Papers

We survey recent developments on the null energy condition (NEC) from the perspective of optimal transport, with emphasis on non-smooth Lorentzian geometry. After recalling the role of classical energy conditions in general relativity, we discuss the relation between Ricci curvature and entropy convexity in optimal transport. The main focus is the authors' optimal-transport characterization of the NEC. In the smooth setting, the NEC is equivalent to displacement concavity of the Shannon entropy power along suitable null-geodesic transport plans supported on null hypersurfaces and measured with respect to rigged measures. We review the geometric ingredients of this characterization and applications, including a weighted Hawking area theorem. We then turn to the synthetic theory introduced in a second paper of the authors. Synthetic null hypersurfaces are defined using achronal boundaries, gauge functions, and reference measures in topological causal spaces. In this framework, the displacement-concavity characterization becomes a synthetic curvature condition, $\mathsf{NC}^{e}(N)$, meaningful without differentiable structure. We discuss compatibility with the smooth theory, invariance under natural changes of gauge and reference measure, and stability under convergence. Finally, we present a synthetic version of Penrose's singularity theorem for continuous Lorentzian metrics. These results indicate that geometric and causal consequences of the NEC persist beyond the smooth setting and highlight optimal transport as a framework connecting curvature, causality, and singularity formation in Lorentzian geometry.

The mechanical performance of structural materials is governed by their microstructure, which is itself set by the thermal history imposed during processing. Predicting how that microstructure evolves along an arbitrary thermal trajectory, however, remains a challenging problem. Conventional continuous-cooling-transformation diagrams, for instance, provide only a static description, and they do not exist for all materials, especially emerging and novel ones. Deep-learning approaches address this issue, but they treat image-based characterization, temporal prediction, and microstructure forecasting as separate tasks. Here, we introduce a probabilistic deep-learning framework that unifies these tasks for in situ high-temperature confocal laser scanning microscopy data. The initial surface image is compressed with a beta-variational autoencoder into a low-dimensional latent representation, which, together with the cooling rate and the full temperature history, is passed to a temporal fusion transformer that produces a quantile forecast of the Widmanstatten ferrite ratio. In parallel, a Gaussian process predicts the end-state Widmanstatten ferrite ratio and corrects the forecast equilibrium level, yielding probabilistic prediction intervals. We demonstrate the efficacy of our method on Widmanstatten ferrite evolution in S235 steel across five cooling regimes and showcase significant accuracy. This verifies the relevance of our framework and paves the way for a streamlined and highly accelerated investigation of material evolution under thermal treatment.

We introduce a robust quantum communication protocol that integrates classical error-control coding, entanglement distribution, and superdense coding. Classical error-correcting codes are used to mitigate dark counts and photon losses by determining the positions of qubit transmissions and protecting the data embedded through superdense coding. We derive conditions on the employed codes that guarantee successful error correction under a bounded error-frequency model. Moreover, upper bounds are derived on the performance of conventional superdense coding protected by classical error correction. It is shown that, under the same constraints on error frequency, suitable code configurations of the proposed protocol can exceed those upper bounds both in terms of data rate and energy efficiency. Finally, we develop a physical error model based on fiber attenuation, detector efficiency, dark counts, and time-slot duration, and use it to evaluate the effective performance of different configurations of error-correcting codes. The proposed approach is primarily suited for short-distance quantum links, as in Quantum Local Area Network (QLAN) where it can provide high communication throughput while integrating entanglement distribution directly into the communication process.

Nonlocal magic quantifies the minimum nonstabilizerness attainable under independent local unitary transformations on the two subsystems. Here, we use min-relative nonlocal magic (NLM) to characterize the crossover from ergodicity to many-body localization (MBL) in the random-field XXZ chain. Unlike entanglement entropy, NLM probes how entanglement is organized through the distance of the Schmidt spectrum from dyadic-flat stabilizer spectra. From weak to intermediate disorder, NLM evolves from an $O(1)$ Haar-like value into a size-enhanced dome, while entanglement remains volume-law, revealing a spectral reorganization not visible in the entropy. Deep in the MBL regime, a two-level cut-hybridization model captures the nearly binary Schmidt spectrum and explains why the mean and median NLM decay approximately as $W^{-1}$ and $W^{-2}$, respectively. Following a product-state quench, NLM overshoots and relaxes in the ergodic regime, whereas at strong disorder it grows slowly and approximately logarithmically. These results show that NLM resolves Schmidt-spectrum structure not visible in the entanglement entropy and provides a complementary probe of ergodicity breaking.

