Papers

Gravitational-wave data are beginning to reveal a structured landscape of black-hole masses and spins, suggesting multiple formation processes are now being resolved observationally. We analyze data from LIGO–Virgo–KAGRA's (LVK's) fifth Gravitational-Wave Transient Catalog and find that the population is naturally described by four distinct subpopulations. The dominant component, contributing $\simeq70\%$ of the astrophysical merger rate, is characterised by a low-mass population centred near $10M_\odot$ and is separated from heavier systems by a depletion near $14M_\odot$. This component may be associated with black holes formed from failed supernovae. Above this depletion, we find two intermediate-mass components: an unequal-mass branch pairing the lower-mass, $\simeq10M_\odot$ black hole with a heavier black hole, perhaps associated with isolated-binary/stable-mass-transfer formation, and a nearly equal-mass branch peaking near $30$–$35M_\odot$ whose low spins and mass distribution favour first-generation systems possibly born in dense stellar environments. A fourth, percent-level component extends to higher masses and is characterized by a broad mass-ratio distribution and large spin magnitudes, consistent with a hierarchical-merger population. Our four-component model is overwhelmingly preferred over a standard LVK population model by a natural-log Bayes factor of $\ln(\mathrm{BF}) = 19.2$. Our work observationally unveils a new subpopulation of black-hole mergers utilising a new hybrid data-driven and parametric-model discovery method, bringing us one step closer to understanding stellar-mass black-hole archaeology.

We revisit the relation between disc stellar mass and disc velocity dispersion (M_*-\sigma_e) and extend it to thin and thick subcomponents using orbit-based dynamical models of 161 SAMI galaxies and counterpart measurements for 31 disc galaxies in the NewHorizon simulation. On the observational side, we apply Schwarzschild orbit superposition to recover orbital circularity distributions and component kinematics. On the simulation side, we sample thin and thick discs by circularity and, separately, by stellar age to test classification dependence. Our analysis reveals three main results. (1) Discs follow a tight M_*-\sigma_e relation, nearly parallel to the bulge relation. (2) For both circularity- and age-based definitions, the thick-disc component is systematically hotter than the thin-disc component, and the thin-thick dispersion ratio varies only weakly with mass. However, age cuts yield a smaller kinematic contrast, indicating that stellar age and orbital circularity do not map one-to-one and that no single global age threshold reproduces the circularity-based split. (3) Method and data systematics are present, with Schwarzschild modelling returning slightly higher disc \sigma_e than spectroscopic bulge-disc decompositions, and simulated discs showing lower \sigma_e at fixed mass than observed. All these results are consistent with a baseline set by vertical-equilibrium scalings, with secular heating accumulating over time and modulating the dispersion at fixed mass. Occasional minor interactions may add localised heating but do not appear to be essential for explaining the qualitative, global trends reported here. Future tests with chemo-dynamical modelling and higher-resolution, chemistry-tracking simulations will provide stronger constraints on disc substructures in external galaxies.

Recent experimental advances could soon allow the accurate extraction of not only the root-mean-square radius but also the fourth radial moment of the nuclear electric charge density distribution. The fourth radial moment of the nuclear density distribution, $R_4\equiv\sqrt[4]{\left<r^4\right>}$, provides a sensitive probe of the nuclear surface thickness, as it is more susceptible to the large-$r$ distributions than the root-mean-square radius ($R_2$). In this work, we examine the utility of $R_4$ for constraining the nuclear equation of state (EoS) at subsaturation densities, specifically for the proton distribution and within the framework of Skyrme energy density functionals. Using a statistical analysis based on predictions from one hundred Skyrme functional models, we demonstrate strong correlations between the energy per particle curvature $K(\rho)$ at $\rho = 0.08 \text{ fm}^{-3}$ and $R_4$ (or the ratio $R_{4/2}=R_4/R_2$) in representative nuclei such as $\text{}^{48}\text{Ca}$ and $\text{}^{208}\text{Pb}$. We establish that $R_{4/2}$, being sensitive to the density tail, serves as an efficient proxy for sub-saturation $K(\rho)$ within the tested Skyrme functional space. Knowledge of $R_{4/2}$ within 0.5% precision or better, for example in $^{48}$Ca or $^{208}$Pb, could constrain the curvature of the energy per particle of symmetric matter at $0.08$ fm$^{-3}$ within 20 MeV or less.

