Papers

Dark Energy Spectroscopic Instrument (DESI) Collaboration reported an evidence for dynamical dark energy model using baryon acoustic oscillation (BAO) measurements from DESI Data Release 2 (DR2), cosmic microwave background (CMB), and three Type Ia Supernovae (SNe) data (Pantheon+, Union3, and DES-SN5YR) recently. The significances are from $2.8\sim4.2\sigma$ depending on which SNe sample is used, and the highest significance is obtained based on the DES-SN5YR data. But more recently, Dark Energy Survey (DES) Collaboration recalibrated the DES-SN5YR data, and a significance of only $3.2\sigma$ is obtained based on the calibrated data, which is called DES-Dovekie. In this work, we perform a comprehensive analysis of five representative dynamical dark energy parametrization models, including Chevallier-Polarski-Linder (CPL), Barboza-Alcaniz (BA), exponential (EXP), logarithmic (LOG), and Jassal-Bagla-Padmanabhan (JBP), using DESI DR2 BAO, Planck CMB, ACT DR6, and SNe data from Pantheon+, Union3, and DES-Dovekie. Similarly, a significance of around $3\sigma$ is obtained based on the DES-Dovekie data. And the significances are $2.1\sim3.7\sigma$ for different SNe samples and different parametrization models, indicating an evolutionary dark energy dynamics. Furthermore, a phantom-like behavior in the past and has transitioned into quintessence-like behavior today for the dynamical dark energy is preferred.

Effective and thermodynamic derivations of gravity theory in the context of generalized entropy can give rise to multiple, and potentially varying, gravitational couplings, raising the question of which quantity should be identified with the physically measured Newton constant. Motivated by this ambiguity, we formulate the notion of gravitational susceptibility of the horizon. In spacetimes containing multiple horizons, if the entropy is a function on a multi-horizon state space, $S=S(A_1,A_2,\cdots,A_n)$, then the derivatives of the entropy with respect to individual horizon areas define the horizon gravitational response coefficients. Different horizons may possess distinct gravitational responses without requiring multiple fundamental gravitational couplings, analogous to direction-dependent response coefficients in macroscopic media. This framework naturally suggests the possibility that horizon entropies (such as the cosmological apparent horizon and a black hole horizon) can influence one another, which could explain the black hole-cosmology coupled "black hole mass growth" previously discussed in the literature.

We study scalar and electromagnetic perturbations of the root–Kerr object. Its source-free exterior carries the electromagnetic field obtained in the vanishing-mass limit of the Kerr–Newman black hole, while its physical source is a rotating disk with an essential distributional rim contribution. For a massless charged scalar, the minimally coupled Klein–Gordon equation separates into a confluent-Heun (CHE) radial equation with a direct coupling between the scalar and background charges. We give the associated Nekrasov–Shatashvili (NS) dictionary and show that the disk is an ordinary radial point. For electromagnetic pertubations, we derive the equations for the radiative Newman–Penrose Maxwell scalars directly. They have the same confluent-Heun structure, but a Maxwell perturbation has no bulk coupling to the background potential. Charge-dependent photon scattering data therefore cannot be fixed by the exterior equation alone. We formulate the disk/rim boundary-data problem, identify the response data required to convert exact CHE/NS connection coefficients into a physical scattering matrix, and show how all-spin helicity-flip Compton information constrains the response on the radiative subspace. The analysis separates exact exterior propagation and amplitude matching from the additional source dynamics required for a microscopic response.

