General Physics
On Computational CUDA Studies of Black Hole Shadows
S. E. Baddis, A. Belhaj, H. Belmahi et al. (+2 more)
Combining high-performance CUDA numerical codes with the Hamilton–Jacobi formalism, we investigate the shadows properties of rotating charged Euler–Heisenberg black holes in the presence of global monopoles. Then, we discuss the associated energy emission rate by varying the involved black hole parameters. As a result, we show that both the shadow structure and the energy emission rate depend on the global monopole parameter, the electric charge, and the rotation parameter. However, we observe that the Euler–Heisenberg nonlinear parameter does not significantly affect either the shadow or the energy emission rate. In order to reconcile the present theoretical predictions with the shadow observations reported by the Event Horizon
Telescope collaboration, we employ a CUDA-based computational approach to establish strict bounds on the GM parameter, the electric charge, and the rotation parameter.
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All authors S. E. Baddis, A. Belhaj, H. Belmahi, S. E. Ennadifi, M. Jemri
Classical Physics
Potentials of axisymmetric razor-thin disks
J.An
We investigate the gravitational potentials generated by axisymmetric, razor-thin disks. Within certain limitations, the potential on one side of the disk is shown to be equivalent to the potential produced by a linear mass distribution along the axis perpendicular to the disk. We first establish the connection between two mass distributions that generate the same potential. We then consider all disk surface density profiles that produce the potential equivalent to those generated by linear mass distributions, specifically those defined by the elementary beta distribution and its relatives on the interval $[0,1]$, $[1,\infty)$ or $[0,\infty)$. These families of models are important because the potentials in all cases are given by, at most, a single real quadrature of elementary functions of the coordinates, and furthermore, many cases result in closed-form expressions. The potential of many realistic disks may be constructed from some combinations of these disk models.
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Fluid Dynamics
Collective Dynamics of Vortex Clusters in Compact Fluid Domains: From Pair Interactions to a Quadrupole Description
Aswathy KR and Rickmoy Samanta
Clusters of co-rotating
vortices on compact fluid domains exhibit a simple collective dynamics, combining coherent global rotation with a slow breathing of the cluster size. In this work, we investigate an analytic model of
vortex interactions on a doubly periodic inviscid fluid domain, based on an exact representation in terms of the Schottky-Klein
prime function and its $q$-representation. The two-
vortex problem reduces to a single complex degree of freedom, from which explicit expressions for the orbital rotation frequency and dipole translation velocity are obtained. Building on this framework, we derive a small-cluster expansion that reveals a decomposition of the dynamics into universal planar interactions, isotropic
torus corrections, and geometry-induced anisotropic modes. At leading order, the collective dynamics is encoded in a single complex quadrupole moment: its real part governs corrections to the rotation rate, while its imaginary part controls the slow breathing of the cluster. These predictions are quantitatively confirmed by direct numerical simulations, establishing a reduced description of
vortex clusters on the flat
torus and compact fluid domains.
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Optics
Aperiodic metalenses with identical nanocylinders: positional phase control and passive chromatic aberration suppression
Ivan Moreno and J. Carlos Basilio-Ortiz
Conventional metalenses control light by varying meta-atom geometry, a design strategy that inherently couples phase modulation to structural dimensions, contributing to chromatic dispersion. Here, we introduce a new design paradigm in which phase control is decoupled from meta-atom geometry. We present an aperiodic metalens architecture composed exclusively of structurally identical, polarization-insensitive dielectric nanorods. In this framework, full 2{\pi} phase coverage is achieved solely through local periodicity modulation, that is, by varying the spacing between adjacent nanocylinders. We theoretically demonstrate that this
geometric invariance yields a linear dependence of the effective refractive index on the local fill factor, which naturally approaches the dispersive condition required for achromatic focusing. The resulting architecture provides a deterministic, library-free, and analytically tractable approach to metalens design, in which positional degrees of freedom replace
geometric variation as the phase-encoding mechanism. An extensive set of full-wave numerical simulations demonstrates the viability of the concept for visible-light focusing, showing that the aperiodic architecture can passively suppress chromatic aberration while maintaining near-diffraction-limited focusing. These results establish geometry-invariant positional phase control as a distinct design framework and provide a simple, reproducible baseline for further dispersion-engineered flat optics.
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Plasma Physics
Forecasting the first Edge Localized Mode (ELM) after LH-transition with a neural network trained on Doppler Backscattering data from DIII-D
Nathan Qi Xuan Teo, Kshitish Barada, Valerian Hall-Chen et al. (+2 more)
In H-mode tokamak and stellarator
plasmas, edge localized modes (ELMs) lead to the expulsion of heat and particles beyond the edge transport barrier. ELMs cause a loss of energy and have the potential to damage the divertor and other
plasma facing components, which motivates efforts to forecast such events to work alongside mitigation systems. In this paper, we use the Doppler backscattering (DBS) diagnostic data as input to train a neural network model, adapted from DeepHit [Lee et al., Deephit, AAAI 2018], to forecast the first ELM crash of H-mode discharges in DIII-D. The model takes 50 ms of DBS spectrogram data and predicts the probability of an ELM crash occurring within set time windows. Training and testing on shots found in the DIII-D database, we find the initial results promising, with the model reliably
forecasting the first ELM 100 ms before it occurs. This successful proof-of-concept lays a strong foundation for a predictive tool that can deploy ELM-mitigation techniques before an ELM crash occurs. Future work will expand the training set with carefully selected shots and refine the neural network architecture to improve model robustness to noise and data variation.