This study investigated using of silicon photomultiplier (SiPM) for scintillation {\gamma}-spectrometry with CdWO4, CsI(Tl), and NaI(Tl) crystal scintillators. At room temperature, CsI(Tl) crystal scintillator provides the best performance, while the achievable energy resolution is lower compared to that obtained with conventional photomultiplier tube (PMT) with green-enhanced photocathode. These findings highlight the potential of SiPMs as a compact and cost-effective alternative to PMTs in nuclear physics applications, particularly for light portable spectrometers, such as radiation monitoring systems based on small unmanned aerial vehicles.

Rare-regime discovery in parameterized dynamical systems is an active-search problem: find one verified parameter at which a scientifically defined qualitative threshold is crossed, even when acceptable candidates are rare, nonconvex, or fragmented. We introduce Quantum-Classical Phase-space and Stability-Threshold Search (QC-PHAST), an evidence-gated decision protocol and query-accounting framework for finite candidate libraries. A candidate induces a dynamical object, simulator-derived criticality score, and verified first-hit predicate. Scientific metadata and charged pilot evidence are used to assess whether equation-aware search, scalar-score active search, predicate-only search, or only a query-model comparison is admissible. The quantum row is the inherited Grover/Boyer–Brassard–Hoyer–Tapp (BBHT) unknown-$M$ marked-set query reference; it is not a new quantum-search theorem, materialized circuit, or hardware-speedup claim. The result is a regime map. Direct boundary constructions, geometry controls, online simulator loops, and learned-label accounting further identify when classical structure, false positives, calibration cost, or state preparation erases the query-model margin. QC-PHAST is therefore an auditable protocol for deciding when a finite-pool marked-set reference is informative and when classical or resource-aware search should dominate.

In this article, we discuss some supersymmetric probe D5 brane solutions embedded in a type 2b supergravity background solution obtained by deforming the global $AdS_5 \times S^5$ spacetime. The D5 brane solution of interest has a non-compact worldvolume, and when it is placed alongside overlapping with an anti-$\overline{\text{D5}}$ solution, the combination is dual to a codimension-1 defect of the boundary $SYM$ theory. The dual defect becomes a topological symmetry defect of a $U(1)$ $0$-form global symmetry if we move the profile of the probe D5 more towards the boundary of the bulk spacetime, resulting in a U-shaped profile hanging from the boundary. We show how the hanging D5 brane can be used holographically to measure the $U(1)$ global symmetry charge of the heavy determinant operators in the SYM theory by moving them across dual giant graviton D3 branes in the bulk spacetime.

Hypercritical fallback accretion can advect the surface magnetic field of a newborn neutron star into the newly accreted outer layers. Before this material joins the solid crust and enters the Hall-Ohmic regime, part of it may remain hot, dense, and liquid, allowing turbulent magnetic amplification. We investigate whether a small-scale dynamo (SSD) can operate under these conditions using six local 3D resistive MHD simulations performed with FLASH 4.7 in a periodic domain with externally forced subsonic turbulence. We explore magnetic Reynolds numbers from about 700 to 3700 and examine the effects of the equation of state, neutrino cooling, and numerical resolution. The magnetic field grows exponentially from an initial strength of 1e12 G and saturates at about (3-7)e13 G within milliseconds. The saturated magnetic energy remains below equipartition, with magnetic-to-kinetic energy ratios of about 0.2-0.3, consistent with SSD behavior for magnetic Prandtl number near unity. The reference simulations at 128^3 and 256^3 resolution agree within a few percent. Neutrino cooling has little effect over the simulated times, while the equation of state only weakly modifies the dynamo properties. These results indicate that a local SSD can efficiently amplify magnetic fields in the liquid post-hypercritical accretion layer and support scenarios for magnetic field reemergence in newborn neutron stars.

