Papers

Quantum error-correction decoders are typically benchmarked against synthetic circuit-level noise, under the assumption that a decoder's ranking under such noise transfers to hardware and improves as the noise model becomes more realistic. The Willow processor, the first to operate below the surface-code threshold, allows us to test this assumption. We rank a panel of six decoders using a four-rung ladder of noise models with increasing fidelity, evaluated against real data across three code distances, two bases, and fifteen round counts. Rank agreement with hardware appears once the noise model gives each operation type its own error rate. Calibrating the model to the device improves absolute error rates but not rank agreement. We additionally provide the first independent evaluation of NVIDIA's Ising pre-decoder on hardware, at code distances below its training receptive field and via a mapping onto the lattice on which it was trained. Under these conditions, it holds no accuracy-latency advantage: another panel decoder matches or improves on it in both per-cycle error rate and decode latency in 278 of the 280 evaluations. We release the full pipeline and the per-shot outcome of every evaluation, so future decoders and devices can be compared.

We report the first soft x-ray photoelectron spectroscopy (XPS) measurements of Ar and Kr clusters formed inside superfluid helium nanodroplets (HNDs) through consecutive pickup of dopant atoms. Ar and Kr atoms and clusters are selectively inner-shell ionized (Ar 2p, Kr 3d) using monochromatic soft x-ray synchrotron radiation. In the regime of strong doping, the electron spectra exhibit features characteristic of free Ar and Kr atoms as well as their clusters. The Kr cluster spectra agree well with literature data for bare Kr clusters. Backed by detailed simulations of the pickup process, this agreement indicates that the observed spectra originate from nearly bare Ar and Kr clusters, from which most or all He has evaporated in the course of cluster aggregation. These results establish HNDs as a platform for XPS of various types of molecular complexes and nanostructures.

Dynamic self-consistent field theory (DSCFT) provides an efficient continuum framework for studying structure formation in inhomogeneous polymer systems, but its predictive accuracy depends on the choice of the nonlocal mobilities. Here, we construct mobility functions for moderately and strongly entangled homopolymer and diblock copolymer systems from the relaxation dynamics of single-chain structure factors, based on molecular dynamics (MD) simulations of the Kremer-Grest model and analytical reptation theory. Single- chain mobilities are combined such that the resulting DSCFT accounts for the dependence of fluxes on local chain densities. The theory is then applied to the spinodal decomposition of symmetric homopolymer blends and diblock copolymer melts following a quench into the (micro)phase-separation regime. Predictions of DSCFT are systematically compared with MD simulations. Mobility functions derived from single-chain dynamics are found to reproduce the kinetics of structure formation more accurately than conventional Debye-type mobilities. We additionally investigate the influence of adding stochastic currents (noise) that are correlated according to the fluctuation-dissipation relation. At low noise levels, they enable the generation of equilibrium initial states and facilitate defect annealing. At high noise levels, however, nonolinear effects lead to discrepancies between DSCFT and MD simulations.

This work provides evidence that established error field penetration threshold scalings remain applicable during plasma current ramp-up. In dedicated DIII-D experiments with imposed $n=1$ perturbations during extended $I_p$ ramps, an apparent empirical threshold is found between $2$ and $3$~kA of applied 3D coil current, above which MHD modes are seeded. The imposed perturbation couples to the rational surfaces present during the ramp, seeding near the $q=4$ surface and penetrating as an $m/n=3/1$ mode by the end of the perturbation phase. To interpret these observations, multi-machine penetration threshold scalings are combined with equilibrium-based overlap metrics from the GPEC code, including the in-situ error fields of the device. This modeling reproduces the observed onset in the amplitude scan and classifies mode seeding across a database of 12 ramp-up discharges spanning a range of plasma currents and densities. Across this database, the seeding appears to be controlled primarily by the applied 3D coil current rather than by the plasma current or its ramp rate. Accounting for the in-situ error fields is found to be important for reliable prediction. These results are consistent with the robustness of scaling-based penetration metrics when coupled to detailed 3D field modeling under transient ramp-up conditions, and suggest the importance of accounting for in-situ error fields when assessing additional externally induced perturbations. This work is motivated by future tokamaks in which transient, non-axisymmetric error fields can arise during startup, for example from runaway electron mitigation coils.

Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective responses that span more than an order of magnitude in apparent stiffness and exhibit strongly nonlinear, composition-dependent, and rate-dependent dissipative behavior. Classical finite-strain viscoelastic models represent such behavior with closed-form strain energy functions for equilibrium and non-equilibrium stresses as well as evolution of internal variables, which may limit flexibility when a single constitutive model is expected to generalize across materials and loading rates. Here, we present a data-driven multi-material constitutive modeling framework that generalizes a formulation by Bergström and Boyce. The proposed framework retains the structure of the classical model, namely multiplicative kinematics, invariant-based strain-energy functions, and a scalar dissipative evolution law directed along the normalized nonequilibrium deviatoric stress. For the equilibrium branch, the data-driven discovery framework either directly predicts closed-form model parameters as functions of composition or automatically constructs a polyconvex strain-energy function using neural ordinary differential equations (NODEs). The nonequilibrium branch kinetics are learned similarly, either by directly identifying closed-form parameters across compositions or by using appropriately constrained artificial neural networks. Using multi-rate uniaxial compression data across multiple material compositions, we show that the proposed formulation captures rate-dependent stiffness and hysteresis across compositions while preserving thermodynamic consistency.

The complex flow behavior of viscoelastic fluids emerges from a feedback between fluid stress and the out-of-equilibrium conformational dynamics of their constituent polymer chains. While constitutive models can predict flow and stress fields in simple geometries, a fundamental understanding of the mechanisms regulating viscoelastic flows with mixed kinematics and non-trivial polymer dynamics remains elusive. Quantifying the history-dependent conformation of polymer chains in flow is essential to resolve these complex systems. To address this knowledge gap, we employ direct Lagrangian tracking of individual DNA molecules to reveal their conformational dynamics in microfluidic viscoelastic flows for different polymer contour lengths and concentrations. Comparing the measured molecular extension and orientation reveals discrepancies with canonical constitutive models, which do not fully capture the transient dynamics. Our measurements show a distinct anisotropy in polymer relaxation and shape, which couple the polymer's rotation and extension through hydrodynamic drag, regulating their Lagrangian mechanics. These findings emphasize the importance of Lagrangian polymer history and the need to account for non-trivial intra-molecular mechanics to improve constitutive descriptions of viscoelastic flows.

The discovery of superconductivity in Ruddlesden-Popper nickelates has raised a central question: how does layer architecture shape the electronic, magnetic, and lattice interactions relevant to pairing? Here, we report a detailed comparative study of the two polymorphs of La3Ni2O7–the alternating monolayer-trilayer (LNO-1313) and bilayer (LNO-2222) structures–and the related trilayer compound La4Ni3O10, using both Ni L3- and O K-edge RIXS. We find that LNO-1313 and La4Ni3O10 share strikingly similar electronic, magnetic, and lattice excitations, whereas bilayer LNO-2222 exhibits distinct features. Compared to LNO-2222, LNO-1313 and La4Ni3O10 have weaker orbital polarization, enhanced 3d8L character, a reduced out-of-plane magnetic-exchange scale, and stronger EPC. Within an effective local-moment framework, an entangled-dimer scenario provides a natural description of the spin excitations generated by strong antiferromagnetic interlayer coupling. Its advantage over conventional spin-wave theory is clearest in bilayer LNO-2222, where the interlayer coupling dominates the intralayer interactions. These findings provide critical experimental constraints for future theoretical models for the low-energy physics relevant to superconductivity in these layered nickelates.

We introduce a reduced-order learning framework that couples a convolutional autoencoder to a structured latent neural ordinary differential equation. A learned scalar generates the latent dynamics through an antisymmetric operator and a positive-definite dissipative operator. The antisymmetric component permits motion along scalar level sets, while the dissipative component decreases the scalar. Adding a coercive quadratic term gives bounded continuous-time latent trajectories and approach to the scalar's critical set under the stated assumptions. The scalar provides a structural constraint rather than an approximation of physical energy. The encoder, decoder, scalar, and operators are trained jointly on short snapshot windows without time-derivative supervision, physical-energy labels, or dissipation penalties. At inference, one initial state is encoded and evolved continuously in latent space, with decoding only at requested times. We demonstrate the framework on micromagnetic magnetization trajectories from NIST $\mu$MAG Standard Problem 4 and compare it with dissipative-only dynamics and an unstructured latent neural differential equation. Repeated training runs show competitive observed-horizon accuracy, while selected longer rollouts illustrate improved retention of approximately stationary states relative to the unstructured baseline. These results motivate scalar-generated latent dynamics as an inductive bias for reduced-order prediction and other learning tasks where bounded evolution and equilibrium behavior are important.