Near-critical null geodesics generate image-order channels with distinct mappings, delays, and weights, so one observer-time data set samples multiple source epochs. We organize the inverse problem by historical reach, dimension, and held-out recovery. Using continuum-noise-consistent, matrix-free Kerr/Schwarzschild operators for orders n = 0, 1, 2, we audit 12 spin-inclination geometries. At normalized SNR0 = 100, resolved orders extend anchor-connected history beyond direct imaging in all cells by 40 M, 24 M, and one 4 M grid step at inclinations 20 degrees, 50 degrees, and 75 degrees. Operational rank rises from 153 to 202 and old-age innovation from 8.29 M to 40.72 M; complete order summation retains only 9.66 M. Compact temporal support gives an exact result: temporal basis functions outside the direct retarded-time footprint generate identically zero direct columns, while higher orders can lift those old-epoch blind blocks. Direct old-structure rank is zero, while resolved orders support 38, 93, and 185 operational directions across three classes. Yet source enrichment leaves reach nearly fixed while rank fraction and singular-value stability collapse; this is the Mahakal phenomenon. Held-out tests show bounded benefit, not a movie. Resolved orders extend stable baseline-inclusive emissivity-level span from 48 M to 80 M, but that endpoint is level dominated. On 60 histories, physical morphology error falls by 13.3 percent with TSVD and 16.4 percent with ridge. Reliable two-feature recovery fails and the stable morphology interval is zero. Complete order summation yields no material morphology gain. The positive results are therefore best-case, known-geometry image-domain benchmarks under ideal order separation; SNR0 is an operator normalization, not an observation.

Black holes hold considerable fascination for the general public and students alike, and are commonly included in general-science courses on astronomy or modern physics. But teaching about the basics of black holes poses a considerable challenge: Any rigorous description requires concepts and techniques from general relativity, Einstein's theory of geometry and gravitation. And any half-way rigorous introduction to that theory, including the required mathematical tools, is significantly beyond the level of general-science courses. Inevitably, accounts of relativistic physics at the introductory undergraduate level make use of analogies, approximations and simplified models to teach about topics like black holes, gravitational waves, gravitational lensing, or cosmology. The purpose of this article is to given an account of one particular set of analogies for teaching about black holes, all of which are based on modelling the motions of observers in the vicinity of the black hole and rely on the concept of (relative) speed to describe properties of the black hole. While most elements of what I am about to describe can be found in the existing literature, I am not aware of any text that attempts to pull them together into a unified picture at a suitable level of presentation for undergraduate-level teaching; that is the goal of the present text.

We study a phenomenological fixed-ADM prescription for an extended-uncertainty-principle (EUP) correction to the Schwarzschild exterior. Rather than treat mass constancy as a consequence of the EUP, we hold the charge measured at spatial infinity fixed so that we can compare exteriors at the same physical mass. We impose the EUP-shifted horizon radius and Hawking temperature and select the slowest admissible inverse-power representative. Its Einstein tensor defines an effective anisotropic tensor, but we do not infer an independent matter model, quantum state, or perturbation theory from that definition. For a positive EUP parameter, the effective energy density is negative throughout the exterior, while the transverse null and strong energy conditions fail over an extended radial region. The area entropy and the formal entropy obtained from the reduced relation $\mathrm{d}M=T\,\mathrm{d}S$ disagree at leading order; we interpret this mismatch as evidence that the thermodynamic description lacks work terms or an action-based entropy law, not as a definite EUP entropy correction. An explicit one-parameter family with the same mass, horizon, and temperature shows that the orbit, lensing, and test-field eikonal shifts depend on the chosen representative. Our results therefore characterize the minimal member of a phenomenological family rather than universal consequences of the EUP.

The XENONnT Collaboration recently reported a new measurement of the solar \(pp\) neutrino flux via neutrino-electron elastic scattering using a multi-ton-scale liquid xenon detector. We comment here on an important omission in their summary plot.

This paper discusses the metaphor, originating with Whittaker, according to which Maxwell's model of molecular vortices was the scaffolding by means of which the equations of electromagnetism were erected, removed afterwards without consequence for the building. It is argued here that the metaphor describes badly what happened, and it is shown that whoever upholds it today transcribes, within that same text, the quotations that belie it. The paper examines what was actually removed in 1865, which materials of the model still operate inside the equations under other names, and proposes in its place the figure of the template, taken from sheet-metal layout work. The discussion is situated with respect to the work of Daniel M. Siegel and Cameron Lazaroff-Puck, who maintain from the history of science the constitutive character of the model. It closes with a criterion for telling one case from the other.