We present a diffusion-guided full waveform inversion (FWI) study in which pretrained diffusion generative models are used as learned regularizers inside a PDE-constrained seismic inversion loop. We compare three training-free guidance strategies: Manifold-Preserving Guided Diffusion (MPGD), SDEdit-based initialization, and Split Gibbs Diffusion Sampling (SGDS), which alternates between FWI likelihood updates and diffusion-prior denoising. The proposed workflow keeps wave-equation modeling in the inversion loop and uses a geological prior to stabilize model components that are weakly constrained by the seismic data. Controlled GeoFWI experiments, benchmark-scale Marmousi and Overthrust tests, a difficult Sigsbee2A salt test, and noise-degradation studies show that SGDS improves reconstruction quality relative to conventional L2 and total-variation regularized FWI in clean and moderately noisy synthetic settings. Overall, these experiments demonstrate that diffusion-guided optimization can serve as a practical learned regularization strategy for synthetic FWI benchmarks while preserving the wave-equation modeling loop.

A prevailing consensus holds that flow reversals of the large-scale circulation (LSC) are suppressed in low-Prandtl-number (Pr) fluids, as high thermal diffusivity rapidly dissipates the energy required to fuel the corner-vortex mechanisms. Here, we report Direct Numerical Simulations of liquid metal convection (Pr=0.029) revealing that strong lateral confinement defies this consensus, enabling sustained LSC reversals. We show that confinement triggers a “plume condensation" transition, reorganizing chaotic thermal plumes into highly coherent, quasi-linear structures. A thermal dissipation analysis demonstrates that this coherence drastically reduces heat loss during transport, allowing plumes to deliver sufficient buoyancy to corner vortices to drive reversals. We map a distinct “island of reversal" in the parameter space, establishing lateral confinement as a control parameter capable of overcoming the stabilizing effects of high thermal diffusivity.

Interacting hysteretic spins are an appealing model for cyclically-driven athermal disordered matter. Because they provide a basis for storing and processing information from their environment, such models are also being pursued as a framework for intelligent matter. Recent proof-of-concept designs have begun to demonstrate the strong, controlled, pairwise interactions that are necessary for this endeavor. But, it is not yet clear what are the limits—practically or fundamentally—on such interactions. Here we build rotor-based mechanical hysterons that extend the generality of their interactions in three ways: (i) splitting the interaction strength based on the hysteron state, (ii) building a non-reciprocal interaction of mixed sign, and (iii) incorporating tunable three-body effects. We access these effects within a simple, replicable design platform, and we rationalize our results. Our work expands the space of behaviors for designed structures that compute on mechanical inputs.

Sexual contact networks are fundamental to understanding the spread of sexually transmitted infections (STIs), yet their large-scale structure remains difficult to observe because of technical, ethical, and privacy constraints. Here, we analyze a large bipartite commercial-sex contact network comprising 1,436,738 client-sex worker links, constructed from reviews posted between 1999 and 2024 on The Erotic Review (TER), a review platform covering sex workers listed across 97 countries and territories. We examine the network's cross-country structure, focusing on how clients, sex workers, locations, and time periods are connected. The network formed a giant connected component containing most clients and sex workers and exhibited small-world-like properties, including short average path lengths, high bipartite clustering, and disassortative degree correlations. Although most reviews were associated with the United States, a small fraction of clients reviewed sex workers in multiple countries or territories, creating spatial bridges between otherwise distant regions. Long-term users created temporal bridges, making the aggregated network more compact than networks built from shorter time windows. Contacts involving transgender female sex workers were structurally concentrated: clients who reviewed both cisgender and transgender female sex workers represented a small minority but accounted for a large share of transgender-related reviews and showed higher cross-country activity. These findings show how a small number of highly connected clients with cross-country review activity can shape the large-scale connectivity of commercial-sex contact networks. More broadly, our results demonstrate the value and limitations of digital trace data for studying cross-country contact structures relevant to STI transmission.

