Papers

Describing and predicting the response of multistable materials to external driving is central to memory formation, programmable metamaterials, soft robotics, and in-materia computing. While scalar driving is captured by transition graphs (t-graphs), vectorial driving produces path-dependent responses that require the recently introduced path-transition graphs (pt-graphs). In both cases, transitions are governed by singularities in the energy landscape: for scalar driving these correspond to saddle-node bifurcations, but for vectorial driving, higher-order singularities become important. Combining experiments on chain-like metamaterials with a minimal spring model, we investigate how higher-order singularities shape pt-graphs and the resulting path-dependent responses. We moreover discuss the role of non-generic driving paths through higher-order singularities, where the response is governed by spontaneous symmetry breaking. Finally, we demonstrate how t-graphs emerge as the one-dimensional limit of pt-graphs, unifying scalar and vectorial driving within a common graph-based framework. These results establish a singularity-based approach to path-dependent responses and provide a foundation for designing multistable materials with programmable sequential functionality for smart sensing, soft robotics, and in-materia computation.

The classical Frank-Bilby equation (FBE) is commonly used to predict the structure of interfaces in crystalline materials in terms of interfacial dislocation networks. However, in general, the line defects in interfaces are disconnections, possessing both dislocation and step character, which are not captured by the classical FBE. As a result, the FBE cannot fully describe the structure of most interfaces of practical interest. To address this issue, we derive a generalized Frank-Bilby equation (GFBE) that explicitly incorporates both dislocation and step components of interfacial defects. We demonstrate its application to several representative interface systems.

The barren plateau phenomenon severely limits the scalability of parameterized quantum circuits (PQCs). We present an analytical framework for zero-shot classical-to-quantum parameter transfer and manifold-based model merging without quantum-side optimization. Our parameter transfer map converts classical neural-network weights into low-dimensional quantum evolutions using Stiefel subspace selection, nearest-unitary polar projection, and logarithmic generator extraction. We prove a Subspace Quantization Theorem that bounds the reconstruction error by geometric truncation and exact non-unitarity, with the latter becoming second order in the near-unitary regime. Identity-centered architectures naturally produce generators near the identity, avoiding logarithm branch-cut singularities and mitigating initialization-time gradient concentration. We further derive an explicit error bound for manifold-based model merging and show that the same construction provides a warm-start initialization whose active dynamics remain confined to a k-dimensional subspace. Experiments on IBM ibm_kobe demonstrate Hellinger fidelity of 0.987 at k=8, while subspace gradients remain resolvable up to 128 physical qubits.

We apply the soft photon approximation to QED processes in a strong plane-wave laser field. New compact expressions for the emission factors are presented, which simplify the generalization to multiple soft photon emission. The method is applied to nonlinear single, double, and $N$-photon Compton scattering. For single Compton scattering, we show that the soft photon approximation reproduces the exact spectrum with accuracy $\mathcal{O}(\omega/\varepsilon)$, significantly outperforming the classical radiation theory. For double Compton scattering, we derive analytical expressions for the differential probability in two kinematic regimes: when both photons are soft, and when one photon is soft while the other has arbitrary energy. We present a compact analytical expression for the amplitude of $N$-photon emission within the soft photon approximation. A numerical implementation in Wolfram Mathematica for single and double Compton scattering is provided.

Employing the relativistic continuum Hartree-Bogoliubov (RCHB) theory with intrinsic electromagnetic structure corrections, this work primarily investigates the charge properties of halo nuclei along the Ne and P isotopic chains. Our results characterize halo nuclei by an extended tail in the charge density and distinct signatures in the charge form factors at low momentum transfer. Moreover, the higher-order radial moments of nuclear charge density, particularly the eighth moment, exhibit pronounced oscillations, serving as a key indicator of the neutron halo structure. These special features in nuclear charge distributions will serve as key references for identifying halo nuclei.

In recent years, NICER data have been extensively used to determine neutron star masses and radii using pulse profile modeling. Pulse profile modeling is implemented with the X-PSI package and the best-fit parameters are typically obtained using Bayesian inference. Using simulated data, we demonstrate the first ever application of frequentist inference to determine the neutron star radius, where the nuisance parameters are treated using profile likelihood. We find that the profile likelihood technique can recover the true radius to $< 1\sigma$. The uncertainty in the estimated radius is also comparable to that obtained from Bayesian analysis while being computationally much faster. Therefore, this work serves as a proof-of-principle application of frequentist inference to estimate the neutron star radius using pulse profile modeling and complements the Bayesian inference technique currently used. We have also made our analysis codes for frequentist inference using X-PSI publicly available.

