Papers

Students' explanations of scientific phenomena provide important evidence of their conceptual understanding. However, because conceptual understanding can only be inferred through language rather than observed directly, distinguishing conceptual understanding from linguistic quality represents a fundamental challenge for assessment. This challenge can also be expected to arise when artificial intelligence (AI) is used to score students' text-based explanations. We investigated whether AI-based scoring approaches assess students' conceptual understanding independently of the linguistic quality of their explanations about a phenomenon taken from a physics context. Conceptual understanding scores assigned by nine machine learning-based scoring approaches and two large language model-based scoring approaches were compared with expert-assigned scores for 116 explanations produced by secondary-school students in Germany. Multinomial logistic regression analyses examined whether expert-rated linguistic quality was associated with under- or overestimation of conceptual understanding. Across all eleven AI-based scoring approaches, lower linguistic quality was associated with a greater likelihood of underestimation, although the strength and statistical significance of this association varied between approaches. AI-based scoring approaches may thus systematically underestimate the conceptual understanding expressed in linguistically weaker explanations. This pattern resembles language bias previously documented in STEM teachers' assessment practices. The findings highlight the importance of examining not only agreement with expert ratings but also whether AI-based scoring approaches inadvertently rely on construct-irrelevant information. Addressing language bias is therefore essential for the validity and fairness of AI-supported assessment in physics education and STEM education more broadly.

Electromagnetically driven centrifuges (EMDCs) rotate fluids using the Lorentz force, but their separative performance is limited by thermal dissipation that is coupled to electromagnetic forcing. This follows because local centrifugal strength is characterized by {\lambda}=mV_{\theta}^2/2k_B T, which compares directed kinetic energy that drives species separation to thermal energy that smooths concentration gradients and counteracts separation. In this work, we develop a two-temperature magnetohydrodynamic model to determine how radial geometry, current density, and magnetic field strength control the coupled evolution of rotation and heating that dictates {\lambda}. The model is benchmarked against Ar velocity, temperature, and pressure measurements spanning current density up to 15 kA/m2, magnetic field up to 0.57 T, feed pressures of 0.5-3 Torr, and annulus sizes of 1-5 cm. The results show that volumetric Lorentz forcing sustains elevated {\lambda}, and therefore greater local compositional shifts throughout a larger radial portion of the fluid volume than wall-bounded shear centrifuges, despite producing a lower peak {\lambda}. Simulations of a 40Ar/36Ar isotopic mixture demonstrate that, when electromagnetic force and geometry are jointly optimized, EMDCs can approach or match the separative performance of shear-driven centrifuges operating near material speed limits while requiring lower area-averaged values of {\lambda}, and can sustain greater radial compositional shifts than the SDC reference over much of the annulus. These results challenge assertions that viscous dissipation constrains weakly ionized plasma centrifuges to {\lambda}<1, and indicate that enhanced radial mass separation can be achieved by broadening the radial region over which {\lambda} remains elevated rather than maximizing its peak or area-averaged value.

We study the observational signatures of kinetic gravity braiding (KGB) models in relativistic cosmological probes constructed along the past light cone. Using the relativistic $N$-body code KGB-evolution, we generate light-cone outputs and compute several observables that directly probe the gravitational field, including weak gravitational lensing convergence, Shapiro time delay, the integrated Sachs-Wolfe and Rees-Sciama (ISW-RS) effects, and gravitational redshift. Full-sky maps and angular power spectra of these quantities are constructed and compared with $k$-essence models and predictions from linear perturbation theory. We find that the derivative coupling between the scalar field and the metric modifies both the amplitude and the time evolution of the gravitational potentials, producing scale-dependent deviations ranging from a few percent to tens of percent. In particular, the ISW-RS signal exhibits the largest fractional response, as the slower decay of the Weyl potential suppresses the KGB signal in the ISW-dominated regime, whereas nonlinear evolution reverses this trend at higher multipoles, producing differences of tens of percent relative to $k$-essence. Weak gravitational lensing also provides a strong complementary probe and, for the model considered here, exhibits clear deviations from the $k$-essence prediction at small scales with enhancements up to $\sim 10$-$12\%$ at multipoles $\ell \sim 10^2$-$10^3$. Our results show that linear perturbation theory accurately describes the large-scale behaviour, while nonlinear effects become important at smaller scales, particularly for the ISW-RS signal and, more moderately, for the convergence, and must therefore be included for reliable theoretical predictions.