The preparation of high-fidelity logical magic states is a crucial subroutine for universal fault-tolerant quantum computation (FTQC). Predicting the performance of FTQC and developing improved protocols rely on numerical methods to classically simulate logical magic state preparation in the presence of noise. Clifford logic on Pauli-stabilizer codes with circuit-level Pauli errors can be efficiently simulated using Pauli-stabilizer formalism, but the non-Clifford operations required to prepare logical magic states render generic simulation inefficient. We introduce Clifford-stabilizer simulation, an exact and efficient algorithm based on updating a Clifford-stabilizer group to simulate noisy preparation protocols for a broad class of logical magic states used to implement non-Clifford gates in the third level of the Clifford hierarchy under circuit-level Pauli errors. Clifford-stabilizer simulation applies to a range of operations that commonly appear in preparation protocols for such logical magic states, including Pauli-stabilizer measurements, logical Clifford measurements, and transversal non-Clifford gates. Our algorithm for Clifford-stabilizer simulation maps a non-Clifford circuit with sampled circuit-level Pauli errors to a Clifford circuit that exactly reproduces its measurement outcome distribution, achieving time and space complexities polynomial in relevant protocol parameters. We perform exact simulation of magic state cultivation up to fault distance 7 by Clifford-stabilizer simulation. Our method provides a route to perform exact benchmarking of large-scale logical magic state preparation protocols required for useful FTQC.

For a low-energy Effective Field Theory (EFT) to admit a consistent ultraviolet (UV) completion, it must adhere to the fundamental principles of locality, unitarity, analyticity, and Lorentz invariance. This leads to positivity constraints on certain Wilson coefficients via dispersion relations of $2\rightarrow2$ forward elastic amplitudes, which carry significant implications for both theoretical consistency and experimental phenomenology. In this work, we extend this bootstrap framework to $\mathcal N = 1$ supersymmetric theories, where the super-Poincaré algebra relates the bosonic and fermionic degrees of freedom within a single supermultiplet. Supersymmetric Ward identities (SWIs) enforce exact linear relations among component scattering amplitudes. Consequently, the Wilson coefficients associated with different $2\rightarrow2$ processes are mutually constrained, rendering their respective positivity bounds dependent. Focusing on dimension-8 operators, we explicitly show the positivity bounds on quartic interactions involving scalars, fermions and gauge fields, and demonstrate how they are related as a direct consequence of the SWIs. Furthermore, we expect that the algebraic nature of this framework naturally extends to loop-level superamplitudes and higher-point processes, providing a unified bootstrap perspective on supersymmetric EFTs.

Significant observational effort has been spent on identifying stellar analogues to solar type II and III bursts, which are plasma emission that is produced when coronal mass ejections and fast electron beams, respectively, propagate through the corona and wind. The sensitivity of these bursts to ambient plasma and their association with energetic transient particle flux makes them powerful diagnostics of exo-solar space weather. However, analyses of stellar bursts often rely on the assumption that the coronae of the stars that produce them are similar to the solar corona or that the instability requirements are the same as for the Sun. By extension, the assumption is that the forms of stellar bursts are very similar to that of solar bursts. I introduce a simple framework and the python package, swabs, to predict the shapes of stellar type II and III bursts to understand how they may deviate from the shapes of solar bursts. I present the burst expectations in the range of 10–170 MHz for two well-studied active stars: the M dwarf AD Leonis, and the G dwarf EK Draconis. The results for these stars suggest that the coronae of active stars may lead to bursts that have substantially (1) delayed arrival times to the observing band, (2) shallower drift rates, and (3) longer durations than solar bursts. The hot and fast winds may also inhibit the development of type~II and III bursts, regardless of the Alfvén speed. Non-isothermal winds reduce this inhibition, but may increase the delays and durations of the bursts. This work demonstrates that stellar bursts may deviate radically from their solar analogues. These deviations need to be considered in how we conduct searches for stellar bursts and how we evaluate the coronal conditions that cause the burst structures.

The interaction between magnetic and elastic degrees of freedom is increasingly central to advanced spintronics technology. In magnetostrictive materials, a moving domain wall carries a localized elastic deformation. Here, we investigate these elastic deformations and their back-action on field-driven domain wall dynamics using a fully coupled micromagnetic-elastodynamic framework and an analytical collective coordinates model. We show that mechanical damping acts as a non-magnetic dissipation channel, extracting energy from the magnetic texture and sustaining translational domain wall motion even in the complete absence of intrinsic Gilbert damping ($\alpha=0$). Specifically, elastic damping impacts domain wall dynamics in a manner equivalent to magnetic losses, allowing us to map it onto an effective magnetoelastic contribution to the Gilbert damping parameter, $\alpha_\text{me}$. We calculate the localized stress tensor profiles co-moving with the domain wall and show that mechanical dissipation breaks their spatial symmetry. Using our collective coordinates model, we demonstrate that these asymmetric stress profiles-specifically the localized stress gradients across the wall core-exert direct propelling forces that balance field-induced magnetic precession, enabling steady-state motion. Overall, these findings reveal a fundamental energy transfer and relaxation mechanism between magnetic and elastic subsystems, providing valuable insights for controlling magnetic texture dynamics via strain engineering and mechanical damping design in acoustic spintronic devices.