In this paper, we discuss the conjugate boundary condition (CBC), which has recently been studied as a way of realizing a Majorana fermion within a compactified five-dimensional theory ($\mathcal M^4\otimes S^1/Z_2$). The Majorana fermion which plays a crucial role in the seesaw scenario arises as a zero mode (lowest mode) by imposing the CBC. Although each nonzero Kaluza-Klein (KK) mode is also naively expected to be described by two Majorana fermions, we show by direct calculation that they can be combined into a Dirac spinor in the free theory or up to the level of the quadratic term in the Lagrangian. This difference comes from the fact that an accidental U(1) symmetry exists in the KK mode sector, though such a symmetry does not appear in the zero mode sector. We also point out that the compatibility between the CBC and the axial U(1) transformation plays a crucial role in the diagonalization of the KK mode. We also investigate interactions compatible with both the CBC and the chiral orbifold projection. We find that a nontrivial bulk interaction between a CBC fermion and a chiral-orbifold fermion is allowed. As an application, we construct an extended seesaw scenario by utilizing both boundary conditions and such nontrivial bulk interactions. The resulting seesaw scenario has a different property in contrast with the conventional extended seesaw scenario; namely, the above new non-trivial interactions cause lepton number violation (LNV), while the Majorana mass term in our model does not violate lepton number.

The dark matter and baryon asymmetry problems remain two of the most pressing questions in fundamental physics. Considering lepton-flavored dark matter, it has recently been shown that the cogenesis of dark matter and the baryon asymmetry can be economically achieved via conversion-driven freeze-out. This mechanism leverages semi-efficient conversions to drive a departure from equilibrium while preserving independence from initial conditions through early thermalization of the dark sector. In this work, we develop this mechanism further, providing a detailed analysis of the chemical-equilibrium conditions and demonstrating that the framework can be extended to quark-philic scenarios, where the matter-antimatter asymmetry is generated resonantly through baryon-number-conserving $CP$-violating conversions of a mediator field into Standard Model quarks and dark matter. The strong QCD interactions of the colored mediator, including bound-state formation effects during freeze-out, substantially enlarge the viable parameter space and allow dark matter masses from a few hundred GeV up to the TeV scale. We furthermore assess the impact of thermal effects by comparing a minimal treatment with a setup that approximately accounts for thermal masses and their kinematic consequences. The resulting scenario predicts striking long-lived particle signatures with soft displaced decay products that remain only partially explored at the LHC and motivate dedicated searches at the HL-LHC.

High-redshift binary black hole (BBH) events are promising candidates for primordial black holes (PBHs) detectable by next-generation gravitational wave (GW) detectors. A redshifted mass distribution of detected PBH candidates can be obtained from GW observations, from which the underlying PBH mass function can be reconstructed. In this work, we develop a framework that applies the gradient-descent method to the observed redshifted mass distribution and reconstructs the PBH mass function and, subsequently, the primordial power spectrum (PPS) on small scales. As an illustrative application, we analyze BBH events in the LIGO–Virgo–KAGRA (LVK) catalogs under a specified PBH selection criterion. We find a regularization-stable candidate bump-like enhancement of order $\mathcal{O}(10^{-2})$ in the reconstructed PPS, centered around $k_{\mathrm{peak}}\simeq 5.7\times 10^5~\mathrm{Mpc}^{-1}$ under the adopted assumptions. Our results demonstrate the feasibility of reconstructing the small-scale PPS from high-redshift BBH observations with next-generation GW detectors.

