Non-reciprocal systems exhibit extreme sensitivity to boundary conditions, typically manifesting as the non-Hermitian skin effect (NHSE) under open boundaries. By bridging the boundaries with a tunable impurity bond, one can access intermediate regimes where scale-free localization (SFL) can emerge. Here, we investigate the competition between such boundary coupling and quasiperiodic disorder in a non-reciprocal lattice. Our analyses reveal a quasiperiodicity-induced breakdown of the SFL regime, which evolves into either the NHSE or an extended regime, depending on boundary conditions. These results uncover the crucial role of quasiperiodicity in non-Hermitian systems.

The undoped Kondo necklace in 1D is a paradigmatic and well understood model of a Kondo insulator. This work performs the first large-scale study of the 1D Anderson-lattice underlying the Kondo necklace with quasi-exact numerical methods, comparing this with the perturbative effective 1D Kondo-necklace model derived from the former. This study is based on an exact mapping of the Anderson model to one of a superconducting pairing layer connected to a metallic reservoir which is valid in arbitrary spatial dimensions, thereby linking the previously disparate areas of reservoir-enhanced superconductivity, following Kivelson's pioneering proposals, and that of periodic Kondo-systems. Our work reveals that below the length-scales on which the insulating state sets in, which can be very large, superconducting and density-density correlations are degenerate and may both appear to approach an almost ordered state, to a degree that far exceeds that of any isolated 1D pairing layer with short-range interactions. We trace these effects to the effective extended-range coupling that the metallic layer mediates within the pairing layer. These results translate directly to the appearance of near-long-range magnetic order at intermediate scales in the Kondo-systems, and explain the strong renormalization of the RKKY-coupling that we effectively observe, in terms of the back-action of the pairing layer onto the metallic layer. The effects we predict could be tested either by local probes of quasi-1D heavy fermion compounds such as CeCo$_2$Ga$_8$, in engineered chains of ad-atoms or in ultracold atomic gases.

LHAASO J2108+5157 is one of the few ultra-high energy gamma-ray sources in the LHAASO catalogue without secure counterpart at longer wavelengths. Several Galactic scenarios have been proposed, including an evolved supernova remnant and a pulsar wind nebula. Yet, no shocked gas, shell-like structure, or compact pulsar candidate has been identified. Follow-up observations with VERITAS and the LST-1 prototype have not firmly clarified its nature. A recent microquasar candidate from GMRT radio data remains uncertain. Here we present the first dedicated near-infrared study of the field, combining deep JHKs imaging with narrow band observations targeting the H2 v=1-0 S(1) line. Our observations were initially planned to encompass the full source region, but now only partially cover the latest updated position and size of LHAASO J2108+5157. We find no evidence of shocked emission, extended nebular structures, or an accreting compact object signature in the covered field. The GMRT radio source, despite its jet-like morphology, exhibits near-infrared properties incompatible with both a Galactic microquasar and a nearby radio galaxy, discouraging an association with the gamma-ray emission. Our analysis reveals no convincing counterpart consistent within the positional uncertainty, leaving LHAASO J2108+5157 as an enigmatic ultra-high energy emitter that requires deeper observations.

The MicroBooNE detector is a liquid argon time projection chamber (LArTPC) that produces three-dimensional images of particle interactions using ionization charge collected by anode wire plane arrays and scintillation light collected by a light detection system. In addition to testing long-standing experimental neutrino anomalies and performing measurements of neutrino interactions with argon nuclei using the Fermilab Booster Neutrino Beam, MicroBooNE aims to develop methodologies for rare beyond the Standard Model and off-beam physics searches. Looking ahead to the upcoming Deep Underground Neutrino Experiment (DUNE), with MicroBooNE serving as a valuable testbed, achieving high sensitivity and livetime for off-beam physics while satisfying data processing and storage constraints will require data-driven, intelligent, and online or real-time data selection techniques. These techniques are essential for reducing data rates and preserving rare signals with high accuracy. In this paper, we describe a fast data selection algorithm suitable for online execution to identify electrons from stopping cosmic ray muons in the MicroBooNE detector utilizing ionization charge information, and present its performance. This represents the first demonstration of online data selection in a LArTPC using real data and charge information exclusively and provides an important proof-of-principle for applying such techniques to other LArTPC experiments such as the Short-Baseline Near Detector and DUNE.

