Papers

We present an empirical calibration of the effective spectral resolution of the JWST NIRSpec PRISM disperser based on a large sample of galaxies drawn from archival observations. Our sample spans a wide redshift range, $0.5 \lesssim z \lesssim 10$, enabling a wavelength-dependent characterization of the PRISM resolving power across the full bandpass using the [O III]$\lambda\lambda4959,5007$ doublets. To ensure robust measurements, we impose strict signal-to-noise requirements on both continuum and emission-line detections. Emission-line profiles are fitted with multi-Gaussian models, and the full width at half maximum (FWHM) is measured for each source. The derived empirical resolution curve shows a systematic offset from the nominal pre-launch estimate, lying between the tabulated instrumental resolution and the point-source shutter limit. We also discuss how source size affects the final spectral resolution, and provide an analytical formula that combines wavelength and morphology dependence in a root-sum-square framework. This work delivers a population-averaged, statistically robust calibration of the effective PRISM spectral resolution for typical galaxy observations. The resulting calibration is directly applicable to statistical analyses of galaxy spectra obtained with NIRSpec PRISM. Our empirical calibration exceeds the pre-launch JDox values by a factor of 1.03 to 1.63, depending on source morphology, and provides the first wavelength and morphology dependent calibration of the NIRSpec PRISM spectral resolution.

We prove the Berezinskii-Kosterlitz-Thouless (BKT) phase transition in the large-distance behaviour of the two-point correlation function of the $\mathsf{Sine}_\beta$ point process for all $\beta>0$, confirming the first term of an asymptotic expansion predicted by Forrester.

When investigating the performance of supercapacitors (SCs), it was found that they offer high-power density and long life but are limited in capacity and energy density. Water-in-salt electrolytes, i.e., 21___m lithium bis(trifluoromethanesulfonyl) imide (LiTFSI), offer a solution by expanding the electrochemical window compared to a diluted aqueous environment. While significant research has been dedicated to improving these electrolytes, the impact of carbon properties on energy storage remains relatively underexplored. This study investigated the influence of carbon structures, defects, and surface functional groups on supercapacitive performance in LiTFSI electrolytes. For this purpose, a series of carbon materials, including carbon nano-onions and porous conductive carbon black (ENSACO), were synthesized and systematically modified by oxidative and reductive thermal treatments. Our results indicated that higher specific surface area and active surface area improve the ion adsorption and capacitance, while specific oxygen functionalities, such as carboxylic and anhydride groups, were found to significantly enhance capacitance. Furthermore, crystallinity and graphitic characteristics play a role in capacitance, with smaller graphene domains and greater disorder promoting ion storage. The study demonstrates a direct correlation between carbon microstructure and electrochemical behavior, providing insights into the rational design of carbon electrodes in high-performance SCs.

We derive approximation rates for Sobolev functions defined on the complex unit sphere and complex projective space, representing quantum states, and on the special and projective unitary groups, representing quantum unitary operations. By using harmonic analysis on compact groups and compact homogeneous spaces, we characterize the corresponding Peter–Weyl decompositions and Sobolev spaces via irreducible representations and the Laplace–Beltrami operator. Combining spectral truncation with finite-depth quantum-circuit approximation, invoking the Solovay–Kitaev theorem, we establish quantitative rates that separate the spectral truncation error from the circuit approximation error.

These notes summarize the content of a one-week course given by Luis Vega on the binormal flow model applied to describe the dynamics of three-dimensional vortex filaments in incompressible fluids following the Euler equations. This topic touches connections between the binormal flow, the Schrödinger map and the 1d cubic nonlinear Schrödinger equation, providing a rich casuistry linked to the physical and numerical phenomenology. This course was part of the $2^{\text{nd}}$ Advanced Summer School held at the Institut d'Etudes Scientifiques de Cargèse (IESC), at Cargèse (Corsica), from $20^{\text{th}}$ to $30^{\text{th}}$ June 2023. These notes have been written and completed by two of its attendees, Elena Danesi and Pablo Merino.