Binary black hole mergers without electromagnetic counterparts are expected to dominate O5 gravitational-wave catalogs. Recent CHIMERA~2.0 forecasts typically fix $\Omega_m$ to a CMB-informed value and use spectroscopic hosts when available, but the corresponding sensitivity of the $\Xi$ posterior has not been assessed for pure dark sirens on the public O5 mock catalog. We analyze 300 O5-sensitivity mock events without galaxy catalogs, varying $\Omega_m$ over $[0.20,\,0.35]$ (including Planck $0.315$), and compare fixed-background inference with joint $(H_0,\,\Omega_m,\,\Xi)$ inference. The marginalized $\Xi$ posterior is $0.9783 \pm 0.3548$ and shows no change across this interval. Only GW luminosity distances enter the analysis, so the likelihood constrains $\Xi\, D_L^{\rm EM}(H_0,\,\Omega_m)$; when $\Omega_m$ is changed, $H_0$ shifts to compensate and the $\Xi$ marginal remains unchanged. Joint inference gives $\Xi^{\rm joint} = 0.9550 \pm 0.3710$, with $|\rho| \lesssim 0.05$ for the $\Omega_m$–$\Xi$ and $H_0$–$\Xi$ pairs, whereas $\rho_{H_0 \Omega_m} \simeq -0.4$ and the $H_0$ median moves by $\simeq 4.2\,{\rm km\,s^{-1}\,Mpc^{-1}}$ over the adopted $\Omega_m$ range. Galaxy-catalog analyses on the same events at fixed $\Omega_m = 0.3$ reach $\sim 7.5\%$ precision on $\Xi$, compared with $\pm 36.3\%$ here; the larger uncertainty is driven mainly by missing host redshifts. Sub-percent $\Xi$ tests will therefore still require measured redshifts even if dark sirens dominate the detection rate.

Seismic swarms are commonly associated with fluid migration and other transient processes, yet their spatial migration remains difficult to quantify using conventional diffusion models. Here we analyze ten persistent earthquake swarms identified within the relocated Southern California earthquake catalogue using a consensus clustering approach. We characterize their migration through the multiscaling spectrum $\varepsilon(q)$ obtained from the temporal evolution of the moments of interevent distances. All analyzed swarms exhibit a common scaling signature: the spectrum is approximately linear for negative moments but departs systematically from a single linear behavior for positive moments, indicating that small and large interevent distances evolve with different effective scaling laws. In contrast, the Landers tectonic sequence displays a high-order saturation of $\varepsilon(q)$, consistent with spatially bounded diffusion. These results reveal that earthquake swarms are characterized by strong anomalous diffusion and suggest that the multiscaling spectrum provides a quantitative fingerprint capable of distinguishing swarm migration from conventional tectonic earthquake sequences.

A long-standing bottleneck in GeV-scale accelerator experiments lies in reconstructing long-lived neutral hadrons in conventional electromagnetic calorimeters (ECALs), where hadron–nucleus interactions fall outside the detector's native response regime. In this paper, we develop a physics-inspired representation approach for antineutron reconstruction using a large corpus of real collision data. Motivated by two distinct energy deposition patterns from the penetrating high energy antineutrons in ECALs, we propose a Mixed-representation Calorimetric Network (MrCAL) that integrates complementary visual and sequential representation branches within a unified object-detection architecture. This architecture jointly predicts particle identity, momentum direction, and momentum magnitude. Our approach improves the precision of antineutron momentum-direction reconstruction by up to 96% and, for the first time, enables direct measurement of momentum magnitude solely from ECAL readouts, achieving a momentum resolution of approximately 17% at 1 GeV/c. The model maintains robust performance through comprehensive generalization tests spanning a wide variety of physics processes and background environments. This work unlocks a novel measurement capability for legacy ECAL systems at large experimental facilities, broadening their scientific scope via innovative final-state neutral-hadron detection.

We investigate the quantum dissipative dynamics of a charged magneto-oscillator(CMO) coupled to a heat bath of harmonic oscillators in the presence of an additional field-bath interaction. The problem is formulated within the framework of the generalized quantum Langevin equation, where the external confining potential modifies the random force correlations and the memory kernel of the environment, and the effective spring constant. Using the corresponding non-Markovian master equation, we examine the influence of the field-bath coupling on the temporal decay of the reduced density matrix of the system. We show that the additional interaction leads to an enhancement of the decoherence rate by altering the dissipative response of the bath. Thus our results shed light on the role of field-bath coupling in the quantum to classical transition of a CMO. The resulting fluctuation-dissipation relation is analyzed, and the position autocorrelation, position-velocity correlation and velocity autocorrelation are obtained explicitly. The dependence of these experimentally accessible quantities on the field-bath coupling parameter is discussed. We outline an experimental proposal for testing our theoretical predictions. The study sheds light on reservoir engineering which is central to quantum technologies.