Transmon qubits are a cornerstone of modern superconducting quantum computing platforms. Temporal fluctuations of energy relaxation in these qubits are widely attributed to microscopic two-level systems (TLSs) in device dielectrics and interfaces, yet isolating individual defects typically relies on tuning the qubit or the TLS into resonance. We demonstrate a novel spectroscopy method for fixed-frequency transmons based on multilevel relaxation: repeated preparation of the second excited state and simultaneous $T_1$ extraction of the first and second excited states reveals characteristic correlations in the decay rates of adjacent transitions. From these correlations we identify one or more dominant TLSs and reconstruct their frequency drift over time. Remarkably, we find that TLSs detuned by $\gtrsim 100\,\mathrm{MHz}$ from the qubit transition can still significantly influence relaxation. The proposed method provides a powerful tool for TLS spectroscopy without the need to tune the transmon frequency, either via a flux-tunable inductor or AC-Stark shifts.

Accelerating the discovery of high-performance materials remains a central challenge across energy, electronics, and aerospace technologies, where traditional workflows depend heavily on expert intuition and computationally expensive simulations. Here we introduce the Materials Knowledge Navigation Agent (MKNA), a language-driven system that translates natural-language scientific intent into executable actions for database retrieval, property prediction, structure generation, and stability evaluation. Beyond automating tool invocation, MKNA autonomously extracts quantitative thresholds and chemically meaningful design motifs from literature and database evidence, enabling data-grounded hypothesis formation. Applied to the search for high-Debye-temperature ceramics, the agent identifies a literature-supported screening criterion (Theta_D > 800 K), rediscovers canonical ultra-stiff materials such as diamond, SiC, SiN, and BeO, and proposes thermodynamically stable, previously unreported Be-C-rich compounds that populate the sparsely explored 1500-1700 K regime. These results demonstrate that MKNA not only finds stable candidates but also reconstructs interpretable design heuristics, establishing a generalizable platform for autonomous, language-guided materials exploration.

The early universe grants access to energy scales far beyond those achievable in terrestrial experiments and allows unstable Standard Model particles to play an active dynamical role. In this contribution, we focus on recent studies aimed at quantifying the potential of the early universe to probe the properties and interactions of axions. The discussion is organized around four classes of axion scenarios, ordered from long to short lifetimes: (i) stable or long-lived axions contributing to dark radiation; (ii) stable or long-lived axions produced out-of-equilibrium and constituting dark matter; (iii) metastable axions whose decays inject energy into the primordial plasma and leave observable signatures in the global 21 cm signal; and (iv) very short-lived axions that act only as portals to additional degrees of freedom. Together, these scenarios highlight the interplay between axion phenomenology and early universe cosmology and demonstrate the potential of cosmological data to probe axions over a broad range of masses and lifetimes.

This work investigates the Floquet dynamics of electrons and excitons (particle-hole pairs) in a Dirac material referred to as Kekulé-distorted graphene. Specifically, we examine the role played by a high frequency driving electromagnetic field on the tunneling and blocking by a potential barrier on both the charged single particles as well as the neutral composite particles. We demonstrate that the small effective masses of the electron and hole for the energy spectrum of this Kekulé distorted graphene leads to practically almost perfect transmission across a symmetric potential barrier for any angle of incidence of impinging excitons. However, this unexpected Klein paradox for excitons does not hold for the single-particle electrons. The reduced total transmission of electron due to Kekulé distortion is more suppressed due to irradiation. Additionally, we calculate and investigate the exciton binding energy since the quantum tunneling of a bound electron-hole pair across a potential barrier is governed by its mass measured in the center of mass and binding energy of the composite pair. Thus, irradiation with circularly polarized light fundamentally modifies exciton formation, coherence and transport properties, thereby producing unusual topological behaviors. These behaviors are unlike conventional Dirac materials. Possible technical applications of the results arising from our investigation include valleytronics due to the folding of the valleys, thereby making intervalley coupling feasible. Other practical applications include optoelectronics due to Floquet tuning of energy spectrum and transport properties.