Time-dependent density functional theory, though exact in theory, is in practice applied in an adiabatic approximation using exchange-correlation functionals with only local temporal dependence. Simultaneously, the exact correlation potential formally depends on, among other quantities, the time-history of electron densities. Here we develop a framework to learn neural network models of correlation functionals that feature explicit memory-dependence. Our framework includes two different approaches, both linked through their use of adjoint-based optimization. One approach decouples the learning of the functional from inversion to find ground truth values of the correlation potential. The other approach learns the functional directly without requiring inversion. We apply these methods to modeling the electron dynamics of two-electron systems in two spatial dimensions. In both cases, our methods yield correlation functionals with low test set propagation error, outperforming standard local density and generalized gradient approximation functionals by one to two orders of magnitude. Overall, our framework points at one strategy to move beyond the adiabatic approximation and develop memory-dependent correlation functionals that yield accurate propagation for excited state and/or non-equilibrium dynamics.

Distributed quantum computing (DQC) offers a promising approach to scale quantum computing by overcoming the resource limitations of a single quantum processor. However, inter-node communication remains a major bottleneck of DQC due to inefficient and error-prone entanglement distribution. Optimizing inter-node communication can not only reduce the amount of entanglement resource needed to execute a quantum circuit but also improve execution speed and accuracy of the results. This paper proposes DPRQ, a qubit routing algorithm for minimizing inter-node communication in distributed quantum circuits divided into collective communication blocks. Unlike current approaches that utilize greedy block-level qubit routing strategies, DPRQ employs a dynamic programming-based technique focused on global circuit-level optimization, while capturing inter-block dependencies. We evaluated DPRQ on four sets of quantum circuits and a variety of DQC configurations. The results demonstrate that DPRQ's innovative routing strategy achieves an average of 24.40% reduction with a maximum of 85.06% reduction in inter-node communication, when compared to the state-of-the-art collective communication-based DQC compiler QuComm.

Cities must allocate limited resources to maintain mobility, with uncertainties about the resulting state of the system. Analyzing roughly 3,000 bus routes with more than 4 billion yearly riders across 19 metropolitan areas worldwide, we uncover a robust scaling law of the form $f \sim (d/t)^\alpha$ with exponent $\alpha \in [1/2,\,2/3]$, linking the service frequency $f$ to passenger demand $d$ and route duration $t$. We show that this scaling emerges from a simple optimization principle: cities implicitly minimize total passenger waiting time under a fixed operational budget when both schedule frequency and crowding are taken into account. This mechanism produces two universal regimes: a frequency-dominated regime with $\alpha = 1/2$ when crowding is negligible, and a capacity-dominated regime with $\alpha = 2/3$ when most routes are overloaded. Intermediate exponents arise when only part of the network operates near capacity. Furthermore, we find that the benefits of additional investment are highly uneven across systems. For instance, our model suggests that a $20\%$ budget increase yields nearly a 5-minute reduction in daily waiting time per passenger in Boston, compared to only about 1 minute in Paris. These findings place urban transit within a broader class of constrained capacity-allocation problems, while highlighting a distinct regime in which prescribed route demands shape the allocation of limited service resources. The resulting scaling laws show how simple optimization principles can generate systematic exponents in complex transport systems, beyond the dissipation-based frameworks usually considered in physical and biological flow networks.

A numerical simulation of an annular rotating detonation engine with stoichiometric hydrogen-oxygen is performed. A generic, well posed, and easily implemented approach using a quasi-two-dimensional method to model the area variations through the rotating detonation engine's injector and combustor is presented. The detonation–injector interaction is studied for the case with a ratio of four between the combustor and injector's throat areas. A shock wave is formed in the divergent portion of the injector due the high back pressure created by the detonation in the combustor. A Favre-averaged steady-state analysis of stream lines and particle paths reveals that the shock causes an irrecoverable loss of stagnation pressure. Stagnation pressure gain in the combustor is insufficient to make up for the loss and the flow leaves the engine with lower stagnation pressure than in the plenum.