Dense suspensions of rod-shaped granular particles are widespread in nature and manufacturing, where their fluid mechanical properties are often paramount. We have developed a particle-based simulation that models such suspensions under simple shear flow, providing predictions of the viscosity and microstructure for a given solids volume fraction and particle aspect ratio. The model tracks the trajectories of spherocylindrical rods under the action of short-range frictional contact and hydrodynamic forces, inspired by similar tools that have generated new insight into suspensions of granular spheres. It incorporates new schemes for the computation of lubrication forces between spherocylinders and the dynamic determination of the timestep. For aspect ratios up to 20, the model predicts a viscosity spike at shear start-up, giving way to steady state viscosities that increase systematically with volume fraction and aspect ratio. Likewise, particle alignment increases with volume fraction up to an aspect-ratio-dependent critical point. Our model corroborates the limited experimental rheology data available for suspensions of granular rods, and offers a tool for fundamental exploration of the fluid mechanics, microstructure and rheology of this widespread material.

Frequency-domain thermoreflectance (FDTR) is a laser pump-probe technique widely used to measure thermal properties at the micro- and nanoscale; however, it relies on a complex data analysis procedure that demands substantial domain expertise and is susceptible to subtle human errors. Here, we present Vibe-FDTR, an agent-oriented framework that enables large language model (LLM) agents to perform reliable and reproducible FDTR analyses directly from natural language requests. This framework couples a configuration-driven FDTR code package, which enforces physical and parametric consistency, with procedural agent skills that translate user intentions into organized and verifiable analysis steps. We evaluate Vibe-FDTR using a controlled benchmark with two levels: synthetic single-step tasks and real-data multi-step tasks based on measurements of gold-coated graphite samples. Across the two levels, agents using Vibe-FDTR achieve success rates of 100% and 98.9%, respectively. In sharp contrast, ablating skills (Code-agent) reduces performance to 91.4% and 36.7%, which drops further to 38.6% and 0% when the domain package is also omitted (Agent-only). Beyond success rate, Vibe-FDTR also reduces computational cost by 87.7% relative to the Code-agent variant and cuts execution time by more than 60%. Finally, an optional expert mode supports experimental planning via autonomous sensitivity and uncertainty evaluations, and formulates physically grounded recommendations for underspecified tasks. These results demonstrate that encapsulating domain code and expert knowledge into agent skills offers a promising route toward low-barrier, autonomous, and trustworthy thermal metrology.

Depth uncertainties for wide-angle crustal models are usually obtained by propagating an assumed picking error, yet picks from one shot gather are mutually dependent and velocity-depth trade-offs act at the level of the model class. We revisit the 210-km Perur-Chikmagalur profile across the Dharwar Craton using a permanently identified ledger of 2,000 primary P1-P2 first arrivals and 9,919 reflections from seven shots, with 852 later-turning P4 observations held aside for a conditional test. At fixed production velocity, a reciprocity-based fast-marching table makes it affordable to refit all six reflectors inside nested complete-shot validation and a receiver-coordinate block bootstrap. Withheld reflections are predicted to 68 ms RMS with standardized mean-squared residual 2.07 and 85 per cent coverage inside nominal 95 per cent limits; calibration is strongly phase-dependent, the Moho reflection specifically giving 73 ms and 2.12. First arrivals degrade from a 17 ms all-data RMS to 103 ms on untouched shots, driven by aperture extrapolation and a parameterization blind to the weathered near-surface. Exact window means place the Moho at 46.9 km beneath the western block and 39.4 km beneath the central block, a raw contrast of 7.47 km. Resampling receivers within shots gives a +/-0.10 km conditional spread; deleting whole shots gives +/-0.40 km, and two independent experiments agree the raw contrast is biased low by 0.36-0.48 km. Admissible velocity models span 6.46-8.64 km. Paired LVL-boundary reflections at 1,260 receivers separate beyond uncertainty everywhere, though the internal velocity shape stays unresolved. Sampling uncertainty and model-class uncertainty must be reported apart.

