Papers

The microstructural characteristics of soot generated during thermal runaway events in lithium-ion batteries have been examined, with a focus on the associated health risks. X-ray computed tomography (Nano-CT) offers advanced 3D imaging for accurate analysis of particle shape, size, and distribution. Its primary benefit is the detection of core-shell structures that contain thin shell of carcinogenic materials. In contrast, traditional techniques like X-ray diffraction can only quantify the mass fraction of these hazardous phases, which is often minimal. Nevertheless, even a small mass fraction of carcinogenic materials on carbonaceous surfaces poses significant health risks when inhaled, potentially compromising internal organ function. This research underscores the need for a deeper understanding of particulate composition to inform safety regulations and respiratory protection measures.

Socio-demographic factors influence social contact patterns and play a fundamental role in shaping the transmission dynamics of infectious diseases. However, compartment-based models of infectious disease dynamics commonly consider the dependence of contact patterns on age, but ignore other factors that are likely to have compounding effects. Methods that stratify the population by multiple socio-demographic factors are few and require social contact surveys that contain information about all factors of interest. Here we present a method that can stratify an existing social contact matrix with an additional socio-demographic factor using information about the population structure of the socio-demographic factors and assumptions about the aggregate mixing rates within and between groups. We then analyse hypothetical populations and a projection of a social contact survey onto Aotearoa New Zealand's age-ethnic structure to show how these extended social contact matrices can change epidemic dynamics and outcomes. The inclusion of the additional factor has a big impact on the model reproduction number and final epidemic size. We find that minority group epidemic outcomes are most sensitive to variation in model parameter values.

Atmospheric correction is a critical preprocessing step in optical remote sensing, but repeated high-fidelity radiative transfer simulations remain computationally expensive for dense look-up-table generation, sensitivity analysis, retrieval support, and operational preprocessing. This study presents a physics-aware multi-fidelity surrogate framework for emulating atmospheric correction coefficients using paired 6S and libRadtran simulations. Atmospheric and geometric states are sampled using Latin Hypercube Sampling, and both radiative transfer models are evaluated under matched conditions for Sentinel-2 bands using spectral-response-function-aware coefficient generation. The high-fidelity targets are path reflectance, total transmittance, and spherical albedo. A physics-guided Kolmogorov-Arnold Network, termed pKANrtm, receives the atmospheric state and low-fidelity 6S coefficients, predicts the residual relative to libRadtran, and reconstructs the high-fidelity coefficients. The pKANrtm model uses an Efficient-KAN architecture and is trained with a physics-consistency penalty applied in the original coefficient space. The proposed model is evaluated against state-of-the-art regression-based RTM surrogates. Across both standard and out-of-distribution evaluation settings, pKANrtm achieves the strongest overall predictive performance among the compared models. Runtime benchmarking demonstrates substantial acceleration relative to libRadtran, with GPU inference providing approximately four orders of magnitude single-sample speedup and batched inference reaching tens of thousands of samples per second. These results indicate that physics-aware multi-fidelity pKANrtm emulation provides an accurate, physically structured, and computationally efficient strategy for atmospheric correction coefficient generation.

Rotation of conducting and dielectric spherical particles levitating in the uniform electrostatic field is considered. A dipole moment of the spherical particle induced by the external uniform electrostatic field is inclined to the field if the particle rotates. This causes the torque braking the rotation. Vectors of dipole moment and torque depend both on an angular velocity of the particle and its electric properties. Equations of rotary motion of the particle levitating in the external field are integrable in quadratures. Few examples of the conducting and dielectric particles are solved explicitly.