This work describes the calculation of spin-orbit spillage from a crystal structure. Spin-orbit spillage provides a measure of the likelihood that a material has topological character. The spillage also provides the reference quantity for the machine-learning classifier of Choudhary et al., which predicts whether the spillage exceeds a specified threshold rather than the calculation of its numerical value directly. The complete computation workflow was applied to the insulating compound BaMg2Bi2, yielding a spillage of 2.094 compared with the published VASP spillage of 2.075, corresponding to a difference of 0.9%. The calculation is described in terms of two Quantum ESPRESSO (QE) calculations of spillage performed with and without spin-orbit coupling, the role of relativistic pseudopotentials, and the subsequent wavefunction-overlap analysis. The limitations of the same calculation procedure for semimetals are also examined.

We identify dynamical processes in the near-wall region of turbulent wall-bounded flow by representing coherent-structure energy as trajectories in a low-dimensional state space and analyzing recurring trajectory patterns using network motifs. Direct numerical simulations (DNS) are performed for three configurations: a minimal flow unit (MFU) at $Re_\tau \approx 180$ to isolate the self-sustaining process (SSP), a full-scale channel at the same Reynolds number to study interactions between near-wall structures, and an MFU at $Re_\tau \approx 2200$ to investigate near-wall/outer-layer coupling. Proper orthogonal decomposition (POD) is used to identify modes corresponding to streaks, rolls, and meandering structures of the SSP, and the flow is projected onto these modes to track their energy over time. Motif analysis then identifies statistically significant dynamical pathways in the resulting state-space trajectories. Several motifs are common to all three configurations, indicating robust near-wall dynamics across different background flows. While motifs associated with the classical SSP are recovered, equally prominent motifs with no direct SSP analogue are also identified, demonstrating that preferred near-wall dynamics extend beyond the canonical regeneration cycle. Conditioning the $Re_\tau \approx 180$ MFU on outer-layer energy further shows that quiescent outer-layer states produce dynamics resembling those of the low-Reynolds-number full channel, whereas energetic outer-layer states promote high-energy bursting events. These results demonstrate that near-wall turbulence evolves along preferred dynamical pathways whose prevalence is modified, but not eliminated, by outer-layer activity.

Triboelectric nanogenerators (TENGs) incorporating metal-organic frameworks (MOFs) have largely been designed around porous architectures, based on the assumption that high internal surface area is the primary driver for triboelectric enhancement. Herein, we demonstrate that a dense, nominally nonporous MOF called ZIF-zni can instead function as an effective high-loading filler within a tribopositive polyurethane (PU) matrix, offering a design strategy for harnessing interfacial electromechanical effects. A 20 wt% ZIF-zni@PU composite delivers an output voltage of 470+/-15 V and a peak power density of 1.31+/-0.03 W m-2 against polydimethylsiloxane (PDMS). The device exhibits stable performance over ~94,000 cycles under a contact force of ~100 N, and it remains operational under higher impact beyond 500 N. This concept enables the demonstration of a proof-of-concept triboelectric floor tile. Combined experimental and theoretical studies indicate that the performance enhancement arises from favourable interfacial polarization, reduced dielectric screening, and surface accessible ZIF-zni rich domains, rather than porosity alone. These features are accompanied by reduced work of adhesion and modified surface roughness, hence improving contact electrification. These findings establish dense MOFs as an effective triboelectric filler and identify interfacial electronic structure and polarization as key design parameters for engineering mechanical energy harvesters and self-powered sensors.

Parametric component separation methods used in cosmic microwave background (CMB) analyses commonly assume instrumental noise to be uncorrelated between frequency channels. This approximation may become insufficient for experiments whose design naturally introduces cross-channel correlations, such as interferometric or multi-band bolometric systems. This work aims to provide a consistent framework to include frequency-correlated noise in Commander2 and to quantify its impact on component separation in controlled and instrument-motivated simulations. We generalized the amplitude sampling of the Commander2 framework to consistently handle dense frequency-frequency noise covariance matrices, modifying the likelihood evaluation and parallel architecture. The implementation is validated on low-resolution simulations of a simplified CMB-S4-like configuration. We applied the generalized framework to instrument-motivated simulations of the QUBIC bolometric interferometer, using representative noise models derived from end-to-end simulations. Validation tests show excellent convergence and agreement between sampled parameters and analytical maximum-likelihood estimates. Accounting for frequency correlations reduces the width of the maximum-likelihood residual distributions by up to 8% on average, and up to 11% for individual parameters, relative to the diagonal-noise approximation. In QUBIC-based simulations, accounting for an anti-correlated noise produces posterior widths reduced by about 60%, while a positive-correlation test increases them by about 40%. This shows that neglecting cross-channel correlations can lead to incorrect posterior estimates in either direction. The generalized implementation presented here enables the Commander2 software to consistently propagate such correlations into parameter estimation, providing a framework for assessing their impact in future CMB analyses.