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All authors Nathan Qi Xuan Teo, Kshitish Barada, Valerian Hall-Chen, Lin Gu, Terry Lee Rhodes
Medical Physics
Maximum Likelihood Estimation Yields Accurate Line-of-Response Assignment for Positron + Prompt Gamma Ray Events in Multiplexed PET (mPET)
Sarah J. Zou, Garry Chinn, Muhammad Nasir Ullah et al. (+1 more)
For accurate disease characterization using positron emission
tomography (PET), it is desirable to image multiple radiotracers in a single scan. Conventional PET methods cannot do this due to the indistinguishable annihilation
photons produced by different radiotracers. One approach is to label one radiotracer with a positron+
prompt-gamma ($\beta^+\!\!-\!\!\gamma$) isotope producing triple coincidences, and another with a pure positron-emitting ($\beta^+$) isotope producing double coincidences. However, $\beta^+\!\!-\!\!\gamma$ emitters present challenges in correctly identifying the two annihilation
photons, or equivalently, assigning the correct line-of-response (LOR) to triple-
photon coincidence events. Here, we propose a maximum likelihood estimation (MLE) framework leveraging spatial, timing, and energy information to determine the most probable LOR. Simulation studies validated the method: simulations showed over 96% and 94% accuracy for LOR assignment of $\beta^+\!\!-\!\!\gamma$ emitters $^{22}$Na and $^{124}$I point sources, respectively. Furthermore, simulated phantom imaging of $^{22}$Na or $^{124}$I distributions alongside a $\beta^+$ emitter demonstrated that MLE LOR assignment achieved comparable image quality – measured by contrast recovery coefficient (CRC) and cross-talk ratio (XR) – to
benchmark methods, where the
prompt gamma was identified using an energy threshold ($\geq 650$ keV) for $^{22}$Na and as the highest-energy
photon for $^{124}$I.
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All authors Sarah J. Zou, Garry Chinn, Muhammad Nasir Ullah, Craig S. Levin
Medical Physics
Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)
Jiacheng Xie, Hua-Chieh Shao, Can Wu et al. (+7 more)
Time-resolved volumetric MR imaging that reconstructs a 3D MRI within sub-seconds to resolve deformable motion is essential for motion-adaptive radiotherapy. Representing patient anatomy and associated motion fields as 3D Gaussians, we developed a spatiotemporal Gaussian representation-based framework (DREME-GSMR), which enables time-resolved dynamic MRI reconstruction from a pre-treatment 3D MR scan without any prior anatomical/motion model. DREME-GSMR represents a reference MRI volume and a corresponding low-rank motion model (as motion-basis components) using 3D Gaussians, and incorporates a dual-path MLP/CNN motion encoder to estimate temporal motion coefficients of the motion model from raw k-space-derived signals. Furthermore, using the solved motion model, DREME-GSMR can infer motion coefficients directly from new online k-space data, allowing subsequent intra-treatment volumetric MR imaging and motion tracking (real-time imaging). A motion-augmentation strategy is further introduced to improve robustness to unseen motion patterns during real-time imaging. DREME-GSMR was evaluated on the XCAT digital phantom, a physical motion phantom, and MR-LINAC
datasets acquired from 6 healthy volunteers and 20 patients (with independent sequential scans for cross-evaluation). DREME-GSMR reconstructs MRIs of a ~400ms temporal resolution, with an inference time of ~10ms/volume. In XCAT experiments, DREME-GSMR achieved mean(s.d.) SSIM, tumor center-of-mass-error(COME), and DSC of 0.92(0.01)/0.91(0.02), 0.50(0.15)/0.65(0.19) mm, and 0.92(0.02)/0.92(0.03) for dynamic reconstruction/real-time imaging. For the physical phantom, the mean target COME was 1.19(0.94)/1.40(1.15) mm for dynamic/real-time imaging, while for volunteers and patients, the mean liver COME for real-time imaging was 1.31(0.82) and 0.96(0.64) mm, respectively.