A finite dusty plasma cluster of charged microparticles confined in an anisotropic potential well is investigated experimentally and through Langevin dynamics simulations. As the confinement anisotropy is increased, the cluster undergoes a structural transition from near isotropic concentric shells to a linear chain configuration. The spatiotemporal modes of the cluster are analyzed by using Singular Value Decomposition. At weaker confinement anisotropies, two modes are dominant. Mode 1, corresponding to a breathing-type oscillation of the cluster, and mode 2, representing an azimuthal rotational motion of the cluster, together carry around 99$\%$ of the signal energy. With increasing anisotropy, the dominance of mode 2 decreases and that of mode 1 increases. This mode restructuring is accompanied by an increasingly non-Gaussian particle displacement statistics as evidenced by positive values of the Non-Gaussian Parameter maintained over an extended time duration. Simultaneously, the increasing dominance of mode 1 and suppression of mode 2 is accompanied by a significant slowing down of structural relaxation, with the cluster eventually exhibiting signatures of structural arrest. At weaker anisotropy, the experimentally measured dynamical quantities are very sensitive to the initial conditions which account for the discrepancy between the experiment and initial condition averaged simulation results for these observables. This study offers insight into the mechanisms underlying anomalous transport and structural relaxation in anisotropically confined many-body systems.

Accurate Surface Solar Irradiance (SSI) estimation is increasingly important for photovoltaic energy monitoring and forecasting. The recently introduced Meteosat Third Generation (MTG) satellite constellation provides imaging data with higher spatial resolution compared to the Meteosat Second Generation (MSG) satellite constellation, but its benefits for machine-learning-based SSI retrieval have not been well established. In this work, we introduce a multi-imager and multi-resolution convolutional neural network architecture for 10-minute SSI retrieval over Northern Europe (Estonia) using MSG/SEVIRI and MTG/FCI satellite imagery together with solar-geometry and clear-sky irradiance features. Model performance is evaluated against ground-based pyranometer measurements from eight Estonian meteorological stations using site-based cross-validation and multiple training seeds. Model performance is also compared with the SARAH-3 physics-based satellite SSI product. The hybrid SEVIRI-FCI model significantly outperformed the SEVIRI-only model under overcast and cloudy conditions, reducing RMSE by 8.2 W m$^{-2}$ and 5.7 W m$^{-2}$, respectively. However, under partly cloudy or clear skies, no statistically significant difference in RMSE was observed between the SEVIRI-FCI hybrid and the SEVIRI-only models. Compared with physics-based SARAH-3, the hybrid model yielded skill scores of 35 % under overcast conditions, 21 % under cloudy conditions, and 20 % overall. Furthermore, both models underperformed SARAH-3 in clear-sky conditions. These results show that higher-resolution MTG/FCI imagery improves CNN-based SSI retrieval when clouds dominate irradiance variability, but also indicate that higher spatial resolution alone is insufficient to address clear-sky limitations in machine-learning-based SSI retrieval.

Achieving magnetically switchable chiral light emission is an important goal for 2D opto-spintronics. However, conventional strategies face a fundamental trade-off between dynamic tunability and polarization contrast. Nonlinear optics, particularly the emerging mechanism of chiral second-harmonic generation (SHG), offers a distinct strategy to bypass this restriction, yet its experimental realization remains elusive due to stringent symmetry requirements. Here, we report giant nonlinear optical magnetochirality in a centrosymmetric 2D ferrotoroid, bilayer (2L) CrSBr. We reveal that a field-induced spin-canting state breaks the parity-time (PT) symmetry of the unperturbed antiferromagnetic (AFM) ground state, activating a spin-chirality-driven i-type susceptibility. The coherent interference between this emergent i-type and intrinsic c-type SHG susceptibilities generates a macroscopic circularly polarized SHG signal whose helicity is magnetically switchable. Leveraging this sensitive mechanism, we uncover remanent magnetic states after field saturation that evade conventional linear probes. By exploiting the non-volatility of these states, we demonstrate magneto-optical memory and logic operations. Our work establishes a general symmetry-driven strategy for tailoring nonlinear magnetochirality, while providing a sensitive optical probe for subtle spin textures in the 2D limit.