We present a framework for the scale-invariance characterization of stochastic processes in reconstructed finite-dimensional phase spaces. This framework analyses the structural and dynamical properties of the phase space and is based on a Takens embedding reconstruction followed by the definition of ensembles of analogue states. We define the analogues of a target state as its nearest neighbors. Then, we specify a collection of target states densely sampling the full phase space. For each target state, we search for the ensemble of its k-best analogues and we analyze its volume and dynamics. First, we study the probability distribution of the volumes and relate its mean and variance to the scale-invariance properties of the stochastic process. Second, we study the Lagrangian properties of the analogues by characterizing how they disperse in time. More particularly, we study the volume occupied by the analogue's successors in function of time and of their initial volume. We link these dynamical properties to the scale-invariance properties of the process. We analyze two types of stationary and dissipative 1-dimensional scale-invariant processes: regularized fractional Brownian motion and regularized multifractal random walk. For both processes, the structure and dynamics of the phase space are determined by their scale-invariant properties.

In this study, a mathematical model of neutron moderation is developed that accounts for the temperature of the fissile medium and the contribution of inelastic neutron scattering on heavy nuclei of uranium 238 in reactor cores. Within the gas model framework, an analytical expression for the inelastic neutron scattering law for an isotropic neutron source is derived for the first time, incorporating the temperature of the moderating medium as a parameter. The scattering law is obtained from the general kinematic solution of inelastic neutron nucleus interactions in the laboratory system, where both particles possess arbitrary velocity vectors. Based on the newly derived inelastic and previously obtained elastic scattering laws, analytical formulas for the neutron flux density and moderation spectrum are presented, both of them dependents on the medium temperature. The calculated deceleration spectra exhibit two distinct maxima (a high energy and a low energy peaks). The low energy maximum is consistent with the analytical solution of the neutron balance equation, confirming the validity of the proposed model. The developed approach provides a deeper understanding of neutron energy distribution in temperature dependent reactor media and can be applied to improve the accuracy of neutron kinetic calculations in thermal and fast reactor systems.

This manuscript provides a detailed and extended analysis of the breakeven conditions for nuclear fusion based on beam-target interactions, distinct from conventional plasma-based approaches. Building on the energy-based criterion introduced in the accompanying Letter~\cite{TK_arXiv}, we formulate a self-consistent description of stopping power in electron-suppressed targets and derive quantitative, implementation-agnostic conditions under which fusion energy generation can exceed beam energy loss.

Ultraviolet (UV) exciton-polaritons (EPs) enable nonlinear optics, polaritonic lasing, and polariton-mediated photochemistry in the short-wavelength regime, yet progress has been limited due to the scarcity of materials that combine large oscillator strength with stable and narrow UV excitons. Here, we demonstrate UV EPs in silver phenylthiolate (Thiorene, AgSPh), a van der Waals (vdW) layered metal-organic chalcogenolate (MOC) that forms a natural multi-quantum-well (MQW) architecture showing strong excitonic features. Imaging spectroscopic ellipsometry reveals a pronounced in-plane excitonic resonance at 3.46 eV with a narrow linewidth of ~ 60 meV, strong UV birefringence ({\Delta}n ~ 0.3), and a high refractive index (n ~ 2.1). Temperature-dependent photoluminescence (PL) shows excitonic emission with a large Stokes shift and substantial exciton-phonon coupling. In both open (self-cavity) and closed cavities, thickness-dependent and angle-resolved reflectance spectra exhibit clear anticrossing, yielding large Rabi splittings of approximately 500 meV. These values are among the largest reported in the UV, positioning thiorene as a promising platform for UV polariton lasers and polariton-enabled photochemistry.

Photothermal imaging is a powerful noncontact and nondestructive technique for subsurface inspection of composite materials, yet its performance is fundamentally limited by the diffusive and irreversible nature of heat transport, leading to severe image blurring and ambiguous depth interpretation. The concept of virtual waves provides a route to overcome this limitation by linking diffusion fields to propagating wave fields, but existing approaches are largely restricted to idealized impulsive excitation. Here, we propose a generalized virtual-wave photothermal tomography framework that extends the diffusion-to-wave transformation to arbitrary boundary excitations, including pulsed, harmonic, and chirped waveforms. Starting from the heat equation with a general source term, we derive a Fredholm integral mapping between the measured diffusion field and a virtual wave field governed by a wave equation, explicitly enforcing causality and thermodynamic irreversibility. The resulting ill-posed inverse problem is solved using ADMM or truncated SVD, depending on the excitation characteristics. Numerical and experimental results demonstrate that the proposed method converts blurred thermal responses into wave-like fields with clear wavefronts and reflections, enabling improved depth localization and tomographic reconstruction. Experiments on carbon fiber reinforced polymer samples with embedded defects show enhanced contrast, sharper boundaries, and more reliable depth interpretation compared with conventional thermographic techniques. This work establishes a unified and physically grounded framework for wave-based photothermal tomography under realistic excitation conditions.