Many deformed nuclei exhibit low-lying rotational bands with negative parity. In mean-field calculations, such bands follow from symmetry restoration when the variational solution spontaneously breaks parity. However, they can also be computed in cases where the breaking of parity is not spontaneous but rather enforced by an octupole constraint. In this paper, we extend these techniques to coupled-cluster computations. We introduce octupole-constrained coupled-cluster theory, which explicitly breaks the parity of the correlated intrinsic state and restores parity and angular momentum as good quantum numbers by projection after variation. We perform coupled-cluster singles-and-doubles (CCSD) computations for $^{20}$Ne using two- and three-nucleon forces from chiral effective field theory. The energies of the positive-parity ground-state rotational band are close to data and depend only weakly on the constrained octupole moment and the Hartree–Fock reference. In contrast, the energies of the negative-parity rotational band are more sensitive to these auxiliary quantities.

Altermagnets combine compensated magnetic order with momentum-dependent spin splitting, which makes them promising for future spintronic and magnonic applications, but their macroscopic responses can be strongly reduced by domain averaging. In hexagonal $\alpha$-MnTe, three symmetry-related in-plane Néel-vector orientations and their time-reversal counterparts form six magnetic domain states, complicating both magnetic structure determination and measurements of the chiral magnon response. Using single-crystal neutron diffraction, we show that an in-plane field applied along the $[-1,1,0]$ direction produces a common magnetic domain orientation that saturates near $\mu_0H=2$ T. A constrained refinement of 61 nuclear and magnetic reflections collected at 2 K and 2 T yields $\mathbf{M}\parallel[1,1,0]$, an ordered moment of $4.62(8)~\mu_\mathrm{B}$ per Mn, and an orientational domain population at $r_A:r_B:r_C=0.97:0.03(5):0$ that is compatible with a nearly complete alignment of orientational domains. Furthermore, an abnormal behaviour in the temperature dependence of the selected reflections is observed in 60-88 K, its microscopic origin is discussed within the context of possible intrinsic piezomagnetic or magnetoelastic effects. Because unpolarized diffraction neither distinguishes domain repopulation from continuous Néel-vector rotation nor resolves time-reversal antiphase domains, the field-prepared state is a near-single-orientation state rather than a truly single-domain state. This non-contact domain control provides a practical complement to strain and field-cooling approaches for preparing well-defined magnetic states in bulk $\alpha$-MnTe.

Anomaly detection, the identification of collision events that deviate from Standard Model expectations, is one of the most promising model-agnostic strategies for new-physics searches at the Large Hadron Collider. Searching for rare events in learned low-dimensional representations removes the need to commit to hand-crafted observables a priori, but sacrifices the immediate physical interpretation those observables provide. For anomaly detection to enter a discovery pipeline, flagged events must therefore be diagnosed locally, both to distinguish potential new physics from mismodeling or detector effects and to identify observables for targeted follow-up searches. We evaluate supervised and self-supervised neural embedding strategies on COLLIDE-2V, a 750-million-event dataset simulated under realistic High-Luminosity LHC conditions, using the reconstruction error of an autoencoder trained in the learned latent space as the anomaly score. Across the configurations and benchmark signals considered, supervised objectives, which exploit simulation process labels during training, generally outperform their self-supervised counterparts and, in their best configurations, improve on an autoencoder trained directly on kinematic features, raising the signal efficiency at 10% background acceptance on all held-out processes. We further introduce a local diagnostic pipeline that fits a Gaussian Mixture Model to the SM latent space, profiles each component through high-level observables without process labels, localizes the flagged events, and ranks observables by their local discriminating power. The ranking recovers the defining kinematics of the held-out processes, while correlating the anomaly score with the same observables quantifies how closely the score tracks them, revealing cases where the most discriminating observable is not the one the score follows most closely.

Saturn's icy moon Enceladus is a prime target in the search for life beyond Earth. Abundant H2 and CH4 in Enceladus's south-polar plume have been shown to be consistent with hydrogenotrophic methanogens feeding on products of long-lived serpentinization. However, thermo-permeability models make sustained core-wide H2 production by serpentinization uncertain. We therefore explore whether an alternative metabolism based on fermentation-driven heterotrophy could reproduce Cassini H2 and CH4 escape fluxes under limited serpentinization. We develop a coupled ecosystem model of fermentation and methanogenesis in a hydrothermal-ocean-plume framework, using glycine as a proxy for amino-acid-like fermentable organics. With effectively no abiotic H2 and CH4 input, purely heterotrophic communities with fermenters and methanogens can reproduce both Cassini-derived flux intervals given sufficient hydrothermal glycine-equivalent supply, while the estimated organic stock could sustain the required H2 production on gigayear timescales if fully available. We then test a scenario with input ranges for fermentable organic matter, H2, and CH4 informed by asteroid-sample measurements and geochemical estimates of limited serpentinization. Simulations within both Cassini-derived flux intervals are concentrated in communities with acetoclastic methanogens; Bayesian model comparison favors the same communities. This holds even allowing for greater abiotic H2 and CH4 inputs that reduce the fermentable-organic supply required to reproduce the plume. We therefore identify fermentation heterotrophy as a testable alternative to purely hydrogenotrophic, serpentinization-dominated biotic interpretations and motivate future plume searches for co-exported fermentation products, including acetate and ammonium, together with measurements of methane carbon isotopes.