We consider an electrostatic system whose spatial factor is conformal to an $n$-dimensional Euclidean space. We provide a complete characterization of the most general ansatz, thereby reducing the associated electrostatic system of partial differential equations to an ordinary differential equation system. We prove that there are only two possibilities: either the cosmological constant is nonzero, in which case the solutions are necessarily invariant under rotations or translations, or the cosmological constant vanishes, and the solutions belong to the Majumdar-Papapetrou class with a degree of freedom associated with an invariant $(n-1)$-dimensional subgroup. As a result, we introduce a new solution to the electrovacuum system in the Majumdar-Papapetrou class that is invariant under an $(n-1)$-dimensional group of dilations.

Reciprocity is a fundamental symmetry present in many natural phenomena and engineered systems. Distinct situations where this symmetry is broken are typically grouped under the umbrella term "nonreciprocity", colloquially defined by: the action of A on B $\neq$ the action of B on A. In this review, we elucidate what nonreciprocity is by providing an introduction to its most salient classes: nonvariational dynamics, violations of Newton's third law, broken detailed balance, nonreciprocal responses and nonreciprocity of arbitrary linear operators. Next, we point out where to find these manifestations of non-reciprocity, from ensembles of particles with field mediated interactions to synthetic neural networks and open quantum systems. Given this breadth of contexts and the lack of an all-encompassing definition, it makes it all the more intriguing that some general conclusions can be gathered, when distinct definitions of nonreciprocity overlap. We explore what these universal consequences are with a special emphasis on collective phenomena that arise in nonreciprocal many-body systems. The topics covered include nonreciprocal phase transitions and non-normal amplification of noise and perturbations. We conclude with some open questions.

The clustering of self-motile and repulsive particles, so-called motility-induced phase separation (MIPS), is one of the clearest signatures of active physics. Typically, increasing the amplitude of self-motility increases the degree of clustering, however for high enough self-motility the homogeneous phase is reentered. Here, we report that such reentrance naturally emerges in a Hamiltonian (conservative) model known to recapitulate properties of (active) bird flocks, and exhibits clustering behaviour reminiscent of MIPS. We numerically demonstrate the reentrance of the homogeneous phase and identify the underlying mechanism as a competition between the amplitude of a spin-velocity coupled drive and mobility-limited kinetic frustration. Specifically, we reveal that strong spin-velocity coupling suppresses transverse diffusion, thereby leading the system into an arrest that closes the window for phase separation. Overall, our work offers a Hamiltonian, conservative, bridge between reentrant physics across equilibrium and non-equilibrium materials.

Lyotropic liquid crystals can display rich phase behaviour and self-organisation, yet the physical principles underlying their self-assembly into large scale patterns remains understudied. Here, we combine theory, simulations and experiments on Sunset Yellow-water chromonic mixtures to show that such materials spontaneously phase separate, even without assuming any underlying microscopic attraction between the molecular species. In our minimal model, demixing depends solely on the Onsager-like coupling between local nematogen density and orientational order. If such a coupling is sufficiently strong, nematic defects trigger the nucleation of isotropic droplets, which then coalesce due to elastic or interfacial tensions. We further show that strong anchoring of the director field at the interface arrests this coarsening process, resulting in a stable microphase separated lamellar pattern. This self-assembled smectic phase has striking and unusual features, including spontaneous undulations, heterogeneous layer spacing, long-lived glassy defect patterns and lamellar onions. Our results identify orientational-density coupling and elastocapillarity as fundamental mechanisms to guide self-assembly in lyotropic and chromonic liquid crystals.

We derive updated cosmological bounds on light axion-like particles (ALPs) coupled to leptons or photons, using a full phase-space treatment of their production from the primordial thermal plasma. The ALP phase-space distribution, obtained by solving the momentum-dependent Boltzmann equation for the relevant production processes, is consistently propagated into the computation of cosmological observables, allowing us to assess the impact of non-thermal spectral distortions on the effective number of relativistic species, $ΔN_{\rm eff}$. Using state-of-the-art measurements of the cosmic microwave background from Planck, the Atacama Cosmology Telescope, and the South Pole Telescope, complemented with Big Bang Nucleosynthesis determinations of primordial deuterium and helium abundances, we obtain the following 95\% credible limits on the ALP decay constant: $f_a > 1.63 \times 10^6 \, {\rm GeV}$, $9.41 \times 10^6 \, {\rm GeV}$ and $8.06 \times 10^4 \, {\rm GeV}$ for ALPs coupled to electrons, muons and taus, respectively. For the ALP-photon coupling we find $g_{aγ} < 1.98 \times 10^{-8} \, {\rm GeV}^{-1}$. Including baryon acoustic oscillation data from the Dark Energy Spectroscopic Instrument mildly relaxes the constraints, in line with previous analyses of extra relativistic degrees of freedom. Finally, we present forecasts for the LiteBIRD$+$Simons Observatory and LiteBIRD$+$CMB-HD configurations, discussing the importance of an exact phase-space treatment for robust cosmological bounds on ALP interactions.