We report on the use of ditellurium ($\mathrm{^{130}Te_2}$) as a frequency reference for laser locking at 391 nm and 498 nm optical wavelengths, which are of interest in titanium (Ti) laser-cooling experiments. In the ultraviolet region near the optical wavelength of 391 nm, 36 previously unobserved transitions were found using laser absorption spectroscopy in a 256 GHz range. Based on the established molecular structure of $\mathrm{^{130}Te_2}$, we attribute these lines to the $\mathrm{0_u^+\rightarrow 0_g^+}$ subsystem of the $\mathrm{^3\Sigma_u^-\rightarrow\ ^3\Sigma_g^-}$ transition with possible vibrational transitions of $\nu=(28,27,26,25,24)\rightarrow 0$ and $(27,26)\rightarrow 1$. We measure the frequencies of these lines, and also of lines near 498 nm wavelength, and subsequently stabilize lasers at wavelengths of 391 nm (and 498 nm) to $\sim$60 MHz ($\sim$50 MHz) wide resonances in $\mathrm{^{130}Te_2}$, near the optical-pumping (laser-cooling) transitions in $\mathrm{^{48}Ti}$. We observe robust laser frequency locks, with Allan deviations of $4.9\times 10^{-10}$ ($3.6\times 10^{-11}$) at 10 s of averaging time for the 391 nm (498 nm) wavelength lasers.

Why does the mammalian vascular tree maintain a conserved branching exponent $\alpha^* \approx 2.72$ across a $10^7$-fold range in body mass, despite a fundamental shift from viscous to wave-dominated transport? We prove this universality cannot emerge from local optimization: any junction-level coupling of incommensurable costs requires scale-dependent fine-tuning varying by $O(10^2$–$10^3)$ across the hierarchy. Real networks resolve this through structural heterogeneity, and vascular geometry emerges as a scale-free attractor of a network-level minimax principle. Grounding the fitness penalty in ATP stoichiometry, we prove a Topological Rigidity theorem: the optimal branching exponent depends only on dimensionless structural parameters $(G, N, p, \alpha_w)$, independent of all metabolic quantities. A self-consistency condition on the viscous–inertial energy partition yields a dual-threshold framework with $\mathrm{Wo}_c^{\mathrm{fluid}} = \sqrt{3}$ and $\mathrm{Wo}_c^{\mathrm{wave}} = 3/\sqrt{2}$. The symmetric model yields $\alpha^*_{\mathrm{model}} \approx 2.626$, in agreement with mammals near the allometric transition; morphometric heterogeneities shift large-mammal values toward $2.72$. The framework explains developmental stability of cardiovascular networks as a consequence of architecture being decoupled from biochemistry.

We report the design, commissioning and benchmarking of a compact AmBe-based neutron irradiation facility capable of providing both fast and thermal neutron dominated fields through multiple detector positions within a moderator assembly. Detailed radiation transport simulations using the FLUKA Monte Carlo code were performed to model the radiation environment at different detector positions. The inclusion of a single-crystal CVD diamond neutron detector in the simulations enabled direct comparison with experimental measurements, providing confidence the radiation fields are well understood. The simulations also provided a detailed breakdown of energy deposition mechanisms in the diamond sensors, including nuclear recoil, neutron capture reactions and secondary proton production from surrounding materials, highlighting the influence of detector housing materials on the local radiation environment and detector response. The facility provides a practical and accessible platform for neutron detector development and benchmarking in typical university laboratories, with dose rates outside the facility below typical natural background levels.