Self-consistent numerical simulations of free boundary instabilities, such as resistive wall modes (RWMs) and external kink modes, require special attention given their relevance on the path towards magnetic fusion based nuclear reactors. In this work, a new implementation and a verification benchmark are presented for the nonlinear three-dimensional (3D) full magneto-hydrodynamics (MHD) code SpeCyl, which self-consistently combines a free flow 3D treatment for the edge plasma velocity with double resistive wall thin shell magnetic boundary conditions, capable of capturing the essential features of common setups in experimental devices. Two coaxial resistive shells surround the plasma. Vacuum conditions are assumed between and outside the resistive shells, whose relative positions and time constants can be varied on purpose. The implementation is extensively verified against Shafranov's analytic theory, valid for a circular cross section large aspect ratio tokamak and assuming a flat current profile. The correctness of newly implemented Boundary Conditions (BCs) is confirmed by the good agreement obtained for the linear growth rates of (m=2,n=1) RWM and external kink, over a wide range of wall positions, resistive shell time constants, and for different initial reference equilibria. The (m=2,n=1) RWM growth rates are also benchmarked against previous different RWMs modelling and successful validation studies for RFX-mod tokamak plasmas. The new BCs make SpeCyl a flexible and robust tool to investigate free boundary MHD modes in large aspect ratio magnetic confinement configurations, such as those achieved with the RFX-mod machine and expected in RFX-mod2.

We report a soft-aperture Kerr-lens mode-locked Tm:Y2O3 ceramic laser stabilized by suppressing wavelength competition under high population-inversion conditions. The laser delivers an average output power of 0.5 W with a pulse duration of 112 fs at a repetition rate of 90 MHz and a central wavelength of 2.08 um. Further optimization for shorter pulses yields a pulse duration of 107 fs, through with a reduced output power of 0.22 W. These results demonstrate that spectral-control is an effective approach for achieving stable mode-locking in such laser with strong spectral competition.

Reduced order modeling provides an efficient alternative to high fidelity simulations for the rapid prediction of fluid flows in many query settings. Among non intrusive approaches, Operator Inference (OpInf) enables the identification of reduced dynamical operators directly from data while preserving a prescribed mathematical structure. The present work extends physics structured OpInf to the construction of parametric Reduced Order Models for transient incompressible flows governed by the Navier-Stokes equations. The velocity and pressure fields are represented using separate global reduced bases, and their reduced operators are identified directly from temporal and parametric POD coefficients. The identification procedure is formulated as a sequence of uncoupled least squares problems, leading to an efficient offline stage while avoiding the explicit projection of the governing equations. The approach is first evaluated on a lid driven cavity flow in a periodic regime to assess temporal extrapolation and prediction at previously unseen parameter values. Its applicability is then demonstrated for transient flow through a porous medium with a square obstacle, for which the nonlinear terms are not directly amenable to a standard Galerkin projection. The results demonstrate that the approach can accurately reproduce both velocity and pressure fields while providing computationally efficient predictions for unseen parameter values.

Micro- and nanostructures fabricated by focused ion beam (FIB) milling invariably deviate from input designs due to systematic and stochastic effects such as ablation-rate fluctuations, and redeposition. As such, nominally identical iterations of a given process yield a distribution of non-identical fabrication outcomes, leading to variations in the physical (optical, mechanical) properties of fabricated structures. Existing models of the milling process typically predict a single deterministic fabrication result, neglecting this variability. Here we present a conditional diffusion model that learns the statistical distribution of process outcomes conditioned on input design, enabling it to predict a realistic distribution of possible output sample structures. These predictions accurately reproduce edge morphology, linewidth variations, and defect probabilities observed in fabricated samples, enabling statistical analysis of milling performance and the identification of dominant structural and process parameter-dependent deformation modes. The work establishes a probabilistic modelling framework for fabrication variability analysis and fabrication-aware design optimization.