Active microrheology infers rheological properties from the motion of a force-driven probe. In small samples, however, the probe is often close to walls or other probes, and the resulting hydrodynamic interactions bias the inferred parameters. We present a Bayesian framework in which these interactions are built into simplified analytical models for Newtonian and linear viscoelastic fluids and test it on noisy synthetic data from finite-element simulations of Newtonian and Oldroyd-B fluids. Accounting for the interactions substantially improves the accuracy of the inferred parameters. Near a wall, oblique forcing allows the material parameters and the wall distance to be identified jointly from a single experiment. In the nonlinear viscoelastic regime, posterior predictive checks over multiple force levels reveal the inadequacy of the linear model. Finally, when the probe size is comparable to the heterogeneity length scale, the framework distinguishes spatial variations in the modulus from measurement noise.

Ensemble nitrogen-vacancy (NV) diamond magnetometers combine high sensitivity with vector-field reconstruction, but practical deployment is limited by errors arising from high-frequency laser noise during short-duration operations and slow-varying gain fluctuations and offset drift during long-term operation. Here, we present an integrated digital architecture for achieving NV magnetometry stability across distinct timescales. A dynamic differential readout continuously balances fluorescence and reference channels to suppress correlated laser noise. Second-derivative Lorentzian lineshape tracking enables in-situ correction of slope variations arising from slow changes in gain and optical excitation. We further identify temperature-induced bias-magnet fluctuations as a dominant source of long-term drift and introduce a magnetic eigenvector transformation that uses the intrinsic response of NV resonances to eliminate these variations. We show, under unshielded ambient conditions, that this architecture achieves an off-resonance noise factor of 1.0 $\pm$ 0.1, matching the fundamental limit with ten-fold suppression of long-term drift, enabling stable, field-ready quantum magnetometry.

Integrating scattering amplitudes over the forward direction, and consequently the unitarity constraints one extracts from such integrals, can appear ambiguous in theories with long-range forces. Standard techniques for bounding EFT couplings then typically produce bounds that depend on an arbitrary infrared scale. We study a non-relativistic model in which the standard techniques produce such an ambiguous bound, but which is simple enough that the exact bound can also be derived non-perturbatively and shown to involve no infrared scale. We then show how to derive bounds perturbatively, with no infrared scale entering at any stage. This requires two ingredients: accounting for the modified distributional structure of long-range amplitudes, and using distorted-wave perturbation theory (DWPT), which treats the Coulomb dynamics exactly. Together they give amplitudes with well-defined partial-wave projections and no spurious infrared divergences. Interpreting the model as an EFT with an ultraviolet cutoff $\Lambda_{\rm EFT}$, we derive cutoff-dependent bounds valid for arbitrary UV completions. Taking $\Lambda_{\rm EFT}\to\infty$ at each order yields bounds that rapidly converge to the exact bound, reaching $0.002\%$ accuracy at fourth order in the expansion. Finally, we demonstrate that every order of the DWPT expansion resums infinitely many Feynman diagrams of short-range perturbation theory, which individually evaluate to multiple polylogarithms and complete elliptic integrals, into a compact expression. At the orders we compute, no integration is even required, suggesting that DWPT may offer a simpler representation of scattering amplitudes than standard perturbation theory.

A physics-inspired dynamical model of interpersonal communication is proposed to describe how information interpretation can influence the evolution of a relationship between two interacting systems. The model distinguishes between intended information and observed information, with the former represented as an asymptotic limit in an abstract information space. An information potential landscape with local minima and increasing potential barriers is introduced to represent stable information states and limited information accessibility. Information transmission and processing are described through a general thought-processing function, while the separation between information states determines the coupling strength between the interacting systems. The relationship dynamics is then defined from the coupling strength and the independent sentiments of the two systems. Numerical simulations demonstrate the potential landscape, information dynamics, and several relationship regimes under deterministic and fluctuating information processing. An illustrative dynamical scenario further shows that an emerging information discrepancy can lead to either relationship recovery or deterioration depending on the subsequent evolution of information separation, coupling, and individual sentiment. The model provides a simple physics-inspired framework for describing interpersonal communication as a coupled dynamical process.

