Atmospheric and Oceanic Physics
OCELOT: Direct Atmospheric Forecasting from Heterogeneous Earth Observations Using a Graph-Transformer Hybrid Model
Azadeh Gholoubi, Ronald McLaren, Mu-Chieh Ko et al. (+7 more)
This study presents OCELOT (Observation-Centric Estimation and Learning for Outlook Trajectories), a global machine-learning
forecasting system that predicts future Earth observations directly from heterogeneous satellite and in-situ measurements. Unlike data-driven weather models trained on gridded reanalysis states, OCELOT operates natively in observation space, preserving instrument-specific sampling, viewing geometry, and measurement characteristics. The system combines per-instrument graph-
attention encoders, a shared spherical icosahedral latent mesh, a hybrid sliding-window
Transformer/spatial graph neural network processor, and metadata-conditioned decoders to produce forecasts up to 12 h ahead. OCELOT is trained on observations for the years 2015 through 2023, validated on the year 2024, and evaluated out of sample on 2025 observations across satellite radiances, radiosondes, aircraft, and surface networks. In the 2025 evaluation, OCELOT produces spatially coherent +12 h forecasts across independent observing systems: microwave temperature-sounding channels show RMSE values of 1.24-1.87 K, while the more surface- and cloud-sensitive AVHRR infrared window channel shows a higher RMSE of 3.95 K. Vertical profile diagnostics show physically consistent radiosonde and aircraft temperature structure. Surface forecasts remain stable through 12 h, with 2-m air-temperature RMSE increasing from about 3.2 K at +3 h to about 3.6 K at +12 h. In paired observation-space comparisons, OCELOT remains less accurate than operational GFS but substantially outperforms persistence at longer lead times for 2-m temperature and 10-m wind components. These results demonstrate that observation-space
forecasting can recover large-scale atmospheric structure and provide meaningful short-range skill without reanalysis supervision.
Read the full abstract ▾
All authors Azadeh Gholoubi, Ronald McLaren, Mu-Chieh Ko, Xin Jin, Nicholas Esposito, Andrew Collard, Cory Martin, Russ Treadon, Daniel Holdaway, Daryl Kleist
Medical Physics · Cross-listed from stat.ML
Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastography
Boyuan Deng, Kshitiz Upadhyay and Michael Shields
The Helmholtz equation governs time-harmonic wave propagation, and in dissipative media a complex modulus renders its squared wavenumber $\kappa^2$ complex. Inferring such fields from sparse, noisy data calls for solvers that also quantify their own uncertainty. Physics-informed Gaussian-process (GP) regression supplies this by returning a posterior over the solution, yet operator-conditioned formulations have been developed almost exclusively for real-valued fields. We extend operator-informed GP regression to complex-valued Helmholtz problems by realifying the complex operator into an equivalent coupled real block, which enables inference with standard real-valued GP conditioning. The construction admits a family of priors, from a proper diagonal prior to coregionalized and multiscale variants, and conditions on PDE residuals and boundary traces. On
benchmark problems in one to three dimensions, the solver is competitive with finite-difference and neural-network baselines at a far smaller interior-constraint budget. Unlike those deterministic baselines, it returns a posterior over the complex wavefield rather than a point estimate. Applied to in vivo
brain magnetic resonance elastography, a proper multiscale prior reconstructs the shear curl field to a correlation of $0.77$ with measurement, above a $0.75$ target. The gain arises from the multiscale kernel rather than from real–imaginary coupling. We further identify a low-frequency accuracy ceiling set by model mismatch and a posterior uncertainty that is not yet calibrated. Calibrated uncertainty therefore emerges as the central next step for probabilistic wavefield inference in dissipative media.
Read the full abstract ▾
Applied Physics · Cross-listed from quant-ph
Sail membranes for optomechanical accelerometry
Atkin D. Hyatt, Mitul Dey Chowdhury, Mahir Chowdhury et al. (+2 more)
Strained membrane resonators have emerged as a promising platform for optomechanical accelerometry; however, the desired combination of low frequency and high $Q$-mass product requires a rethinking of their dissipation dilution engineering. Applying
Bayesian optimization to a Si$_3$N$_4$ membrane, we discover a class of sail-like trampoline resonators in which the frequency is decreased by an order of magnitude while preserving the $Q$-mass product. We demonstrate centimeter-scale sails with kHz frequencies, $Q\sim10^7$ and $Q\times\text{mass}\sim$ 10 g. Vertically integrating a 7 kHz device with a nanoribbon, we realize a monolithic cavity optomechanical accelerometer with a room temperature thermal noise of $40\;\text{n}g_0/\sqrt{\text{Hz}}$, sufficient to resolve $\mu g_0/\sqrt{\text{Hz}}$ ambient vibration over a bandwidth of 4 kHz with a displacement imprecision of $10^{-14}\;\text{m}/\sqrt{\text{Hz}}$. Cryogenic arrays of sail membranes may be attractive for new physics searches and distributed
quantum sensing experiments.