Efficient simulations of grain growth are essential for investigating the process-structure-property relationships in polycrystalline materials. We introduce a two-dimensional von Neumann-Mullins-based graph grain growth simulation (VN-GraphGG), which couples the grain-scale kinetics with microstructure reconstruction. The polycrystalline microstructure is represented as a graph with grain and junction nodes. The grain-area evolution on the grain graph is governed by the von Neumann–Mullins (VN) law, while a graph neural network is trained to reconstruct the microstructure from the graph. This separation allows kinetic coefficients to be varied without retraining the neural reconstruction model. Topological changes during grain growth such as disappearance of grains and changes in neighbor relations are handled straightforwardly through reconstruction and postprocessing. Simulations demonstrate accurate predictions of VN kinetics and coarsening statistics, and significant speedup over the phase-field simulations. The proposed method provides a foundation for efficient grain growth simulation based on grain-scale kinetics, opening the way to incorporate a broader range of physical mechanisms. An application example is presented for abnormal grain growth, in which kinetic heterogeneity significantly influences the grain microstructure evolution.

Josephson junctions are the central nonlinear element of superconducting qubits, yet their standard description is reached through a chain of approximations. We propose a purely quantum-mechanical model of the junction, formulated directly in Hilbert space: a charged particle moves on a one-dimensional loop, and the junction is a point at which the wave function obeys self-adjoint boundary conditions, labelled by a coupling strength $\lambda$, which controls the transparency of the junction, and a phase jump $\delta$, which plays the role of the superconducting phase difference. For a loop with a single junction we solve the spectral problem and show that the ground-state momentum expectation, identified with the supercurrent, factorises exactly as $\langle\hat{p}_0\rangle = I_c(\delta,\lambda)\sin\delta$. For opaque junctions $I_c$ becomes independent of $\delta$ and the first Josephson relation $I=I_c\sin\delta$ is recovered; for strongly attractive coupling, where the ground state is bound to the junction, the $\sin\delta$ law is also recovered, but with an exponentially suppressed amplitude. For more transparent junctions, however, the model predicts deviations from the pure $\sin\delta$ law: $I_c$ acquires a dependence on $\delta$ already at order $\lambda^{-2}$, compatible with the higher Josephson harmonics recently found necessary to describe superconducting qubit spectra, and a fully transparent contact yields a current linear in $\delta$. When the phase varies in time, a gauge transformation shows that the voltage drop across the junction is proportional to $\dot\delta$, so that the second Josephson relation emerges without being imposed. The boundary-condition approach thus reproduces both Josephson relations from first principles and predicts corrections to them, offering a route towards higher-fidelity models of superconducting qubits.

The Filoche–Mayboroda (FM) localization landscape predicts where the spatially localized modes of a self-adjoint elliptic operator reside without diagonalizing it, but the construction fails for the complex, non-normal Green's matrix G of an open scattering array of electric dipoles. We propose three real-valued, positive landscapes built from the Feinberg–Zee hermitization of G: a regularised Gramian inverse (Gram), the diagonal Schur block that carries the Metz–Neri–Rogers spectral density (SY), and the full hermitized inverse contracted against a polarization-isotropic source pair (Herm). The three form a progression from spectrum-blind to fully hermitisation-faithful, agree in the limit $z\to 0$, and diverge where it matters: at nonzero spectral shift SY resolves modes that Gram collapses, and Herm resolves modes that SY collapses. We demonstrate the progression on random and deterministic aperiodic scattering arrays with a wide range of structural correlations, extend to the vectorial case of transverse electric polarization (TE), and provide rigorous justifications along with mathematical proofs and a complete numerical recipe in the appendices and in the supplementary material.

Specific heat of graphite at high temperatures demonstrates significant deviation from the “3R” Dulong-Petite law. It can be explained by accumulation of defects with formation energy as low as 1.45 eV. Based on the density functional theory calculations, we propose a possible candidate of topological defects, whose energy is comparable to the growth activation energy.

