Papers

We investigate the dynamics of two-component flat-top–bubble vector solitons using a variational approximation and numerical simulations. The system is described by coupled cubic-quintic nonlinear Schrödinger equations with repulsive intercomponent coupling. We introduce a self-induced mechanism in which a localized flat-top soliton (FTS) generates a density bubble in a second component: the localized density acts as a repulsive potential for the background field, creating a composite bound state. A background-subtracted formulation and normalized super-Gaussian profiles yield collective-coordinate equations for the component centers and an effective interaction potential for their relative separation. The potential forms a binding well that is nearly triangular over its global displacement range but smooth and locally parabolic near its minimum. The variational approximation consequently predicts harmonic internal oscillations whose frequency is independent of amplitude in the small-oscillation regime. Real-time simulations verify this weak-kick behavior with good agreement between the variational and numerical frequencies. For strong phase imprints, equating the exact kick energy to the variational binding depth predicts a kick scale \(K_{\mathrm{escape}}\) for the onset of partial FTS escape. Real-time simulations support this prediction: below this scale, the composite remains nearly intact, whereas above it, a substantial fraction of the localized component leaves the bubble.

Variational approaches based on neural quantum states and physics-informed neural networks provide powerful paradigms for simulating non-Markovian open quantum dynamics. However, extending these methods into the strongly non-Markovian regime reveals a critical bottleneck: even minute errors in the time evolution can translate into substantial deviations in physical observables. The fundamental origin of this stringent precision requirement, as well as how non-Markovianity governs it, remains an open question. Here, we develop a theoretical framework that systematically characterizes error propagation in variational non-Markovian dynamics. By combining analytical derivations with numerical verification, we present the first quantitative description of variational error evolution over time. Our analysis uncovers an intrinsic error-backflow mechanism driven by long-lived environmental memory. This mechanism establishes a fundamental precision barrier and provides concrete guidance for designing robust variational algorithms for strongly non-Markovian quantum systems.

The Einstein Telescope (ET) is a proposed underground infrastructure in Europe to host future generations of gravitational-wave (GW) detectors. Two configurations are under investigation. The first is the two-site solution with an L-shape detector at each site similar to the LIGO configuration. The second configuration is the single-site triangular detector. While numerous analyses over the last decade have already proven that the distributed network of GW detectors is the scientifically preferred solution, it was also argued that the advantage might not be decisive enough to dismiss ET's triangle configuration. The modeling of aspects such as noise correlations in these configurations has only been treated superficially in the past. In this article, we introduce a time-domain simulation of environmental noises in the ET triangle using available data from ET's candidate sites. We simulate and analyze correlated environmental noise as well as coincident glitches produced by transients in the environmental fields. We find that these features pose an important limitation to the observational capabilities of the ET triangle and limit the utility of its null stream.

Powder X-ray diffraction (PXRD) is the routine probe of crystalline matter, yet its analysis is the rate-limiting step as laboratories automate acquisition. Deep-learning analyzers excel on simulated patterns and degrade on measured ones. This simulation-to-real gap is structural, not additive: synthetic denoising gives no measurable lift on real spectra, whereas correcting a small peak-position drift more than doubles median retrieval correlation. Real-spectrum fine-tuning, peak-aligned reranking, and recalibration narrow what remains and restore the coverage synthetic anchors lose. Xtalyst integrates these in an agent-orchestrated system spanning phase identification, refinement, and calibrated property prediction. On a frozen held-out partition (n=534) each module measured on both splits reproduces its development finding – including the synthetic-anchor under-coverage, whose magnitude differs between the two pools – while held-out refinement converges and preserves symmetry without reaching profile-quality fits, and on a diffractometer its wet-dry recommend-rescan-reanalyze loop flips a blinded silicon standard to a gated PASS and changes which minor phase is resolved on a multi-metal alloy.