Read the full abstract ▾
All authors Atkin D. Hyatt, Mitul Dey Chowdhury, Mahir Chowdhury, Mohamed J. Ahamed, Dalziel J. Wilson
Atomic Physics · Cross-listed from quant-ph
Trigonometric Continuous-Variable Quantum Gates: Realization with Trapped Ions and Nonperturbative Wigner Negativity
Tommaso Rainaldi, Jake Montgomery, Christopher G. Yale et al. (+8 more)
We experimentally realize trigonometric continuous-variable gates on a trapped-ion processor, for which a motional mode acquires a phase proportional to the cosine of its position quadrature, and, for the first time, implement the two-mode generalization, coupling two modes through a single nonlinear phase. Such gates provide an experimentally accessible, nonpolynomial primitive for periodic interactions acting on both compact and noncompact degrees of freedom, including rotor models, sine-Gordon-type systems, and
lattice gauge theories. Scanning gate strength, spatial frequency, and
circuit depth, we resolve via blue-sideband spectroscopy the parity selection rule that fingerprints the exact cosine
evolution, and find that an open-system model incorporating residual thermal occupation and motional dephasing reproduces the data. We then derive the asymptotics of the Wigner negativity generated by these gates and find three scaling regimes. The negativity is beyond all algebraic orders in the gate strength while the negative regions sit in far phase-space tails, becomes linear once they reach the bulk, where it saturates a first-order bound we establish, and logarithmic at strong gate strength. These results expose a general mechanism, first identified here through the cosine gate, by which every finite-order perturbative estimate of a non-Gaussian resource can vanish even though the resource itself remains nonzero. Together, our results establish trigonometric gates as controllable, experimentally realizable building blocks for bosonic
quantum simulation, expanding the class of nonlinear dynamics accessible to continuous-variable
quantum processors.
Read the full abstract ▾
All authors Tommaso Rainaldi, Jake Montgomery, Christopher G. Yale, Brian K. McFarland, Melissa C. Revelle, Daniel Lobser, Edward C. Tortorici, Susan M. Clark, George Siopsis, Matt Grau, Felix Ringer
Fluid Dynamics
Dynamics of a microroller under confinement
Han Gao, Nan Xie, Zaiyi Shen et al. (+3 more)
Rotating particles can translate when placed near a surface, forming microrollers with a wide range of biomedical and microfluidic applications. In this work, we investigate the dynamics of microrollers in confined microchannels with different geometries by combining experiments, numerical simulations, and scaling analysis. In constricted channels, we find that the translational velocity of a microroller decreases as it approaches the constricted region. In both rectangular and cylindrical channels, velocity reversal occurs as the characteristic channel width decreases. Using the force-free condition for free translation, we develop a systematic scaling framework that can be generalized to different channel geometries. The scaling analysis yields functional dependences of the translational velocity on the degree of confinement, which agree well with both experiments and simulations. Importantly, we demonstrate that the viscous stress generated by the far-field rotlet flow governs the observed velocity reduction and reversal, while the translational resistance resulting from the near-field shear flow suppresses translation under tight confinement. The distinct roles of these flow components revealed by our analysis may provide practical guidance for controlling microroller dynamics in confined fluid environments.
Read the full abstract ▾
All authors Han Gao, Nan Xie, Zaiyi Shen, Xiaoping Hu, Shiyuan Hu, Ye Xu
Data Analysis and Statistics · Cross-listed from nucl-th
Beyond Constant Error: Heteroscedastic Bayesian Model Combination for Modeling Unmeasured Nuclei
B. Knight, S. Lalit, P. Giuliani et al. (+4 more)
Experimentally inaccessible regions of the
nuclear chart remain a challenge for global models of atomic
nuclei to predict. This includes exotic
nuclei near particle drip lines, superheavy elements at the extremes of mass and charge, and the
neutron-rich pathways of astrophysical processes in explosive
stellar environments where heavy elements are created. Given that individual
nuclear models are imperfect, deep extrapolations are best approached using model ensembles, which allow for the systematic combination of diverse theoretical predictions. In this study, we employ the recently introduced
Bayesian Model Combination (BMC) method, based on statistical machine learning, that provides robust uncertainty quantification for forecasts using model ensembles. To account for the inherent degradation of predictive power as models extrapolate into the yet-unexplored domain, we introduce a heteroscedastic BMC framework in which the combined theoretical uncertainty is treated as a dynamic quantity. We apply this methodology to an ensemble of realistic energy density functionals with a specific focus on the $Z=46\text{--}52$ isotopic chains. We rigorously validate the approach using both experimental data and synthetic data designed to assess performance in the deep extrapolation regime. Our results demonstrate that the proposed heteroscedastic approach yields superior calibration metrics and provides statistically principled assessments of the particle drip lines.
Read the full abstract ▾
All authors B. Knight, S. Lalit, P. Giuliani, K. Godbey, W. Nazarewicz, A. Ravlić, P. -G. Reinhard
Physics and Society
The Wulff bio-heat transfer model revisited: directional blood enthalpy transport, the biological Peclet number, and implications for laser-induced thermal therapy
Valerio D'Alessandro, Matteo Falone, Luca Giammichele et al. (+1 more)
Bio-heat transfer models play a fundamental role in predicting temperature fields during
laser-induced thermal therapy (LITT). Among continuum bio-heat transfer models, the Pennes equation remains the most widely adopted formulation. However, by representing blood perfusion as an isotropic volumetric source, it neglects the directional transport of thermal energy associated with blood flow. The Wulff model overcomes this limitation by incorporating blood averaged enthalpy transport directly into the heat flux. Despite its physical significance, the derivation of the Wulff formulation and the assumptions required to obtain its governing equation remain only briefly discussed in the original work. The aim of the present work is twofold. First, the physical formulation originally proposed by Wulff is revisited in order to clarify its derivation and the assumptions required to obtain the governing equation. In particular, the physical origin of the modified heat flux is investigated and an equivalent formulation with an independent metabolic source term is discussed. Second, the resulting model is assessed against the classical Pennes formulation through representative
laser-induced thermal therapy
benchmark problem. The numerical results show that accounting for blood flow directionality may substantially alter the predicted temperature field and the extent of thermal damage. A dimensionless analysis further leads to the definition of a biological Peclet number, which quantifies the magnitude of directional blood enthalpy transport relative to thermal
diffusion. Finally, a simple extension accounting for venous stasis is proposed, mitigating the excessive temperature rise predicted by the original Wulff formulation
Read the full abstract ▾
All authors Valerio D'Alessandro, Matteo Falone, Luca Giammichele, Renato Ricci
Computational Physics · Cross-listed from cond-mat.str-el
$\texttt{iNORG}$: An open-source quantum impurity solver package based on the natural orbitals renormalization group
Jia-Ming Wang, Yi-Heng Tian, Yin Chen et al. (+3 more)
In the context of dynamical mean-field theory (DMFT) calculations for strongly correlated electron systems,
quantum impurity solvers play a central computational role in treating correlated
lattice models and realistic materials. Consequently, developing efficient and robust
quantum impurity solvers remains a key challenge. In this paper, we present an open-source
quantum impurity solver package based on the natural orbitals renormalization group (NORG) method, dubbed $\texttt{iNORG}$. This software delivers high accuracy with reduced computational cost by optimizing the bath representation using natural orbitals and incorporating advanced features such as efficient
Hilbert space selection and efficient
algorithms for computing Green's functions. We first introduce the basic principle of the NORG method and then discuss the implementation details. The software framework, major features, and installation procedure for $\texttt{iNORG}$ are explained as well. Finally, several simple examples are presented to demonstrate the usage of $\texttt{iNORG}$.