We introduce chameleon gates as a natural generalization of conventional quantum controlled-gates. Chameleon gates are state-based quantum controlled-operations that retain standard elements such as control and target systems, while introducing a new feature: the quantum knob. This knob is a quantum signal (state) that determines the operation performed by the gate. Consequently, the action and form of a chameleon gate depend dynamically on the quantum knob, allowing the gate to adapt its operation and implement transformations that are not necessarily unitary. This shapeshifting property is in stark contrast to conventional quantum controlled-gates, whose actions are fixed and cannot be modified. We also propose how chameleon gates can be realized using conventional quantum gates available in current quantum technologies. We then employ chameleon gates as a useful building block within the recently proposed state-based quantum computation (SBQC) framework. Using this approach, we demonstrate the simulation of state-dependent (nonlinear) quantum evolutions.

We study the one-shot and concurrent hitting for the discrete-time Grover-coined quantum walk on cubelike graphs $G=\text{Cay}(\mathbb Z_2^d,\Omega)$ of degree $\Delta=|\Omega|$. Starting from the vertex labeled $0$, we identify $\sigma=\bigoplus_{\omega\in\Omega}\omega$ as a natural target vertex; for the hypercube, $\sigma$ is precisely the antipodal vertex. For families with $\Delta\to\infty$, let $T$ be an integer having the same parity as $\Delta$ and satisfying $ \left|T-\frac{\pi\Delta}{2}\right|\leq 1. $ We show that the probability $p_T(\sigma)$ of finding the walker at $\sigma$ when it is measured at time $T$ satisfies $$ p_T(\sigma)=1-O(\Delta^{-1/5}). $$ Thus the target is found with probability tending to one after $\Theta(\Delta)$ steps. For the concurrently measured walk, let $H_T^{\mathrm{Conc}}(\sigma)$ denote the probability that the target is detected at or before time $T$ when it is tested after every step. We prove $$ p_T(\sigma)\leq T H_T^{\mathrm{Conc}}(\sigma), $$ which implies $H_T^{\mathrm{Conc}}(\sigma)=\Omega(\Delta^{-1})$ over the same time scale. The proof uses the Walsh-Fourier decomposition, an exact two-dimensional reduction of each Fourier mode, and a universal second-moment identity for the associated character sums. Our results extend Kempe's hypercube hitting phenomenon (J. Kempe, Probab. Theory Relat. Fields 133, 215-235, 2005) to arbitrary cubelike generating sets and establish the conjectured asymptotic hitting behavior for cublelike and augmented cubes in Mulherkar, Rajdeepak and Sunitha (Int. J. Quantum Inf. 20,2250020, 2022)

Electrical conductivity (EC) provides important constraints on the composition and volatile distribution of Earth's deep mantle, yet the EC of davemaoite (CaSiO3 perovskite), a major lower-mantle phase, remains poorly constrained. We measured the EC of nominally dry CaSiO3 perovskite at pressures up to 89 GPa and temperatures up to 2200 K using impedance spectroscopy in a laser-heated diamond anvil cell. Conductivity increases with temperature but decreases systematically with pressure and it is substantially lower than previously reported, yet broadly consistent with recent theoretical predictions for oxygen-vacancy-mediated ionic transport. A distinct change in the temperature dependence coincides with the tetragonal-to-cubic phase boundary, revealing a modest enhancement of ionic transport across the structural transition. Along a normal lower-mantle geotherm, dry davemaoite is comparable in conductivity to bridgmanite near the top of the lower mantle but becomes progressively less conductive with depth. Under cold-slab conditions, dry davemaoite is substantially less conductive than dry subducted MORB and cannot account for the observed high-conductivity anomalies. Dry davemaoite therefore contributes little to bulk lower-mantle conductivity.