The nature of the pseudoscalar $X(2370)$ remains unresolved: glueball, hybrid, conventional $q\bar q$, and mixed configurations are all viable at the level of present information. We test a flavor-singlet $J^{PC}=0^{-+}$ radial-hybrid interpretation using a channel-dependent one-node overlap surrogate at $m_X=2.359~\mathrm{GeV}$ and a separate flavor-singlet radiative production factor. The resulting decay template naturally suppresses $K^{*}\bar K$ and can be conditionally normalized to the measured width, although its dominant scalar and tensor channels remain model dependent. Reproducing the inferred central $J/\psi\to\gamma X$ rate requires an effective singlet-production amplitude about $4.4$ times the fixed-$V(0)$ continuation of the available $N_f=2$ lattice anchor. In a common-nuisance screening comparison, the hybrid-mixed-glueball integrated-score difference is $-1.29$, $+0.04$, and $+0.41$ for tight, baseline, and wide nuisance settings, respectively; these values constitute a conditional-score tie rather than a composition measurement. We identify coherent amplitudes in $f_0(980)\eta$, $a_0(980)\pi$, and $K_0^{*}(1430)\bar K$ as particularly informative. Under the stated proxy forecast covariance, including theory uncertainty for both templates, their joint separation is $\Delta\chi^2\simeq6.5$ from the shared glueball template and $\simeq7.6$ from the radial-$q\bar q$ surrogate. A flavor-singlet radial hybrid is therefore compatible with current data but remains indistinguishable from a mixed-glueball interpretation; a coherent amplitude analysis can test the competing decay patterns.

Local discrimination of quantum states is a fundamental task in distributed quantum information processing and underlies applications such as quantum communication, data hiding, and secret sharing. Here we investigate a weak form of local indistinguishability by asking how easily it can disappear when the candidate set is reduced or an additional copy of the unknown state is supplied. We introduce inclusion-minimal locally indistinguishable sets, namely, locally indistinguishable sets for which every proper subset is perfectly distinguishable by local operations and classical communication (LOCC), and show that every finite locally indistinguishable set contains such a subset. We further find that any inclusion-minimal locally indistinguishable sets becomes perfectly distinguishable by LOCC when two identical copies are available, although a single copy is insufficient. Fininally, We construct explicit inclusion-minimal locally indistinguishable product-state sets in $(\mathbb C^d)^{\otimes n}$ for every odd $d=2k+1$ and $n\ge2$. These results show that local indistinguishability can be nontrivial at the single-copy level yet fragile under either the removal of candidate states or a modest increase in copy resources, providing a complementary perspective on the structure of quantum nonlocality.

Motivated by the recent mass measurement of the black-widow pulsar PSR~J0952$-$0607 with $M=2.35\pm0.11\,M_\odot$, we investigate how the masses of heavy, rapidly rotating millisecond pulsars can be used to constrain relativistic mean-field (RMF) models containing hyperonic degrees of freedom. In our approach, hyperons are incorporated following the spin-flavor SU(6) symmetry scheme for the vector-meson couplings. We find that increasing the nonlinear $\omega$-meson vector self-coupling parameter $\zeta$ suppresses the hyperon fraction and can alter the onset ordering of the $\Sigma^-$ and $\Xi^-$ hyperons. By computing rotating neutron-star configurations at the observed spin frequency $707\,\mathrm{Hz}$ of PSR~J0952$-$0607, we identify RMF models compatible with this pulsar's observed lower-mass bound. Using an empirical relation for the maximum neutron star mass, the PSR~J0952$-$0607 observational contraint is mapped onto the allowed RMF parameter space in $n_0$, $m^\ast$, and $\zeta$.

Can a probability distribution measured in one basis determine complex quantum geometry? The answer is affirmative for a lattice Laughlin quasihole. The exact occupation law at one generic reference position, together with the known analytic quasihole factor, fixes the complete complex Gram kernel. A finite snapshot ensemble estimates this kernel without preparing another member of the family, and thereby determines finite-distance overlaps, Bargmann phases, the quantum metric, and the Berry curvature. The same analytic structure confines the family to an exact projective subspace whose dimension grows at most linearly with particle number. Exact enumeration of a Nielsen-Cirac-Sierra state demonstrates finite-shot reconstruction of both the metric and a geometric phase. Moreover, a Rényi-2 divergence sets the statistical range of the reconstruction while, at nondegenerate points, occupation readout also attains the local single-copy multiparameter information bound.