Accurately estimating the coupling strength in oscillator networks from macroscopic observations is essential for predicting synchronization transitions. We consider the inverse problem of reconstructing the unknown coupling strength $K$ in the globally coupled Kuramoto model from scalar observations of the macroscopic order parameter $R(t)$, assuming that the natural frequencies and the initial phase configuration are known. After initialization, individual phase trajectories are treated as hidden, and only the scalar order parameter is observed. We employ an extended Kalman filter with an augmented state representation that recursively estimates the coupling strength from observations of $R(t)$. By exploiting the mean-field structure of the globally coupled Kuramoto model, the covariance prediction step can be computed efficiently, substantially reducing the computational cost. Numerical simulations demonstrate that the proposed estimator accurately reconstructs the coupling strength and remains stable even when $R(t)$ is small and strongly fluctuating.

The survival of many-body coherence depends on the competition between correlation buildup and decoherence. In Dicke superradiance, collective emission builds up correlations, producing a peak intensity scaling as $N^2$ for $N$ emitters. We develop a scaling theory including local dephasing and spontaneous emission and obtain fully collective, partially collective, and independent-emitter scaling regimes. The boundary of the fully collective regime defines a continuous phase transition in a transient observable. Local decoherence can prevent $N^2$ scaling despite increasing $N$.

Chimera states, characterized by the coexistence of synchronized and desynchronized dynamics in identical oscillators, are typically studied in systems with pairwise interactions. Recent studies have shown that purely higher-order interactions can support chimera states. Here, we show that breathing chimera states arise in a model governed solely by a triadic phase interaction, whose intrinsic $\pi$-symmetry permits bimodal phase distributions. We construct a bimodal Ott-Antonsen reduction that incorporates an asymmetry parameter via prescribed asymmetric initial conditions, thereby achieving an exact low-dimensional description of the macroscopic dynamics. This allows us to derive an analytic condition for the emergence of chimera states and identify a bifurcation to a breathing chimera regime characterized by persistent oscillations. Furthermore, the reduced dynamics can be expressed as a Riccati-type equation, providing a geometric interpretation of the chimera state as a closed periodic orbit in the complex plane. Our results establish purely triadic coupling as a minimal higher-order mechanism for chimera formation and provide a tractable framework for studying symmetry-broken collective dynamics in systems dominated by many-body interactions.

Accurate atomistic modelling of iron (Fe) oxidation requires a reliable interatomic potential, which necessitates an extensive and representative first-principles dataset for training the interatomic potential. However, Fe-oxygen (O) system is known for its structural and magnetic complexity, rendering the generation of high-quality dataset challenging. In this work, we propose the Perturbation Augmented Space group structure Sampling (PASS) method to generate extensive and representative dataset consisting of small-cell structures with less than 10 atoms. We present a systematic approach to developing a first of its kind transferable machine learning interatomic potential (MLIP) for Fe-O system based on the atomic cluster expansion (ACE) framework. We thoroughly validate the accuracy and capability of the ACE MLIP across both pure Fe and Fe-O systems through bulk, surface, and interface properties. We showcase the formation of FeO-like structure in large-scale Fe oxidation simulation using the ACE MLIP. This work demonstrates that the PASS method yields an accurate and transferable MLIP which is capable of capturing the reactive complexity of oxide growth while remaining computationally practical for extended systems.

The Mpemba effect, wherein a system prepared farther from equilibrium relaxes faster than one initially closer to equilibrium, has been extensively investigated in a wide range of physical systems. In contrast, its role in biologically relevant non-equilibrium processes remains largely unexplored. Here, we investigate anomalous relaxation in the six-state chemomechanical network model of the Kinesin molecular motor under both equilibrium and non-equilibrium conditions. We first establish the existence of the Mpemba effect in chemical equilibrium and show that many of its qualitative features can be understood from the underlying free-energy landscape. We then examine the effects of mechanical and chemical driving, showing that breaking detailed balance primarily reshapes the Mpemba phase diagram without qualitatively altering the relaxation phenomenology over the physically relevant parameter regime. Finally, we demonstrate that the relaxation of the motor velocity also mirrors the anomalous relaxation of the underlying stochastic dynamics, thereby identifying an experimentally accessible signature of the Mpemba effect. Our results establish molecular motors as a promising baseline for studying anomalous relaxation in living systems and suggest a broader framework for exploring the Mpemba effect in non-equilibrium biochemical networks.