We theoretically investigate the thermally-driven curvature and lipid density fluctuations of a quasi-spherical vesicle, accounting for the dissipation due to monolayer viscosity and intermonolayer friction. The theory predicts that membrane curvature makes long-wavelength undulations sensitive to membrane viscosity and speeds up the relaxation of the lipid density fluctuations. Implications for the dynamic roughness and Dynamic Structure Factor measurements of submicron liposomes on nano-second time scales are discussed. Specifically, a clear stretched-exponential relaxation regime may not exist, in contrast to the behavior of planar membranes for which an anomalous diffusion exponent of 2/3 has been predicted [Zilman and Granek, Phys. Rev. Lett. (1996)].

We define non-Markovian quantum dynamics as evolution in which the current state depends on all past states, and completely characterize its structure under the assumptions of complete positivity and non-signalling. The resulting continuous-time dynamics is an integro-differential equation that augments the Gorini-Kossakowski-Sudarshan-Lindblad equation with a memory integral, and is capable of describing the quantum state of systems exposed to noise with any integrable power spectral density with no further approximations. We then establish a formalism to evaluate multi-time correlations of measurement outcomes in this general setting, obviating the need for a regression theorem. As an application, we derive the emission spectrum of a driven two-level system coupled to a non-Markovian bath: the familiar Mollow triplet acquires a frequency-dependent linewidth that encodes the memory of the bath. Our work provides a rigorous yet transparent description of the quantum state of non-Markovian systems, opening the door for state estimation and state-based quantum control beyond the Markovian regime.

In the current era of AI transforming the research-education environment of physics, variety of issues and concerns arise. The KITP program "Generative AI for High and Low Energy Physics” offered a discussion session on this, and here presented is a summary of the opinions provided in the discussion. The material is formulated such that it can serve as a starting point for further discussions in readers' research community/institution/group.

In proliferating cell populations, adaptive changes to biochemical reactions can change a cell's division time, which in turn can change the population size. However, biochemical reactions are subject to noise, and therefore the conditions for optimal information transmission from the molecular to the population scale are poorly understood. Here, we model cell proliferation as a Bellman-Harris branching process with age-dependent division times. We identify a class of division time distributions, built from a series of Markovian steps, for which the population size distribution at all times is hierarchically calculable. We use this feature to characterize the amount of influence that a given reaction step has on the population size via the mutual information. We find that information transmission is optimal for a characteristic number of steps until division: too few and the population size is unpredictable; too many and any given step has vanishing influence on the population size. Our work reveals the potential tradeoffs involved in adaptive decision making at the sub-cellular, cellular and population scales.

Spectroscopy of laser-produced plasmas offers an avenue for real-time, standoff and non-preparatory sensing of rare-earth elements (REEs) within a mineralogical context with applications spanning exploration geology to ore body mapping to ore sorting. Demonstrations of laser-induced breakdown spectroscopy (LIBS) in rock samples have employed both atomic and molecular detection for REE sensors. In this work we evaluate a complementary technique of absorption spectroscopy, realized with dual-frequency combs. This approach provides multi-THz (nm) spectral coverage with simultaneous sub-GHz (pm) resolution. It can improve accuracy and line identification confidence in congested multi-species spectra, which makes it ideal for multi-species evaluations present within mineralogical samples. We analyze REE signatures in calibrated reference materials (CRMs) and a synthesized, REE-containing alloy for atomic, ionic and molecular (oxide) absorptions across three spectral windows. We identify lines from rare-earth and matrix elements, compare absorption line strengths and investigate their temporal evolution. For La I, Sm I and Ce I, preliminary limits of detection from 54-583 ppm are estimated for CRMs, using univariate analysis of selected transitions. Comparing the CRM signatures to those of REEs synthesized in a copper alloy, we observe that all REE lines appear earlier and disappear faster in the CRM samples. We attribute these dynamics to matrix effects: Among other elements, the increased oxygen content in the CRM could favor molecular formation. For rock samples, observations will once again differ due to grain sizes and bonding mechanisms. Compared to LIBS, we can resolve individual REE and matrix lines with minimal spectral overlap. These proof-of-principle results form a foundation for further development of this laser-based method as a mining sensor.