Many biological processes involve numerous coupled degrees of freedom, yet free-energy estimation is often restricted to one-dimensional profiles to mitigate the high computational cost of multidimensional sampling. In this work, we extend Fokker--Planck Score Learning (FPSL) to efficiently reconstruct two-dimensional free-energy landscapes from non-equilibrium molecular dynamics simulations using different types of collective variables. We show that explicitly modeling orthogonal degrees of freedom reveals insights hidden in one-dimensional projections at negligible computational overhead. Additionally, exploiting symmetries in the underlying landscape enhances reconstruction accuracy, while regularization techniques ensure numerical robustness in sparsely sampled regions. We validate our approach on three distinct systems: the conformational dynamics of alanine dipeptide, as well as coarse-grained and all-atom models of solute permeation through lipid bilayers. We demonstrate that, because FPSL learns a smooth score function rather than histogram-based densities, it overcomes the exponential scaling of grid-based methods, establishing it as a data-efficient and scalable tool for multidimensional free-energy estimation.

We study the training and performance of physics-informed learning for initial and boundary value problems (IBVP) with physics-informed neural networks (PINNs) from a statistical learning perspective. Specifically, we restrict ourselves to parameterizations with hard initial and boundary condition constraints and reformulate the problem of estimating PINN parameters as a statistical learning problem. From this perspective, the physics penalty on the IBVP residuals can be better understood not as a regularizing term bus as an infinite source of indirect data, and the learning process as fitting the PINN distribution of residuals $p(y \mid x, t, w) q(x, t) $ to the true data-generating distribution $δ(0) q(x, t)$ by minimizing the Kullback-Leibler divergence between the true and PINN distributions. Furthermore, this analysis show that physics-informed learning with PINNs is a singular learning problem, and we employ singular learning theory tools, namely the so-called Local Learning Coefficient (Lau et al., 2025) to analyze the estimates of PINN parameters obtained via stochastic optimization for a heat equation IBVP. Finally, we discuss implications of this analysis on the quantification of predictive uncertainty of PINNs and the extrapolation capacity of PINNs.

Processes controlled by stochastic synthesis and degradation (SSD) are widespread in biology but their reaction kinetics are not well understood. Using methods borrowed from the theory of resetting processes, we determine the first-passage properties of a collection of independent particles that are synthesized and degraded at constant rates, and follow an arbitrary diffusive process in space. At equal synthesis and degradation rates, the mean reaction time with a target site can be minimized as in stochastic resetting, and a $CV$-criterion is derived. When the degradation rate is held fixed and the synthesis costs are taken into account, an optimal synthesis rate is obtained. In bounded domains, despite particle degradation, SSD improves the mean search time compared to a single non-degrading particle if the synthesis rate exceeds a critical value. The latter obeys a universal relation. We illustrate these findings with Brownian diffusion on the infinite line and in an interval.

Black hole jets represent one of the most extreme manifestations of astrophysical processes, linking accretion physics, relativistic magnetohydrodynamics, and large-scale feedback in galaxies and clusters. Despite decades of observational and theoretical work, the mechanisms governing jet launching, collimation, and energy dissipation remain open questions. In this article, we discuss how upcoming facilities such as the Event Horizon Telescope (EHT), the Cherenkov Telescope Array (CTA), the Vera C. Rubin Observatory (LSST), and the Whole Earth Blazar Telescope (WEBT) will provide unprecedented constraints on jet dynamics, variability, and multi-wavelength signatures. Furthermore, we highlight theoretical challenges, including the role of magnetically arrested disks (MADs), plasma microphysics, and general relativistic magnetohydrodynamic (GRMHD) simulations in shaping our understanding of jet formation. By combining high-resolution imaging, time-domain surveys, and advanced simulations, the next decade promises transformative progress in unveiling the physics of black hole jets.