We further develop Kernel Quantum State Estimation (KQSE), a fully data-driven nonparametric method for continuous variable quantum state reconstruction and characterization, introduced in our recent work and rooted in nonparametric kernel density estimation (KDE). Unlike approaches relying on finite-dimensional basis truncations, parametric ansätze, or prior models, KQSE combines a new kernel estimator of the characteristic function of the tomographic quadrature distribution with suitable kernel integral transformations. The characteristic function is estimated directly from experimental homodyne or heterodyne data and subsequently transformed to estimate quantum state representations and characteristics, including the Wigner function and the density matrix kernel. Observing that several other physically relevant representations and characteristics admit transformations of a closely related form, we derive convergence rates for the corresponding broad class of KQSE-based estimators. The resulting framework covers all phase-space representations, the photon-number tomogram, trace products of quantum states, and purity. We derive mean squared error convergence rates for these kernel transformed estimators and show that the corresponding KQSE applications inherit the near optimal rate O(T^{-1}), where $T$ is the total number of measurements. The proposed theory applies equally to Gaussian and non-Gaussian continuous variable quantum states without imposing a Fock space cutoff. Numerical experiments on simulated Gaussian and non-Gaussian states and real homodyne data demonstrate the advantages of KQSE over state-of-the-art methods, establishing it as a statistically consistent and computationally modest framework for continuous variable quantum state estimation and characterization, particularly in the non-Gaussian regime.

Recent detections of Galactic diffuse neutrinos by IceCube and $\gamma$-rays by LHAASO offer direct probes into the origin of Galactic cosmic rays. Conventional diffuse templates typically assume a single cosmic-ray injection spectrum across a wide energy range, without accounting for independent contributions from distinct accelerator populations. Here, we present a numerical framework that models Galactic diffuse neutrino and $\gamma$-ray emission that incorporates contributions from both microquasar and supernova remnant populations. By anchoring CR injection to local observations and utilizing high-resolution 3D target gas distributions, our model suggests multi-population contributions to the diffuse sky: escaped cosmic rays from supernova remnants dominate below $\sim10\text{ TeV}$, while those from microquasars become the primary driver at higher energies. Our predicted neutrino flux agrees well with the recent 12-year IceCube measurements, establishing a physically motivated baseline for the diffuse hadronic background while leaving room for unresolved point-like sources. This flexible framework provides testable predictions for current and future multi-messenger observatories, accommodating diverse accelerator populations and updated observational constraints.

Structured coherence – partially coherent light spanned by a finite number of modes – is emerging as a powerful tool in optical communications, computation, cryptography, and spectroscopy. Key to these prospects is the recent development of on-chip processing of structured coherence, in which large meshes of interferometers implement unitary and non-unitary transformations on the Hermitian coherence matrix representing multimode partially coherent light. Two related critical tasks for the applications of structured coherence are the reconstruction of an unknown coherence matrix and its diagonalization. Stokes tomography has been utilized in reconstructing the coherence matrix, whereas variational processing has been employed in its diagonalization. We show here that Stokes tomography can also be exploited in the on-chip diagonalization of an unknown coherence matrix, which we verify for two-mode and four-mode structured coherence in an integrated hexagonal mesh of Mach-Zehnder interferometers. This photonic circuit implements a predetermined sequence of configurations to estimate the generalized Stokes parameters, which – in a final step – inform a reconfiguration of the photonic circuit that diagonalizes the coherence matrix. The field is thus left in a coherent-mode representation comprising uncorrelated, orthogonal modes whose weights correspond to the eigenvalues of the original coherence matrix. Moreover, the integrated photonic circuit can be configured to provide the original field alongside its diagonalized counterpart at the circuit output. We verify the diagonalization procedure for coherence matrices of different coherence rank, entropy, and structure. Finally, we dispel the common notion that O(N^2) steps are required for reconstructing an N x N coherence matrix and show that only O(N) steps are needed.

As a lepton-number-violating process whose observation would establish the Majorana nature of neutrinos, neutrinoless double-beta decay ($0\nu\beta\beta$) is an important low-energy nuclear probe of beyond-the-Standard-Model physics. Effective field theory (EFT) provides a general, model-independent description, and interference among effective mechanisms is essential for disentangling their contributions to the total decay rate. We focus on scalar-type terms of the long-range Lagrangian in the EFT approach to $0\nu\beta\beta$. We simultaneously retain the two scalar interactions involving a common $(S+P)$ leptonic current coupled either to an $(S-P)$ or an $(S+P)$ hadronic current. Their coherent contribution produces mutual interference between the two scalar mechanisms, controlled by the relative complex phase of their couplings, in addition to their separate interference with the standard mass mechanism. We give a compact half-life expression, the explicit scalar–scalar interference coefficient in terms of scalar nuclear-matrix-element ratios and integrated phase-space factors, and a positivity bound on that coefficient. The calculation uses the closure and single-nucleon impulse approximations, retains the dominant scalar recoil subset, and includes the $s_{1/2}$ and $p_{1/2}$ Coulomb waves of the outgoing electrons. The resulting dimensionally explicit formulation provides a basis for correlated analyses of the two scalar coefficients.

