing the contact-interaction scalar superfluid system in a broader landscape of nonlocal, multicomponent, fermionic, and driven-dissipative
quantum fluids, where
turbulence concepts can be tested for universality.
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All authors Ashton S. Bradley, Tyler W. Neely, Xiaoquan Yu, Brian P. Anderson
Computational Physics · Cross-listed from cs.LG
Latent PDE mapping for efficient physics-informed learning across geometries with limited data
Ingvild Askim Adde, Mary M. Maleckar and Gabriel Balaban
In this study, we introduce latent PDE mapping, a broadly applicable physics-informed learning technique designed to enable efficient
geometric generalization with sparse training data. Latent PDE mapping pulls back geometry-specific PDE residuals and boundary conditions to a predefined latent geometry via the deformation gradient, thereby enabling the automated calculation of geometry-consistent shape gradients that are missing in conventional physics-informed machine learning formulations. We demonstrate the utility of latent PDE mapping in solving the anisotropic Aliev-Panfilov PDE of cardiac electrophysiology using both physics-informed neural networks and physics-informed deep operator networks. The Aliev-Panfilov PDE serves as a challenging exemplar: a nonlinear, time-dependent PDE
benchmark with sharp gradients that are expensive to capture using traditional numerical solvers. To represent the limited data regime, we train the networks using just fifteen
geometric samples drawn from parameterized distributions in two and three spatial dimensions. While modest improvements appear for geometries parameterized by affine and shear deformations, latent PDE mapping demonstrates significant benefits on select
geometric families, achieving a factor ~4-6 reduction in mean relative L2 error. Furthermore, our results show that the computational cost of applying latent PDE mapping was modest during network training, and negligible at inference. Taken together, our study highlights how latent PDE mapping facilitates the creation of generalizable physics-informed machine learning models from limited sets of training geometries.
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Fluid Dynamics
Bubble bursting in a sessile droplet
U. J. Gutiérrez-Hernández, B. Muñoz-Sánchez, A. Agúndez-Cruz et al. (+3 more)
We analyzed experimentally and numerically the bursting of a bubble within a sessile droplet. Our experiments show that both sessile droplet
curvature and confinement enhance the energy focusing. In the low-viscosity regime, this effect results in thinner, faster Worthington jets. In the high-viscosity regime, droplets are ejected for values of the Laplace number (the Reynolds number based on the visco-capillary velocity) smaller than the threshold for a bubble in an infinite
liquid bath. This is probably the major result of the present work. Numerical simulations show the critical role of the additional pressure gradient arising from the
curvature of the sessile droplet interface. The resulting force drives the
liquid towards the bottom of the cavity, compressing it and accelerating jet formation. In the low-viscosity limit, the bottom of the cavity becomes smoother before jet ejection. This effect resembles the energy-focusing enhancement that occurs in an infinite
liquid bath at the critical Laplace number, where short-wavelength waves are damped by viscosity.
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All authors U. J. Gutiérrez-Hernández, B. Muñoz-Sánchez, A. Agúndez-Cruz, J. M. Montanero, D. Fernández Rivas, M. G. Cabezas
Computational Physics
Producing a complete nuclear data library for adjoint Monte Carlo simulations
Paul Rovel, Coline Larmier, Davide Mancusi et al. (+1 more)
Radiation shielding applications related to reactor design typically involve situations where the source region (the core) is much larger than the detector region (a dosimeter). In such cases, the efficiency of Monte Carlo simulation might be significantly increased by solving the adjoint transport equation: adjoint particles are born from the detector, undergo adjoint ('reversed') flights and collisions, and accumulate their tallies in the source region. A key prerequisite to sample the adjoint collision events is the preparation of adjoint
nuclear data. In this work, we propose a general method able to handle the diversity of
nuclear reactions available in modern
neutron data evaluations, and ultimately create a full adjoint
nuclear data library. This is a stepping stone in view of implementing adjoint sampling schemes in TRIPOLI-5 __ , the next-generation Monte Carlo code developed by CEA and ASNR. We validate this strategy based on a relevant continuous energy
benchmark configuration involving mixtures of heavy and light nuclides, and we compare our results to those obtained by standard forward Monte Carlo simulations.
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All authors Paul Rovel, Coline Larmier, Davide Mancusi, Andrea Zoia