Predicting the thermodynamic properties of an alloy requires sampling its configurations across many chemical conditions and recovering free energies on a common absolute scale. Markov chain Monte Carlo (MCMC) is the standard tool, but it requires separate simulations at different conditions, and auxiliary free-energy methods such as thermodynamic integration are used to place results on a common absolute scale. Modern discrete neural samplers typically use reverse KL divergence as the objective and can be mode-seeking or biased. We present Free energy Offering Generative Sampler (FrOGS), a hybrid discrete neural sampler that couples an autoregressive model to a continuous-time Markov chain (CTMC) to be trained jointly under a single shared loss. FrOGS draws i.i.d. configurations, returns an unbiased estimate of the partition function, and gives consistent estimates of thermodynamic observables. We train a single model across a wide range of chemical conditions to produce estimates on a common absolute free-energy scale. FrOGS matches exact finite-size results on the 2D Ising model and reference phase diagrams for AgPd and CuAu, without mode collapse. We additionally compare to SEGAL, a published autoregressive baseline, and find that only FrOGS recovers the stability range of the CuAu$_3$ phase.

Anisotropies in two-dimensional materials are responsible for a variety of effects that significantly modify their physical properties. In this paper, we present a covariant modification of the Dirac equation that incorporates anisotropies into the effective low-energy description around the Dirac point. The model is constructed by analogy with a Lorentz-violating extension of the Standard Model of elementary particles and yields a (2+1)-dimensional framework describing physical effects such as shifted, tilted, and distorted Dirac cones through its free parameters. The proposed model thus provides a general and unified framework in which distinct anisotropy-induced modifications of the Dirac spectrum and their combinations can be systematically characterized. We further apply the model to strained graphene and show that its effective parameters can be quantitatively extracted from first-principles electronic band structures while retaining a direct geometrical interpretation. These results establish a connection between the microscopic electronic structure and a material-independent effective description of anisotropic two-dimensional Dirac systems.

High-order CFD simulations provide detailed resolution of the heterogeneous interstitial flow in pebble-bed reactors, but their computational cost is high, especially during the initial flow-development period required to reach statistically stationary conditions. This work investigates the use of a Super-Resolution Graph Neural Network (SR-GNN) to improve the initialization of high-order NekRS simulations. Lower-order P = 2 velocity fields are used as inputs to reconstruct higher-order representations, which are then used as initial conditions for P = 7 restart simulations. The approach is evaluated using a 146-pebble bed at Re = 1000, Re = 2500, and Re = 5000, with pressure-drop convergence used as the main figure of merit. The SR-GNN models were trained using paired low- and high-order snapshots and were first evaluated through qualitative inference comparisons. High-order restart simulations showed that, for Re = 1000 and Re = 2500, the SR-GNN initialized cases produced pressure-drop histories similar to direct restarts from true P = 2 fields. For Re = 5000, however, the super-resolved field restart approached the statistically stationary P = 7 pressure-drop range faster than both the direct P = 2 restart and the reference P = 7 simulation initialized from a uniform velocity field. The trained Re = 5000 model was also applied to a larger 1568-pebble bed, demonstrating qualitative applicability of the workflow to a significantly larger packed-bed geometry. These results indicate that SR-GNN-based initialization is a promising strategy for reducing high-order flow-development cost, while also motivating further work on broader Reynolds-number and geometry generalization.

We define the notion of superintegrability of a Hamiltonian system on a stratified symplectic space. We focus on spin Calogero-Moser-Sutherland (sCMS) systems, where the phase space is a stratified symplectic space obtained by the Hamiltonian reduction of the cotangent bundle over a compact Lie group, and demonstrate that the sCMS systems for $SU(3)$ are superintegrable.

One-dimensional quantum–classical hybrid walks are introduced by combining quantum and classical steps under periodic and random protocols. Exact diffusion coefficients are derived for both protocols throughout the diffusive regime. The exact results show how diffusion depends on the average frequency and temporal arrangement of classical steps. As the average frequency approaches zero, both diffusion coefficients diverge inversely with the frequency. At matched average frequencies, the ratio of the diffusion coefficients for the random and periodic protocols approaches two. A finite-mode approximation is also developed and tested against the exact diffusion coefficient for the random protocol. The exact results for the one-dimensional model provide a foundation for future studies of more general quantum–classical hybrid walks.