After decades of efforts, there are now fruitful high-resolution radio observations of low-luminosity active galactic nuclei (LLAGNs), and the observed frequency has extended from $\sim$10 GHz up to $\sim$200 GHz. In this work, based on a model that combines a magnetically arrested disk (MAD) and a Blandford-Znajek-like jet, we carried out detailed analysis on size and location of the radio core of LLAGNs. The radio core size of nearby LLAGN M104 is re-visited based on this new model. We successfully reproduce a $size\propto\nu^{-1}$ scaling between 1 GHz and tens of GHz, if more than $50\%$ of electrons in jet follow a power-law (PL) distribution. We further confirm that, at high radio frequencies emission from MAD exceeds that from jet, and a flatter size-frequency slope is observed. The impact of PL electrons in MAD is also investigated. For those $L_{\rm bol}/L_{\rm Edd} \gtrsim (3-8)\times 10^{-6}$ LLAGNs and black hole binaries in their hard state, PL electrons are expected to be highly suppressed due to strong radiative cooling (so-called `synchrotron boiler' effect).

Computation is the controlled evolution of a state. Asynchronous evolutions, where all parts of the state change in their own time without stopping each other, put this control in jeopardy. It is in fact a mystery how natural processes perform asynchronous computations using many units with no global orchestration. Here we demonstrate how collective computational abilities can emerge in asynchronous many-body systems. The key insight is to split the physical "hardware" underlying the computation into two asymmetrically coupled parts, analogous to position and momentum in a harmonic oscillator. The resulting inertia nudges the evolution of the state so that the asynchronous computation proceeds in the right order. By treating our inertial asynchronous computer as a nonequilibrium material, we map out its phase diagram numerically and analytically using a framework we dub loop dynamical mean-field theory. We experimentally demonstrate our approach using analog spiking neuromorphic chips designed to mimic actual neurons in the brain. In addition, we construct software that can run on asynchronous hardware: we denoise movies whose clean versions were never seen during training, an instantiation of the generalization transition underlying modern machine learning. Our results point to a general strategy for reliable, decentralized computation in energy-constrained settings from dynamics self-assembly to cell differentiation.

Binary (super-)massive black holes (BHs) are expected to reside in the center of some galaxies. In this work, we re-visit accretion onto binary massive BHs, incorporating recent advances in both accretion theory and the mass transfer rate between the two massive BHs. We focus on relatively bright systems with an Eddington ratio of 0.1 for a binary with total BH mass $10^8\,M_\odot$, but consider a wide range of mass ratios $10^{-4} \le q \le 0.5$. The binary system consists of two mini-disks surrounding two individual BHs and a circumbinary disk surrounding the mass center of binary BHs. Depending on the mass ratio, the two mini-disks can be hot accretion flows, standard thin (cold) disks, or Slim disks. The radiative contributions from all three disks, each potentially in different accretion modes, are taken into account self-consistently. The spectral energy distributions of the binary BH system show universal “notch” features from the near-infrared to ultraviolet bands, caused by the gap or cavity in the accretion disk, consistent with previous studies. Binary with different mass ratios exhibit distinct spectral energy distribution properties, offering opportunities for testing (identifying candidates) with future broad band (infrared up to X-rays) observations. We also investigate the evolution of these binary systems, and find that, for systems with initial mass ratios $q \lesssim \text{a few} \times 10^{-3}$, the mass ratio evolves toward an equilibrium value $q \sim 10^{-3}$. For binary BH systems with a larger initial mass ratio, their mass ratio instead evolves toward unity.