We consider the open XXZ spin chain with unparallel boundary fields, in the special case in which the Vertex-IRF transform of the K-matrix labelling the boundary field at site 1 is diagonal and invariant under the value of the gauge parameters, the other K-matrix being arbitrary. We discuss here several properties related to this special gauge invariant boundary condition. Such a model can be solved in the Vertex-IRF framework with arbitrary gauge parameters: we can diagonalise the transfer matrix by means of generalised Sklyanin's Separation of Variable (SoV) approach in a family of SoV bases that depend on two arbitrary gauge parameters $\alpha$ and $\beta$, $\beta$ being the so-called dynamical parameter and $\alpha$ an arbitrary shift of the spectral parameter. We show here that such SoV bases depend actually on $\alpha$ and $\beta$ through the difference $\alpha-\beta$ only. Considering the ungauged limit in which $\alpha-\beta$ tends to infinity, we show how to compute all elementary building blocks for the correlation functions of this model. We finally show how to compute overlaps between eigenstates of this model and eigenstates of a spin chain sharing the same boundary condition at site $N$ and in which the gauge invariant boundary condition at site 1 has been changed to a more general one.

Interacting Bose–Hubbard dynamics is formulated on the concrete Buchholz algebra in Fock space. Under repulsive finite-range interactions and a low-activity gap, a discrete functional integral gives volume-uniform exponential local-number bounds at all temperatures. These bounds remove the local-moment hypothesis and yield finite-volume approximation and KMS accumulation states in arXiv:2505.13170.

Finite datasets can share the same size and low-order statistics while differing strongly in structural complexity. We connect this dataset complexity to loss-landscape geometry by pairing local label mixing across neighborhood scales with local entropy around trained neural-network solutions. Adapted from the Franz–Parisi construction in spin-glass theory, local entropy measures the effective volume of low-loss, solution-like parameter configurations at each distance from a reference. We estimate it in finite networks using adaptive sequential Monte Carlo. In a controlled synthetic sweep, greater dataset complexity produces a larger decrease in local entropy near the reference. Farther away, its radial derivative becomes weak and nearly common across conditions. Dataset complexity therefore changes where the effective solution volume contracts, rather than making it decrease uniformly faster. Experiments on real image data show the same qualitative trend, with label randomization further amplifying the effect. These results show that dataset structure shapes how low-loss neighborhoods are organized across finite distances from trained solutions.

Deep atmospheric convection governs South Asian monsoon variability, yet attempting to learn its latent world model directly from zero-inflated, heavy-tailed precipitation yields suboptimal predictive representations. Continuous atmospheric proxies, such as outgoing longwave radiation (OLR), express this convective organization far more coherently. We address this mismatch with cross-variable proxy learning: M-JEPA, a multiscale Monsoon Joint-Embedding Predictive Architecture, is pretrained on five continuous proxy fields over Lagrangian patches tracking moving convective systems—without rainfall supervision at any point. The resulting frozen representation is transferred to daily precipitation forecasts through a shared decoder trunk featuring parallel probabilistic and deterministic branches. Because rainfall is strictly unobserved during pretraining, downstream skill directly measures the predictive information captured in the latent rollout. A frozen-backbone probing framework with two controls (an identical architecture trained on rainfall alone, and a randomly initialized backbone) attributes the transfer specifically to proxy pretraining: direct rainfall training exhibits $36\%$ higher CRPS error ($7.52$ vs.\ $5.54$\,mm/day). Against the 51-member operational ECMWF ensemble, the transferred model attains a statistically resolved CRPS advantage ($6.81$ vs.\ $6.89$\,mm/day) and higher Brier skill ($+0.05$ vs.\ $-0.04$) using $15.4$M parameters on a single consumer GPU, concentrated at heavy-rain thresholds and fine spatial scales, while the ensemble retains an advantage in neighborhood skill and deterministic references on point metrics. The result provides a competitive monsoon precipitation forecast grounded in intraseasonal dynamics and a diagnostic framework for evaluating transferred atmospheric representations.