We study first-order phase transitions in continuum Gibbs point processes with saturated interactions. These interactions form a broad class of Hamiltonians in which the local energy in regions of high particle density depends only on the number of points. Building on ideas of Pirogov-Sinai-Zahradnik theory and its adaptations to the continuum, we develop a general method for establishing the existence of two distinct infinite-volume Gibbs measures with different intensities in this setting, demonstrating a first-order phase transition. Our approach extends previous results obtained for the Quermass model and applies in particular to a new class of diluted pairwise interactions introduced in this work.

The inclusion of long-range electrostatics in atomistic machine learning (ML) is receiving increasing attention for achieving quantum-mechanical accuracy in predicting a wide range of molecular and material properties. However, there is still no general prescription on how long-range physical effects should be incorporated into the model while preserving well-established locality principles underlying most transferable ML representations. Here, we provide a physical perspective on the problem, by discussing how distinct contributions to the system's electrostatics can be captured through the adoption of different learning paradigms. Specifically, we discern between local charge models, which rely either on explicit charge-density decompositions or implicit auxiliary variables, and models where a notion of nonlocality is deliberately introduced, either via self-consistent procedures or by using nonlocal descriptors and learning architectures. We further address the related aspect of incorporating finite-field effects through the coupling with the system's polarization, relevant for the application of an external electric bias. We conclude by discussing the implications for the simulation of electrochemical interfaces, where long-range electrostatics are essential to capture the interplay between charge redistribution, interfacial dynamics, and ionic screening, and for ionic transport phenomena, which, although less explored, appear far less sensitive to their inclusion.

Reservoir Computing (RC), a type of recurrent random neural network, is a powerful framework for modeling complex and chaotic dynamics. However, its autonomous (closed-loop) operation is often plagued by inherent instability. Moreover, performance is highly sensitive to the reservoir's random initialization, leading to vulnerability to noise and/or behaviour that bears no resemblance whatsoever to the target dynamical system. Here we identify a primary cause of this unreliability: the emergence of excessive, spurious unstable or neutral modes in the closed-loop dynamics. We introduce a simple deterministic input layer design principle that directly addresses this vulnerability by structurally suppressing the emergence of these spurious modes a priori (before training). Our approach dramatically improves robustness to both initialization sensitivity and internal noise, doubling the prediction horizon. Furthermore, we demonstrate on chaotic dynamical systems that this design enables robust estimation of the full Lyapunov spectrum (100\% success rate across 50 seeds), signifying that the autonomous RC faithfully emulates the essential properties of the target dynamical system. This work provides a systematic explanation for a common RC failure mode and offers a concrete design guideline, advancing RCs from heuristic trial-and-error tuning toward a reliable tool for modeling complex systems.

Coherent virtual absorption refers to time-limited storage of optical energy in lossless configurations due to excitation of a complex zero frequency through proper temporal engineering of the incident wave. Given the dynamics underlying the effect and the storage-release mechanism occurring for finite excitation pulses, studying and understanding the associated time dynamics are crucial for enabling future applications. In this work, we carefully investigate this phenomenon in symmetric and asymmetric geometries, shedding light on practical considerations in situations when a closed-form analytical solution is not readily available. Combinations of time domain analysis and spectral filtering are used to enable systematic analysis of these structures. Our approach can be generalized to more complex structures, including multilayered and inhomogeneous cases, providing new opportunities for optimized energy storage and advanced sensing applications utilizing complex-frequency dynamics in lossless designs.

The present work reports, for the first time, the growth of high-quality free-standing InAsSb nanoflags and their electronic properties. Different growth conditions have been explored, and zinc-blende InAsSb nanoflags of various composition have been obtained. In particular, InAs0.77Sb0.23 nanoflags are on average (2000+-180) nm long, (640+-50) nm wide, and (130+-30) nm thick. We show that these nanoflags have a Landé g-factor larger than InAs and InSb and a mobility comparable to those of the best performing InAs and InSb nanoflags. Besides, we show evidence for a surface Fermi level pinning in the conductance band of these InAs0.77Sb0.23 nanoflags, similar to the well-known behavior of InAs. This promises to make InAsSb easy to couple to superconductors, while keeping or improving many of the features that make InSb an interesting material for quantum applications.