We examine whether tropical cyclones (TCs) obey ordinary Brownian or anomalous diffusion using a huge ensemble (HENS) of hindcasts for summer 2023. Anomalous diffusion has been inferred for actual TCs from the fluctuations in their tracks from the shortest paths between the initiation and termination of each cyclone. We reproduce the same anomalous diffusion power laws connecting spatial position and time using HENS. In addition, we show that the variance in the position of a single TC across HENS since initiation follows a scaling law with time that, in some cases, corresponds to ballistic motion of the TC through the background atmospheric flow. This determination was enabled by the exceptional statistics determined from thousands of plausible yet counterfactual recreations of 34 individual TCs. HENS consists of 7424 15-day hindcasts initiated from observed atmospheric conditions each day from June 1, 2023 to August 31, 2023 using the ECMWF ERA5 meteorological reanalysis. The hindcasts were generated using NVIDIA's Spherical Fourier Neural Operator (SFNO) machine-learning-based weather and climate emulator. We identify tropical cyclones in HENS using a variant of the Tempest Extremes detection and tracking frameworks for TCs with adjustments to the disposable parameters to minimize the numbers of false positives and negatives relative to the International Best Track Archive for Climate Stewardship (IBTrACS) records for TCs observed in summer 2023. We conclude with the implications of our findings for the predictability of TC tracks and landfall locations on lead times of days to weeks.

Quark confinement, the fact that colored quarks are permanently bound inside color-neutral hadrons and have never been observed as isolated particles, remains one of the central issues of the Standard Model. Recently, Ji et al.\,\cite{Ji:2026lyj} proposed a framework to define and measure the force on quarks in the proton, obtaining strong evidence for a net confining force and thus opening a new perspective on the study of confinement. In this work, we improve this analysis by incorporating light-cone QCD sum rule results to supplement the limited experimental and lattice QCD information in the large-$|q^2|$ region. We further formulate the reconstruction of the quark force as a regularized inverse problem, thereby reducing the model dependence associated with the prescribed functional parametrizations used before. The resulting quark force provides a complementary, less parametrization-dependent determination and remains consistent with that implied by a linear QCD potential, which also supports the robustness of the framework proposed in Ref.\,\cite{Ji:2026lyj}. We also show that improved future inputs can substantially reduce the uncertainty in the reconstructed quark force.

Extreme debris disks provide time-domain probes of rocky planet assembly after giant impacts. We compare Spitzer and JWST spectra of the warm debris disk around HD 15407A, obtained 14.95 yr apart. Across 6-25$\mu$m, the stellar-subtracted mid-infrared excess declined by $14.0\pm1.2$%, while the mineral fingerprint is nearly unchanged. Within the adopted common reduced mineral basis, Mg-rich pyroxene dominates the normalized fitted mineral weights at about 65%, while silica and forsterite contribute about 22-23% and 12-13%, respectively. In both epochs, about 93-94% of the fitted mineral weight is carried by 5.0$\mu$m grains. The fitted optically thin surface-component amplitude drops by 32%, consistent with reduced mineral-emitting flux from the disk surface rather than the appearance of a new dust composition. A simple collisional-cascade guide fitted to the IRAS-to-JWST multi-epoch ratios gives $t_c=87^{+17}_{-12}$ yr, for which the Spitzer-normalized dust-excess ratio reaches 0.5 after one $t_c$. HD 15407A is consistent with an evolved post-impact reservoir with limited recent supply of very small grains: its rocky mineral fingerprint has remained stable since at least 2008, while its warm mineral-emitting flux is gradually fading over several hundred years.

The transverse-field Ising model is a fundamental quantum spin system that captures the competition between quantum fluctuations and interactions, playing a central role in studies of quantum phase transitions and non-equilibrium dynamics. However, classical computations of ground and excited states in large-scale or high-dimensional systems are severely limited by the exponential growth of the Hilbert space. Here, we propose a novel approach using a D-Wave quantum annealer, where a triangular-wave oscillating magnetic field is applied to induce Rabi oscillations, allowing the estimation of energy gaps between the ground and excited states. Unlike conventional quantum annealing methods limited to ground-state searches, this approach can directly access excited-state information. It is potentially applicable to larger systems, providing a new avenue for quantum-device-based simulation. The validity of the method is demonstrated through numerical simulations of relatively small systems.