Optical skyrmions have been widely explored in recent years. Among them, Stokes skyrmions require sophisticated wave engineering or photonic devices for their generation. We show that Stokes skyrmions can emerge spontaneously in second-order harmonic generation in underdense plasmas driven by optical vortices. The nonlinear response produces a structured frequency-doubled field whose polarization texture maps the Poincaré sphere. When plasma inhomogeneities are taken into account, the electron density gradient deforms the skyrmionic texture, enabling topological diagnosis of plasma density.

Frequency-multiplexed entangled photon pair sources with narrow bandwidths and high pair generation efficiency are a key enabling technology for quantum networking. We present a simulation-based design study of an integrated photonic racetrack resonator source for spontaneous parametric down-conversion (SPDC) that simultaneously achieves all three properties. The central result is a simulated set of 90 doubly resonant signal/idler frequency-mode pairs with an effective Schmidt number of 89.62, average bandwidths of 1.08 GHz, a mean free spectral range of 51.9 GHz, and a total internal pair-generation-rate efficiency of 1.16 GHz/mW. Under deterministic wavelength-based splitting, the accessible frequency-state Schmidt number is reduced to 44.93. To support these predictions, we derive a closed-form analytical connection between classical cavity parameters (resonant frequencies, decay rates, coupling coefficients) and the quantum joint spectral amplitude and pair generation rate, extending the dispersive-medium quantization formalism of Raymer to the nonlinear optical cavity case. We demonstrate how classical electromagnetic field simulations can be combined with this analytical framework to predict quantum figures of merit for an integrated photonic source prior to fabrication. Fabrication and experimental validation are left for future work.

We present iGENE, a fully-differentiable TensorFlow implementation of the electromagnetic local nonlinear gyrokinetic model, which allows us to compute gradients of any simulation output with respect to any input via automatic differentiation. We show that even if the stochastic nature of turbulence prevents the exact evaluation of gradients of nonlinear quantities of interest, they can still be successfully used to perform outer-loop tasks, such as profile predictions. This work enables the integration of gyrokinetics into automated parameter optimization, uncertainty quantification, sensitivity analysis, and AI workflows.

Road-traffic NO2 hotspots are still often modelled with static emissions and generic temporal profiles, although near-road concentrations respond strongly to rapidly changing traffic conditions. Here, we test whether detector-informed dynamic traffic emissions improve hyperlocal NO2 modelling relative to a conventional static baseline. To this end, we couple an online-calibrated mesoscopic traffic model (SUMO) with the LES-based urban dispersion model CAIRDIO in a nested high-resolution framework for Leipzig, Germany. We compare two otherwise identical experiment setups: a static reference simulation and a coupled simulation in which road-traffic emissions within the SUMO domain are replaced by dynamic emissions derived from simulated traffic states. The framework is designed for city-wide high-resolution application, while the present evaluation focuses on two traffic-oriented hotspot settings during two one-week periods. Compared against hourly NO2 observations of official air quality monitoring, the coupled setup performs better overall, with the clearest improvement at the street-canyon hotspot and in the representation of concentration peaks. Dynamic traffic emissions therefore provide clear added value for hyperlocal NO2 prediction where hotspot realism and exposure-relevant peaks matter.