Multivariate oscillatory signals from complex systems often exhibit non-stationary dynamics and metastable regime structure, making dynamical interpretation challenging. We introduce a ``dynamical microscope'' framework that converts multichannel signals into circular phase--amplitude features, learns a data-driven latent trajectory representation with an autoencoder, and quantifies dynamical regimes through trajectory geometry and flow field metrics. Using a coupled Stuart--Landau oscillator network with topology-switching as ground-truth validation, we demonstrate that the framework recovers differences in dynamical laws even when regimes occupy overlapping regions of state space. Group differences can be expressed as changes in latent trajectory speed, path geometry, and flow organization on a shared manifold, rather than requiring discrete state separation. Speed and explored variance show strong regime discriminability ($η^2 > 0.5$), while some metrics (e.g., tortuosity) capture trajectory geometry orthogonal to topology contrasts. The framework provides a principled approach for analyzing regime structure in multivariate time series from neural, physiological, or physical systems.

Anisotropic, non-thermal, and multi-temperature distributed particle momenta are commonly observed in collisionless space plasmas, such as the solar wind. Using Liouville's theorem, we argue that anisotropic compression or expansion of the plasma, followed by a relaxation of the resulting anisotropic stress must lead to non-equilibrium states that are either anisotropic, non-thermal distribution functions, different electron and ion temperatures, or a combination of these effects. We present arguments showing that a plasma in thermal equilibrium undergoing anisotropic compression or expansion cannot return to thermal equilibrium in the absence of particle collisions. Since most astrophysical plasmas are practically collisionless and experience significant anisotropic compression or expansion, we expect anisotropic, non-thermal, and multi-temperature particle distributions to be ubiquitous, in agreement with solar wind measurements.

Binding energies (BEs) of adsorbates on interstellar dust grains critically control adsorption, desorption, diffusion, and surface reactivity, and therefore strongly influence astrochemical models of star- and planet-forming regions. While recent computational studies increasingly report full distributions of BEs rather than single representative values, these distributions are typically derived for either bare grain surfaces or thick water-ice mantles. In this work, we bridge these regimes by systematically investigating the BE distributions of water on partially and fully ice-covered dust grain surfaces. We employ machine-learning interatomic potentials (MLIPs) based on graph neural networks to model water adsorption on graphene and on the Mg-terminated (010) surface of forsterite, representing carbonaceous and silicate grains, respectively. The models enable extensive sampling of adsorption sites on water clusters, monolayers, and bilayers generated under both crystalline (thermally processed) and amorphous (low-temperature) growth conditions. At submonolayer coverage, the chemical nature of the underlying grain strongly affects both ice morphology and binding energies, with Mg-O interactions on silicate surfaces producing particularly deep binding sites. From monolayer coverage onward, adsorption on both substrates is dominated by hydrogen bonding within the ice, reducing the influence of the grain material. Across all coverages, amorphous ice structures systematically shift the BE distributions toward stronger binding compared to crystalline ice, introducing highly stable defect and pocket sites. These results demonstrate that BE distributions in the submonolayer to few-layer ice regime are broad and highly surface dependent, and they provide physically motivated input for next-generation astrochemical models incorporating surface heterogeneity.

The Habitable Worlds Observatory (HWO), NASA's next flagship science mission, follows in the tradition of the Nancy Grace Roman Space Telescope and other preceding great observatories. HWO will directly image and characterize Earth-like exoplanet and their atmospheres, with the capability to detect biosignatures and potentially answer the question of whether we are we alone. HWO will also serve as a powerful general astrophysics observatory, enabling breakthroughs in galaxy evolution, stellar astrophysics, and dark matter studies. Currently in pre-formulation, the project has established Exploratory Analytic Cases (EACs), a series of architectural concept designs used to assess the mission's demanding science objectives while exploring challenging engineering parameters. This paper describes the first three EACs, starting with observing strategies and error budget formulation and then progressing to design formulations, trade studies and lessons learned; this paper also discusses the integrated modeling pipeline, a key multidisciplinary system-level analysis capability, and analysis findings as applied to the first EAC. These activities set the stage for the follow on EACs 4 and 5, which will further explore the trade space and prepare for the baseline design that will support the Mission Concept Review (MCR).