We study the recovery of a three-dimensional Riemannian manifold from the areas of minimal surfaces. We show that areas of minimal surfaces determine the metric on manifolds foliated by minimal surfaces, up to boundary-fixing isometry. This resolves the conjecture on boundary rigidity for least-area data in an exterior formulation, even without topological restrictions on the minimal surfaces. We also prove global rigidity for Einstein metrics on compact three-manifolds with boundary, assuming $\(\pi_1(M,\partial M)=0\)$ and a non-trapping condition. The Einstein result includes the

The $S=1/2$ Kitaev model provides an exactly solvable setting in which spin fractionalization produces itinerant Majorana fermions coupled to an emergent $\mathbb{Z}_2$ gauge field. In realistic Kitaev magnets, however, non-Kitaev interactions break integrability and make their finite-temperature dynamics difficult to calculate. We develop a finite-temperature parton mean-field theory based on the Abrikosov-fermion representation and incorporate the effects of exchange interactions through the random phase approximation to calculate the dynamical spin structure factor. For the pure Kitaev model, the method reproduces a strong, nearly dispersionless peak at a finite low energy together with a broad continuum at higher energies. It also reproduces the momentum dependence of the dynamical spin structure factor and its temperature evolution, including the downward shift of the low-energy spectral weight, in qualitative agreement with continuous-time quantum Monte Carlo calculations. Within the present theory, the contraction of the continuum is traced to the temperature-induced reduction of the bond amplitudes, which narrows the parton bands and compresses the two-parton phase space. For ferromagnetic Kitaev coupling, the energy of the low-energy peak is minimized at the $\Gamma$ point for a ferromagnetic Heisenberg interaction and maximized there for an antiferromagnetic interaction. In the ferromagnetic Kitaev-$\Gamma$ model, the static response develops a finite-momentum softening, indicating an instability toward the period-three magnetic order reported in numerical studies. For uniform-ferromagnetic states selected by a $[111]$ magnetic field, we find that a broad parton continuum coexists with magnonlike dispersive structures, producing spectral features consistent with a spinon-to-magnon crossover.

How can human researchers use artificial intelligence (AI) to make progress on difficult mechanics problems while retaining control of physical significance, mathematical rigor, and validation? We explore this opportunity through a quantitative stability problem rooted in the Eshelby conjecture. For a uniform dilatational eigenstrain in a homogeneous isotropic solid, we establish a nonlinear two-sided relation between whole-interior strain heterogeneity and a geometry-only distance from the ellipsoid family, with half-order Sobolev scaling that captures morphological length scale. We then develop a complementary finite-shape theory: ellipsoidal subtraction yields an exact residual-field budget from which systematically convergent upper and lower bounds are obtained for a prescribed geometry. Applied to protruded, indented, and necked inclusions, the bounds agree with independent displacement-based finite-element calculations. The study also documents a human-led, AI-assisted research process in which researchers select consequential problems, shape targets, audit arguments, require independent checks, and interpret mechanics, while AI broadens analytical exploration and supports computation. This experience also led us to develop Open Problems in Mechanics, a versioned community-facing map that makes selected mechanics questions, their known/open boundaries, and criteria for progress explicit.

Redshift space distortions (RSD) provide a powerful measurement of structure formation in modern cosmology. In this work, we explore the potential of 21cm intensity mapping with the MeerKAT Large Area Synoptic Survey (MeerKLASS) to detect RSD and probe gravity within the redshift range of MeerKAT's UHF band ($0.4<z<1.45$). Using simulated sky observations, we examine the impact from observational effects caused by blind foreground removal and MeerKAT's primary beam, plus $k$-cuts to the measurements. With a multipole expansion formalism, we demonstrate how the quadrupole of the HI power spectrum can be modelled and also show how a foreground transfer function can reconstruct the distorted signal. With 1,115 deg$^2$ of simulated MeerKLASS observations at $0.4<z<1.0$, our proposed analysis pipeline reveals a $6.7\sigma$ detection of an RSD-induced quadrupole relative to one caused by anisotropic observational effects alone. We also explore parameter inference with the full MeerKLASS 10,000 deg$^2$ survey. Within forecasts, we demonstrate how a tomographic analysis across the full UHF redshift range can be leveraged to address certain parameter degeneracies with e.g. the HI bias ($b_{\rm HI}$) and cosmic density of HI ($\Omega_{\rm HI}$). By adopting a redshift-dependent model for the bias and HI density combined with simulation-informed priors, we demonstrate how degeneracies can be alleviated, and indicate the potential for MeerKLASS to reach competitive precision on simple probes of modified gravity.