We study a spinning optomechanical cavity that is coupled to a meandering waveguide at multiple spatially separated points, forming a giant-cavity configuration. The resulting self-interference makes the effective optical driving, linewidth, and frequency shift strongly dependent on the propagation phase in the waveguide, while the rotation-induced Sagnac-Fizeau shift causes the clockwise (CW) and counterclockwise (CCW) cavity modes to experience distinct interference phases. Under single-tone driving, this mechanism enables phase-controlled phonon cooling and, in the presence of cavity rotation, nonreciprocal cooling, with one propagation direction approaching the mechanical ground-state regime while the opposite direction remains less efficiently cooled. Under two-tone driving, the same interference mechanism engineers a squeezed reservoir for the mechanical mode, producing steady-state squeezing that likewise becomes nonreciprocal under cavity rotation. These results establish multi-point self-interference as a versatile mechanism for phase-controlled optomechanical reservoir engineering and show that, when combined with cavity rotation, it provides a route to nonreciprocal quantum effects.

High-resolution spacecraft images provide important astrometric constraints for orbit refinement, but measurements of resolved bodies are often limited by labor-intensive control-point selection and the difficulty of achieving consistent reductions over large image archives. We present an automated shape-model-based astrometric pipeline for Phobos and apply it to Mars Express Super Resolution Channel (SRC) images. For each exposure, a synthetic image is rendered from a high-resolution 3D shape model under the nominal spacecraft-target-Sun geometry. Feature correspondences between the observed and synthetic images are established using SuperPoint and SuperGlue, followed by RANSAC filtering. The matched synthetic-image keypoints are then associated with surface points through ray-shape intersection. The geometric adjustment fixes the adopted body orientation, spacecraft state, and corrected camera pointing and estimates only two effective plane-of-sky position offsets using the exact perspective-projection model. These offsets are used to derive the center-of-figure position of Phobos. We first test the method on an image set previously analysed with a control-point approach and obtain comparable astrometric performance. We then extend the analysis to a larger SRC dataset spanning 2007-2025 and obtain 1113 successful measurements. Relative to the JPL MAR099 ephemeris, the resulting observed-minus-computed residuals have mean values of 0.186 km in $\alpha \times cos(\delta)$ and 0.053 km in $\delta$, with corresponding standard deviations of 0.609 km and 0.583 km. These results demonstrate that the proposed pipeline provides a practical approach to large-scale, homogeneous astrometric reduction of archival spacecraft images of Phobos, with potential application to other resolved bodies.

Low-field MRI scanners based on permanent magnet arrays require accurate B0 shimming to achieve sufficient field homogeneity for imaging. Here, we present Shimmer, an open-source methodology for passive shimming of low-field MRI magnets using additional NdFeB magnets placed outside the main magnet array. The end-to-end methodology combines magnetic field mapping, magnetic field simulations, numerical optimization of shim magnet positions and orientations, and automated generation of 3D-printable shim holders. Continuous magnet rotations and several optimization strategies are considered and evaluated. The method was validated and applied to three different permanent magnet arrays ~50 mT in 200 mm diameter spherical volumes, reducing initial field inhomogeneities of 9361ppm, 31721 ppm and 34143 ppm to 888 ppm, 1812 ppm and 1602 ppm respectively. The improved homogeneity enabled undistorted MR imaging of a phantom. Shimmer provides a reproducible and adaptable approach for improving the performance of low-field MRI magnets.

Recent technical advances in atomic force microscopy (AFM) have led to the development of high-speed AFM (HS-AFM), which enables video-rate imaging and direct visualization of the nanoscale dynamics of biomolecules functioning in solution. Because HS-AFM rapidly generates large datasets, dedicated software is essential for efficient processing, organization, and analysis. Recent improvements in imaging speed have further increased data throughput, underscoring the need for faster analysis software with improved usability. To address this need, we developed Ultrafast Microscopy Exploration (UMEX) Viewer, a software suite for HS-AFM data analysis. UMEX Viewer provides an integrated environment for HS-AFM data handling, including image processing, drift correction, image and movie export, data management, and quantitative analysis. These capabilities have enabled the software to be applied to a wide range of recent studies. In this article, we describe the core functions of UMEX Viewer and demonstrate its practical applications for HS-AFM data analysis aimed at elucidating biological functions.