We investigate static, spherically symmetric charged black holes in Einstein gravity coupled to Euler–Heisenberg (EH) nonlinear electrodynamics, including purely electric, purely magnetic, and dyonic configurations. Rather than adopting the Hamiltonian formulation based on the auxiliary electromagnetic invariant $\mathcal{P}$, we work directly with the physical electromagnetic invariant $\mathcal{F}$ in the Einstein-Euler–Heisenberg Lagrangian, thereby describing all charged configurations without introducing auxiliary variables. Within this approach, we derive an exact analytical solution for the purely electric case, recover the purely magnetic solution directly from the field equations, and construct the dyonic solutions numerically. We systematically study the horizon structure, causal properties, and thermodynamics of these solutions. While the purely electric branch exhibits the familiar Reissner–Nordström horizon structure, the purely magnetic branch naturally admits a novel three-horizon configuration consisting of one event horizon and two inner horizons. The dyonic solutions continuously interpolate between the electric and magnetic limits and exhibit either one- or three-horizon configurations, depending on the magnetic-to-electric charge ratio and the EH coupling. We further show that the EH nonlinear interaction significantly modifies the horizon structure and thermodynamic properties of charged black holes while leaving the central curvature singularity unresolved. These results demonstrate that EH nonlinear electrodynamics gives rise to qualitatively new causal structures beyond Einstein–Maxwell theory.

As the only galaxy enabling temporally-resolved observations from an internal vantage point, the Milky Way as a galaxy offers unique insights into galactic chemical enrichment history, establishment of fundamental scaling relations, and the underlying astrophysical processes. However, this insider perspective also introduces strong selection effects, hindering direct measurement of the Milky Way's global properties and comparison with the broader galaxy population, for the vast majority of which only integrated properties can be measured. {Here we report our measurements of the Milky Way's temporally-resolved galaxy-scale average metallicity using data from the APOGEE survey after correction for the selection function. Our findings unveil a present-day metallicity of the Milky Way close to the Sun, an interrupted integrated age-metallicity relation, and a disturbed evolutionary trajectory in the mass-metallicity diagram, likely caused by dilution and inside-out growth associated with external gas accretion around 7~Gyr ago. Our results highlight the critical role of gas accretion in disrupting the galactic enrichment histories and introducing scatter in mass-metallicity relations.

We report the discovery of long tidal tails associated with the globular cluster (GC) NGC 5024. A modified matched filter applied to Gaia DR3 data reveals a broad stellar stream spanning $\alpha \approx 230^{\circ}-175^{\circ}$. The stellar stream overlaps on the sky with the LMS-1 and with the simulated stream of the GC NGC 5053, and all three share similar proper motions and metallicity. Our member candidates may therefore be a mixture of stars from NGC 5024, NGC 5053, and LMS-1. Nevertheless, the trailing tail extends roughly $20^{\circ}$ beyond the known LMS-1 stream. Furthermore, the radial velocity (RV) as a function of $\alpha$ is used to distinguish the genuine stream candidates of NGC 5024 from the streams of NGC 5053 and LMS-1. Among the sources in common with DESI (Dark Energy Spectroscopic Instrument) DR1, we found two distinct sequences in RV-$\alpha$ plane, corresponding to the stellar streams of NGC 5024 and NGC 5053 (or LMS-1), respectively. This constitutes the first strong evidence for the existence of extensive tidal tails around NGC 5024.

Enabling reversible spin-splitting switching in stray-field-free altermagnets is promising for spintronic applications, but currently limited to a narrow class of polar materials. We propose a broader approach based on spin-induced improper polarization in nonpolar dual-sublattice magnets. We demonstrate this mechanism in DyFeO3, where the product of nonpolar Fe and Dy spin modes transforms as an induced polar mode. Density functional theory shows that the relative Dy–Fe spin alignment selects the polarization, while the Fe sublattice controls nonrelativistic spin splitting, thus enabling reversible switching. These results establish spin-induced improper polarization as a route to switchable altermagnetism in nonpolar bulk systems.