The tryptophan residues in tubulin $\alpha\beta$-dimers form an ordered aromatic network that has been proposed to support quantum exciton transport even under physiological environmental noise. Existing studies of this system mostly assume white-noise dephasing, but the statistical properties of the protein-solvent bath coupled to tryptophan sites remain uncharacterised under physiological conditions. Here we characterise this fluctuation bath via all-atom molecular dynamics simulations of a solvated tubulin dimer at 310 K, combining high-frequency and long-time trajectories with 10 fs and 10 ps sampling intervals. The resulting autocorrelation of the site-energy fluctuations is tri-exponential, with three well-separated decay modes: sub-100-fs and picosecond fluctuations driven by water dynamics, and a nanosecond mode originating from protein conformational rearrangements. All three modes fall deep within the non-Markovian regime. We further demonstrate that the slow protein mode introduces strong quasi-static disorder, which results in Anderson localisation, while the two fast water modes frequently tune chromophore pairs through resonance, enabling environment-assisted quantum transport (ENAQT). On the full eight-site network, the coloured-noise bath confines excitons predominantly to strongly coupled proximal tryptophan pairs, in marked contrast to the more uniform delocalisation predicted by the standard white-noise Haken-Strobl model. Our workflow generalises to other pigment-protein systems with solvent-exposed chromophores.

We study the propagation of high-frequency electromagnetic waves coupled to an axionic scalar field in curved spacetime. By applying the effective action approach, we go beyond the standard geometric optics limit and derive spinoptics equations for the axion–Maxwell theory that are valid for arbitrary curved spacetimes and arbitrary axion profiles. These equations allow us to analyze helicity-dependent corrections to photon trajectories arising from the photon's interactions with both the axion field and spacetime curvature. We present representative examples to demonstrate how light trajectories deviate from null geodesics due to spinoptics effects in the presence of an axion field.

Recent analyses of Proca theories with non-minimal curvature couplings have uncovered an additional scalar degree of freedom with an identically vanishing propagation speed, $c_s^2=0$, signaling a scale-dependent strong-coupling problem. In this Letter, we show that an additional derivative interaction can lift this zero-speed degeneracy and restore a non-degenerate quadratic dynamics for the scalar perturbations. In an open region of parameter space, the scalar kinetic matrix is positive definite and the high-frequency propagation speeds are real and positive. At quadratic order, this removes the specific signature of strong coupling associated with the $c_s^2=0$ mode. We also uncover a non-uniform limit as the temporal vector condensate approaches zero: at fixed nonzero $A_0$, the formal extreme-ultraviolet regime develops a ghost, while the kinetic structure of the exactly vanishing-condensate branch is different. The momentum scale at which this ghost appears is pushed toward increasingly high values as $A_0\to0$. Whether the ghost scale ultimately lies above the EFT cutoff depends on an independent determination of the EFT cutoff. Finally, we identify a stable de Sitter fixed point with $A_0\neq0$, surrounded by a finite region in which the scalar no-ghost and high-frequency stability conditions remain satisfied.

The nature of the superconducting pairing symmetry in the kagome metal CsV$_3$Sb$_5$ and its relationship with the charge density wave (CDW) order are central unresolved issues. Here, we investigate the evolution of superconductivity in CsV$_3$Sb$_5$ under in-situ uniaxial pressure using $^{121}$Sb nuclear quadrupole resonance (NQR). We find that tensile strain significantly enhances the superconducting transition temperature, $T_{\rm c}$, while the CDW remains unchanged, demonstrating that superconductivity can be tuned independently of the bulk charge order. At a tensile strain of $\varepsilon$ = +0.90%, the nuclear spin-lattice relaxation rate reveals a remarkable double transition: an upper transition at $T_{\rm c1}$ = 3.6 K to a nodal gap state, and a lower one at $T_{\rm c2}$ = 3.0 K characterized by a nodeless gap. These results evidence degenerate superconducting states with different gap symmetry in the kagome metal at ambient pressure which split under strain. Our work demonstrates a high tunability of superconductivity by uniaxial pressure.