Sampling from high-dimensional Gibbs measures poses a challenge when the energy landscape consists of multiple metastable states. Enhanced-sampling methods mitigate this difficulty by introducing adaptive biasing potentials to facilitate the exploration along prescribed collective variables (CVs), but their scalability is often limited by the dimension of the CV space. Motivated by the Wasserstein-gradient-flow interpretation of adaptive biasing, we propose a regularized path-dependent McKean–Vlasov formulation for high-dimensional enhanced sampling. The formulation replaces the variational regularization of the Wasserstein functional by a direct regularization of the CV marginal density in the McKean–Vlasov drift, avoiding the outer convolution over the CV domain. Furthermore, it replaces the instantaneous law by a weighted path-history measure to improve statistical stability in the small-replica regime. We establish well-posedness of the resulting regularized and path-dependent stochastic dynamics under suitable assumptions. For numerical realization, the history-averaged CV marginal density is approximated using an optimization-free functional hierarchical tensor representation, leading to a scalable density-based adaptive biasing scheme. Numerical experiments on benchmark potentials and molecular systems demonstrate the effectiveness of the proposed method for sampling problems with CV dimensions up to 64.

Reconnection-driven nanoflares are widely considered a leading mechanism for coronal-loop heating, but their direct fingerprints in the tenuous coronal plasma remain elusive. The recently discovered coronal nanojets offer a potential probe of reconnection dynamics, but their extreme collimation, directionality and multi-wavelength visibility are not fully understood. Here we present a 3D rMHD simulation that unprecedentedly reproduces the key properties of nanojets, offering a viable model to explain their nature. These results provide a unified picture in which nanojet morphology, dynamics and detectability are contingent on the thermodynamic environment of reconnection. Together, our results point to a cool origin of coronal nanojets, where cool and dense material permits narrow, multi-band jet signatures to emerge from reconnection.

Despite their importance in turbulence theory, a unifying and predictive rule determining the direction of the cascades of conserved quantities is lacking. In this work, we show that the direction of the cascades in two-dimensional turbulence is encoded in the complex phases of the Fourier transform of the velocity field. We develop a closure for the dynamics of a triad phase, the sum of the phases of three modes forming a triad, based on the observation that neighboring triad phases are weakly correlated. The resulting stochastic model can be solved analytically to find the triad phase probability distribution function (PDF). We validate our model's assumptions and predictions using an ensemble of two-dimensional turbulence simulations. From the triad phase PDF we develop a novel closure of the energy equation, and prove that the cascade directions are determined by our model without adjustable parameters and given only the energy spectrum. Triad phase dynamics occur in any quadratically nonlinear partial differential equation, making this a promising new direction in the study of strongly out-of-equilibrium systems.

Hyperbolic and quasi-flat isofrequency contours (IFCs) are used for beam canalization and can be created by tilted Dirac points in photonic systems. Dirac points in microcavities are generated by the combination of transverse-electric/transverse-magnetic splitting and linear birefringence. We show that the canalization is here strongly assisted by the coupling between the spatial dynamics and polarization pseudospin precession. This dynamics is well described analytically and numerically as the action of a non-Abelian gauge field on emergent charges (spin current). We demonstrate a ten-fold enhancement of the canalization for a Gaussian beam by the gauge field, as compared to a description based solely on the group velocity associated with the IFCs.

Building on recent progress in the understanding of particle transport in magnetized plasmas, we derive a scaling law for the formation of nonthermal spectral tails in mildly and strongly magnetized turbulent environments. We validate this scaling using driven-turbulence particle-in-cell simulations that incorporate particle escape, allowing the system to reach a steady state. The simulation results show good agreement with our theoretical predictions. We then discuss the astrophysical implications of these findings, focusing on proton acceleration in the coronae of supermassive black holes and the resulting high-energy neutrino emission.

We establish the conditions under which scalable spin squeezing can be achieved in interacting spin ensembles embedded in arbitrary, inhomogeneous graph geometries. We identify two different forms of squeezing: OAT-like scalable squeezing is governed solely by the universal properties of the interaction graph and is controlled by its spectral dimension. In critical squeezing, on the other hand, the value of the spectral dimension only furnishes the necessary condition for scalable metrological gain, while the sufficient condition requires the model to lie below the symmetry breaking transition. Therefore, in systems with random interaction graphs, the scaling of the spin-squeezing critical point emerges from a nontrivial interplay between xy-ferromagnetic universality and percolation universality. We apply this general theoretical framework to several experimental scenarios and discuss sharp and experimentally relevant conditions for achieving robust metrological gain on generic inhomogeneous structures, giving a unifying perspective for designing scalable quantum sensors across diverse quantum simulation platforms.