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All authors Jiacheng Xie, Hua-Chieh Shao, Can Wu, Ricardo Otazo, Jie Deng, Mu-Han Lin, Tsuicheng Chiu, Jacob Buatti, Viktor Iakovenko, You Zhang
Applied Physics
Blue Organic Light-Emitting Diodes with External Quantum Efficiencies over 20% Based on Europium(II) Emitters
Mahmoud Soleimani, Toni Bärschneider, Felix Kaden et al. (+4 more)
The realization of blue electroluminescence with high efficiency and lifetime remains a long-standing hurdle for organic light-emitting diode (OLED) technology to overcome. Divalent Europium [Europium(II)] complexes offer a fundamentally distinct pathway toward this goal, as their atomic 4f-5d transitions yield single-Gaussian, spectrally pure emission with theoretical 100%
exciton utilization in electroluminescence and no involvement of fragile organic bonds in the emissive process. Here, we present a rigid aza-crown europium(II) complex (Eu5NHCrown) that achieves near-unity photoluminescence
quantum yield with bright, pure-blue emission and its incorporation in OLEDs. The emitter complex sublimes without decomposition and can be processed by industry-standard
vacuum deposition. A bottom-emitting, single-host OLED architecture delivers an external
quantum efficiency (EQE) of 20.7% with minimal roll-off (19.3% at 1000 cd m${}^{-2}$ ) and a narrowband electroluminescence with Commission Internationale de l'Eclairage (CIE) coordinates of (0.12, 0.25). This ligand design provides enhanced steric shielding of the Eu(II) center, enabling the highest reported efficiency among
vacuum-deposited Eu(II)- based blue OLEDs while maintaining performance at high luminance. These results reveal the true potential of divalent Europium 4f-5d transitions for high-efficiency blue OLEDs, establishing a molecular design concept that bridges atomic-transition efficiency with the processability of organic materials.
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All authors Mahmoud Soleimani, Toni Bärschneider, Felix Kaden, Roman Tkachov, Sebastian Schellhammer, Sebastian Reineke, Carsten Rothe
Applied Physics
High-Efficiency Deep Blue Single-Gaussian Europium(II) Emitters and their Emitter-Host Interactions
Mahmoud Soleimani, Paulius Imbrasas, Jan-Michael Mewes et al. (+6 more)
Eu(II) complexes are attractive emitters for deep-blue organic light-emitting diodes (OLEDs) due to their narrow, parity-allowed 4f-5d emission; however, their implementation in
vacuum-processed OLEDs has remained limited. Here, we introduce a new molecular design concept for Eu(II) emitters, in which a crown-ether ligand is combined with carborate anions to define the coordination environment and improve steric shielding of the europium center. Based on this design, we present two emitters that combine narrow deep-blue photoluminescence with
quantum yields approaching 90% and sufficient thermal stability for
vacuum deposition. As the excited state dynamics of this emitter class are different from most conventional OLED emitters and the pathway to maximum luminescence efficiency in thin films is not fully established, we study interactions between Eu(II) complexes and the host environment, based on density functional theory and time-resolved experiments. We identify steric shielding of the Eu(II) core and energetic confinement of the excited 5d electron, defined by molecular design as key factors governing efficient luminescence, providing a roadmap for rational design of Eu(II) emitters. Together, these results establish a basis for higher-efficiency and deeper blue OLEDs incorporating Eu(II) emitters.
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All authors Mahmoud Soleimani, Paulius Imbrasas, Jan-Michael Mewes, Felix Kaden, Stephanie Anna Buchholtz, Karl Leo, Sebastian Schellhammer, Carsten Rothe, Sebastian Reineke
Applied Physics · Cross-listed from cs.LG
Weighted Bayesian Conformal Prediction
Xiayin Lou and Peng Luo
Machine learning predictors are rarely deployed on data that match the data they were calibrated on. Conformal prediction and its risk-control generalization promise distribution-free guarantees at deployment, but only under exchangeability or a
covariate shift whose likelihood ratio is known exactly – and the estimated-ratio case, the one that occurs in practice, is explicitly open, out of reach of the
Bayesian construction available under exchangeability. We reach it from a different starting point: pushing a Dirichlet process prior on the calibration distribution through the likelihood ratio yields the exact
Bayesian posterior over the deployed risk. That posterior is exact given the weight function, which at deployment is itself estimated, so we further prove finite-sample upper and lower bounds on the risk the selected threshold actually incurs, in terms of the weight-estimation error and the conditional shift. On synthetic and real
covariate shifts, in regression and classification, the realized failure rate then stays close to target where shift-blind selection fails, and what deployment receives is a posterior over the risk of every threshold rather than a single number.
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Instrumentation and Detectors
Development of a Modular Current-Mode NaI(Tl) Detector Array for Parity Odd (n,{\gamma}) Cross Section Measurements
J. T. Mills, J. G. Otero Munoz, K. Dickerson et al. (+26 more)
The
Neutron Optics Parity and Time-Reversal Violation Experiment (NOPTREX) Collaboration has developed a modular array of 24 NaI(Tl) detectors to measure parity and time-reversal
symmetry violation in
neutron-
nucleus interactions. These detectors feature custom electronics that allow for operation in pulse or current mode. This paper describes the design, construction, characterization, and testing of the detectors in this array. We demonstrate the ability of the array to detect parity-odd asymmetries in
neutron resonances by observing the known 0.7 eV parity-violating resonance in 139La in measurements at LANSCE.