Read the full abstract ▾
All authors Jia-Ming Wang, Yi-Heng Tian, Yin Chen, Ru Zheng, Rong-Qiang He, Zhong-Yi Lu
Optics
Magneto-optical hologram lens with microsecond focal switching in magnetic garnet
Hibiki Miyashita, Toshiaki Watanabe, Eri Negishi et al. (+3 more)
Active lenses based on metasurfaces have been demonstrated across broad applications, and their response speeds remain limited to the microsecond range. Here we report the experimental demonstration of a magneto-optical (MO) active lens based on a
magnetic hologram formed on a
magnetic garnet film. A Fresnel zone plate pattern was created using an array of permanent magnets, focusing a 633 nm
laser beam of 12.5 mm diameter to a spot size of 0.7 mm at a focal length of 3.1 m with a modulation depth of ~91%. The focusing state was reversibly switched by applying an external
magnetic field pulse. Through optimization of the
magnetic field application system, a switching speed of 10.8 $\mu$s was achieved, surpassing the operating speeds of conventional active lenses. The intrinsic picosecond-scale response of the MO effect indicates that further improvement in switching speed is achievable, establishing this work as a proof-of-concept for ultra-fast active lenses.
Read the full abstract ▾
All authors Hibiki Miyashita, Toshiaki Watanabe, Eri Negishi, Mitsuteru Inoue, Kazushi Ishiyama, Taichi Goto
Computational Physics
Contact-resolved deployment of the Contour Neurovascular System in patient-specific intracranial aneurysms
Ratnadeep Pramanik, Fina Gießler, Martin Frank et al. (+5 more)
While intrasaccular flow disruptors are increasingly used to treat wide-necked intracranial aneurysms (IAs), many patient-specific computational workflows prescribe a pre-seated device geometry and omit deployment mechanics. This simplification is particularly restrictive for the Contour Neurovascular System (CNS), for which neck coverage, wall apposition, and post-contact motion depend on the deployment process. To address this, we present a contact-resolved finite-element framework that models CNS deployment within patient-specific IAs. We represent the device as a dual-layer interwoven Nitinol braid using geometrically exact beam models, and the aneurysm wall as a deformable hyperelastic shell. Frictional contact governs wire-wire and wire-wall interactions during staged release. The framework is demonstrated across three patient-specific anatomies. In the reference anatomy, the seated configuration is sensitive to the assumed device-wall tangential friction and to release depth relative to the aneurysm neck plane. Frictionless wall contact permits pronounced post-contact sliding, whereas finite tangential friction strongly suppresses residual pole motion. Release depth alters both the onset of wall engagement and the subsequent deployment path.
Geometric placement approaches that prescribe the implanted configuration cannot recover this contact history or the associated wall-supported state. Our framework provides a mechanics-based route to deployed geometries for downstream hemodynamic, fluid-structure interaction, and mechanobiological analyses.
Read the full abstract ▾
All authors Ratnadeep Pramanik, Fina Gießler, Martin Frank, Ivo Steinbrecher, Matthias Mayr, Naomi Larsen, Sylvia Saalfeld, Alexander Popp
Biological Physics
Confinement effects on protein stability in a freezing water environment
Yanis R. Espinosa, H. Ariel Alvarez and C. Manuel Carlevaro
Understanding how
proteins behave at low temperatures remains a central challenge in biophysics, with direct implications for cold denaturation and cryopreservation. While cold denaturation of
proteins in the supercooled
liquid regime has been studied extensively, the behavior of a
protein embedded in a growing ice
lattice remains largely inaccessible to experiments. Here we use molecular dynamics simulations that explicitly capture ice Ih formation to characterize the conformational dynamics of yeast frataxin (Yfh1) as its aqueous environment crystallizes. Using four independent ice-seeded replicas and
liquid-water controls at three temperatures, we first validate the
liquid-solid transition through convergent changes in solvent density, potential energy, and local bond-order parameters (W4, W6). Principal component analysis (PCA), dihedral PCA (dPCA), and free-energy landscapes then reveal that crystallization of the solvent markedly reshapes the accessible conformational space, shifting it from a continuous, highly connected regime in
liquid water toward a discretized landscape dominated by confined states. Complementary analyses of solvent-accessible surface area (SASA), radius of gyration, and hydrogen bonding indicate a solvent-driven reorganization of
protein-water interactions: although first-shell water remains
liquid-like, its surface density increases under freezing, while conformational sampling contracts. Together, these results indicate that
protein behavior at low temperatures is governed not by temperature alone but by the structural organization of the surrounding water. By imposing geometrical constraints on the solvent, ice formation restricts conformational sampling while preserving – and even densifying – the interfacial hydration layer, highlighting the role of water structure as a determinant of
protein stability under freezing conditions.