This paper presents a scalable Bayesian framework for the inference of modern-day sea-level change and surface mass redistribution. To motivate this approach, we first review and quantify the performance of some standard methods for the analysis of satellite gravity and ocean altimetry observations. We find that these methods substantially underestimate uncertainties and are subject to systematic biases, deficiencies that stem from their incomplete treatment of sea-level physics and from uncertainty estimates based solely on the propagation of observational noise. To address these limitations, our approach combines three advances. First, we use recent developments in adjoint sea-level theory to embed the full physics into the forward and inverse modelling. Second, we formulate the Bayesian inverse problem in an infinite-dimensional setting, thereby avoiding discretisation artefacts and the underestimation of uncertainties inherent in truncated model spaces. Finally, our computational methods render such inversions tractable at full observational resolution while supporting joint model spaces and multiple data types. By employing a matrix-free approach with iterative solvers and randomised low-rank decompositions – implemented in the linked open-source libraries pygeoinf and pyslfp – basic calculations are possible on a single laptop, with the most intensive tasks parallelising trivially across available cores. We demonstrate the methodology through a series of synthetic experiments, culminating in a joint inversion of satellite gravity and ocean altimetry data that decomposes regionally averaged sea-level change into steric and manometric components with quantified uncertainties, including the degeneracies that remain.

The objective of this study was to characterize the human-made object that impacted the Moon on 5 August 2026, to estimate the dimensions of the crater it produced, and to compare the estimates with the crater subsequently measured by the Lunar Reconnaissance Orbiter (LRO). The impactor was the discarded upper stage of a SpaceX Falcon 9 rocket, which struck the surface near Einstein crater at a velocity of 2.43 km/s. The kinetic energy and linear momentum of the impactor were calculated, and the crater diameter was estimated using pi-group scaling relationships and measured rocket-body impacts as empirical analogs. The kinetic energy of the impactor was found to be 1.18 x 10^10 J, equivalent to approximately 2.8 tonnes of TNT. Compact-body pi-group scaling yielded a final rim-to-rim diameter of 46-57 m, and the empirical analogs yielded 26-27 m, leading to a pre-observation preferred estimate of 25 m within a range of 20-30 m. Between 11 and 12 August 2026, LRO imaged a crater approximately 18 m in diameter and less than 3 m deep. The results revealed that the compact-body scaling overpredicted the observed diameter by a factor of 2.6-3.2, whereas the empirical-analog method overpredicted it by 39-50%. When the nearest-mass analog was applied as a single crater rather than as a double crater, the estimated diameter was approximately 18 m, in close agreement with the observation. It was found that the hollow and elongated geometry of the stage, rather than a single equivalent bulk density, is the principal factor governing the crater size. This study provides a direct comparison of impact-scaling methods against a measured artificial lunar crater.

Superconducting cosmic strings (SCSs) are characterized by three parameters: the dimensionless string tension $G\mu$, the current amplitude $Y$, and the vector coupling, $\tilde{e}$ as recently explored in \cite{Rybak:2024our}. Using the Charge-Velocity-dependent One-Scale (CVOS) model, we compute the stochastic gravitational wave background (SGWB) emitted by current-carrying loops, including the suppression effect of vector radiation. While previous studies have shown that large $\tilde{e}$ suppresses the SGWB, allowing larger $G\mu$ to evade pulsar timing array (PTA) bounds \cite{Rybak:2024our}, we demonstrate that this parameter space is definitively constrained by an independent, astrophysically clean probe: the Dark Ages global $21$-cm signal. We recalculated the recently derived $21$-cm bounds \cite{Si:2025vsj} into the generic $(\tilde{e},Y,G\mu)$ parameter space and find that at the threshold where vector radiation emission starts dominating, any coupling $\tilde{e}\gtrsim 10^{-7}$ and $Y\simeq 0.67$ and $G\mu\gtrsim 10^{-13}$ injects sufficient ionizing radiation to completely erase the $21$-cm absorption signal at redshift $z\simeq89$. Greater current amplitudes impose more stringent constraints. This excludes a large parameter space considered viable. Our model-independent results highlight the complementarity between future lunar-based $21$-cm experiments and gravitational wave (GW) observatories.