The intrinsic alignment (IA) of galaxies and the dark matter haloes that host them is one of the leading astrophysical systematics for weak-lensing cosmology, yet how the IA signal depends on the large-scale environment in which haloes reside is not yet fully characterised. We use the high-resolution $N$-body simulations of the Dark Quest suite to measure the environmental dependence of the IA of dark matter haloes over the redshift range $z=0.1$–$1.5$. We quantify each halo's environment through the overdensity $\delta_8$, defined from the number of neighbouring haloes within $8\, h^{-1}Mpc$, and we isolate the environmental effect from its degeneracy with the halo-mass dependence by comparing the most overdense and most underdense haloes constructed to share the same halo-mass distribution. We find that haloes in underdense environments exhibit systematically larger IA amplitudes $A_{\rm IA}$ than haloes of the same mass in overdense environments, by a factor of $\sim1.5$–$1.8$, and that this trend persists across the mass and redshift ranges probed, strengthening towards low redshift. Using an orientation-only (unit-ellipticity) estimator, we further show that this environmental contrast is driven by a combination of two effects: haloes in underdense regions are both intrinsically less spherical and more strongly aligned with the large-scale tidal field than their overdense counterparts of the same mass. These results indicate that the large-scale environment is a non-negligible variable in modelling halo and galaxy alignments, and may be a particularly important factor for beyond-two-point weak-lensing analyses.

We consider the classical satellite and splice constructions of knot theory from the perspective of link-complement states in three-dimensional topological quantum field theory (TQFT). These states are prepared by a TQFT on a link-complement manifold, namely a manifold with multiple disjoint torus boundaries, obtained by removing a thickened link from a closed ambient three-manifold. In this setting, satellite and splice constructions are realized by gluing manifolds along torus boundaries, and the TQFT assigns to these building blocks multilinear operators acting on the corresponding torus Hilbert spaces. We give explicit prescriptions for these operators and show how they can be used to build link-complement states. This construction is closely related to the JSJ decomposition of link-complement manifolds. While splicing builds manifolds by gluing along tori, the JSJ decomposition cuts a manifold along essential tori into canonical pieces. From the TQFT point of view, this expresses a link-complement state as a network of elementary operators associated with the JSJ pieces. We illustrate this framework by studying the entanglement entropy of Seifert-fibered link complements, Hopf-chain complements, and complements of Whitehead-doubled Hopf chains, showing how their different JSJ structures lead to distinct entanglement patterns.

The stable adatom configurations of a semiconductor surface have traditionally been sought by sampling: density functional theory (DFT) energies steer a heuristic or Bayesian search through a configuration space far too large to cover. Here we show that, for the GaN(0001)-$(6\times6)$ surface under the electron counting (EC) rule, the search can instead be posed as a discrete tiling problem and solved exhaustively. Enumerating all rhombus tilings of the surface lattice, together with all EC-compatible adatom arrangements built on them, yields the complete catalog of 416,683 configurations at fixed stoichiometry (3 Ga adatoms and 18 H atoms), organized by symmetry into 14 Ga placement classes. The number of tilings compatible with a given configuration, its tiling decomposition multiplicity $n_\mathrm{til}$, predicts stability. Within each class, the configuration maximizing $n_\mathrm{til}$ is the most stable. The rule holds strictly in 13 of the 14 classes; in the remaining class the minimum is itself among the highest-multiplicity configurations, with the $n_\mathrm{til}$-max configuration only 8.5 meV above it; this ordering is reproduced by independent DFT calculations, and the difference is negligible at growth temperature. Stability screening uses a machine-learning interatomic potential validated against 710 DFT-computed structures. The rule reduces the candidate set for first-principles evaluation from 416,683 to 24 configurations, all of which have been evaluated with DFT. Analysis of the rule identifies the local mechanism, the avoidance of adjacent bare surface sites, while the existence of a compatible tiling remains a separate requirement with an energy cost of its own. Enumeration thus provides what sampling cannot: a coverage guarantee, and a route to stable-structure prediction in which first-principles input enters only at the final ranking step.