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All authors J. T. Mills, J. G. Otero Munoz, K. Dickerson, I. Britt, A. Couture, J. Doskow, J. Fry, I. Ide, M. Kitaguchi, R. Kobayashi, M. Luxnat, A. Moseley, R. Nakabe, I. Novikov, K. Oikawa, T. Oku, T. Okudaira, A. Quintinar-Peña, A. Richburg, S. Samiei, D. Schaper, H. M. Shimizu, D. Slone, W. M. Snow, S. Takada, S. Takahashi, Y. Tsuchikawa, G. Visser, J. Winkelbauer
Data Analysis and Statistics · Cross-listed from nlin.CD
Anticipating tipping in spatiotemporal systems with machine learning
Smita Deb, Zheng-Meng Zhai, Mulugeta Haile et al. (+1 more)
In nonlinear dynamical systems, tipping refers to a critical transition from one steady state to another, typically catastrophic, steady state, often resulting from a saddle-node bifurcation. Recently, the machine-learning framework of parameter-adaptable reservoir computing has been applied to predict tipping in systems described by low-dimensional
stochastic differential equations. However, anticipating tipping in complex spatiotemporal dynamical systems remains a significant open problem. The ability to forecast not only the occurrence but also the precise timing of such tipping events is crucial for providing the actionable lead time necessary for timely mitigation. By utilizing the mathematical approach of non-negative
matrix factorization to generate dimensionally reduced spatiotemporal data as input, we exploit parameter-adaptable reservoir computing to accurately anticipate tipping. We demonstrate that the tipping time can be identified within a narrow prediction window across a variety of spatiotemporal dynamical systems, as well as in CMIP5 (Coupled Model Intercomparison Project 5)
climate projections. Furthermore, we show that this reservoir-computing framework, utilizing reduced input data, is robust against common
forecasting challenges and significantly alleviates the computational overhead associated with processing full spatiotemporal data.
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All authors Smita Deb, Zheng-Meng Zhai, Mulugeta Haile, Ying-Cheng Lai
Biological Physics · Cross-listed from quant-ph
Agent Choice via Quantum Flux in Living Systems
R. E. Kastner
A basic model is provided that places active, intentional choices by biological organisms on a solid physical footing. The model is provisionally called "
Agent Choice via
Quantum Flux." It brings to bear specific physics on living systems in a way that allows for intentional choices not pre-determined by physical laws, but remaining consistent with those laws. It does so by exploring a possible many-to-one relation of
quantum states to
agent choices, with a parallel to the relation of thermodynamic microstates to macrostates.
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Computational Physics
Operator Learning for Predicting Bulk Wave Parameters of Spectral Wave Models
Shukai Cai, Sourav Dutta, Mark Loveland et al. (+4 more)
The impact of wave-induced forcing on the mean water level and nearshore currents is typically modeled through excess momentum fluxes, also known as radiation stresses, and their spatial gradients. Accurate storm surge prediction requires coupled circulation and wave models, but the high computational cost of numerical wave models limits their temporal resolution. In this work, we explore a proof-of-concept application of Deep Operator Networks (DeepONets) as a surrogate for the Simulating WAves Nearshore (SWAN) numerical wave model. Unlike grid-dependent surrogate models, DeepONets learn the underlying continuous operator, and thus, can provide highly efficient prediction while enabling discretization-invariant inference. The proposed surrogate model is evaluated using two distinct 1-D and 2-D steady-state numerical examples with variable boundary wave conditions and wind fields. When applied to a realistic numerical example of steady-state wave simulation in Duck, NC, the DeepONet surrogate improves computational efficiency by four orders of magnitude. Furthermore, the model demonstrates consistently high accuracy in predicting the significant wave height and the x- and y- components of the radiation stress gradient, by achieving relative L_2 errors bounded by 1.91%, 10.98%, and 6.88%, respectively, across all unseen test scenarios.
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All authors Shukai Cai, Sourav Dutta, Mark Loveland, Eirik Valseth, Peter Rivera-Casillas, Corey Trahan, Clint Dawson
Data Analysis and Statistics · Cross-listed from gr-qc
Gauge Theoretic Signal Processing I: The Commutative Formalism for Single-Detector Adaptive Whitening
James Kennington and Joshua Black
We present a
geometric framework for adaptive whitening in gravitational-wave detectors, reformulating the problem from a sequence of spectral factorizations to parallel transport on a principal bundle. We identify the whitening filter as a section over the
manifold of power spectra and derive the minimum-phase connection as the unique
geometric structure that enforces signal causality while preserving signal-to-noise ratio. This construction yields a rigorous definition of
geometric drift, a coordinate-independent scalar measuring the intrinsic instability of the detector noise floor. The central result is the flatness
theorem, which proves that the
curvature of the connection vanishes for scalar fields. This establishes a holonomic update law, guaranteeing that the optimal filter correction is path-independent and determined solely by the instantaneous noise state, free from
geometric phase or hysteresis. This framework unifies the static theory of Wiener-
Hopf factorization with the dynamic requirements of real-time control, providing a rigorous certification for the stability of zero-latency calibration routines and establishing a foundation for gauge-theoretic signal processing (GTSP) in next-generation detector networks. In the wider context of gravitational-wave astronomy, this result governs the latency and fidelity of low-latency searches and provides the single-detector foundation for the multi-detector network extensions of the framework. The update law is integrated into a production matched-filter pipeline and validated on detector data in a companion paper.