In this study, we aim to theoretically investigate antipolar cell-cell adhesion, in which adhesion sites are located on the opposite side of the leading edge of migrating cells, as a candidate for irregularly polarized adhesion that induces disorder in collective cell migration. We employ the cellular Potts model to simulate the effects of antipolar adhesion on collective migration driven by cell motility. Antipolar adhesion induces a collective motility disorder, which exhibits a disordered configuration in the motility direction, even when collective motion occurs in the absence of adhesion. Consequently, antipolar adhesion inhibits collective migration. The effect is in contrast to that of polar adhesion, which accelerates the directional intercellular order of cell motility. At a specific motility strength, a depinning transition emerges from a collective motility disorder to a collective motion. The collective motility disorder can be physically explained by the cooperative effect between antipolar adhesion and motility persistence within the mean-field approximation.

Theoretical models of the effective interactions between microwave-shielded polar molecules typically rely on the assumption of pairwise additivity. In this work, we test the validity of this pairwise approximation by evaluating the exact many-body potential energy for configurations of three and four molecules. We demonstrate that this approximation fails for molecule configurations in the plane of the circularly polarized microwave field. In these planar geometries, many-body effects significantly increase the depth of the attractive potential well and shift the position of the repulsive barrier. Although out-of-plane interactions in three-dimensional arrangements weaken this non-additivity, the effective interactions between microwave-shielded dipoles are not a simple sum of pair interactions. In particular, their attractive well, crucial for self-bound clusters, can only be described by the full many-body potential energy.

Here we present a detailed theoretical investigation of the environment-induced interaction correction to the ground states and addition energies in a short superconducting junction. We derive general formulas for these corrections linking them to the frequency-dependent admittance of the junction and the environment impedance. We specify two environmental models: that of an external impedance and the Mattis-Bardeen model. In both cases we assume $G_Q Z \ll 1$, where $G_Q$ is the conductance quantum and $Z$ the typical environmental resistance. While an expectation is that the typical scale of the relative correction is of the order $G_Q Z$, we have found that in many cases the dimensionless scale $L G_Q \Delta$ provides a better estimation, where $\Delta$ is the superconducting energy gap and $L$ an external inductance. We discuss the logarithmic renormalizations of the energies by low- and high-frequency phase fluctuations and single out a non-superconducting contribution related to the renormalization of transmission eigenvalues. We investigate in detail the peculiarities of the corrections at: i. small phases, where the interaction causes a current jump, ii. Andreev bound state energies close to the gap edge, iii. Andreev bound state energies close to Fermi level. In all these cases the correction may become comparable with the unperturbed energy, even for $G_Q Z \ll 1$, and we briefly sketch non-perturbative models relevant for the situations.

Metadynamics is a class of enhanced-sampling methods that is widely used in molecular modeling. Here, we consider metadynamics with a one-dimensional collective variable and explore the limitations of using Gaussian hills in light of existing convergence results. We reduce the corresponding evolution problem to a replicator-type differential equation and analyze its long-time behavior. For a periodic collective variable, we prove the convergence of metadynamics. However, the situation is fundamentally different when the collective variable is defined on a finite interval. In this case, the stationary solution only exists in the weak sense as a finite atomic measure. The solution to the differential equation weakly converges to it. Consequently, metadynamics is ineffective due to the absence of a clear quasi-stationary state. A quasi-stationary, transient solution can only be captured in the "INTERVAL" framework if the free energy outside the finite interval remains nearly constant across sufficiently large regions compared to $\sigma$.