The double-parton scattering process off light and heavy nuclei is proposed as an effective tool to open a novel window for accurately studying the effects of nuclear binding on the internal structure of the nucleon, in the spirit of the European Muon Collaboration (EMC) effect. The key ingredient is the actual calculation of nuclear double-parton distributions that contain information about double-parton distributions and generalized-parton distributions of the nucleon, folded with nuclear light-front distributions. It allows to introduce a new quantity, which we call double-EMC ratio that appears to be much more sensitive than the standard EMC effect to both the nucleon partonic structure and to the nuclear binding. Hence, it could help solving long-standing questions about how nucleons change when they are bound in atomic nuclei.

Strongly lensed gravitational waves (GWs) are commonly searched for using population priors, including time-delay and magnification-ratio distributions, to suppress false alarms. Current LIGO, Virgo, and KAGRA (LVK) searches mainly adopt priors calibrated with galaxy-scale lenses; however, these may penalize the longer time delays produced by galaxy clusters, which could contribute 10%–20% of lensed events. Cluster member galaxies may also perturb the macro-lens potential and alter image magnifications. We investigate the time-delay and magnification-ratio distributions of binary black hole GWs lensed by galaxy clusters, together with microlensing systematics. In the full lensed sample, the median time delay is about 76 days for cluster lenses, compared with 6.9 days for galaxy lenses. After imposing an SNR threshold of $\rho>8$ at LVK O4 sensitivity, the medians are 15.4 and 0.24 days. The detected cluster sample retains a long-delay tail, with 9.1% of image pairs delayed by more than 365 days and 3.3% by more than 1000 days, so galaxy-based time-delay priors can suppress cluster-lensed pairs. By contrast, although cluster member galaxies broaden the magnification-ratio distribution of the full cluster sample, the detected cluster- and galaxy-lensing samples are very similar at O4 sensitivity, suggesting little bias in candidate selection. Cluster lenses also generally produce weak microlensing distortions: only about 0.8% of single-image mismatches exceed ${\cal M}=0.03$, while 4.8% exceed the parameter-estimation threshold $1.35/\rho^2$. Thus, microlensing is subdominant for most cluster-lensed events, mainly because images often form far from the brightest cluster galaxy, where the stellar density is low and dominated by intracluster light, although nearby satellites can locally enhance it. These results motivate a dedicated cluster-lensing pipeline for GW lensing searches.

The accurate description of electron correlations remains a major challenge in atomic calculations. In order to perform accurate calculations, it is necessary to consider the various types of electron correlations what often leads to extensive CSF expansions. This work presents further development of the method based on the second-order perturbation theory to identify the most significant CSFs that have the greatest influence on core-valence, core, core-core and valence-valence correlations. This method is based on a combination of the relativistic configuration interaction method and the stationary second-order Rayleigh-Schrödinger many-body perturbation theory in an irreducible tensorial form [G. Gaigalas, P. Rynkun, L. Kitovienė, Lithuanian Journal of Physics, 64, No. 1, 20-39 (2024) (https://doi.org/10.3952/physics.2024.64.1.3), G. Gaigalas, P. Rynkun, L. Kitovienė, Lithuanian Journal of Physics, 64, No. 2, 73-81 (2024) (https://doi.org/10.3952/physics.2024.64.2.1) and G. Gaigalas, P. Rynkun, L. Kitovienė, Lithuanian Journal of Physics, 64, No. 3, 139-161 (2024) (https://doi.org/10.3952/physics.2024.64.3.1)]. The method is extended to include additionally valence-valence electron correlations. It can be applied for an atom or ion with any number of valence electrons for calculation of energy spectra and other properties. Meanwhile, the correlations which can not be included according to perturbation theory are accounted for in a regular way. The use of the developed method allows a significant reduction of CSFs especially for complex atoms and ions. As an example of its application, the atomic calculations of the energy structure for Se~III ion are presented.

A novel semi-implicit second-order finite volume scheme integrating the scalar auxiliary variable (SAV) approach is proposed for solving the pseudo-time Eikonal equation in wall-distance computation. Unconditional energy stability under zero boundary conditions is rigorously proved, eliminating the dependence of the time step on the grid scale and enabling large-time-step computation. The scheme is a priori accuracy-preserving, and its discretization matrix forms an M-matrix, thereby guaranteeing strict non-negativity of the numerical solution inherently. The framework extends readily to any non-conservative scalar equation and, being independent of the specific finite-volume reconstruction, is compatible with schemes of arbitrary order of accuracy. A vanishing artificial viscosity is introduced to smooth the solution without compromising formal accuracy, and upwinding is incorporated through directional weighting in the weighted least-squares (WLS) reconstruction. Numerical experiments confirm that the scheme achieves the designed accuracy and permits stable computations with uniformly large time steps. For complex configurations such as a three-element airfoil and the three-dimensional ONERA M6 wing, accurate results are obtained on high-aspect-ratio grids, with relative errors in the computed wall distance below 3% relative to the search-based reference, except near geometric singularities.