We extend classical nucleation theory (CNT) by explicitly incorporating the multidimensional nature of nucleation and the coupled roles of kinetics and thermodynamics. Specifically, we treat the cluster density as an independent variable, within both sharp-interface and diffuse-interface descriptions. The kinetics are governed by dynamical density functional theory. Applied to liquid condensation in the Lennard-Jones system, our two-variable (size–density) and three-variable (size–interface width–density) models reveal nonclassical nucleation mechanism. At low supersaturation, both models recover the classical picture, in which clusters nucleate and grow at the equilibrium liquid density. As supersaturation increases, a nonclassical behavior emerges: the critical cluster density decreases, and the nucleation pathway involves concomitant evolution in cluster size, density, and, within the diffuse-interface description, interfacial width. Our model with diffuse interface reveals a rapid increase in interfacial diffuseness at high supersaturation. Near the spinodal limit, both models predict the critical cluster with diverging sizes, densities approaching that of the metastable initial phase, and vanishing work of formation, which provides a smooth connection between nucleation and spinodal decomposition. Comparison with molecular dynamics simulations demonstrates that both models substantially outperform CNT. However, the weak non-monotonic dependence of the critical cluster density observed at very low supersaturation is captured only by diffuse-interface models. Overall, our findings indicate that CNT should be applied only in the low-supersaturation regime, and our work provides a robust foundation for its refinement beyond this limit.

We show that gyrokinetics in a spatially uniform equilibrium (or `astrophysical gyrokinetics') possesses a previously unknown quadratic invariant, which we call the gyrokinetic helicity. We derive the limiting forms of the gyrokinetic helicity in five subsidiary expansions of astrophysical gyrokinetics: the isothermal electron fluid (ITEF) approximation, kinetic reduced magnetohydrodynamics (KRMHD), electron reduced magnetohydrodynamics (ERMHD), finite-Larmor-radius magnetohydrodynamics (FLR-MHD), and the kinetic reduced electron heating model~(KREHM). We subdivide the real part of the gyrokinetic helicity into `magnetofluid' and `phase-space' components, which are separately conserved in KRMHD, ERMHD, and FLR-MHD (in which there is no phase-space helicity). The magnetofluid helicity is equivalent to the Alfvénic part of the cross helicity in KRMHD, the magnetic helicity in~ERMHD, and the generalized helicity in FLR-MHD and~KREHM. We derive an analytic expression for the rate at which magnetofluid helicity is converted into phase-space helicity at length scales comparable to the proton gyroradius in the ITEF approximation. We also explain how this conversion circumvents the helicity barrier and enhances turbulent heating in coronal holes and the near-Sun solar wind.

In the absence of experimental data for molecular spectra in strong magnetic fields, high resolution and reliable computational spectra are required for the interpretation of spectra collected from highly magnetic astrophysical objects. In this paper, we extend the Wilson-Hamiltonian framework, recently implemented and benchmarked by us (J. Chem. Theory Comput., 21, 9753 (2025) ), to a general three-dimensional framework suitable for computing the rovibrational spectra of diatomic molecules in strong uniform magnetic fields. The field-dependent electronic and nuclear Hamiltonians capture full non-perturbative coupling between particle motion and the field making the method applicable to all field strengths. The electric quadrupole transition moment integrals for rovibrational transitions in external static magnetic fields are formulated, implemented, and computed to yield spectra which respect the selection rules of the molecule-field system. The spectral changes with increasing field strength such as shifting, splitting, merging, appearance and disappearance of peaks are noted. Contributions from electrons and nuclei are studied individually, as well as in unison to reveal the underlying physics such as stiffening of the bond, emergence of a rotational barrier, field-induced coupling/decoupling of states, and symmetry-breaking in rotational and vibrational states. These results provide the first fully quantum mechanical computational results for the rovibrational signature of $^1\mathrm{H}_{2}$ in extreme magnetic field environments with accuracy suitable for experimental interpretation. The methodology developed herein has direct relevance for high-field spectroscopy and astrochemical modeling, both for providing computational data as well as for understanding the spectral impact of strong magnetic fields on electronic structure and nuclear motion in molecules.