Understanding how individual protection and population density influence epidemic spreading remains a central challenge in epidemiology. While classical compartmental models successfully describe the temporal evolution of epidemics, they do not explicitly account for the microscopic motion and spatial organisation of individuals. Here, we investigate contagion dynamics using an agent-based model of Active Brownian Particles (ABPs), where self-propelled agents interact through local contact and a prescribed fraction of the population is protected. By systematically varying the protected-agent fraction and the population density, we identify two distinct contagion regimes separated by a crossover protection of approximately $30\%$. Below this threshold, the maximum contagion rate follows the universal scaling $\nu_{\mathrm{max}}\propto\phi^{1/2}$, indicating that disease transmission is governed primarily by frequency of encounters. Above the threshold, universal scaling is lost and protection becomes the dominant mechanism controlling epidemic spreading, with the strongest suppression occurring in low-density populations. These results demonstrate that microscopic active-matter models provide a powerful framework for investigating epidemic dynamics beyond the assumptions of well-mixed population models and reveal how spatial organisation and individual protection jointly determine contagion dynamics.

Electron-phonon interactions govern carrier dynamics, transport, and many optical and quantum phenomena in solids, but finite-displacement calculations within the projector augmented-wave (PAW) framework require up to 6N self-consistent supercell calculations for an N-atom system, making them costly for large, low-symmetry, and disordered materials. We introduce MLLocP, a machine learning strategy that learns the self-consistent local potential on real-space grids and supplies its displacement derivatives to the evaluation of all-electron PAW electron-phonon matrix elements. Analysis of the PAW decomposition identifies the local-potential contribution as a natural target for machine learning, while the remaining PAW quantities are retained within the established VASP/PHELEL framework. Using targeted grid-point sampling and feature-diversity selection for efficient model training, we validate MLLocP for elemental Cu, polar BAs, and chemically disordered Cu3Au. The learned potentials and their resulting displacement derivatives closely reproduce direct density functional theory calculations, yielding transport coefficients within approximately 3.5% for BAs mobility, 8% for Cu conductivity, and 11% for Cu3Au conductivity. For the 32-atom Cu3Au special quasirandom structure, using 40 sampled configurations reduces the number of self-consistent calculations by about fivefold relative to the 192 displaced structures required directly; using 20 or 10 configurations increases the acceleration to approximately 10- and 20-fold, with conductivity errors of 14.3% and 18.6%, respectively. MLLocP thus provides a scalable route to PAW electron-phonon calculations in complex materials while preserving the underlying all-electron formalism.

Precise calibration of LIGO's strain readout plays a vital role in our ability to extract information from gravitational-wave detections. We present a comparative analysis of two independent direct-force calibration methods: the Newtonian Calibrator (NCal) and Photon Calibrator (PCal) systems. Currently, LIGO relies solely on the PCal system as its absolute calibration reference. To facilitate comparison, a series of direct-force injections were performed using the NCal and PCal systems at the LIGO Hanford observatory just after the completion of the third observing run. We compute the ratio of the measured to expected strain amplitudes for every injection. For each injection frequency across a 30 Hz band, the ratios of NCal and PCal differ by $\sim$0.5-1%, demonstrating a small systematic difference between the two systems. This offset highlights the importance of maintaining multiple independent calibration references to validate LIGO's calibration.

Reliable attractor recall conventionally requires broad basins of attraction. However, in reservoir-computing based associative memory, temporal cues reliably recover dynamical memories despite basins dominated by unpredictable, riddled-like regions. We reveal that memory basins exhibit an “octopus-like” structure: a robust “head” near the attractor and thin, intertwined “tentacles” spanning state space. Initial states in tentacular regions yield near-zero uncertainty exponents, making the recalled memory effectively unpredictable at finite precision. Yet, cue-driven generalized synchronization bypasses this unpredictability, driving the system into the robust basin head. This mechanism yields a quantitative relation linking minimum cue duration, synchronization rate, and basin-head radius. Trained recurrent neural networks exhibit similar geometry, suggesting this phenomenon extends beyond reservoir computing.