Contributions of parton's orbital motion to the nucleon spin are encoded in the transverse-momentum dependent distributions (TMDs) and generalized parton distributions (GPDs). Currently these distributions are accessed through the lepton-induced processes such as semi-inclusive deep-inelastic scattering (SIDIS) and deeply virtual Compton Scattering (DVCS). The (un)polarized Drell-Yan process with hadron beams provides a unique and complementary probe, enabling critical tests of the universality properties of TMDs and GPDs in the space-like and time-like approaches. This talk reviews recent results of TMDs obtained from the Drell-Yan measurements and discusses future prospects for accessing nucleon GPDs through measurements of the exclusive pion-induced Drell-Yan process at J-PARC.

Context. The radiative stage of a supernova remnant (SNR) constitutes approximately 90% of its lifetime. Yet, only a few tens of radiative supernova remnants (r-SNRs) have been observed in the Milky Way, while catalogs of young SNRs in their adiabatic stage contain $\sim$ 300 -400 confirmed sources. This deficit reflects the absence of unambiguous tracers for the radiative phase, and stands as a major obstacle to understanding the impact of SNRs on the energetic balance and the chemical state of the interstellar medium. Aims. We aim to identify spectral tracers of r-SNRs to enable their individual or statistical detection across the Galactic plane, and, more generally, to quantify the collective contribution of the Galactic r-SNR population to interstellar line emission. Methods. We present SKYNET, a new predictive framework that couples a physically motivated model of individual r-SNRs, built upon a dedicated version of the Paris-Durham shock code, with a Galactic model that describes the spatial distribution of r-SNRs and the properties of the medium into which they expand. Together, these components allow SKYNET to predict the cumulative emission of Galactic r-SNRs in nearly one million spectral lines. As a first application, we compare the predictions of SKYNET with an Herschel survey of the two fine-structure lines of singly ionized nitrogen. Results. The Galactic distribution predicted by SKYNET shows that random lines of sight toward the Galactic plane inevitably intercept multiple r-SNRs, whose cumulative emission may be associated with specific spectral tracers. SKYNET successfully reproduces the longitudinal profile of N + emission along the Galactic plane, which results from the projection on the sky of the spiral arms structure. The predicted line-ratio distribution matches the mean, the dispersion, and the tail of the observed distribution, showing that the model naturally explains the unexpectedly narrow range of physical conditions of the medium responsible for the N + emission. Comparison with the observed column densities reveals that SKYNET accounts for $\sim$ 20-25% of the total Galactic N + content, demonstrating that radiative SNRs constitute a significant, and previously underappreciated, source of ionization in the Milky Way. The difference between the observed and predicted column densities could be due to the clustering of supernova remnants into superbubbles, which is currently not taken into account by the model, and to the contribution of non-SNR sources such as H II regions. Conclusions. SKYNET offers a new framework for quantifying the cumulative emission of r-SNRs at Galactic scales that can be directly compared with observational surveys of atomic and molecular lines. The first application reveals that SNRs contribute significantly to the ionization of the ISM, highlighting a role that has largely been overlooked.

This work considers the Enskog kinetic theory for moderately dense confined granular mixtures within the framework of the $\Delta$-model, an effective collisional model that accounts for the transfer of energy from the vertical to the horizontal degrees of freedom of grains through an additional velocity increment during collisions. The main objective is to complete the Navier–Stokes hydrodynamic description of dense binary granular mixtures by explicitly determining the heat-flux transport coefficients, namely the Dufour and thermal conductivity coefficients, together with the first-order contributions to the partial temperatures and the cooling rate. These quantities are determined in the steady state from the Enskog kinetic equation by means of the Chapman–Enskog method and are expressed in terms of the masses and diameters of the particles, mixture composition, density, and coefficients of restitution. The results show that finite-density effects have a significant influence on transport, leading to behaviors that differ markedly from those observed in the dilute regime. The complete set of transport coefficients is considered further to study two different problems. First, we perform a linear stability analysis of the homogeneous steady state (HSS). The analysis demonstrates that no unstable transverse or longitudinal hydrodynamic modes are found over the parameter space explored, indicating that the HSS is linearly stable even at moderate densities. As a second problem, the violation of the Onsager reciprocal relations for a confined dense granular mixture is quantified in terms of the parameter space of the problem.