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Chemical Physics
Coupled-Cluster Imaginary-Time Evolution and the Coupled-Cluster Energy Variance
Yuhang Ai, Huanchen Zhai and Garnet Kin-Lic Chan
We discuss a coupled-cluster formalism for carrying out imaginary-time
evolution from an arbitrary reference, and study the properties of the resulting
evolution trajectories. The
evolution converges to a solution of the standard coupled-cluster amplitude equations in the long-time limit if a finite valued limit exists, but when such a limit does not exist, the trajectories still contain additional information beyond the standard solutions. We introduce the coupled-cluster energy variance which through its minima identifies physically regularized coupled-cluster amplitudes when the solutions of the amplitude equations are unreasonable. We demonstrate the value of this formalism in several exploratory examples within single- and multi-reference coupled-cluster formulations.
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Computational Physics · Cross-listed from cond-mat.soft
Two-dimensional active polar semiflexible polymer under shear flow
A. Lamura and R. G. Winkler
The nonequilibrium structural and dynamical properties of semiflexible active polar
polymers subject to linear flow are studied by numerical simulations. Filaments are confined in two dimensions and immersed in a fluid described by the Brownian Multiparticle Collision Dynamics approach. The applied shear flow causes conformational changes of a
polymer, aligns it along the flow direction, and induces a tumbling motion at large flow rates. In an intermediate, activity-dependent shear-rate regime, a characteristic scaling exponent for the mean-square end-to-end distance along the gradient direction is observed. This exponent appears to be determined by the semiflexibility of the
polymer. The tumbling dynamics exhibits a characteristic time, with a stronger dependence on the Weissenberg number than that of flexible active or passive
polymers. Activity strongly impacts the rheological properties of the semiflexible
polymers, and even implies a negative viscosity for weak flows. At very large values of the shear rate, shear dominates over activity and passive-
polymer behavior is assumed.
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Optics · Cross-listed from quant-ph
Directional and correlated optical emission from a waveguide-engineered molecule with local control
Clara Henke, Thomas Wilkens Sandø, Vasiliki Angelopoulou et al. (+10 more)
Radiative coupling between
quantum emitters leads to a range of spectacular emission phenomena. Dicke studied the foundations of collectively enhanced and suppressed decay, commonly referred to as super- and subradiance. Collective effects can further result in directionality of the emission, thus offering a complimentary implementation of
chiral quantum optics. Waveguide
quantum electrodynamics (QED) allows coupling between spatially separated emitters, enabling selective driving. In this work, we control the emission direction for a pair of
quantum dots embedded in a bidirectional photonic
crystal waveguide offering independent electrical tuning. Notably the emitters are 13 \micro m apart, which corresponds to 26 effective wavelengths, but are nevertheless radiatively coupled. The directionality arises from a dispersive dipole-dipole interaction, which shifts the energy of the collective states, so that the emitter pair effectively forms an artificial molecule. We show that the emission direction can be switched from left- to rightwards by manipulating the relative driving phase while collectively exciting the emitters. In addition, we observe directional
photon statistics under continuous driving, with, for example, single
photons detected on one output port, and
photon pairs on the other. With pulsed excitation, both emitters are fully inverted and correlated
photon pairs are observed in time-resolved intensity correlation measurements. This work demonstrates a novel implementation of
chiral quantum optics using
quantum dots coupled via a non-
chiral waveguide, and reports key steps for scaling up as a multi-emitter waveguide QED platform.
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All authors Clara Henke, Thomas Wilkens Sandø, Vasiliki Angelopoulou, Lena Maria Hansen, Alexey Tiranov, Oliver August Dall'Alba Sandberg, Zhe Liu, Leonardo Midolo, Nikolai Bart, Arne Ludwig, Anders Søndberg Sørensen, Peter Lodahl, Cornelis Jacobus van Diepen
Atmospheric and Oceanic Physics
Calibration of a neural network ocean closure for improved mean state and variability
Pavel Perezhogin, Alistair Adcroft and Laure Zanna
Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To address these biases, parameterization coefficients are typically tuned ad hoc. Here, we formulate parameter tuning as a calibration problem using Ensemble Kalman Inversion (EKI). We optimize parameters of a neural network parameterization of mesoscale eddies in two idealized ocean models at coarse resolution. The calibrated parameterization reduces errors by factors of 1.7-3.3 in the time-averaged fluid interfaces and their variability compared to the unparameterized model, depending on the metric and configuration. The EKI method is robust to noise in time-averaged statistics arising from chaotic ocean dynamics. Furthermore, we propose an efficient calibration protocol that bypasses integration to statistical
equilibrium by carefully choosing an initial condition. These results demonstrate that systematic calibration can substantially improve coarse-resolution ocean simulations and provide a practical pathway for reducing biases in global ocean models.