Machine-learned interatomic potentials have enabled highly accurate atomistic simulations, but extending these capabilities to coarse-grained systems remains challenging due to the loss of geometric and orientational information during coarse-graining. In this work, we present a generalized framework for incorporating molecular geometry and directionality into coarse-grained machine-learned potentials through two complementary approaches: anisotropic density-based descriptors (AniSOAP) and symmetry-adapted equivariant message-passing neural networks (MACE-CG). Using Gay-Berne particles and coarse-grained representations of benzene, formamide, and water, we demonstrate that explicitly retaining molecular anisotropy substantially improves the prediction of energies, forces, and torques relative to isotropic representations. AniSOAP provides an effective linear baseline when molecular shape is well approximated by ellipsoidal symmetry, while symmetry-adapted MACE-CG enables the incorporation of arbitrary molecular point-group symmetries. For water, whose orientational degrees of freedom are poorly represented by ellipsoidal descriptors alone, symmetry-adapted rigid-body features improve energy, force, and torque prediction by resolving orientational degeneracies inherent to isotropic and moment-of-inertia-based representations. These results show that information loss in coarse-grained modeling is governed not only by mapping resolution but also by the symmetry and geometric information retained in the representation, providing a systematic route toward more expressive and transferable coarse-grained machine-learned potentials.

The physical mechanisms controlling the onset of gravity-driven spreading of elastoviscoplastic drops remain unclear because yielding, elastic deformation, and viscous dissipation can occur simultaneously in different regions of the material. To isolate these mechanisms, we perform direct numerical simulations of large axisymmetric drops resting on a thin precursor layer of the same material. Each drop is first equilibrated under gravity, $g$, before being subjected to an additional constant downward acceleration of varying magnitude. The drop rheology is described by the Saramito–Herschel–Bulkley model. Once the rate of deformation becomes negligible under the enhanced forcing, the additional acceleration is removed and the drop is allowed to relax again under gravity alone, enabling the global deformation to be separated into recoverable and unrecoverable components. We find that spreading does not occur simply because some portion of the material yields locally. At low imposed accelerations, yielded regions remain confined within a predominantly solid-like viscoelastic matrix, and the resulting deformation is mostly recoverable. Appreciable spreading is observed when these yielded regions connect to form a continuous fluidised pathway from the drop interior to the free surface, allowing radial material transport. The acceleration required for this transition is governed primarily by the yield stress, whereas elasticity controls the partition between recoverable deformation and spreading by regulating the extent and spatial distribution of yielded regions.

We present a cohesive framework for simulating seismic wave propagation utilizing quantum computing paradigms and their classical tensor network equivalents. We detail a quantum circuit-based formulation for the explicit finite-difference time-domain (FDTD) solution of the two-dimensional acoustic wave equation and map this quantum architecture onto a tensor train representation, namely for Matrix Product State (MPS). The MPS solver enables deterministic simulation of large-scale wavefield dynamics on classical high-performance computing systems. We demonstrate the MPS representation by computing 2D seismic wavefields on the Marmousi model. Our results indicate that the MPS representation is a viable direction for computing and scaling wavefield propagation.

A powerful probe of the physical processes that quench star formation in dense environments is determining where within galaxies star formation is suppressed. At high redshift, the spatial resolution of multi-band imaging limits such measurements. We use deep-learning-based deconvolution to recover spatially resolved optical and near-infrared photometry for galaxies in nine GOGREEN clusters at 1<z<1.4, using customized models trained on HST and JWST imaging. Using resolved rest-frame UVJ colors, we classify galaxies by the star-forming states of their inner and outer regions into predominantly star-forming, predominantly quiescent, inside-quenched, or outside-quenched. We find that 24% of galaxies classified as quiescent from their integrated colors retain significant star formation. The predominantly quiescent fraction increases with stellar mass and is higher in clusters than in the field while the cluster quenched fraction excess is, when limiting to predominantly quenched galaxies, approximately 20%. Contrary to previous GOGREEN studies using integrated colors, we find this excess to be independent of stellar mass, demonstrating that partially quenched galaxies can bias measurements based on integrated colors. Among galaxies retaining significant star formation, outside-quenched galaxies are substantially more common than inside-quenched galaxies and have a fraction excess of (22.8+/-5.8)% in clusters relative to the field at low masses. This provides evidence that clusters preferentially suppress star formation in the outskirts of low-mass galaxies. Our results demonstrate the importance of spatially resolved classifications for interpreting environmental quenching at z~1 and the potential of deep-learning-based deconvolution to recover such information from large ground-based imaging datasets.