Previous literature has documented that black hole shadow images are mainly shaped by spacetime geometry and plasma microphysics governing accretion flows. However, the influence of equations of state (EoS) remains under-explored. In this paper, we quantify how different plasma microphysics prescriptions, specifically the choice of EoS and electron-heating models, affect flow thermodynamics and the corresponding synthetic black-hole images. We perform three-dimensional general-relativistic magnetohydrodynamics (GRMHD) simulations using two constant-$\gamma$ ideal EoSs with $\gamma = 4/3$ and 5/3 and a temperature-dependent variable EoS (TM) in both accretion flow states: Standard And Normal Evolution (SANE) and Magnetically Arrested Disk (MAD) regimes. The dynamical models are post-processed with general-relativistic radiative transfer (GRRT) calculations at 86GHz and 230GHz, employing thermal and hybrid $\kappa$ electron distribution functions for synchrotron radiation. We also compare the two-temperature electron heating prescription based on turbulent heating and magnetic reconnection. The constant-$\gamma$ EoSs systematically overestimate or underestimate gas and electron temperatures across disk and jet regions, whereas the variable EoS provides a smoother trans-relativistic interpolation between the two limiting regimes. These differences substantively impact the synchrotron emissivity, image morphology, and the amplitude of flux variability. In particular, the variable EoS exhibits systematically larger temporal variability than the constant-$\gamma$ models. These results demonstrate that adopting a physically self-consistent description of the EoS is essential for realistic modeling of accretion-flow thermodynamics and horizon-scale images like Event Horizon Telescope (EHT) observations.

{Observations reveal hard X-ray sources in the solar corona with morphologies and locations distinct from typical solar flares. One expected mechanisms for hard X-ray (HXR) production in the solar corona is eruptions of various types. Failed eruptions are promising targets for detecting non-flare-related HXR sources because most of their energy is expected to be dissipated in the corona. } {We investigated the SOL2022-0 to determine the nature of coronal HXR sources associated with the event.} {We used Solar Orbiter's STIX, EUI, and SDO AIA observations. We reconstructed STIX images using the MARLIN algorithm. We used EUI data to cross-check the view of the event from two vantage points. The AIA images enabled kinematic analysis and reconstruction of differential emission measure (DEM) maps. We then used the AIA DEM to predict X-ray emission maps and compared them with STIX images. } {We find temporal and spatial correlations between the failed eruption and HXR sources. During the braking of the filament eruption, we observed non-thermal HXR emission from coronal sources along the path of the eruption. Thermal HXR emission was concentrated in three coronal sources formed with two separate mechanisms: chromospheric evaporation and direct heating. Directly heated sources are the result of the interaction of the eruption with the overlying magnetic field, and were observed to cool for an extremely long time.} {The HXR emission sources related to the interaction between the eruption and the overlying magnetic field might be typical structures in all flares. However, they appear to be weak, since their emission is only 5-20~% of that from the flare coronal source. If footpoint sources were not occulted, then these weak coronal emission sources would not be visible due to the low dynamic range of HXR telescopes.}

The probabilities of syntactic structures in human languages are assumed to emerge fully from language-specific experience. Here, I show that a universal prior over syntactic structures emerges from a model of human language production, in which words are progressively integrated into syntactic structure. Without fitting any parameters to specific language data, the resulting prior assigns higher probabilities to attested than to random dependency trees in all 138 typologically diverse languages examined. These prior probabilities correlate positively with those estimated from corpora in 33 of 34 languages. The results indicate that part of the probability structure of syntax can arise independently of language-specific learning. This identifies human language production as a possible cognitive source of universal statistical structure in language, while providing a data-independent structural bias for probabilistic models, including large language models.

Can deep learning translate physical fields governed by fundamentally different equations? We address this question by introducing Cross-Physics Mapping (CPM), an operator-learning framework for mappings between heterogeneous physical domains. We formulate sufficient conditions for such mappings through compatible latent representations and propose a dimensionless scaling principle that aligns the characteristic evolution scales of the source and target systems without assuming their dynamical equivalence. As a representative test, paired diffusion and wave fields are generated independently from their respective parabolic and hyperbolic equations while sharing the same latent geometry, material heterogeneity, excitation, and dimensionless scale. Seven architectures-ResUNet, DeepONet, Fourier, latent, wavelet, U-shaped, and Galerkin neural operators-are evaluated for both diffusion-to-wave and wave-to-diffusion mappings. The results reveal a strong directional asymmetry. Diffusion-to-wave reconstruction is more challenging because it requires recovering wavefront, phase, and time-of-flight information attenuated by diffusion; U-NO performs best in this direction, achieving a relative $\ell_2$ error of $0.307$ and an $R^2$ of $0.905$. Wave-to-diffusion mapping is considerably more stable, with GNO attaining a relative $\ell_2$ error of $0.154$ and an $R^2$ of $0.935$. Neural operators generally outperform the conventional convolutional baseline, highlighting the nonlocal nature of cross-physics transformations. These findings demonstrate that deep learning can establish useful mappings between distinct physical modalities on a shared latent manifold, while the achievable accuracy remains fundamentally constrained by the direction-dependent information content of the governing physics.