We formulate and implement the phaseless auxiliary-field quantum Monte Carlo (AFQMC) method in the Hartree-Fock-Bogoliubov (HFB) manifold for systems with strong pairing correlations. Our formulation introduces a more flexible representation of HFB walkers with an explicit analytical expression for the normalization factor derived from the representation matrix with respect to the reference vacuum. We also extend our previously developed stochastic gauge formalism from the Slater determinant manifold to the HFB framework, providing additional flexibility for controlling stochastic fluctuations. Benchmark calculations for the Richardson model demonstrate the accuracy and numerical stability of the method.

Gravitational-wave (GW) signals from core-collapse supernovae (CCSNe) contain both stochastic and deterministic components. Among these, the High-Frequency Feature (HFF), associated with PNS oscillations, has emerged as a robust observable for probing dense-matter physics. Previous studies of the HFF have primarily focused on the early-time slope as a diagnostic of PNS contraction and its dependence on the equation of state (EOS). In this work, we extend the analysis beyond the early-time linear regime. In particular, we discuss how higher-order features of the HFF evolution, such as its curvature, may encode additional information about the time-dependent structure of the PNS and the transition between oscillation modes. Using real interferometric noise and reconstruction techniques on detected candidates by the coherent WaveBurst (cWB) algorithm in its cWB-XP implementation, we illustrate how such features can be accessed. By comparing reconstructed signals at different source distances, we assess the relative impact of detector noise.

This paper presents a convolutional neural network (CNN) approach to classifying the nuclear equation of state (EOS). As illustrative examples, we use five two-dimensional core-collapse supernova (CCSN) simulations that differ only in their EOS. We analyze estimates of the initial slope of the high-frequency feature (HFF) reconstructed in real interferometric data from the O3b LIGO-Virgo-KAGRA (LVK) observing run at Galactic source distances of 1, 5, and 10 kpc. The CNN classifier achieves an overall accuracy of 98.58% at 1 kpc and 52.43% at 5 kpc. At 10 kpc, its ability to distinguish among the EOS classes is effectively lost. The successful EOS classification at 1 kpc suggests that this approach may be scalable to next-generation observatories. The expected order-of-magnitude sensitivity improvements of Cosmic Explorer and the Einstein Telescope could enable comparable classification performance at approximately ten times the current distance. More detailed performance metrics, including the macro-averaged one-vs-rest (OvR) area under the curve (AUC), yield values of 0.97 and 0.98 at 1 kpc. These results indicate strong classification performance both across the complete set of EOS classes and for the individual classes.

Many scientific and engineering systems are observed as time-indexed probability distributions whose governing dynamics are unknown and whose individual trajectories are unavailable. These settings challenge conventional system-identification approaches that rely on trajectory correspondence or prescribed evolution equations. This work presents a population-level inference framework that recovers latent stochastic dynamics directly from snapshot probability distributions by decomposing the observed evolution into an intrinsic latent stochastic process and a discrepancy transport map that captures geometric deformation between the latent and observed probability spaces. The latent dynamics are modeled using an Ornstein–Uhlenbeck process, providing a closed-form solution to the associated Fokker–Planck equation, while the discrepancy transport map is parameterized through the Knothe–Rosenblatt rearrangement with monotone neural networks. To mitigate the non-uniqueness inherent in the latent–transport decomposition, the transport map is regularized using a deformation energy motivated by hyperelasticity, promoting smooth, physically interpretable deformations while reducing unnecessary complexity. The latent stochastic model and discrepancy transport map are learned jointly through a unified optimization problem defined over probability distributions. Numerical examples involving nonlinear and multimodal distributional dynamics demonstrate that the proposed framework accurately reconstructs complex probability evolution while preserving a compact and analytically tractable latent representation. The proposed formulation provides a general framework for population-level dynamical inference and establishes a foundation for extending latent stochastic models and transport-based learning to more general and higher-dimensional systems.