Fault-tolerant quantum computation fundamentally relies on encoding a logical qubit into a structured block of physical qubits, typically in the tens to hundreds. As a trade-off for improved fault-tolerance, logical gate operations will incur a linear overhead in terms of both the amount of time and quantum resources than before. For example, in monolithic quantum computing, performing a gate operation on two distant logical qubits will first require using a linear number of SWAP operations in order to move the logical qubits next to each other; while in distributed quantum computing, doing so will first require a linear number of ancilla qubits in order to form entanglement connections (or a logical Bell pair). In this paper, we focus on significantly reducing the overhead involved in logical CNOT operations, a fundamental primitive. We propose NOBOL, a novel approach that requires only one Bell pair to perform a logical CNOT operation on two distant qubits encoded in arbitrary CSS codes. More importantly, NOBOL only requires performing gate operations on the logical X or Z operator subsets of the logical qubits. For many CSS codes, such as the surface code, these subsets are significantly smaller than the size of the code itself. In this paper, we describe various circuit realizations of NOBOL, including a depth-optimal circuit with logarithmic depth in terms of the size of the logical operators. Finally, we propose effective methods to contain error propagation without incurring much additional overhead. Since NOBOL can be effectively applied to a wide range of quantum error-correcting (QEC) codes and, in addition, is agnostic to qubit modalities and effective for various architectures, including those based on either a monolithic QPU or distributed QPUs.

Theoretical models suggest that the adiabatic growth of a black hole immersed in dark matter can lead to the formation of high density regions of dark matter, known as “spikes”, near the black hole event horizon. The density of these spikes is determined theoretically and observationally to be a power law of the form $\rho(r) \propto r^{-\gamma_\text{sp}}$. It has been shown that the spike can potentially have a detectable impact on the emitted gravitational waves and shadow radius of the central black hole. In this work, we model the dark matter spike using a real scalar field with a non-standard potential. More specifically we “reverse engineer” the equations of motion to find a potential for the scalar field that permits a solution to the equations of motion with desired energy density and reasonable background geometry. We show that the emerging geometry is testable. In addition, the fact that the solution is derived from a covariant action makes it possible to study gravitational perturbations of the black hole in the presence of a spike including the backreaction of the spike.

Next-generation X-ray optics will revolutionize high-energy astrophysics, yet they present several challenges to design a complementary focal plane. In particular, the focal surface is curved, requiring many small, flat sensors to achieve a large field. We present work building on MIT Lincoln Laboratory technology to curve the sensor itself, improving image quality and reducing complexity. Applying this technology to back-illuminated, large-format CCDs having well-characterized X-ray response, we describe the process and report success curving functional BI CCDs to a 2.5-m radius of curvature, achieving RMS curvature deviations less than 1 micron. We confirm that there is no appreciable increase in dark current and that the spectroscopic performance across the 0.3-6 keV band remains excellent. These results demonstrate that curved, large-format X-ray sensors are realizable, and the process can be extended to silicon detectors with other architectures, including active pixel sensors.

We present the optimized version of ViSta, a visibility-domain stacking method that combines interferometric observations in the Fourier domain from radio to sub-millimeter wavelengths. By stacking visibilities directly and transforming them into the rest frame, ViSta enhances the signal, suppresses noise, and improves image reconstruction through extended uv-coverage. ViSta outperforms image stacking when individual sources are too faint to detect, achieving higher SNR in the low-signal and extended regime. This new optimized version features a C++/OpenMP kernel which replaces CASA demanding functions, enabling GPU acceleration while minimising memory usage and intermediate data products. The method is highly flexible, allowing stacking regardless of array configuration, spectral setup, or telescope. In the SKA era, where visibilities will not be routinely preserved, ViSta helps in assessing the information lost in the image-plane transition and in exploiting the vast amount of data still stored in interferometric archives.

In this paper, we present a model for classical and quantum reference frames for $C^*$-algebras by utilizing Hilbert modules and equivariant correspondences of $C^*$-dynamical systems. We show the classical model provides a commutative baseline for the quantum model, and we discuss how the quantum model encodes the Hilbert space operational formulation of quantum reference frames. We also include various explicit examples for both classical and quantum reference frames.