A local neoclassical transport module has been developed and validated in the semi-Lagrangian gyrokinetic code NLT for multi-species collisional plasmas. The module incorporates a linearized multi-species Sugama collision operator and provides two complementary solution strategies. In the initial-value formulation, a composite substep source-integration scheme is introduced to accurately evaluate the neoclassical drive along unperturbed particle trajectories while retaining large macroscopic time steps. A direct steady-state solver is also implemented to obtain the stationary neoclassical response without long-time relaxation. The two approaches are benchmarked against the Eulerian neoclassical code NEO for electron-ion plasmas and three-species plasmas with carbon impurities. The NLT results reproduce the NEO particle and heat fluxes, parallel flows, and bootstrap current over a broad collisionality range. As representative applications, the validated framework is applied to EAST-relevant tungsten impurity transport and core trapped-electron-mode stability. The results show that tungsten neoclassical transport is sensitive to local profile gradients, while the increased effective collisionality associated with larger \(Z_{\rm eff}\) can reduce the linear TEM growth rate under the considered EAST-relevant conditions. These developments extend NLT toward realistic multi-species collisional transport simulations.

The generalized equivalent uniform dose (gEUD) provides a biologically interpretable measure of heterogeneous dose distributions and is widely used in radiobiological modeling. However, because gEUD depends solely on dose magnitude, it does not explicitly account for collective cellular interactions or phase-dependent biological responses. Here, we propose a quantum generalized equivalent uniform dose (QgEUD), which extends the conventional gEUD kernel into the complex domain by introducing a phase variable while preserving the original dose-weighting formalism. This formulation yields a two-dimensional response surface that recovers conventional gEUD on the real axis and incorporates interaction-dependent radiobiological effects through phase modulation. The local response of the surface is characterized by a Kähler metric, providing an intrinsic measure of sensitivity to dose weighting and phase perturbations. To demonstrate the framework, local dose elements are modeled by an Ising Hamiltonian with dose- and phase-dependent interactions, and equilibrium response maps are obtained using Metropolis Monte Carlo simulations. Simulations in a virtual radiotherapy phantom preserve the overall dose distribution while producing spatially modulated biological-effect maps governed by collective interactions. The corresponding Kähler response identifies regions exhibiting enhanced sensitivity beyond dose magnitude alone, and parameter sensitivity analysis confirms stable convergence under practical simulation conditions. These results establish QgEUD as a quantum-inspired extension of gEUD that integrates heterogeneous dose aggregation, phase-dependent interactions, and geometric response within a unified mathematical framework, providing a basis for interaction-aware radiobiological modeling and future quantum-compatible optimization.

Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers. We show that finite quantum measurement statistics (shot noise) act as a built-in defense against gradient-based test-time attacks whose cost scales unfavorably for the attacker. Because every gradient component must be inferred from repeated circuit executions under any unbiased gradient-estimation rule, white-box extraction consumes a dimension-dependent measurement budget that measurement grouping cannot remove in expressive circuits. Under stated assumptions, single-step attacks need at least quadratically many shots in the input dimension $d$, growing as $d^{5/2}$ under norm-concentration scaling, with a sufficient-budget analysis for iterative attacks via stochastic gradient Langevin dynamics. Simulations up to 784 input dimensions validate the law: the realized total budget is the $d^{5/2}$ geometric floor for plateau-mitigated models and grows as $d^{3.00}$ for the tested deep circuits, whose gradient norms decay with dimension absent barren-plateau mitigation; folding the measured gradient norm back in recovers the parameter-free $d^{3/2}$ shot-noise geometry. Against a matched classical baseline whose attack overhead is dimension-independent (the cheap-gradient principle of automatic differentiation), the quantum gradient cost ratio grows empirically as $d^{3.00}$, so the attacker's relative cost diverges as the model scales. Experiments on a 156-qubit IBM processor (ibm_boston, 4-qubit circuits, $d=12$) reproduce the effect: at matched budgets the device attack tracks the ideal within a few percent, with the high-shot gradient faithful to the exact one. The defense operates precisely when the forward map is classically hard to simulate: only then is a white-box attacker denied the simulate-and-backpropagate shortcut and must pay the measurement cost we quantify.

Accurate distances to small, opaque Bok globules are difficult to obtain due to their compact sizes and lack of embedded standard candles. We estimate distances to five globules – CB4, CB24, CB56, CB60, and CB188 – using a near-infrared (NIR) extinction technique applied to 2MASS photometry, combined with stellar distances from Gaia DR3 parallaxes. Extinction-distance profiles reveal distinct rises marking each cloud, yielding median distances of 766~pc (CB4), 354~pc (CB24), 483~pc (CB56), 1143~pc (CB60), and 926~pc (CB188). Weighted-mean values agree within $8.0\%$, supporting the internal consistency of the method and interquartile-range-based uncertainties span 3-9% across the sample. Independent validation with Bayestar19 3D dust maps supports the CB4 and CB188 results. These refined distances provide an improved basis for future determinations of cloud masses, densities, and star-formation efficiencies, including the first robust measurement for CB56 and a revised upward distance for CB188.