Optically readable molecular spins are attractive as quantum sensors due to their nanoscale modularity, synthetic tunability, and scope for sensitive readout. In particular, photoexcited spin-triplet states in organic molecules are appealing in supporting high optical-spin contrast at room temperature. Their utility is underpinned by their coherence, which warrants a detailed understanding of it. Here, from first principles, we systematically explore the Hahn-echo decoherence of the benchmark molecular system for room-temperature optically detected spin coherence—pentacene guest molecules coupled to a para-terphenyl host. Using generalized cluster-correlation expansion methods, we investigate the mechanisms of nuclear-spin-induced decoherence from zero to high magnetic field, exploring the role of guest vs host molecules, specific nuclei, zero-field splitting interactions, and hyperfine parameters. We describe how zero-field decoherence is driven by ~6 nuclei on the guest, while high-field decoherence is driven by ~600 nuclei in the host; how the longitudinal zero-field splitting parameter, $D$, can prolong $T_2$; and the magnetic-field-dependent processes which drive decoherence. These results advance our understanding of decoherence in optically readable molecular spins, providing insight for their synthetic enhancement and deployment as quantum probes.

We present an analytical and experimental study of a variable-mass oscillator with constant mass loss. Starting from the equation of motion of a spring-mass system whose mass decreases linearly with time, analytical solutions are obtained in terms of Bessel functions. Simple asymptotic expressions are then derived, providing explicit descriptions of the evolution of the oscillation amplitude and frequency. The analysis is extended to include viscous damping, leading to a model suitable for direct comparison with experimental observations. Experimental measurements of the oscillator acceleration were performed using a simple laboratory setup consisting of a spring-mounted container that continuously loses mass at an approximately constant rate. The theoretical predictions accurately reproduce the measured dynamics over the entire duration of the oscillation, and the massloss rates obtained from the fits are in good agreement with independent measurements. The results provide an accessible example of variable-mass dynamics and illustrate how special-function methods can yield experimentally testable predictions in advanced undergraduate mechanics.

We develop an inverse lensing formalism for reconstructing the metric function of a static, spherically symmetric spacetime directly from the gravitational deflection angle of light. By formulating the inverse problem through an Abel transformation, we derive an integro-differential relation connecting the observable deflection profile to the underlying spacetime geometry. As a consistency check, we consider the case of vanishing deflection, \(\alpha(b)=0\), and recover \(A(r)=1\), corresponding to flat Minkowski spacetime. We then apply the formalism to the gravitational deflection of light by the Sun using the observationally motivated leading-order expression $\alpha(b)=\frac{2(1+\gamma)GM_{\odot}}{c^{2}b}.$The resulting metric function is shown to recover the Schwarzschild form in the weak-field limit \(r\gg M\). We further consider the higher-order correction to the solar deflection angle and reconstruct the corresponding metric beyond the leading-order approximation. Interestingly, the \((M/r)^2\) term vanishes in the resulting weak-field expansion, yielding an improved approximation compared with the metric reconstructed from the leading-order deflection angle. Our results demonstrate that gravitational lensing observations can provide a direct route to reconstructing the underlying spacetime geometry without assuming a specific metric a priori. This formalism therefore, offers a novel framework for connecting observational light propagation with spacetime geometry.

X-ray tomographic particle tracking velocimetry is a promising tool to provide pore-scale insight into fluid flow inside opaque porous media. This is critical for applications ranging from geo-energy to electrochemistry. Current approaches to implement this three-dimensional, three-component (3D-3C) velocimetry technique use conventional micro-computed tomography (CT) workflows to recover tracer particle trajectories. These workflows first reconstruct a sequence of 3D CT images from radiographs which are sequentially acquired at time intervals that span multiple viewing angles. The tracer particles are then detected in each of these CT images and finally linked into trajectories. However, motion-blurring induced by the static CT volume reconstruction impairs the detectability of fast-moving tracer particles, severely limiting the temporal resolution and dynamic range of the velocimetry outcome. In this work, we show that this limitation can be overcome by explicitly recovering particle trajectories directly from the acquired radiographs, bypassing the intermediate tomographic reconstruction step and the static-scene assumptions of traditional 3D CT reconstructions. We implement this approach in a new open-source algorithm, CTracks, and validate it using experimental data on pipe flow and on flow through a complex porous medium, demonstrating a five-fold increase in the maximum detectable velocity compared with state-of-the-art methods. Numerical simulations further confirm improved detection rates and bounded trajectory errors at these increased velocities. Our work thus demonstrates that 3D-3C velocimetry in opaque microscopic geometries can be extended to substantially faster flows by replacing X-ray tomographic image reconstruction with direct particle trajectory reconstruction.