A particle beam with constant intensity that propagates into a plasma triggers a bump-on-tail instability. It has been demonstrated that beam modulation can be used to significantly reduce this instability. However, finding the optimal beam profiles that accomplish this is a complex optimization problem that suffers from an extremely rugged optimization landscape and a relatively high-dimensional parameter space. In this paper, we evaluate a number of optimization algorithms and find that population based methods (in particular, CMA-ES and Differential Evolution) perform best. In particular, we investigate beam profiles where the beam is either completely turned ON or OFF. This configuration is easier to accomplish experimentally, but leads to a higher-dimensional and thus more challenging optimization problem. We obtain ON/OFF beam profiles that reduce the instability by more than 80% and thus perform significantly better than what has been reported in the literature.

While naively simpler than its higher dimensional cousins, 2d physics still has its own share of puzzles. An example of this is instances of dualities of $(0,2)$ theories, passing all consistency checks save for a mismatch in global symmetry. This is common when said duality can be motivated by dimensional reduction of a duality of 4d $\mathcal{N}=1$ theories where said symmetry emerges in 2d, often from an anomalous 4d symmetry. It is commonly believed that these symmetries are broken in the 2d theory, though often the mechanism for such breaking is not understood. We examine various appearances of this phenomenon and show that in these cases the elliptic genus shows signs of the presence of marginal operators that can break these symmetries. Interestingly, these operators do not appear perturbatively but emerge from the calculation, raising the question of what operators the $(0,2)$ elliptic genus actually counts. This provides a potential mechanism to overcome the discrepancy in symmetry, though we point out some potential subtleties.

Quantum reservoir computing and its special case, quantum extreme learning, are powerful tools for predicting classical discrete time-series with quantum devices. Forecasting continuous signals, however, requires careful discretization as oversampling artificially boosts short-term accuracy but fails over realistic intervals, while undersampling captures too few features and yields poor predictions. Here, we experimentally generate four continuous time-series signals from a chaotic electronic circuit for forecasting with quantum extreme learning. Since simplified circuit models do not fully capture real behaviour, it is unclear whether algorithms that predict simulated time-series also work for experimental signals. We address discretization via two approaches: (i) sampling local peaks/troughs; and (ii) uniform sampling at the autocorrelation timescale. The former captures resonant behaviour, while the latter fully captures chaotic features as well as allowing the reconstruction of chaotic attractors. Our algorithm achieves high forecasting accuracy for both simulated and experimental signals, demonstrating its power for continuous signal processing on near-term quantum simulators, and remains robust against moderate decoherence.

We present GRHD simulations of Bondi-Hoyle-Lyttleton accretion onto a rotating black hole in Weyl conformal gravity, described by the Kerr-Weyl metric. The equatorial flow is evolved for spins a=0.9M and a=0.5M and several Weyl coefficients, which enter through an effective curvature parameter k, and is compared with Kerr runs sharing the same setup. For the k considered, the horizon, ISCO and test-particle frequencies differ from Kerr by at most ~2%. However, the curvature term grows as kr^2 and acts at the accretion radius R_acc. The flow is controlled by one quantity, eta=|k|R_acc^3/M, the ratio of curvature-induced to Newtonian acceleration at R_acc. For eta<~0.03 a stable shock cone forms, indistinguishable from Kerr. For eta~0.2-0.3 the anti-de Sitter-like term traps shocked gas behind the black hole and drives persistent flip-flop oscillations. For eta>~1.6 the cone is destroyed and an oscillating torus forms. Spin modifies only the inner few gravitational radii and does not change the regime. In strongly deviating models the mean accretion rate is 1.7-3 times the Kerr value and varies by 20-60% rms, versus 0.1-1% in Kerr-like flows. The power spectra show global QPOs with fundamentals at 2.9-4.8 Hz for a 10 Msun black hole, with harmonic ladders whose 3:2 and 2:1 ratios come from one non-sinusoidal oscillation, not resonances. These lie in the low-frequency QPO band and are unrelated to test-particle frequencies (~70-200 Hz). They scale as f_0=xi f_K(R_acc), with xi~0.7-1.1 for stable cones and xi~0.23-0.38 for flip-flop and torus regimes. Strong oscillations would be detectable within seconds for a bright Galactic source. The required |k| exceeds galactic-rotation-curve values by 18-36 orders of magnitude, so a detected shock-driven QPO would measure a local deviation from Kerr, and its absence would constrain |k|.