Pre-plasma conditions strongly influence laser-plasma interactions in the multi-petawatt (MPW) regime, increasing the need for reliable early-time plasma evolution diagnostics. Among available pre-plasma diagnostics, interferometry remains the most direct method for measuring the spatially resolved electron density of pre-formed plasmas. However, its implementation becomes increasingly challenging at MPW scale dueto steep density gradients, phase-recovery difficulties, strong electromagnetic pulses (EMP), debris accumulation, and high-repetition-rate operation. Compounding these technical challenges, many large-scale facilities lack permanent probe-line architecture and trained diagnostic support, reducing experimental reproducibility and consuming limited beamtime. Future MPW facilities should standardize probe-line architecture, adopt off frequency probing strategies, improve phase-recovery methods for non-symmetric plasmas, integrate emerging real-time analysis capabilities, and engineer diagnostic systems resilient to EMP and high-repetition-rate environments. These advances will enable the user community to reliably characterize pre-plasma formation and laser-plasma dynamics at next-generation MPW facilities.

Long-period cyclic reversals of the large-scale magnetic field are a prominent feature of the dynamo associated with the magnetorotational instability (MRI) in accretion disks, but their physical origin remains unclear. We develop a quasilinear theory (QLT) of the MRI dynamo where the electromotive force (emf) is computed from the linear eigenfunctions under the WKB approximation. The emf depends on the mean field $\mathbf{B}$ more generally than standard mean-field closures allow. In the unstratified case, the leading order contribution to the large-scale dynamo is the shear-current effect: the emf depends on the current $\mathbf{J}$ as $\pmb{\varepsilon} = \pmb{\beta}\cdot\mathbf{J}$, with a tensor $\pmb{\beta}(\mathbf{B},t)$ that oscillates with time $t$ and whose off-diagonal components generate the mean field. The oscillations arise from beats between the two branches of eigenfrequencies. Since the beat frequency varies only weakly with wavenumber, the beats remain coherent and drive the long-period butterfly cycle seen in local shearing box simulations. We predict a dominant cycle period $\sim 30{\left(1+a^2\right)}^{1/2}\,t_{\rm orb}$, with $a$ the vertical-to-radial aspect ratio and $t_{\rm orb}$ the orbital period, and an amplitude scaling $\sim a^2$ before saturation at $a\gtrsim 5$. Both trends agree with zero-net-flux unstratified shearing box simulations with Athena++. A carrier-envelope analysis of the simulation spectra shows that the same interference mechanism extends beyond strict QLT, through higher-order linear combinations of the eigenfrequencies, with observed cycles arising from pairwise beats within this spectral network. These results identify coherent interference between nearly degenerate eigenfrequencies as a key mechanism behind large-scale cyclic dynamos, with implications for magnetic variability in protoplanetary disks, X-ray binaries, and AGNs.

We study hyperedge-removal strategies for suppressing contagion on synthetic hypergraphs. Hypergraphs are generated from Erdős–Rényi, Barabási–Albert, and Watts–Strogatz seed graphs by promoting maximal cliques to hyperedges. For each hypergraph, we construct \(s\)-line graphs whose vertices correspond to hyperedges and whose edges encode hyperedge overlap of size at least \(s\). Spectral \(k\)-way clustering of these \(s\)-line graphs yields a multiscale cut-persistence score used to rank hyperedges for removal. Simulations show that the effect of this intervention is strongly topology-dependent. In the reported Erdős–Rényi case, cut-persistence targeting reduces final infection size more than random hyperedge removal. In the Watts–Strogatz and Barabási–Albert cases, however, random removal is comparable to or better than cut-persistence targeting. These results suggest that spectral overlap structure can identify structurally salient hyperedges, but structural salience alone does not guarantee optimal contagion suppression. The study motivates further comparison with ensemble-level experiments and explicitly higher-order contagion models.