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Fluid Dynamics
Transonic flow past the complex cavity-sub-cavity configurations
A. Kuniyil, H. Bansal, J. J. Patel et al. (+6 more)
The study investigates the physics of unsteady flow in complex cavity geometries operating in the transonic regime. A two-dimensional Detached Eddy Simulation (DES) approach is used for the preliminary analysis. The cavity configuration examined in this work arises from the integration of a scramjet engine with a launch vehicle. In this integrated geometry, the isolator section serves as a deep sub-cavity, while the Single Expansion Ramp Nozzle (SERN) constitutes the primary cavity. The combined arrangement therefore constitutes a complex cavity-sub-cavity system, which is referred to as such throughout the paper. The qualitative analysis revealed a feedback loop within the complex cavity-sub-cavity system, leading to high-pressure oscillations across the geometry. A monotonic increase in pressure loading is observed with increasing Mach number. Varying the cavity
topology demonstrated that modifications to the primary cavity geometry strongly alter shear-layer dynamics and significantly affect the pressure distribution within the cavity-sub-cavity system. To mitigate adverse pressure oscillations, passive control strategies, including trailing-edge wall chamfering and a ventilated (slotted) sub-cavity, are investigated. Among the configurations studied, the slotted sub-cavity case exhibits the most pronounced suppression of pressure loads, particularly on the sub-cavity end wall. Spectral Proper Orthogonal Decomposition (SPOD) analysis also revealed the restructuring of dominant coherent modes in response to topological variations and to the implementation of passive control, providing insight into the underlying governing mechanism.
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All authors A. Kuniyil, H. Bansal, J. J. Patel, R. Kumar, R. Sriram, G. Kanagaraj, Niranjan S. Ghaisas, H. Ogawa, S. K. Karthick
Space Physics · Cross-listed from astro-ph.SR
New insights from cross-correlation studies between solar activity indices and cosmic-ray flux during Forbush decrease events
Mihailo Savić, Nikola Veselinović, Aleksandar Dragić et al. (+4 more)
Observed
galactic cosmic ray intensity can be subjected to a transient decrease. These so-called Forbush decreases are driven by coronal mass ejection induced shockwaves in the heliosphere. By combining in situ measurements by space borne instruments with ground-based
cosmic ray observations, we investigate the relationship between
solar energetic particle flux, various
solar activity indices, and intensity measurements of
cosmic rays during such an event. We present cross-correlation study done using
proton flux data from the SOHO/ERNE instrument, as well as data collected during some of the strongest Forbush decreases over the last two completed
solar cycles by the network of
neutron monitor detectors and different
solar observatories. We have demonstrated connection between the shape of
solar energetic particles fluence spectra and selected coronal mass ejection and Forbush decrease parameters, indicating that power exponents used to model these fluence spectra could be valuable new parameters in similar analysis of mentioned phenomena. They appear to be better predictor variables of Forbush decrease magnitude in interplanetary
magnetic field than coronal mass ejection velocities.
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All authors Mihailo Savić, Nikola Veselinović, Aleksandar Dragić, Dimitrije Maletić, Dejan Joković Vladimir Udovičić, Radomir Banjanac, David Knežević
Physics Education · Cross-listed from astro-ph.SR
The Wonderful World of Binary Stars
Andrea Barone, Henri M. J. Boffin, Beatrice Caccherano et al. (+6 more)
During the 2026 ESO La Silla Observing school, about twenty students attended lectures and performed observations to learn various aspects of observational astronomy. The school, which took place during the first two weeks of February 2026, made use of EFOSC2/NTT and HARPS+NIRPS/3.6m. One of the groups was devoted to the study of binary
stars. Several projects were considered and followed up by some of the six students in this group. The first subgroup used HARPS to study the Rossiter-McLaughlin effect in binary
stars to infer the relative inclination of the rotation axis of the primary with respect to the orbital plane. A detailed study of the contact binary system HD 115264 led to the conclusion that the primary is well aligned, likely as a result of strong
tidal forces within the binary. The second subgroup analysed blue straggler
stars (BSS) in open clusters, using both HARPS and EFOSC2. With HARPS, they looked at some well-known long-period binary with the aim of determining their chemical abundances, thereby confirming their membership to the cluster, as well as looking for any chemical anomalies that might be explained by mass transfer. EFOSC2 was used to derive radial velocities of rapidly varying BSS. For one of them - the
star Rediet - the students clearly detected and analysed the radial velocity variations due to the second overtone pulsation, thereby confirming its delta Scuti character. Finally, one student used EFOSC2 to study planetary nebulae (PN) - taking nice images of some of these intricate objects, as well as doing time-resolved photometry and spectra of some others. In one case, the binary nature of the central
star of the PN was proven, confirming some previous estimates done with ZTF. Each subgroup was thus able to obtain useful research results, which we present hereafter.
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All authors Andrea Barone, Henri M. J. Boffin, Beatrice Caccherano, Simona Di Stefano, Akhila Divakaran, Alexandra S. Murphy, María José Rain, Elyar Sedaghati, Paul V. Steimle
Applied Physics
High Breakdown Field Multi-kV UWBG AlGaN Transistors
Seungheon Shin, Kyle Liddy, Jon Pratt et al. (+6 more)
We demonstrate high-performance UWBG AlGaN PolFETs exhibiting a state-of-the-art combination of nearly 1 A/mm on-state current (~ 960 mA/mm) and large breakdown field (> 4.8 MV/cm) in high carrier density (1.15 x 1013 cm-2). Multi-kV robustness is successfully demonstrated exhibiting 1.28 and 2.17 kV by utilizing a gate-connected field plate structures in 3.9 and 6.8 {\mu}m LGD, corresponding to the extremely low specific on-resistance of 1.25 and 2.86 m{\Omega}cm2, respectively. High RF performance is also achieved, providing fT and fMAX, of 8.5 and 15 GHz, respectively, for 3.9 {\mu}m LGD. These results highlight UWBG AlGaN as a platform for both high-voltage RF and power applications.