Although multi-label k-nearest neighbor (ML-kNN) is able to effectively solve multi-label learning (MLL) problem with local neighborhood similarity, its time complexity is nearly unacceptable with large-scale datasets. To solve this issue, we propose a novel ML-kNN algorithm with quantum computing techniques, which called quantum multi-label k-nearest neighbor (QML-kNN). In particular, we first accelerate the calculation of the prior probability by taking advantage of quantum phase estimation and Grover's amplitude amplification. Then, a controlled-SWAP test and a quantum k-maximal similarity search are used for efficiently identifying the neighbors. Subsequently, a quantum parallel counting circuit (QPCC) is designed to rapidly calculate the posterior probabilities. Experimental results demonstrate that QML-kNN is able to significantly reduce the time complexity of solving multi-label problems with performance improvement, achieving a substantial speedup over the classical MLL algorithm.

Machine learning (ML) emulators offer a cost-efficient alternative to numerical weather prediction models for generating convection-allowing background ensembles in ensemble-based data assimilation (DA). However, few studies have explored ML-based surrogate background ensembles for estimating background-error covariances (BECs). This study develops a convection-allowing emulator, MAPCast, trained on historical convection-allowing simulations from the Model for Prediction Across Scales (MPAS), and evaluates its ability to estimate BECs, paving the way toward multiscale DA. The evaluation uses 10 retrospective convective cases at 15- and 60-min forecast lead times corresponding to subhourly and hourly DA. MAPCast reproduces MPAS forecasts with good fidelity, including realistic storm coverage, temporal evolution, and similar spatial and spectral characteristics of state variables. Discrepancies are primarily confined to small spatial scales near sharp gradients and convective-scale features and variables. For BEC statistics, MAPCast captures ensemble spread magnitude and spatial distribution for most variables, although larger errors occur for storm-related fields that are vertical velocity and reflectivity. Correlation structures are reproduced most faithfully at mesoscale and above, followed by at convective scales, whereas cross-variable correlations are less accurately represented than univariate correlations, indicating that multivariate coupling remains the principal limitation. MAPCast shows weaker replication of full-scale versus decomposed large and small-scale correlations. BEC estimates derived from 15-min forecasts consistently outperform those from 60-min forecasts, suggesting that shorter lead times better preserve flow-dependent error structures.

Scientists increasingly express their ideas as dynamic models of complex processes. It is often much easier to simulate these models than to calculate the probability of their generating a particular outcome, making likelihood-based estimation infeasible. Existing likelihood-free approaches rely either on manually chosen summary statistics or on representations learned by neural networks. The former is error-prone and laborious, while the latter is computationally intensive, leaving many scientists in a difficult position. We show that, for a large class of dynamic models, parameters can be estimated by matching a small number of random features of the observed and simulated data. In particular, we show that models with a $p$-dimensional parameter can be identified from just $2p+1$ generic random Fourier features. We introduce two estimators for stationary and nonstationary processes, respectively, and we establish their consistency under mild regularity conditions. More broadly, our results serve as the foundation for a new class of methods for simulation-based inference.

High-dimensional qudit (i.e., $d$-level or $d$-state) systems outperform two-dimensional qubit (i.e., 2-level or 2-state) systems in some quantum information processing tasks. We exploit entanglement purification protocols (EPPs) for extracting a subset of high quality arbitrary $d$-dimensional $n$-partite Greenberger-Horne-Zeilinger (GHZ) states from a large set of less entangled GHZ states. In our protocols, qudit-flip and phase-flip errors can be corrected, and the fidelity of the output state can be asymptotically improved to unity by iterating the EPP process. Moreover, the schemes are immune to the number of polluted photons, the fidelity thresholds of the proposed EPPs are developed, and the spatial-based single-qudit operations can be well manipulated with a range of balanced beam splitters, and phase shifters. These features make the proposed schemes offer an alternative method for high-dimensional multipartite entanglement purification.