Read the full abstract ▾
Instrumentation and Detectors
Kaleidoscopic-ray-tracing-based model of the scintillation flash energy deposition in the photomultipliers attached to a strip scintillator
I. V. Vovchenko, T. M. Khamitov and L. V. Inzhechik
Strip scintillator detectors are used nowadays in many fields of applied physics, particularly in medicine, civil engineering, mapping of underground resources, and security. In this work we provide an analytical description of light transport in a cuboid-shaped strip scintillator detector from the scintillation location to the detecting surface. We use kaleidoscopic-ray-tracing approach to reproduce the average time profile of the energy deposition (light collection) in the detecting surface. We demonstrate the applicability of the model on the case of PWO
crystal of $1.5\times 1.5 \times 30$ cm$^3$ size. We show that the results achieved in the model are in good agreement with Monte Carlo (MC) simulation. Notably, the developed model requires dozens of milliseconds to be implemented, thus, it is applicable for real-time calibration of the detector, while the MC simulation takes hours. Also, we highlight that the kaleidoscopic-ray-tracing itself being used in MC simulations can highly speed them up in the case of specular reflectors.
Read the full abstract ▾
Optics
Temporal Fourier Optics Reveals Hidden Hybridized Light-Matter States
Juan-Feng Zhu, Wenjie Zhou, Shihao Feng et al. (+5 more)
Spectral measurements provide fundamental insights into wave systems by revealing resonances, mode hybridization, and light-matter interactions. However, intrinsic dissipation and measurement-related spectral broadening often obscure the spectral signatures of the underlying hybridized light-matter states. Here, we establish a temporal
Fourier optics framework based on a space-time
Fourier correspondence, which interprets spectral broadening as the
Fourier counterpart of temporal attenuation. This perspective introduces a temporal point-spread function (TPSF) that enables direct, synthesis-free reconstruction of the underlying spectral response from experimentally measured spectra by compensating for effective temporal decay before transformation back to the frequency domain. We experimentally validate the framework using deterministic single-molecule Au nanosphere dimers and open Au@Ag nanorod- and nanotriangle-based plasmonic nanocavities coupled to J-aggregate
excitons. Across these distinct platforms, TPSF consistently resolves hidden upper and lower polaritonic branches, revealing hybridized light-matter states and strong coupling that remain inaccessible in conventional scattering spectra. The reconstructed spectra agree closely with the recently developed complex-frequency formalism while providing a substantially simpler and experimentally accessible implementation. More broadly, temporal
Fourier optics establishes a general framework for recovering dissipation-obscured spectral information, opening new opportunities for spectroscopy, imaging, sensing, and inverse wave measurements across photonics and wave physics.
Read the full abstract ▾
All authors Juan-Feng Zhu, Wenjie Zhou, Shihao Feng, Zicheng Song, Ruicong Zhang, Cheng-Wei Qiu, Rengming Liu, Lin Wu
Fluid Dynamics
Metamorphosis of transition between states of limit cycle oscillations in aeroacoustic system
Siddharth Kancharlapalli, Beeraiah Thonti, Sivakumar Sudarshanan et al. (+2 more)
Dynamical systems undergoing transition to oscillatory state exhibit change in the nature of the transition from supercritical to subcritical
Hopf bifurcation or vice versa upon variation of a secondary parameter. This phenomenon is referred to as change of criticality. Many real-world systems undergo transition to oscillatory state that do not fit in the framework of
Hopf bifurcation, and hence the change of criticality. We perform experiments on a ducted turbulent aeroacoustic flow constrained by two orifices separated at a distance apart. We vary the Reynolds number (Re), a bifurcation parameter causing a transition between various limit cycles. We change the distance between the orifices as the secondary parameter. We discover that turbulent aeroacoustic flows exhibit a metamorphosis of the transition from continuous to abrupt through a canard explosion, a bifurcation unique for its continuous yet rapid nature. We observe two distinct abrupt bifurcations, differing in their dynamical states associated with the transition. Understanding this metamorphosis from continuous to abrupt aids in developing low-cost control and preventive strategies for systems undergoing a route to oscillatory instabilities.
Read the full abstract ▾
All authors Siddharth Kancharlapalli, Beeraiah Thonti, Sivakumar Sudarshanan, Ramesh S. Bhavi, R. I. Sujith
Optics
ZPAN: An Organic Nonlinear Optical Crystal for High Intensity THz Generation
Matthew J. Lutz, Sin-Hang Ho, Zachary B. Zaccardi et al. (+5 more)
We report an optimized synthesis and
crystal growth protocol as well as structural, optical, and terahertz (THz) generation characteristics of the organic nonlinear optical
crystal ZPAN ((Z)-1-(((4-Phenylamino)phenylamino)methylene)naphthalen-2(1H)-one). Noncentrosymmetric packing of ZPAN is most reliably achieved by slow evaporation from an acetone-based solvent system, producing long rectangular prisms (7 cm \times 1 cm \times 0.2 cm) with (010) as the main face and [001] as both the polar axis and the direction of elongated
crystal growth. [001] is thus the most effective pump polarization direction for generating THz light via optical rectification. The THz generation characteristics of the developed ZPAN
crystals are determined at different near-infrared irradiation wavelengths,
crystal thicknesses, and pump powers, revealing that ZPAN can generate a peak-to-peak electric field of near 1 MV/cm with a smooth spectrum from 0.5-3.4 THz. Calculated second order nonlinear optical coefficients indicate that both the (100) and (010) faces are theoretically capable of THz generation, though (010) is consistently the dominant growth face. The large size of this face (7 cm \times 1 cm) makes it possible to scale
laser power with aperture size, for use in extremely high-power
laser systems.