Recently, a new Trojan Horse Method (THM) measurement of carbon-carbon fusion was reported by Li et al. [Phys. Lett. B (2026) 140675]. The purpose of the present work is to demonstrate the breakdown of the plane-wave approximation used in the analysis of these data and the critical role of Coulomb distortions in the initial and final states. The reaction mechanism underlying the THM analysis of the $^{12}\mathrm{C}+{}^{12}\mathrm{C}$ fusion reaction using the $^{16}\mathrm{O}+{}^{12}\mathrm{C}\to \alpha_s+\alpha+{}^{20}\mathrm{Ne}$ reaction is investigated. Particular attention is paid to the spectator momentum distribution and to the dependence of the THM reaction amplitude on the relative carbon-carbon energy $E$. It is demonstrated that agreement with the measured spectator momentum distribution does not by itself validate the plane-wave approximation. Although the experimental momentum distribution can be reproduced, inclusion of Coulomb distortions in both the initial and final channels leads to an energy dependence of the THM amplitude that is completely different from the plane-wave result. Consequently, the energy dependence of the $^{12}\mathrm{C}+{}^{12}\mathrm{C}$ fusion cross section extracted from the THM data can be strongly distorted by the plane-wave treatment. It is concluded that the astrophysical factor extracted in the plane-wave analysis cannot be regarded as reliable and may lead to misleading conclusions concerning the low-energy $^{12}\mathrm{C}+{}^{12}\mathrm{C}$ fusion reaction.

In single-port non-Hermitian sensors the Petermann factor offsets susceptibility gains, imposing a strict resource bound on metrological precision. We test whether a multi-port geometry can evade this bound: a double-chain optomechanical ladder with opposing non-reciprocal hoppings spatially separates signal amplification from quantum-noise drainage, and gradient-based differentiable optimal control (DOC) maximizes the resource-normalized Fisher information $\Fnorm$ subject to a Hurwitz-stability constraint. Across system sizes $N\in\{\num{6},\dots,\num{16}\}$ the optimizer returns $\Fnorm>0$ in every case, with two coexisting solution classes whose selection is initialization-dependent: deep-stability configurations achieve $\Fnorm\in\numrange{0.937}{0.987}$ with attenuated transmission, while marginal-stability configurations deliver directional gain $\Gfwd\in\qtyrange{13.5}{15.5}{\dB}$ with isolation $\Iso\in\qtyrange{40}{64}{\dB}$. A multi-restart ensemble reveals these classes are the endpoints of a precision–gain frontier. All solutions remain Hurwitz-stable under \qty{5}{\percent} disorder (\qty{87.5}{\percent} recovery), and the deep-stability advantage survives realistic preamplifier noise at $\Fnormeff\approx\num{0.3}$–$\num{0.5}$. Mapped onto circuit-QED parameters, the architecture enables sub-attonewton force sensing and broadband axion searches across the \qtyrange{1}{10}{\giga\hertz} band.

Supermassive binary black holes (SMBBHs) are expected to be surrounded by circumbinary disks (CBDs) which affect the binary through gravitational forces and accretion. It has been reported that the binary can experience “preferential accretion” where one black hole (BH) out-accretes the other for hundreds of orbits, but this asymmetry has yet to be fully described or understood. In this work, we utilize a suite of 80 SMBBH hydrodynamical simulations with varying mass ratios ($q_b$) and eccentricity ($e_b$) in order to robustly delineate the behavior of preferential accretion, determine its relationship to the structure of the CBD, and study its observational consequences. We characterize the accretion-rate ratio $\lambda(t) \equiv \dot{M}_2(t)/\dot{M}_1(t)$ and the mass-ratio rate of change $\dot{q}_b \equiv d/dt(M_2/M_1)$ across the suite. We confirm that the secondary tends to out-accrete the primary ($\lambda \geq 1$), and find this preference to be strongest for low-$e_b$, low-$q_b$ binaries and increasingly time-variable toward high $e_b$. We also find that (i) the time-variability of $\lambda$ tracks the precession of the CBD, (ii) there can be sub- and super-Eddington accretion in a single binary, and (iii) the gas-driven approach toward equal mass becomes particularly slow for highly eccentric, high $q_b$ binaries, suggesting that some binaries may not reach $q_b=1$ within the $30\,\mathrm{Myr}$ lifetime of a quasar and therefore allowing LISA to constrain the accretion history of SMBBHs. Our findings also suggest that periodically flickering jets are a potential observable signature of many binaries.