Grasp package is based on the relativistic configuration interaction in which accurate calculations, accounting for valence, valence-valence, core-valence, core, and core-core electron correlations, often rely on massive CSF expansions. This paper presents further development of the method based on the second-order perturbation theory for finding the most important CSFs that have the greatest influence on the core-valence, core, and core-core correlations. This method is based on a combination of the relativistic configuration interaction method and the stationary second-order Rayleigh-Schrödinger many-body perturbation theory in an irreducible tensorial form [G. Gaigalas, P. Rynkun, L. Kitovienė, Second-Order Rayleigh-Schrödinger Perturbation Theory for the Grasp2018 Package: Core-Valence Correlations, Lithuanian Journal of Physics, 64, No. 1, 20-39 (2024) (https://doi.org/10.3952/physics.2024.64.1.3) and G. Gaigalas, P. Rynkun, L. Kitovienė, Second-Order Rayleigh-Schrödinger Perturbation Theory for the Grasp2018 Package: Core Correlations, Lithuanian Journal of Physics, 64, No. 2, 73-81 (2024) (https://doi.org/10.3952/physics.2024.64.2.1)]. In this extension, the perturbation theory accounts for electron core-valence, core, and core-core correlations where an atom or ion has any number of valence electrons for calculation of energy spectra and other properties. Meanwhile the rest of the correlations are accounted for in a traditional way. This allows a significant reduction of the space of the configuration state function for complex atoms and ions. We also demonstrate how this method works for calculations of the energy structure and E1 transition properties of Fe XV ion.

We present a reanalysis of JWST NIRCam F322W2/F444W transit observations of the hot Jupiter HD 209458 b to extract morning and evening transmission spectra. To determine whether asymmetries in the transit light curves arise from atmospheric structure, we refine the HD 209458 system parameters through a reanalysis of archival transit and radial velocity data. We then extract limb-resolved spectra using two independent light curve modelling frameworks, Harmonica and catwoman,which yield consistent results. We interpret the transmission spectra using both 1D and 1.5D (i.e.,retrievals in which each limb of the planet is modelled independently) atmospheric retrievals. From our 1.5D free-chemistry retrievals, we find a strong preference (Delta ln Z = 2.81 +/- 0.49) for a model in which the morning and evening limbs are retrieved independently over one in which they share atmospheric parameters, despite the additional free parameters this requires. This asymmetry is most pronounced in the retrieved cloud properties, consistent with 3D atmospheric circulation models that predict longitudinally varying cloud distributions, and to a lesser extent in the CH4 and CO abundances. In contrast, the H2O and CO2 abundances and the isothermal temperature structure remain largely consistent between limbs. Similarly, our 1.5D equilibrium chemistry retrieval reveals a uniform composition between the limbs, alongside a marginally hotter evening limb relative to the morning limb. While the retrieved temperatures, chemical abundances, metallicities and carbon-to-oxygen ratios (C/O) are uniform across both limbs, they exhibit significant differences from our 1D retrieval results, highlighting the importance of multidimensional modelling in mitigating 1D retrieval biases.

Many of the students enrolled in the calculus-based physics sequence are pursuing engineering degrees. Lab activities that have a direct connection to engineering, analyzed with the physics they have been studying, can grab students' interest and provide a real-world connection. All the better if the equipment is inexpensive and partly self-built. In addition, we wanted to use smartphones as data collection devices. Smartphones have become valuable tools for physics education because they provide an accessible and inexpensive means of experimental data acquisition through their built-in sensors. Several free applications, such as Physics Toolbox and phyphox, facilitate sensor-based data collection and analysis, making it possible to perform meaningful laboratory activities without expensive equipment. We chose to use a simple crank-slider mechanism for our activity. The crank-slider converts rotary motion into linear motion and has common applications in such things as internal combustion engines and compressors. Educational studies have shown that slider-crank mechanisms and related mechanical devices provide effective contexts for introducing engineering concepts and connecting theoretical models with practical applications. In our implementation, a rotary motor drives a disk connected by a rod to a cart moving along an air track. Students investigate the resulting motion using smartphone data acquisition and video analysis. Experimental measurements are then compared with the predictions of a mathematical model derived from the geometry of the system.