Read the full abstract ▾
All authors Matthew J. Lutz, Sin-Hang Ho, Zachary B. Zaccardi, Paige Petersen, Elisha Jones, Stacey J. Smith, David J. Michaelis, Jeremy A. Johnson
Atomic Physics · Cross-listed from nucl-ex
High-precision measurement of the kaonic hydrogen 1s level shift and width with SIDDHARTA-2
M. Bazzi, F. Clozza, C. Guaraldo et al. (+37 more)
Kaonic atoms provide a unique experimental probe of strong interaction in the low-energy regime. In particular, the strong-interaction-induced shift ($\varepsilon_{1\text{s}}$) and width ($\Gamma_{1\text{s}}$) of kaonic hydrogen directly constrain the low-energy antikaon-nucleon ($\bar{K}N$) interaction at threshold and the theoretical description of the $\Lambda$(1405) resonance. We report a new high-precision measurement of kaonic hydrogen X-ray transitions performed by the SIDDHARTA-2 experiment at the DA$\Phi$NE
collider (INFN-LNF), based on an integrated luminosity of 237 pb$^{-1}$. The extracted values, $\varepsilon_{1\text{s}}\,=\,-303.0\,\pm\,17.0\,(stat.)\,\pm\,2.5\,(syst.)$ eV and $\Gamma_{1\text{s}}\,=\,607\,\pm\,62\,(stat.)\,\pm\,6\,(syst.)$ eV, represent the most precise determination to date, improving the precision by approximately a factor-of-two with respect to the previous SIDDHARTA measurement. These results significantly tighten the experimental constraints on theoretical description of the low-energy $\bar{K}N$ interaction.
Read the full abstract ▾
All authors M. Bazzi, F. Clozza, C. Guaraldo, M. A. Iliescu, A. Scordo, F. Sgaramella, D. Sirghi, F. Sirghi, J. Zmeskal, L. Abbene, C. Amsler, F. Artibani, G. Borghi, D. Bosnar, M. Bragadireanu, A. Buttacavoli, M. Carminati, A. Clozza, L. De Paolis, R. Del Grande, K. Dulski, C. Fiorini, I. Friščić, M. Iwasaki, A. Khreptak, S. Manti, J. Marton, C. Milardi, P. Moskal, F. Napolitano, H. Ohnishi, K. Piscicchia, F. Principato, M. Silarski, M. Skurzok, A. Spallone, K. Toho, M. Tüchler, O. Vazquez Doce, C. Curceanu
Optics
Polariton Topological Insulators with Disclinations
Khalil Sabour and Yaroslav V. Kartashov
We introduce polariton topological
insulator on aperiodic array of microcavity pillars with disclination. This unique dissipative and nonlinear topological system can have discrete rotational
symmetry not achievable in structures constructed on periodic arrays that qualitatively affects the structure of topological modes. In such system resonant optical pump can be used for selective excitation of topological states forming on the disclination core in the center, while imposing vorticity on the pump profile allows to excite not only the simplest disclination states, but also
vortex-carrying disclination states. Strong repulsive nonlinearity of polaritonic system leads to increasing with pump amplitude tilt of the resonance curves and bistability, with rich possibilities for control of shapes of nonlinear disclination states within topological gap by pump frequency detuning. Stability analysis predicts remarkable robustness of both
vortex-free and
vortex-carrying nonlinear disclination states in this system. We show that when topological charge of the pump exceeds maximal allowed charge in the disclination array with a given discrete rotational
symmetry, the stationary states form that contain
vortex clusters in their phase distributions. Our results extend the concept of topo-logical
insulators with disclinations to the realm of nonlinear resonantly driven systems and show that rich bistability effects in them can be observed even for excited
vortex-carrying topological states with different topological charges allowed by discrete rotational
symmetry of the system.
Read the full abstract ▾
Atomic Physics · Cross-listed from quant-ph
Quantum observables for probabilistic classical particles
Christof Wetterich
The classical observables of position and momentum are not well adapted to particles in a microphysical situation where typical probability distributions are characterized by a substantial dispersion. We propose the use of more robust
quantum observables for probabilistic classical particles. The
quantum observables are statistical observables which do not take fixed values for a given classical position and momentum. Solutions of the Liouville equation are discussed in the
quantum formalism for classical statistics. Statistical observables are represented by non-commuting operators. No classical correlation function is defined for these observables and Bell's inequalities do not apply. We demonstrate for a general potential how a
quantum system emerges from classical statistics. For the particular cases of a harmonic potential and a Coulomb potential we investigate subsystems which describe all features of a
quantum particle. This covers the discrete energy spectrum of the hydrogen atom and
quantum harmonic oscillator. We discuss the interference for the double-slit experiment. Conserved statistical observables may also be relevant for the probabilistic dynamics of
dust or
planets.
Read the full abstract ▾
Medical Physics · Cross-listed from cs.CV
A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data
Brunnhilde Ponsi, Thomas Carlier, Lara Marteau et al. (+5 more)
Arrhythmogenic left ventricular cardiomyopathy is a genetic myocardial disease difficult to diagnose due to the lack of gold standard criteria. Simultaneous PET/MR imaging, combined with multiparametric quantitative analysis, could facilitate the identification of different profiles related to the phenotype and progression of cardiomyopathy. This preliminary study focuses on a methodological strategy for dealing with PET/MRI data, including inter-patient data linkage and regional analysis. Two-step clustering was applied to T1 and T2 maps, LGE, and 18F-FDG-PET images of 99 patients genetically diagnosed with arrhythmogenic left ventricular cardiomyopathy. Each patient's images were independently z-scored and summed into a single volume, which was clustered into supervoxels. Thirty-two inter-patient groups of supervoxels were obtained by spectral clustering. An "abnormality" score was assigned to each cluster and modality, and used to visualise abnormal regions likely associated with disease. They enabled the generation of automated textual and bullseye health reports for each patient, which were compared with cardiac imager assessments using balanced accuracy in repeated nested cross-validation. This approach was further validated on a larger cohort of 167 numerical phantoms. The reports generated by clustering accurately identified most of the cardiac physicians' observations (BA = 0.76 $\pm$ 0.04 in repeated nested cross-validation on patients, and BA $\ge$ 0.8 on phantoms). Furthermore, the identified abnormal clusters closely matched their visual observations, facilitating the identification of varying degrees of fibrosis or inflammation on the images. This approach enables a more systematic handling of
multimodal PET/MRI data to characterise myocardial heterogeneity in arrhythmogenic left ventricular cardiomyopathy patients.