Isomerization in molecular systems almost invariably occurs through 3-dimensional motion due to the nature of chemical bonding. Pseudorotation is an unusual type of isomerization that occurs in some high symmetry systems that gives the appearance of rigid-body rotation yet only involves atom rearrangements. This paper demonstrates that pseudorotation occurs in 2-dimensions in an optical matter (OM) system of metal nanoparticle constituents. The difference in dimensionality of the dynamics arises from the electrodynamic field-interference nature of optical binding vs. quantum mechanical bonding in polyatomic molecules. The 8-nanoparticle OM "kite" structure we study in experiments and simulations has D2 (D2h) symmetry and a D4 symmetric transition state. The mechanism for pseudorotation involves correlated motion of all 8 nanoparticles with smooth (continuous) evolution of their interactions and without particles jumping in or out of the OM array. While the OM kite structure only occurs with 10% probability vs. other OM isomers, its rate of pseudorotation is rapid relative to transitions to other structural isomers (e.g., "teardrop"). The other isomers have structures that lie on a trigonal lattice with inter-particle separations at distances that enhance field interference and induced polarizations. Even though the kite isomer has inter-particle separations that would manifest destructive interference on a particle pair (i.e., 2-body) basis, the kite structure is the slowest to rearrange into any other isomer. We show that the unusual structure and dynamics of the kite optical matter system result from N-body interactions and forces demonstrating that N-body effects are important in this class of active matter and presumably more generally.

Extreme mass ratio inspirals (EMRIs) provide unique probes of near-horizon dissipation through the tidal heating. We present a full Bayesian analysis of tidal heating in equatorial eccentric EMRIs by performing injection-recovery studies and inferring posterior constraints on the reflectivity parameter $|\mathcal{R}|^2$ while sampling in the full EMRI parameter space. We find that in the strong-field regime the posterior uncertainties are smaller, indicating a stronger constraining capability on the tidal heating. Using two-year signals with an optimal signal-to-noise ratio (SNR) of $ρ=50$, EMRIs can put bounds on $|\mathcal{R}|^2$ at the level of $10^{-3}$--$ 10^{-4}$ for a rapidly spinning central object. Moreover, we show that neglecting the tidal heating can induce clear systematic biases in the intrinsic parameters of the EMRI system. These results establish EMRIs as promising precision probes for detecting and constraining black hole event horizons.

We compute the entanglement entropy across a Rindler horizon in scalar field theory with Yukawa interaction. Starting from a microscopic scalar-mediator theory in flat spacetime, we integrate out the massive mediator to obtain a quadratic but nonlocal effective kernel that determines the ground-state wavefunctional. The reduced density matrix for a single Rindler wedge is constructed explicitly by tracing over the complementary wedge, allowing the entanglement entropy to be evaluated directly from the kernel without replica or geometric methods. Exploiting translational invariance parallel to the horizon, the problem decomposes into independent transverse momentum sectors that reduce effectively to one-dimensional nonlocal systems and can be diagonalized analytically in the weak-coupling regime. The interaction-induced entropy obeys an area law, with leading corrections controlled by the Yukawa screening mass and logarithmically sensitive to the transverse ultraviolet cutoff, reflecting the localization of correlations near the horizon. Although the modular Hamiltonian depends on the Rindler acceleration, the entanglement spectrum and entropy are independent of this choice, demonstrating the observer-independent nature of vacuum entanglement. Our framework provides a direct and microscopically transparent approach to computing interaction-induced corrections to horizon entanglement using nonlocal effective kernels.

Agent-based models typically treat systems in isolation, discarding environmental coupling as either computationally prohibitive or dynamically irrelevant. We demonstrate that this neglect misses essential physics: environmental degrees of freedom create memory effects that fundamentally alter system dynamics. By systematically transforming linear update rules into exact generalized Langevin equations, we show that unobserved environmental agents manifest as memory kernels whose timescales and coupling strengths are determined by the environmental interaction spectrum. Network topology shapes this memory structure in distinct ways: small-world rewiring drives dynamics toward a single dominant relaxation mode, while fragmented environments sustain multiple persistent modes corresponding to isolated subpopulations. We apply this framework to covert influence operations where adversaries manipulate target populations exclusively via environmental intermediaries. The steady-state response admits a random-walk interpretation through hitting probabilities, revealing how zealot opinions diffuse through the environment to shift system agent opinions toward the zealot mean - even when zealots never directly contact targets.