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All authors Seungheon Shin, Kyle Liddy, Jon Pratt, Can Cao, Yinxuan Zhu, Brianna A. Klein, Andrew Armstrong, Andrew A. Allerman, Siddharth Rajan
Applied Physics · Cross-listed from cond-mat.mes-hall
Tunable Valley Polarization in Diamond
Nattakarn Suntornwipat, Jan Isberg and Saman Majdi
Device stability is essential for
quantum information technologies, where reliable control of electronic states is crucial. Diamond valleytronics offers a promising platform by exploiting the valley degree of freedom to store and manipulate information. In this work, we demonstrate a diamond-based valley transistor with a dual-gate, two-drain architecture that enables tunable valley-polarized transport via gate voltage modulation. By leveraging the significant effective-mass anisotropy of diamond's conduction band valleys, this architecture provides control over spatial distribution and transit times. We further demonstrate that valley-polarized transport in diamond is remarkably robust against thermal variations over macroscopic distances. These results demonstrate the resilience of valley states and highlight diamond's potential for energy-efficient valleytronic devices in next-generation
quantum and high-power electronics.
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Optics
Inverse design of waveguide grating mode converters using artificial neural networks
Ali Mohajer Hejazi and Vincent Ginis
Machine learning techniques, notably various deep neural network methods, are instrumental in processing extensive and intricate data sets in engineering and scientific fields. This paper shows how deep neural networks can inversely design cascaded-mode converting systems, particularly the waveguide gratings that implement selective mode conversion upon reflection. Neural networks can map the grating's physical features to scattering parameters of the modes reflected from the grating. The trained networks can then be utilized to inversely design the gratings based on the desired values of the scattering parameters. The process of the inverse design involves using the technique of gradient descent of a defined loss function. Minimizing this loss function leads to calculating more accurate features fulfilling the desired scattering parameters.
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Optics
Too Big, Too Small, Too $O_2$: The Pandoro Effect from Oxygen Gradients in Tomographic Volumetric Additive Manufacturing
Riccardo Rizzo, Felix Wechsler, Qianyi Zhang et al. (+1 more)
Tomographic Volumetric Additive Manufacturing (TVAM) enables rapid, layerless biofabrication; however, its application to thermoreversible hydrogels is often compromised by complex chemical kinetics. In this study, we identify and characterize a recurrent printing artifact - termed the Pandoro effect - manifesting as a truncated-cone distortion caused by premature polymerization at the vial bottom and inhibition at the top. We demonstrate that this phenomenon originates from a vertical oxygen gradient driven by the thermal hysteresis of resin preparation: heating depletes dissolved oxygen, while subsequent cooling induces
diffusion-limited re-oxygenation from the air-resin interface. To mitigate this, we present a multi-tiered strategy. First, we introduce a coupled ray-optical and photochemical optimization model that rigorously accounts for spatially heterogeneous inhibitor concentrations. Unlike conventional threshold-based approaches, this differentiable framework explicitly simulates the spatiotemporal reaction-
diffusion dynamics of oxygen depletion, allowing the inverse solver to predictively compensate for local inhibition gradients. Complementing this algorithmic correction, we validate two process-based interventions: the elimination of the air-resin interface and the control of headspace atmosphere. We demonstrate that these strategies effectively suppress the Pandoro effect, and are compatible with
cell-laden resins. This work establishes guidelines for reproducible volumetric bioprinting and expands our open-source Dr.TVAM platform with advanced polymerization modeling capabilities.
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All authors Riccardo Rizzo, Felix Wechsler, Qianyi Zhang, Christophe Moser
Computational Physics · Cross-listed from cond-mat.dis-nn
DYNAMITE: A high-performance framework for solving Dynamical Mean-Field Equations
Johannes Lang, Vincenzo Citro, Luca Leuzzi et al. (+1 more)
Understanding the dynamics of systems evolving in complex and rugged energy landscapes is central across physics, economics, biology, and computer science. Disordered mean-field models provide a powerful framework, as exact Dynamical Mean-Field Equations (DMFE) can be derived. However, solving the DMFE – a set of coupled integral-differential equations for two-time functions – remains a major numerical challenge. So far, large-time solutions of DMFE rely either on analytical approaches, such as the Cugliandolo–Kurchan ansatz based on assumptions like weak ergodicity breaking (which is known to fail in some cases), or on numerical integrations that reliably reach times $O(10^3)$ and extend further only via poorly controlled approximations. Consequently, no general method currently exists to solve DMFE at very long times, limiting the study of slow dynamics in complex landscapes. We present Dynamite (DYNAmical Mean-fIeld Time
Evolution solver), a high-performance framework for solving DMFE up to unprecedented times $t=O(10^7)$. It combines non-uniform interpolation, adaptive time stepping, and numerical `renormalization' of
memory, enabling accurate evaluation of history integrals. Its asymptotic runtime is linear, with sublinear
memory scaling. Stability and precision are ensured via an adaptive Runge–Kutta scheme and periodic sparsification of the past. Dynamite achieves orders-of-magnitude speedups over uniform-grid methods while maintaining accuracy and reproducibility on CPU and GPU architectures.