We report a study of the emergent dynamics arising in two-dimensional suspensions of semi-flexible chains whose tip is chemically active, generating a phoretic field. By varying the chain length (number of monomers per chain $N_{pc}$), the area fraction $\phi$, and the sign of the phoretic coupling $J_0$, we map out a rich non-equilibrium phase diagram in the presence of phoretic interactions. For repulsive phoretic interactions ($J_0 > 0$) between the chains, we find that short chains ($N_{pc} = 2$) develop a transient chaotic flow state that crosses over at long times to a global polar flock with super-diffusive mean-squared displacement and long-ranged velocity correlations. Surprisingly, we find this state to have suppressed density fluctuations, indicating the emergence of hyperuniformity. At intermediate chain lengths ($N_{pc} \sim 4$-$8$), the repulsive chemical field drives chaotic mesoscale flows – a dry route to active turbulence – without the need for hydrodynamic interactions or steric alignment interactions. For attractive phoretic interactions ($J_0 < 0$), chains self-organise into hedgehog-like micellar aggregates with heads forming the core and flexible tails radiating outward, in structural analogy with amphiphile micellisation but driven entirely by non-equilibrium self-propulsion. A coarse-grained theory of a tip-emitting active rod predicts the onset of the flocking of dimers, though overestimates the presence of polar order for longer chains. Our results establish phoretic tip activity as a minimal, experimentally realisable mechanism for a spectrum of collective states hitherto attributed to hydrodynamic interactions or steric alignment.

The Minimum Matching Problem consists of finding an independent edge set of minimum weight $M_{\star}(G)$ in a given edge-weighted graph $G$. When $G$ is bipartite, this reduces to the Assignment Problem. We consider a variant of this problem defined by taking the union of optimal matchings across various slightly modified versions of the base graph: $H_{\mathcal{J}}(G)=\bigcup_{U \in \mathcal{J}} M_{\star}(G_{U})$. We establish two families of results: (1) In two distinct settings for the Assignment Problem, we prove that the resulting graphs $H_{\mathcal{J}}$, as well as certain associated graphs $\bar{H}_{\mathcal{J}}$, are spanning trees on the relevant base graphs $G$ and $\bar{G}$. (2) In these same settings, assuming the edge weights are given by the $p$-th power of Euclidean distances for point configurations in the plane, we show that for $p=1$ the tree $H_{\mathcal{J}}$ is non-crossing (i.e., its planar embedding has no crossing edges), whereas, remarkably, for $p=2$ the associated tree $\bar{H}_{\mathcal{J}}$ is non-crossing. Finally, we introduce novel conjectures in Statistical Mechanics, to be explored in future work: in the Random Euclidean Assignment Problem (where points are i.i.d.\ on a planar domain), we conjecture that for $p=2$ the trees $\bar{H}_{\mathcal{J}}$ are asymptotically distributed as Uniform Spanning Trees with free and wired boundary conditions in the two respective settings. In particular, suitable paths on the tree in the second setting, and on its planar dual in the first setting, are asymptotically distributed as $\text{SLE}_{\kappa}$ with $\kappa=2$.

Light thermally produced dark matter is subject to strong bounds stemming from its free-streaming impact on suppressing structure formation. In this paper we show that these limits are significantly alleviated if the dark sector undergoes self-cooling, a new mechanism for lowering the temperature of the dark sector plasma through cannibal self-interactions during freeze-in production. We find that the Lyman-$\alpha$ bounds can be modified by a few orders of magnitude in the sub-MeV region and frozen-in dark matter saturating the observed energy density can be as light as $\approx$ 1.9 (0.7) keV, compared to the $\approx$ 5.7 (1.9) keV warm dark matter limits determined from simulations. Interestingly, a secondary phase of cooling due to simultaneously efficient inverse cannibal- and decay-processes can dominate the modifications of Lyman-$\alpha$ bounds, rather than self-thermalization via cannibal-reactions itself.