Quantization is widely used to reduce the computational and memory demands of neural-network inference. In recurrent networks, however, the quantized state is stored and returned at the next time step, so the rule used to store that state can alter subsequent computations. Here, we introduce recurrent-state write-back to denote this rule and isolate its effect in a compact GRU encoder–decoder for fluorescence lifetime imaging, a molecular imaging modality used in quantitative biological imaging. A central task is estimating two lifetime parameters, the short-lived component {\tau}1 and the long-lived component {\tau}2, from high-noise time-resolved fluorescence signals. Holding the trained model fixed, replacing continuous state propagation with deterministic 4-bit state storage increases estimation errors for {\tau}1 and {\tau}2 by approximately 70x and 300x, respectively. Failure occurs when repeated small updates remain below the write threshold, leaving the stored state nearly fixed while the network continues to propose change. Error feedback, residual memory, and direction memory carry information from these suppressed updates across time and recover accuracy without retraining. Precision sweeps show that increasing state precision can worsen a fixed recurrent solution, while matched training shows that compatibility with the state interface can be learned. To test whether this behavior extends beyond the GRU, we repeat the post-training intervention in an independently trained LSTM, where coarse write-back reproduces the failure, error feedback restores accuracy, and state-specific interventions reveal greater sensitivity of the cell state than the hidden state. Our results establish recurrent-state write-back as a key determinant of low-precision recurrent dynamics and identify the state-storage interface as a central design consideration for quantized recurrent inference.

In this work, we provide the first derivation of the line-intensity mapping (LIM) power spectrum super-sample covariance (SSC) from first principles, and also derive as a by-product the non-Gaussian in-box contributions to the covariance for the first time. Previous studies have typically modelled the LIM power spectrum covariance using either the Gaussian approximation or estimates obtained from mocks or the data itself, neglecting uncertainties related to whether the limited volume surveyed sits in a cosmological overdensity. This contribution, known as the SSC or, depending on the context, the field-to-field variance, cannot be estimated from the data, but it is crucial for a correct inference of global quantities, i.e., for ensemble-averaged parameters rather than the actual values just within the patch of the Universe observed. For our derivation, we employ a combination of the halo model and standard perturbation theory that allows us to capture the nonlinearity and non-Gaussianity of the covariance. After a successful validation of our predictions against painted N-body simulations, we explore different scenarios related to current and future LIM experiments, quantifying the relative importance of the non-Gaussian in-box and SSC. We find that the newly derived contributions to the LIM power spectrum covariance are crucial at intermediate and small scales, especially for cases in which the covariance is not dominated by instrumental noise. We find that the relative relevance of the SSC with respect to the other covariance contributions is roughly independent of the survey volume, but does depend on the specific response of the power spectrum to large-scale modes for each line and redshift. Therefore, the impact of the SSC will be increasingly significant for parameter inference from the current and the next generation high signal-to-noise LIM surveys.

The astrophysical $S$-factor plays an important role in extrapolating the laboratory cross section data to lower energies of relevance in nucleosynthesis calculations. Be it empirical fits or theoretical modeling of the $S$-factors, experimental information on the cross sections over a range of energies is essential to provide reliable $S$-factors and thereby the nuclear reaction rates which enter the nucleosynthesis networks. In view of the scattered and sometimes diverse data on the same reaction in literature and the databases, we present a compilation and review of available data from 318 references involving 371 experiments on the leading and sub-leading reactions in big bang nucleosynthesis (BBN). Salient features such as bumps at certain energies and a steep rise in the $S$-factor at low energies due to screening effects are found to vary in different experiments. The latter depends on the variation of targets and sometimes temperature to extract information on the same nuclear reaction. For instance, 45 cases in which the $S$-factor at low-energy could present electron screening-effect showed deviations from background values with features which are unique to each experiment, target and temperature. The extensive compilation, discussions of the relevance of each reaction in BBN and the features of the $S$-factors is followed by a presentation of empirical fits to the $S$-factors in some selected reactions of interest. This is a preprint of the following work: Carlos A. Salas and Neelima G. Kelkar, Astrophysical S-Factors in Primordial Nucleosynthesis, 2026, SpringerBriefs in Physics. It is the version of the author's manuscript prior to acceptance for publication and has not undergone editorial and/or peer review on behalf of the Publisher (where applicable). The final authenticated version is available online at: https://doi.org/10.1007/978-3-032-30466-7