Research in recent years has focused on the thin-film synthesis of high-quality ternary alloys, identified for their tunable properties and potential in spintronics (e.g., Heusler alloys, Kagome magnets). In a previous study, we identified the conditions for stabilizing Fe$_2$MnSn, a Kagome magnet with a high Curie temperature and magnetic anisotropy. However, ternary phases such as Fe$_2$MnSn are challenging to synthesize and stabilize within a narrow temperature window, as binary and elemental phases can also form during the growth process. To highlight these observations, we investigated the thin film phases in the Fe-Mn-Sn system near the 2:1:1 ratio as a function of annealing temperature, ranging from 400 to 700\degree C. The elemental Fe, Mn, and Sn targets were pre-calibrated to a close to 2:1:1 ratio and co-sputtered at room temperature, followed by annealing. Two binary hexagonal structures, Fe$_3$Sn$_2$ and Fe$_5$Sn$_3$, along with the elemental Fe phase, are stabilized between 400-550\degree C, but disappear at 580\degree C, where Fe$_2$MnSn is the only stable phase. Elemental Mn phase starts to appear starting from 600\degree C, and becomes dominant by 750\degree C. Electrical, magnetic and magneto-optical properties are observed to correlate with the structural findings and the best properties are observed in the temperature range where Fe$_2$MnSn is the dominant phase. In general, our study highlights the difficulty in growing phase-pure ternary alloys such as Fe$_2$MnSn, which is very strongly based on precise temperature conditions. We also observed significant disordered growth below 100 nm for Fe$_2$MnSn, implying poor thickness scaling behavior.

Accurately representing atmospheric aerosol populations is essential for simulating aerosol-cloud interactions, radiative forcing, and ice nucleation, yet existing reduced schemes impose structural assumptions that limit their ability to capture composition diversity and mixing state. Machine-learning approaches offer more flexible representations, but standard autoencoders do not preserve the mathematical structure of aerosol populations and therefore cannot support physically meaningful process operators. We introduce AeroMELD (Aerosol Measure Embedding for Latent Dynamics), a mathematically grounded framework for constructing low-dimensional latent variables that retain this structure. We show that any permutation-invariant linear encoder must take a scale-shape decomposition, with total number concentration represented explicitly and latent shape given by a barycentric combination of per-particle embeddings. This aggregated latent state retains the diagnostic expressiveness of a Deep Sets model by moving the nonlinear post-aggregation stage into the learned diagnostic map while preserving latent linearity. Using particle-resolved data as ground truth, we encode weighted particle populations directly rather than binned aerosol states; size-resolved mass and number distributions serve only as diagnostic targets and visual summaries. The latent space accurately reconstructs these distributions, CCN spectra, optical coefficients, and immersion-freezing behavior while preserving the linear population structure needed for hybrid ML-physics models. Although the experiments focus on diagnostic reconstruction, the embedding is designed so that emissions and mixing can be represented exactly and nonlinear microphysical processes learned in a controlled latent space. This work establishes a foundation for learning aerosol-process evolution directly in latent space.

We investigate nonequilibrium driven dynamics across the special surface phase transition in the three-dimensional classical Heisenberg model with open boundaries, where tuning the surface coupling gives access to an extraordinary-log boundary critical state characterized by logarithmic, rather than power-law, decay of correlations. Using Monte Carlo simulations, we realize four driving protocols: temperature heating and cooling across the special transition, and surface-coupling ramps from the ordinary and extraordinary-log critical states into the special point. For temperature-driven protocols, the surface order parameter obeys a generalization of the finite-time scaling (FTS) and the Kibble-Zurek mechanism. The central finding emerges when the system is driven from the extraordinary-log critical state: the large-rate scaling relation acquires a logarithmic correction and takes the novel form $M^{2}_{s}\propto R^{(1+\eta_{s})/r_{s}}[\log(LR^{1/\eta_{s}})]^{-q}$ , where $R$ is the driving rate, $L$ the system size, $\eta_{s}$ the surface anomalous dimension, $r_{s}$ the scaling dimension of $R$, and $q$ the exponent governing the logarithmic boundary criticality. We demonstrate that this form follows from the general FTS framework by incorporating the logarithmic initial-state memory, and we achieve excellent data collapse over a wide range of system sizes and driving rates. Our results establish that extraordinary-log initial states alter nonequilibrium critical scaling, extending boundary FTS beyond conventional power-law initial conditions.