Benchmarks on glassy mean-field models, including the mixed spherical $p$-
spin system, demonstrate access to aging and relaxation regimes previously out of reach. The framework provides a reproducible and extensible foundation for studying long-
memory dynamical systems.
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All authors Johannes Lang, Vincenzo Citro, Luca Leuzzi, Federico Ricci-Tersenghi
Physics Education
The Quantum Education Ecosystem: A Review of Global Initiatives, Methods, and Challenges
Sara Ayman Metwalli, Aryan Iliat, Steven Thomas et al. (+3 more)
Quantum information science and engineering (QISE) is advancing rapidly, creating an urgent demand for a
quantum-literate, technically proficient workforce. Despite this need,
quantum education initiatives remain fragmented across regions, educational levels, and instructional approaches, which constrains their scalability and overall impact. This paper offers a structured analysis of the current
quantum education ecosystem by synthesizing global initiatives, pedagogical strategies, and emerging trends.
Quantum education is examined through a dual framework that considers both learner progression and instructional methodology, emphasizing the
evolution of educational approaches from conceptual exposure to formal
reasoning and practical application. Analysis of data from international programs and academic literature reveals key challenges, including inequitable access, absence of standardized curricula, limited empirical evaluation, and discontinuities between educational stages.
Quantum education is more accurately conceptualized as a non-linear ecosystem rather than a traditional pipeline, characterized by multiple entry points, feedback mechanisms, and critical transition gaps. Based on this perspective, directions are proposed for developing more coherent, inclusive, and scalable educational frameworks that align with workforce requirements and technological progress. This work presents a unified perspective on the
quantum education landscape and outlines actionable strategies to enhance global
quantum literacy and workforce preparedness.
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All authors Sara Ayman Metwalli, Aryan Iliat, Steven Thomas, Suresh Nair, Zizwe A. Chase, Russell R. Ceballos
Computational Physics · Cross-listed from cs.LG
Asymptotic-Preserving Neural Networks for Viscoelastic Parameter Identification in Multiscale Blood Flow Modeling
Giulia Bertaglia and Raffaella Fiamma Cabini
Mathematical models and numerical simulations offer a non-invasive way to explore cardiovascular phenomena, providing access to quantities that cannot be measured directly. In this study, we start with a one-dimensional multiscale blood flow model that describes the viscoelastic properties of arterial walls, and we focus on improving its practical applicability by addressing a major challenge: determining, in a reliable way, the viscoelastic parameters that control how arteries deform under pulsatile pressure. To achieve this, we employ Asymptotic-Preserving Neural Networks that embed the governing physical principles of the multiscale viscoelastic blood flow model within the learning procedure. This framework allows us to infer the viscoelastic parameters while simultaneously reconstructing the time-dependent
evolution of the state variables of blood vessels. With this approach, pressure waveforms are estimated from readily accessible patient-specific data, i.e., cross-sectional area and velocity measurements from Doppler ultrasound, in vascular segments where direct pressure measurements are not available. Different numerical simulations, conducted in both synthetic and patient-specific scenarios, show the effectiveness of the proposed methodology.
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Instrumentation and Detectors · Cross-listed from astro-ph.IM
Science of Cryogenic sub-Hz cROss torsion bar detector with quantum NOn-demolition Speed meter (CHRONOS)
Yuki Inoue, Hsiang-Yu Huang, Vivek Kumar et al. (+3 more)
The frequency band between $0.1$ and $10\mathrm{Hz}$ remains largely unexplored in gravitational-wave astronomy due to strong seismic, Newtonian, and suspension thermal noise that limit ground-based detectors. The Cryogenic sub-Hz cROss torsion-bar detector with
quantum NOn-demolition Speed meter (CHRONOS) is a novel detector concept designed to access this frequency range from the ground. CHRONOS combines cryogenic torsion-bar test masses with a triangular Sagnac interferometer implementing a speed-meter readout, which suppresses
quantum radiation-pressure noise and enables
quantum non-demolition measurements in the sub-Hz regime. The detector targets a strain sensitivity of $h \sim 10^{-18}\mathrm{Hz^{-1/2}}$ around $2\mathrm{Hz}$ and
stochastic gravitational wave background of $\Omega_{GW} \sim 2\times 10^{-3}$ at $2\mathrm{Hz}$. This sensitivity opens a new observational window between space-based detectors such as LISA and ground-based interferometers, enabling observations of intermediate-mass black hole binaries, searches for
stochastic gravitational-wave backgrounds, and tests of macroscopic
quantum measurements.
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All authors Yuki Inoue, Hsiang-Yu Huang, Vivek Kumar, Mario Juvenal S. Onglao II, Daiki Tanabe, Ta-Chun Yu