What controls heat transport when liquid water interacts with ice? Answering this question is essential for understanding water bodies undergoing phase change. Yet experimental progress remains limited by the lack of a minimally invasive methodology for simultaneously resolving temperature fields in coupled liquid water and non-isothermal ice systems. Here, we introduce a physics-based data-assimilation method for reconstructing temperature fields in buoyancy-driven flows interacting with non-isothermal ice. Particle tracking velocimetry provides the liquid velocity field, while thermal and kinematic boundary conditions constrain the inverse problem. The method couples advection–diffusion in liquid water with conduction in ice to reconstruct simultaneous mean temperature fields and quantify heat transport across the water–ice interface, while remaining minimally invasive and compatible with free-surface systems. We demonstrate the method in laboratory experiments in which the temperature range across the liquid water in contact with ice drives cabbeling-induced convection. This framework enables investigation of coupled thermo-fluid dynamics in cryospheric aquatic systems, including heat exchange at the ice-water interface and liquid-phase energetics, with broader applications to phase-change processes in food and energy industries.

Unstable periodic orbits and steady states are believed to form the backbone of spatiotemporal chaos and turbulence, yet their computation in thermally driven flows remains scarce for transitional regimes. In this work we compute and characterize a steady state and three families of periodic orbits in two-dimensional Rayleigh-Bénard at $\mathrm{Pr}=1$, near the transition to chaos. We find that in its route to chaos, the flow hops between several sets of orbits after becoming quasiperiodic. We use Floquet analysis to study the stability of the orbits obtained and characterize their bifurcations, showing how the appearance of primary and secondary frequencies, as well as phase-locking mechanisms, are all related to the dynamics of the orbits. We study in detail how the flow shadows the orbits found and determine in which regimes each orbit is dynamically relevant or not. Our analysis also reveals two important insights: (1) all symmetries are broken before the onset of chaos, and (2) this onset does not alter the behavior of the heat transport.

Techniques for identifying radio beacons transmitted by extra-terrestrial civilizations are well advanced. However, the casual broadband emission from standard radio communication is much harder to detect, and counter-intuitively casual Earth radio transmissions are becoming more difficult to identify as we more efficiently utilize the radio spectrum. In this paper we examine the electric-field statistics behind radio communication and re-conceptualize standard radio imaging as the first non-zero cumulant. This leads us to images of the higher cumulants where the natural stochastic emission from astrophysical sources disappears, leaving images of only modulated sources of radio communication. Faint SETI and RFI sources stand out in these cumulant images, enabling identification of faint communication that is undetectable with current techniques.

In this work, we investigate exactly the dynamics of a relativistic spinless particle influenced by a static and uncharged black hole surrounded by a quintessence-like anisotropic fluid or Kiselev black hole. Considering two quintessence-like models as examples, we calculate the radial wave function and determine, in both cases, the quasispectrum of energy, as well as the Hawking radiation and temperature. We find that the stronger the influence of quintessence-like anisotropic fluid, the smaller the radiation observed outside the event horizon. Furthermore, the temperatures obtained in both cases are directly influenced by the quintessence-like anisotropic fluid, and we recover the values obtained in other contexts, such as those derived using the surface gravity. Finally, in the absence of quintessence, we recover in both cases the Hawking temperature $T_{H} = 1/(8\pi k_{B} M)$ corresponding to the Schwarzschild black hole.

The scalability of variational quantum algorithms is fundamentally limited by the barren plateau effect, where the cost-function variance vanishes with system size, rendering optimization impractical. Recent Lie-algebraic approaches for deep parameterized have enabled a unified analytical understanding of this challenge but require either the initial state or the measurement observable to belong to the dynamical Lie algebra generated by the circuit. Here, we introduce a representation-theoretic framework under $2$-design hypothesis showing that variational quantum landscapes admit a natural decomposition into irreducible representation channels. This yields exact expressions and analytical bounds for the cost-function variance applicable to arbitrary initial states and observables, with previous Lie-algebraic results emerging as a special case. We illustrate the framework by analyzing the energy landscape of the one-dimensional ANNNI model for several circuit architectures, revealing trainability regimes inaccessible to existing methods. Our results establish a general representation-theoretic framework for analyzing variational quantum landscapes, substantially extending the analytical theory of barren plateaus.