Localization effects are central to disordered electronics and photonics. In electronics, Anderson localization governs electron confinement in randomly perturbed lattices. Similarly, its photonic counterpart inhibits light transport via disorder – but with a unique exception: Brewster-anomaly delocalization, where the Brewster effect prevents multiple-scattering interference and counteracts the localization. Despite extensive research in electronics and photonics separately, the intricate role of localization effects in free-electron–light interactions – vital for lasers, accelerators, microscopy and spectroscopy, and quantum information – remains largely unexplored. At the same time, localization effects are widely regarded as a key factor limiting the efficient coupling between free electrons and light in random media. Here we overcome this key limitation via the unconventional interplay between Brewster-anomaly delocalization and free-electron radiation. In this way, free-electron radiation can be localization-free, intense and directional even in strongly disordered, unengineered multilayers. Essentially, this delocalization-mediated free-electron radiation is remarkably invariant not only to the random-medium configuration, but also to the light frequency and the electron velocity. Our findings unlock new opportunities for particle detectors and achromatic light sources operating in easy-to-fabricate complex media at previously inaccessible frequencies.

An independent air kerma traceability chain for $^{60}$Co radiation protection levels has been successfully established at the CIEMAT Ionizing Radiation Metrology Laboratory (LMRI-CIEMAT), based on the reference provided by the $^{137}$Cs primary standard. To achieve this, a secondary standard ionization chamber with an appropriate energy response was characterized, and its beam-quality correction factor, $k_Q$, was accurately determined via Monte Carlo simulations using the EGSnrc code. The method's accuracy was validated through a comparison with long-term historical calibration data and peer-confirmed through CIEMAT's successful participation in the EURAMET.RI(I)-S19 supplementary comparison, recently officially published in the BIPM KCDB. The excellent agreement achieved with international reference values demonstrates the high robustness and traceability independence of this newly implemented methodology.

The Venusian interaction with the solar wind leads to the formation of an induced magnetosphere structured by plasma boundaries. Their dynamics is complex, due to the combined influence of external (solar photons, solar wind plasma and interplanetary magnetic field (IMF)) and internal (ionized atmosphere) drivers. Studying these drivers helps understanding the transfer of energy and momentum throughout the Venusian system, and has thus implications for the erosion of the atmosphere through its coupling with the solar wind. We here analyze and rank the influence of the main drivers of the Venusian bow shock and ion composition boundary locations. We revisit the results by Signoles et al. (2023) based on Venus Express measurements by combining several methods such as the Akaike Information Criterion, Least Absolute Shrinkage Selection Operator regression, and partial correlations. These methods allow to investigate cross correlations that appear and can bias the interpretation, and allow to rank drivers with robust approaches. The bow shock appears primarily driven by the IMF intensity or Mach number, the IMF $\theta_{bn}$ angle separating quasi-perpendicular vs quasi-parallel shocks, and then the solar extreme ultraviolet fluxes and solar wind dynamic pressure (with little influence of the convective electric field induced asymmetries). The Ion Composition Boundary is primarily driven by extreme ultraviolet fluxes, with a more reduced influence of several solar wind parameters and IMF induced magnetic pileup asymmetries. We also compare the behaviors of both boundaries and then compare the bow shock driver rankings at Mars and Venus. Finally we propose an analysis of the drivers of the extreme bow shock and ion composition boundary excursions.

High-harmonic generation (HHG) in inversion-symmetric systems is typically restricted to odd harmonics by symmetry. Here, we show that this selection rule can be broken without modifying the underlying Hamiltonian. We investigate a boundary-driven Su-Schrieffer-Heeger (SSH) chain coupled to source and sink reservoirs and demonstrate that dissipative dynamics generates a nonequilibrium steady state carrying a finite DC current. While the SSH Hamiltonian retains inversion symmetry, the current-carrying steady-state density matrix does not, leading to the emergence of even harmonics in the emitted spectrum. Using a correlation-matrix approach based on the Lindblad master equation, we obtain the steady state and calculate the resulting HHG response. We find that the intensity of the even harmonics is directly controlled by the transport current, establishing a link between nonequilibrium charge transport and HHG selection rules. Our results uncover a mechanism for even-harmonic generation that relies solely on nonequilibrium steady-state symmetry breaking and provide a route to probing transport currents through ultrafast nonlinear spectroscopy in centrosymmetric quantum systems.