Nanomaterial properties are governed by atomic structure, and driving structural changes on demand could enable materials with programmable properties. Pulsed light is the most controllable route: wavelength, fluence, polarization and pulse timing can each be tuned to inject energy with nanometre and picosecond precision and mould atomic positions. Yet the spatiotemporal dynamics of these transformations have remained hidden, inferred only from before-and-after snapshots. Here we track gold nanorods in a transmission electron microscope under pulsed optical excitation, quantifying the structural transition frame by frame. The rods enter an unexpectedly dynamic regime: material redistributes within and between particles, twin boundaries nucleate and propagate, rods hundreds of nanometres apart reshape in concert, and individual rods almost vanish before recovering their original shape, indicating a structural memory. Driving and following these transformations with pulse-synchronized in situ electron microscopy could turn light-induced reshaping into a tool for setting structure, and consequently properties, on demand.

Surface-enhanced Raman scattering (SERS) is typically achieved by depositing the analyte on a metal or semiconductor substrate. We propose a bottom-up approach, enabling SERS by depositing a metal on the surface of the object being studied. A Mie-theory analysis of electric-field enhancement in dielectric core-shell particles is presented. A correlative approach combining SERS, atomic force microscopy, and scanning electron microscopy is employed to model the conditions required for sensitive SERS detection and spectral analysis of polystyrene microspheres (PMSs) as models of globular and vesicle-like objects. Based on these findings, we propose a SERS-active system that provides strong signal enhancement (up to four orders of magnitude for a single globular object) while maintaining spectral fidelity, where PMSs act both as model analytes and as integral components of a metal-dielectric resonator.

Short-term aftershock incompleteness biases Gutenberg-Richter b-value estimates because many small earthquakes remain undetected immediately after large events. Two strategies address this problem. Earthquake replenishment reconstructs missing events to restore a more complete catalog, whereas robust positive-difference estimators infer the b-value directly from incomplete observations. Whether these approaches provide equivalent information on the magnitude distribution remains open. We address this issue using synthetic aftershock sequences generated with the Epidemic-Type Aftershock Sequence (ETAS) model and degraded through a time- and magnitude-dependent incompleteness model. Incomplete catalogs are replenished using AutoBETA, and b is estimated using the standard maximum-likelihood, b-more-positive, and b-more-incomplete estimators. Replenishment reconstructs the overall structure of the missing-event population and substantially improves conventional maximum-likelihood estimates, although small residual biases remain. By contrast, both positive-difference estimators recover the imposed b-value directly from incomplete synthetic catalogs. Applications to the Ridgecrest, Landers, and Hector Mine sequences show that b-more-incomplete is less sensitive to replenishment than b-more-positive. Ten independent replenishment seeds indicate that seedto-seed variability is small relative to differences among estimators. Replenishment and robust b-value estimation therefore address different aspects of catalog incompleteness: replenishment provides a more complete aftershock representation, whereas robust estimators are preferable when accurate b-value estimation is the primary objective.

The nitrogen-vacancy (NV) color center in diamond has found applications in magnetometry across various geometrical scales. In particular, vector magnetometry using hand-held devices with low power consumption is of interest for mobile and geological applications. However, such a device requires efficient driving of the four different orientations of the NV center, thus placing demands on a broadband microwave resonator that provides a uniform and highly insensitive microwave magnetic field at the resonance frequencies of the NV centers. Here, we propose a unique planar resonator design that enables efficient driving of all four orientations of NV color centers over a 100 MHz range with only 0.5 W of microwave input power and 250 mW of laser power, allowing vector magnetic field measurements. Using the planar resonator to drive NV centers in diamond, a compact vector magnetometer was fabricated and experimentally studied. The sensitivity of the device is about \[{1.2\text{ nT}}/{\sqrt{\text{Hz}}}\;\]. While this is not a record sensitivity for diamond-based magnetometers, this device occupies its own niche as a compact hand-held magnetometer.

We show how the covariant phase space Hamiltonian can be constructed in theories in which some of the fields have gauge freedoms. The addition to the action of boundary terms that guarantee the well-posedness of a variational principle and its exact gauge invariance for the prescribed boundary conditions turn out to be essential in this construction. The same ingredients are essential in the use of the action in the framework of Gibbons and Hawking's Euclidean approach to Quantum Gravity and we show how the on-shell action constructed with a Lagrangian with those properties can be expressed in terms of the covariant phase space Hamiltonian. We compute the Hamiltonian of several theories, one of which has Chern–Simons terms, using our prescription and show that, in black-hole backgrounds, the Hamiltonian gives directly the quantum statistical relation including terms related to non-gravitational charges and chemical potentials, as expected.