We propose an autonomous scheme for stabilizing prescribed Fock states in a Kerr cavity by rectifying anti-Jaynes–Cummings interactions with auxiliary-qubit relaxation. Full Lindblad simulations demonstrate steady states dominated by the Fock states $|1\rangle$ through $|4\rangle$, accompanied by pronounced Wigner negativity. An analytical birth–death model captures the steady-state populations and identifies the operating regime for stabilization. These results establish auxiliary-qubit relaxation as a resource for autonomous reservoir engineering and provide a route to nonclassical bosonic-state preparation in a single Kerr cavity and, through a boundary reservoir, in a Kerr-cavity chain.

Urban wildfire resilience depends on interactions among built form, vegetation condition, and extreme fire weather, yet city-scale risk models often overlook whether predictive skill transfers across neighborhoods. We developed a spatially validated GeoAI workflow for the January 2025 Palisades Fire, linking 12,081 CAL FIRE damage inspections to pre-fire Sentinel-2 vegetation indices, Landsat surface temperature, LANDFIRE fuels, terrain, and OpenStreetMap buildings and roads. Among 9,883 inspected residential structures, 5,566 were destroyed. Random cross-validation yielded ROC-AUC 0.92 for the integrated XGBoost model, but 1 km spatial block validation reduced performance to 0.75; logistic regression performed similarly and was better calibrated. Building count within 100 m was the strongest predictor, with destruction odds increasing 4.12-fold per standard deviation. Vegetation moisture and greenness showed opposing conditional associations: NDMI at 100-300 m was protective (OR 0.52), whereas NDVI at 30-100 m was positively associated with destruction after accounting for moisture (OR 1.74). Predictive information was concentrated at the 100-300 m neighborhood scale. A separate post-fire track mapped burn severity and vegetation recovery without leakage. The results support neighborhood-scale susceptibility screening, moisture-aware vegetation management, and spatial block validation as a minimum standard for single-event urban wildfire modeling.

We propose a thermodynamic description of the genuine Kerr-Bertotti-Robinson (Kerr-BR) black hole spacetimes. Contrary to previous studies, such solutions no longer have electric charge. Consequently, the thermodynamics is governed by simple relations. Namely, upon introducing a suitable normalization factor for the timelike Killing vector, these spacetimes are characterized by the standard Hawking temperature, Bekenstein's entropy, Komar mass and angular momentum, and horizon angular velocity. The corresponding mass is shown to obey the standard Christodoulou-Ruffini formula, along with the standard first law and Smarr relations. In fact, by a proper rescaling of coordinates and solution parameters, and up to a non-trivial parameter space constraint, we formally recover the thermodynamic quantities of the vacuum Kerr solution. This is used to bring the genuine Kerr-BR spacetime into a Kerr-like form.

Travelling-wave-like wall blowing and suction is an effective approach for enhancing heat transfer with a minimal pressure drag penalty. However, achieving such a dissimilar heat transfer enhancement effect in a passive manner remains a challenge. In the present study, we propose introducing parallel perforated plates to induce travelling-wave-like disturbances passively. Pore-resolving simulations of a spatially developing laminar flow between parallel perforated plates are performed across a wide range of Reynolds numbers of $Re = 500-1500$ and pore-to-solid length ratios of $L_\mathrm{p}/L_\mathrm{s} = 0-10$. Dissimilar heat transfer enhancement is confirmed for $9 \leq L_\mathrm{p}/L_\mathrm{s} \leq 10$ at $Re = 1000$ and $4 \leq L_\mathrm{p}/L_\mathrm{s} \leq 7$ at $Re = 1500$. The highest analogy factor, i.e., the ratio of the Stanton number to the friction coefficient is obtained at $Re = 1500$ and $L_\mathrm{p}/L_\mathrm{s} = 6$, yielding an increase of more than $30$% compared to that of an impermeable solid plate. Analysis of the fluctuating fields shows that, in the travelling-wave flow regime, a pressure-induced wall-normal velocity fluctuation transports temperature fluctuations away from the perforated plate, while breaking the correlation between the streamwise and wall-normal velocity fluctuations. This enhances the turbulent heat flux relative to the Reynolds shear stress near the perforated plate. The present results indicate that introducing a perforated plate with a suitable porosity induces travelling-wave velocity disturbances and also achieves a considerable dissimilar heat transfer effect even at low Reynolds numbers where a standard impermeable flat wall yields a steady laminar flow.