Team sports offer a natural laboratory for studying collective motion in competitive environments. While recent tracking technologies enabled detailed analysis of player and ball trajectories, most existing approaches rely on discrete events and local kinematic descriptors. This limits the possibility of studying team collective behavior. Here we introduce a physics-based framework to characterize collective dynamics using empirically reconstructed velocity vector fields defined over the pitch. Taking soccer as a paradigmatic example, we show that both ball and player movements generate smooth, predominantly irrotational vector fields. Each of these fields is generated by a potential providing compact, physically interpretable representations of collective motion and encoding how teams control, or constrain play across space during attacking and defensive phases in each match. Applied to an entire season of top-level competition, the resulting potentials reveal stable team-specific fingerprints and quantitative associations between potential gradients and effective ball progression.

Reliable detection of local earthquakes and accurate identification of P-wave onsets are fundamental tasks in seismology, yet many existing methods to accomplish them require extensive parameter tuning or large training datasets. In this study, we investigate the applicability of Benford's Law - a logarithmic distribution governing the occurrence of leading digits in naturally occurring data - as a statistical framework for local earthquake detection. We analyze continuous seismic waveform data from two contrasting tectonic environments in the Indian subcontinent: the intraplate Deccan Volcanic Province and the actively deforming Himalayan region. Using a sliding-window approach, we quantify the temporal conformity of first-digit distributions of seismic amplitudes to Benford's Law and apply an adaptive normalization scheme to account for station-specific noise. Our results show that local earthquake waveforms consistently conform to Benford's Law during the onset of seismic energy, while pre-event noise does not. Benford-derived statistical anomalies align closely with theoretical P-wave arrival times, with detection performance primarily controlled by window length. These results establish Benford's Law as a computationally inexpensive, parameter-light, and training-free tool for local earthquake detection.

The Vicsek Model represents a paradigmatic framework for understanding the collective motion of active aligning particles, traditionally assuming isotropic interaction fields. Inspired by biological systems characterized by limited perception and blind spots, we propose a generalized Vicsek model featuring two distinct, non-overlapping angular vision cones. We systematically investigate the non-equilibrium phase behavior of this system by tuning the aperture area ({\alpha}) and the front-back orientation (e̱ta}) of the cones. Our results reveal that restricting the lateral vision area destabilizes global order, shifts the critical noise, and induces highly dense traveling bands. Furthermore, breaking the front-back symmetry introduces non-reciprocal interactions that profoundly alter the emergent spatial structures: forward-biased vision drives strong clustering through "follow the leader" alignment, whereas backward-biased alignment stabilizes an exceptionally homogeneous flocking state with suppressed density fluctuations. Finally, we incorporate short-range volume exclusion, demonstrating that the structural integrity of these novel tightly-clustered phases is highly sensitive to steric interactions. Our work provides new insights into the interplay between non-reciprocal perception, spatial anisotropy, and physical constraints in active matter.

AI is accelerating physics discovery, but perhaps away from Einstein-level theory building. To understand this gap, we must recognize a striking trend: while being very successful, the most visible AI contributions to physics discovery appear to mirror the historical development of physics, but in reverse. Human discovery in physics progressed, in broad strokes, from ancient pattern prediction, through phenomenological laws such as Kepler's, to principle-based universal theories such as relativity and the Standard Model. On the AI side, prominent contributions to physics discovery point in the opposite direction: early milestones emphasized explicit equation-discovery methods, such as symbolic regression, whereas more recent frontier contributions are powerful predictors such as AlphaFold and GraphCast, which can be remarkably accurate yet do not provide clear theoretical understanding. If this trend continues, AI would become extraordinarily good at prediction but may struggle to ever propose its first serious contender to quantum gravity or other paradigm-level theories. We review the current landscape of AI for physics discovery and highlight a critical missing skill: the ability to pose the right questions or invent the right principles to guide the development of new theories and the tests to falsify them. This mode of discovery has driven many of the deepest advances since the 17th century, where symmetry, simplicity, and new mathematical frameworks guided theory construction before experimental tests. Equipping AI systems with such skills could move them from predicting within known frameworks to proposing the next paradigm-level discovery in physics.

Gamma-ray imaging with Compton cameras is computationally demanding because conventional reconstruction retains the list-mode acquisition inside the inversion loop: event-dependent cone/voxel interactions must be recomputed as the event count grows. We present a spherical-histogram framework in which each Compton event is encoded online into detector-centred angular histograms. Volumetric reconstruction is then performed from coherent histogram snapshots using a precomputed sparse projection operator. This turns the event stream into a bounded reconstruction state, decoupling event accumulation from iterative inversion. The method supports multi-view and multi-resolution operation, non-blocking acquisition, and iterative forward/backward reconstruction whose dominant cost depends.