Read the full abstract ▾
All authors Brunnhilde Ponsi, Thomas Carlier, Lara Marteau, Aurélien Monnet, Thomas Eugène, Jean-Michel Serfaty, Nicolas Piriou, Hatem Necib
Instrumentation and Detectors
Radiation effects on surface and bulk properties of ATLAS18 silicon sensors under low- and high-dose gamma irradiation and annealing
Marcela Mikestikova, Vitaliy Fadeyev, Pavla Federicova et al. (+10 more)
Silicon strip detectors for the ATLAS Inner Tracker (ITk) at the HL-LHC must withstand harsh radiation conditions, including fluences of up to 1.6E15 1 MeV n$_{eq}$/cm$^{2}$ and total ionizing doses (TID) of up to 66 Mrad. These requirements are met using radiation-hard n+-in-p technology implemented in the ATLAS18 silicon strip sensors currently under production. This work presents a combined study of gamma-irradiation effects in ATLAS18 silicon sensors, including both segmented miniature strip sensors (minis) and unsegmented MD8 diodes fabricated on ATLAS18 production wafers. The samples were irradiated with a $^{60}$Co gamma source to multiple low TIDs between 0.5 and 100 krad, corresponding to the dose range relevant for the early operational phase of the ITk tracker. Additional measurements extending up to a few Mrad were performed to investigate the saturation of surface related damage effects. Post-irradiation characterization included measurements of total, bulk, and surface leakage currents, as well as capacitance-voltage measurements used to extract the full depletion voltage. The thermal stability of radiation-induced defects was studied using isochronal annealing between 80{\deg}C and 300{\deg}C and isothermal annealing at 60{\deg}C and 160{\deg}C. In addition, complementary studies of MD8 diodes irradiated to ultra-high doses of several hundred Mrad, well beyond the ATLAS ITk requirements, are included to investigate possible bulk-related effects induced by pure gamma irradiation and their annealing behavior. The combined analysis of low- and ultra-high-dose irradiation provides a comprehensive picture of surface- and bulk-related gamma-induced effects in ATLAS18 silicon sensors and their thermal
evolution.
Read the full abstract ▾
All authors Marcela Mikestikova, Vitaliy Fadeyev, Pavla Federicova, Petr Gallus, Jana Kozakova, Jiri Kroll, Magdalena Kutova, Jiri Kvasnicka, Igor Mandic, Pavel Tuma, Miguel Ullan, Yoshinobu Unno, Iveta Zatocilova
Computational Physics · Cross-listed from nucl-th
NNStar: An end-to-end AI agent for nuclear matter and neutron star physics
Yao Ma, Yong-Liang Ma and Jia-Ying Xiong
Constraining the equation of state of dense matter requires confronting effective models with massive data that spans many orders of magnitude in scale, from sub-saturation
nuclear matter properties to the masses, radii, and
tidal deformabilities of
neutron stars. Exploring the high-dimensional coupling space of such a model and fine tuning it against all of these constraints is a labor- and time-intensive task. We present NNStar, an end-to-end artificial-intelligence
agent that automates this workflow. Rather than a bespoke application, NNStar is delivered as a portable skill for an open large-
language-model (LLM)
agent platform – a self-describing module that pairs worked usage conventions with symbolic and numerical physics engines that (i) build a relativistic mean-field model directly from a Lagrangian, (ii) solve the mean-field equations of motion and evaluate the saturation properties, (iii) construct the $\beta$-
equilibrium equation of state, splice it to a crust, and integrate the Tolman–Oppenheimer–Volkoff equations, and (iv) score the resulting predictions through a
Bayesian joint analysis against
nuclear matter and astrophysical observations. The
agent can read a model, fit its parameters, and report the full set of
nuclear matter and
neutron star observables without human intervention. NNStar therefore provides a new, AI-driven framework for analyzing
nuclear matter and
neutron-
star observations.
Read the full abstract ▾
Data Analysis and Statistics · Cross-listed from q-fin.TR
Detecting unusual trading patterns on cryptocurrency exchanges by means of complexity measures
Jakub Zwydak, Marcin Wątorek, Jarosław Kwapień et al. (+1 more)
Artificial transaction generation remains an important source of potential
market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce
market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading volume, and transaction counts, using tail distributions, autocorrelation functions, multifractal characteristics, approximate
entropy, and detrended cross-correlations. The methodology is applied to BTC, ETH, and XRP traded on Binance, Bitget, KuCoin, and Kraken over the period from April 1 to June 30, 2025. The results reveal a pronounced anomaly on Bitget for BTC and ETH after mid-May 2025. The number of transactions increases sharply, but there is no proportional increase in traded volume or return fluctuations. This regime is characterised by numerous low-volume trades, weaker autocorrelations, reduced multifractal organisation, higher short-pattern irregularity, and weaker cross-correlations involving the transaction-count series. These features are consistent with a noise-like component in trading activity and may indicate artificially increased transaction counts, although they do not provide direct proof of wash trading. The findings show that complexity-based indicators can be useful for detecting exchange-specific trading anomalies that remain hidden in price-based measures.