X-ray magnetic circular dichroism (XMCD) is a powerful probe of element-specific spin and orbital angular momentum. Conventional analyses based on sum rules, however, rely on integrated spectral intensities and can become insufficient when multiple parameters influence the spectral line shape. Here, we formulate XMCD analysis as an inverse problem and develop a neural-network (NN) based approach to reconstruct spin and orbital angular momentum directly from full spectral line shapes. Using many-body multiplet calculations of Fe, Co, and Ni $L_{2,3}$-edge X-ray absorption spectra (XAS) and XMCD spectra as a physically well-defined training dataset, we systematically vary key parameters including crystal-field splitting, spin–orbit coupling, and exchange field. The NN is trained to map spectral line shapes onto the expectation values of spin and orbital angular momentm $\langle S_z \rangle$ and $\langle L_z \rangle$, and validated using strictly test-only data. The results demonstrate accurate and unbiased reconstruction, establishing a proof of concept for data-driven inverse reconstruction from XAS and XMCD spectra. These findings show that exploiting the full XAS and XMCD line shapes provide access to information beyond conventional sum-rule analyses while remaining consistent with established theoretical frameworks.

Recent advances in time-resolved spectroscopies provide increasing access to collective dynamics in correlated quantum materials. However, computing the corresponding nonequilibrium two-particle correlation functions remains a major challenge. Here, by introducing a contour-dependent virtual probe field within the generalized Keldysh formalism, we propose an approach that computes such correlation functions with the vertex corrections essential for describing collective dynamics. In particular, we introduce a linear integral equation that computes the correlation functions without explicitly constructing the four-time vertex kernel, and develop its matrix-free Krylov solver based on quantics tensor trains. Combining our method with nonequilibrium dynamical mean-field theory, we show that the fluctuation dynamics of the order parameter in a nonequilibrium symmetry-broken state depends significantly on whether vertex corrections are included, and that the fluctuation and its decay time grow near the nonthermal critical point. Our approach thus provides a practical route for evaluating nonequilibrium correlation functions, which are emerging as key observables for characterizing states far from equilibrium.

We present a detailed stellar population analysis of the strongly lensed dusty star-forming galaxy (DSFG) PLCK G165.7+67.0 DSFG-1 at $z = 2.236$, combining JWST NIRCam imaging with new Submillimeter Array (SMA) observations. This source is multiply imaged into two lensed components: image 1a, with a moderate magnification factor of $\mu \sim 5$, and image 1bc, with an extreme magnification factor of $\mu \sim 40$. The new SMA observations detect significant dust continuum emission at 225GHz and 273GHz, with combined flux densities of $S_{\rm cont}=(1.19\pm0.38)$ mJy in image 1a and $S_{\rm cont}=(10.02\pm0.85)$ mJy in image 1bc, indicating active star formation at sub-kpc scale. Based on the integrated SED modeling, DSFG-1 exhibits a lensing amplification-corrected stellar mass of $M_{\star} = (1.2 \pm 0.4) \times 10^{10} M_{\odot}$, and a star-formation rate (SFR) of $(103 \pm 14) M_{\odot}\,\mathrm{yr^{-1}}$, similar to previous $H\alpha$-based results, placing it four times above the star-forming main sequence at this redshift. Its location on the size-mass plane and its morphological properties suggest that the system occupies a transitional phase between star-forming late-type galaxies and compact early-type systems. Together with its elevated star-formation activity, this is consistent with a rapidly evolving galaxy observed during Cosmic Noon. We further investigate the spatially resolved stellar population properties, and found significant spatial variations in stellar age and dust attenuation. These results point to a non-uniform star-formation history and highlight the complex interplay between dust geometry, stellar growth, and gravitational lensing, consistent with a merger scenario.