The quantum-selected configuration interaction identifies important determinantal basis functions through real-time evolution of a reference wavefunction and diagonalizing the Hamiltonian matrix in the resulting selected subspace. However, implementing the full electronic Hamiltonian on noisy quantum devices leads to rapidly increasing circuit complexity, limiting its scalability. To address this issue, we identify the dominant fermionic excitation operators and perform reference-state fidelity loss analysis to construct a compact Hamiltonian, reducing computational overhead while retaining high precision. Applied to Group IIIA monofluorides (BF, AlF, GaF, InF, and TlF), the proposed framework achieves a near-quadratic improvement in Hamiltonian-term scaling, enabling resource-efficient simulations. We employ this framework to compute the relativistic ground-state energies and permanent electric dipole moments (PDMs) of the systems under consideration. After validating the framework via simulations, we demonstrate hardware execution for AlF and TlF on the IBM Marrakesh processor using active spaces of up to 20 qubits. For a 20-qubit TlF system, the reduced Hamiltonian yields a reduction of higher than $ 98\%$ in both circuit depth and two-qubit gate counts, with the resulting PDMs from quantum hardware matching complete active space configuration interaction values within $99.99\%$. These results demonstrate the scalability of this approach on noisy intermediate-scale quantum devices.

Hybrid systems based on magnons in ferromagnetic materials, such as yttrium iron garnet, have achieved remarkable development in the last decade. These include the coupling of magnons to microwave and optical photons, superconducting qubits, phonons, spins, the center-of-mass motion of a ferromagnet, etc. Here, we review both the experimental and theoretical progress in this field, focusing on the generation of magnonic quantum states and their applications in a broad range of fields. Since the strong coupling is a prerequisite for achieving coherent quantum control of magnons and preparing magnonic quantum states, we start by introducing representative strong-coupling experiments in cavity magnonics, then review a series of protocols for creating various magnonic quantum states, such as Fock, cat, squeezed, and entangled states, and discuss their potential applications in macroscopic quantum studies, quantum information science, quantum sensing, magnonic quantum devices, dark matter detection, and so on. Finally, we summarize the review and give an outlook for the future study of quantum magnonics.

Graph neural networks (GNNs) can operate on large graphs but become infrastructure-sensitive at the scale of millions of nodes and typically require scalable training techniques for even larger graphs. This raises a central question: when can a model trained on a smaller, scaled-down replica of a graph be deployed on the full-resolution graph without retraining? We introduce a zero-shot transfer protocol in which a GNN is trained on a graph coarse-grained by geometric renormalization (GR), and the resulting weights are transferred directly to the original network. Across synthetic and real-world networks, training on GR scaled-down replicas preserves much of the original-scale predictive performance while significantly reducing training cost. We further find that learned representations and predictive trajectories remain aligned across scales. These findings suggest that structural similarity may be more important than network size in determining GNN transferability, opening a path toward scale-equivariant graph architectures.

PET provides functional images relying on two-photon coincidences from positron-electron annihilation. In human tissue, about 40% of annihilations are preceded by Ps formation, of which o-Ps component partially decays into three photons, with the remainder annihilating via pick-off or spin-exchange into two photons. This three-photon channel carries additional information about the surrounding micro-environment, including the three-to-two-photon yield ratio as a potential diagnostic marker. We propose the TRIO algorithm, a novel three-photon event-by-event image reconstruction algorithm formulated as a Bayesian maximum a posteriori inference problem. TRIO unifies time-based trilateration, energy-based reconstruction and, for the first time, a physics-informed prior derived from the QED description of Ps decay within a single probabilistic framework. In contrast to positronium lifetime imaging, which requires a prompt photon and is therefore restricted to specific radionuclides, TRIO relies solely on the three photons and is fully compatible with standard radionuclides such as 18F. Monte Carlo simulation modelled after the Siemens Biograph Quadra scanner demonstrates a mean position error of 1.62~cm, improving by approximately a factor of two over the time-based trilateration (3.05 cm) and by about an order of magnitude over energy-based reconstruction alone (18 cm). More importantly, the proposed Bayesian approach is compatible with existing TOF-PET scanners that can register three-photon annihilation coincidences.