Read the full abstract ▾
All authors Jakub Zwydak, Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
Space Physics · Cross-listed from astro-ph.SR
Long-Baseline VLF Observations of Solar Flares from Antarctica
Kamen Kozarev, Peter Petkov, Ivaylo Nachev et al. (+4 more)
We present long-baseline Very Low Frequency (VLF) observations of
solar flare-induced ionospheric disturbances obtained at the Bulgarian Polar Astronomical Observatory (St. Kliment Ohridski Base) on Livingston island, Antarctica. Using continuous VLF transmissions at 21.4 kHz (NPM, Hawaii) and 24.0 kHz (NAA, Maine), propagating over trans-hemispheric paths exceeding 11 000 km, we analyze observations of
solar flares during the period 24 January-8 February 2025. After removing the strong diurnal signal via superposed epoch analysis, we analyse the
flare-related perturbations in VLF amplitude and their correlation with GOES soft X-ray flux for 250
flares of C and M class. The long propagation paths provide enhanced sensitivity to
flare-driven changes in D-region ionization. The observations reveal clear, frequency-dependent responses and measurable time delays between X-ray peaks and VLF extrema. These delays, including cases of near-zero or negative correlation lag for stronger events, are consistent with a possible role for
flare spectral characteristics and D-region recombination processes, although alternative explanations related to waveguide propagation and signal-profile differences cannot presently be excluded. Our results demonstrate the scientific value of long-baseline Antarctic VLF observations for detecting and timing GOES-class
flares, while also highlighting key limitations, such as background variability and path-dependent propagation effects, which must be quantified for reliable VLF-based
flare monitoring.
Read the full abstract ▾
All authors Kamen Kozarev, Peter Petkov, Ivaylo Nachev, Veselka Radeva, Momchil Dechev, Galin Borisov, Anton Atanasov
Chemical Physics
Solid-State NMR Dipolar Recoupling in Presence of Large Chemical Shielding Anisotropies by Quaternion-Based Effective Hamiltonian Optimal Control
Enikő Baligács, Nino Wili, José P. Carvalho et al. (+2 more)
Dipolar recoupling is a key element in magic-angle-spinning (MAS) solid-state NMR spectroscopy with reintroduced dipole-dipole coupling interactions providing information about internuclear distances and enabling transfer of polarization between
spins in resolution-enhancing multiple-dimensional experiments. Such methods may be challenged in many important applications by the presence of large anisotropic
nuclear spin interactions such as chemical shielding anisotropy. In this paper, we address this challenge by presenting quaternion-based optimal control. This is founded in single-
spin operations enabling optimization of effective
Hamiltonians with reduced influences from anisotropic shielding. Along with the principles underlying such optimizations, we present numerical and experimental demonstration of 19F to 13C polarization transfer in presence of 19F chemical shielding anisotropy.
Read the full abstract ▾
All authors Enikő Baligács, Nino Wili, José P. Carvalho, Anders Bodholt Nielsen, Niels Chr. Nielsen
Biological Physics
Stochastic Yield Catastrophe in Delay-Facilitated Self-Assembly
Richard Swiderski, Severin Angerpointner and Erwin Frey
Self-assembly of supramolecular structures in
cells and synthetic applications often proceeds under unfavorable biochemical conditions and at low copy numbers of final target structures, ranging from tens of bacterial microcompartments to a single bacterial flagellum per
cell. Spatial organization through coupled reaction compartments of different reactivity (delay-facilitated assembly) can recover high yield in such environments at the mean-field level, but its robustness to
stochastic fluctuations at low target numbers is unclear. Using
stochastic simulations of a minimal two-compartment model, we show that delay-facilitated assembly is susceptible to a
stochastic yield catastrophe at low target numbers: even when each compartment in isolation allows for high-yield assembly, slow exchange between them induces a substantial drop in the final yield. We trace the mechanism to a specific assembly stage, where the random order of rate-limiting exchange events of subunits and partially completed structures determines the ratio of productive growth to excess nucleation. Restricting the exchange of larger structures – either by suppressing it entirely or letting exchange rates decrease with size – restores most of the yield without altering the mean-field behavior. The same phenomenology appears for two-dimensional hexagonal subunits and in a cytosol-membrane geometry, where
diffusion-limited exchange naturally implements the required size dependence. Our results show that equal success of assembly strategies at high target numbers does not imply their equal success at low target numbers, and that competing slow events occurring in random order are a common signature of
stochastic yield catastrophes.
Read the full abstract ▾
Physics Education · Cross-listed from quant-ph
Continuous limit of the square well problem in quantum mechanics
Matheus D. Moro, Thiago T. Tsutsui, Antonio S. M. de Castro et al. (+1 more)
The free-particle and square-well potentials are two of the most emblematic problems in
quantum mechanics, illustrating essential concepts such as matter waves, energy quantization, and bound states. It is therefore natural to consider how the free-particle solutions emerge from the square well as the width approaches infinity. In this work, we present a systematic procedure to demonstrate this transition by applying a
Fourier transform to the wave equation.
Read the full abstract ▾
All authors Matheus D. Moro, Thiago T. Tsutsui, Antonio S. M. de Castro, Fabiano M. Andrade
Medical Physics
Direct probabilistic IMPT treatment planning with setup and range errors for neuro-oncological patients
Jelte R. de Jong, Sebastiaan Breedveld, Steven J. M. Habraken et al. (+4 more)
To show clinical feasibility of a previously proposed probabilistic planning approach that can precisely optimize for clinical goals with patient-specific acceptance probabilities on a neuro-oncological patient group, we compared probabilistic plans with (automated) robust plans for one patient (group A) that could achieve sufficient clinical target coverage and for four patients (group B) where target coverage had to be compromised due to organ-at-risk (OAR) dose constraints. The probabilistic approach is percentile-based and uses the fact that a (dose) percentile can be approximated as a linear combination of its expected value and standard deviation. The optimization has a nested structure: the inner optimization optimizes the beam weights for a given percentile estimate, while an outer loop iteratively updates and improves the accuracy of the percentile estimate. For every outer iteration, the optimization is warm-started from the previous iteration. Percentiles are efficiently calculated by sampling a
polynomial chaos expansion of the dose-influence
matrix. The patient in group A achieved cumulative OAR dose reductions (of OAR-related DVH-metrics) of 19 GyRBE, for identical target coverage. Target coverage improved for all patients in group B (the 10th percentile of $D_{99.8\%}$ increased up to 0.93 GyRBE), at the same time reaching cumulative OAR dose reductions (of OAR-related DVH-metrics) up to 33 GyRBE. Probabilistic plans were optimized in 44h to 141h. For two representative patients, eliminating warm-starting (i.e., the outer loop) from the approach reduced total optimization times to below 10h (which took originally 80h and 141h). Compared to robust optimization methods, the probabilistic approach achieves improved trade-offs between probabilistic target coverage and OAR sparing, potentially leading to better treatments.
Read the full abstract ▾
All authors Jelte R. de Jong, Sebastiaan Breedveld, Steven J. M. Habraken, Mischa S. Hoogeman, Jenneke I. de Jong, Danny Lathouwers, Zoltán Perkó
Fluid Dynamics
Evaluation of State-of-the-Art Deep Learning Architectures for Aerodynamical Predictions
Jan Scherz, Derrick Hines and Philipp Bekemeyer
Surrogate models are used to substitute classical numerical solvers in engineering applications where the computational cost of the latter becomes infeasible. For instance, in aerodynamics such models offer cost-effective alternatives to computational fluid dynamics in problems such as shape optimization and load analysis, which oftentimes require high-fidelity simulations for a multitude of different parameter combinations. A specific class of deep learning-based surrogate models termed operator learning models directly approximates the solution operators to the partial differential equations underlying the physical phenomenon, thereby learning to replicate solutions to entire families of problems. However, while nowadays numerous architectures of this type get published, corresponding
benchmark studies remain scarce. In this article, we advance the study of AI-based surrogate methods by thoroughly benchmarking four state-of-the-art operator learning models on their aptitude for applications in aerospace engineering. In two experiments, we assess the models' capabilities of predicting the surface pressure distribution on two-dimensional airfoil shapes of varying complexity and on an industrial-scale three-dimensional aircraft configuration. Thereby, we evaluate the models' abilities to fulfill frequent requirements in aerodynamics such as capturing discontinuities (shocks) in the solutions, scalability towards excessive amounts of mesh points and handling of data scarcity. Accompanied by a careful analysis, our findings drive forward the field of AI-based surrogate modeling by providing detailed insights into the strengths and weaknesses of the individual architectures, thus allowing to identify priorities for future developments. In particular the Bi-Stride Multi-Scale Graph Neural Network and Transolver(++) are highlighted as promising surrogate models for aerodynamical applications.
Read the full abstract ▾
Fluid Dynamics
Condition for $1/f$ noise to occur along with an example for a diffusion equation
H. Mouri
While $1/f$ noise is ubiquitous and has been found in various systems, its physics remains uncertain. From an analytical study of an ordinary
diffusion equation, we find an additional example of the $1/f$ noise. The formula for this example, together with existing knowledge about scaling in fluid
turbulence, implies a necessary and sufficient condition for the occurrence of any stationary $1/f$ noise. That is, the noise needs to be characterized by two constant frequencies of $f_{\rm low} \ll f_{\rm high}$. For a frequency range from $f = f_{\rm low}$ to $f_{\rm high}$, it is further needed that, except for the mean amplitude of the noise, there is no other constant parameter. Then, at $f_{\rm low} \ll f \ll f_{\rm high}$, the noise scales asymptotically as $1/f$. Being statistical and simple, our condition applies to any system and hence explains the ubiquity of the $1/f$ noise. It is also applicable to some systems with noise of $\alpha \ne 1.0$ for $1/f^{\alpha}$, via intermittency analogous to that of the
turbulence.
Read the full abstract ▾
Optics
Maximum non reciprocity in metasurfaces of gyrotropic rods
Ioannis Katsantonis, Constantinos Valagiannopoulos and Anna C. Tasolamprou
Efficiently breaking time-reversal
symmetry at the subwavelength scale remains a cornerstone challenge for advanced electromagnetic wave manipulation. This work presents a rigorous analytical framework, based on cylindrical wave expansion, to investigate and optimize the nonreciprocal scattering of transverse electric waves by magnetically biased plasmonic rods. An intuitive metric is introduced to quantify the breaking of time-reversal
symmetry via the asymmetric lifting of degeneracy between azimuthal modes of opposite angular momentum, hosted by the gyrotropic particles. Leveraging this metric, a comprehensive mapping of the multiparametric space of operational frequency, cyclotron frequency, and cylinder optical size isolates regimes of maximum nonreciprocity. A detailed multipolar decomposition reveals that this extreme behavior stems from the phase-matched asymmetric excitation and interference of localized electric and
magnetic dipole modes. Moving from individual, isolated meta-atoms to collective photonic systems, the optimized cylinders are arranged into a periodic grating. Under oblique incidence, the combination of
geometric asymmetry and
magnetic mode splitting, forces the metasurface to transmit light in a totally different way when excited by opposite sides. The reported findings and design principles offer a versatile blueprint for the development of dynamically tunable flat-optics isolators, directional transceivers, and advanced wavefront routers.
Read the full abstract ▾