Papers

Matched filtering remains the most sensitive method for detecting gravitational waves from compact binary coalescences. The efficiency of such searches depends on how well a discrete template bank covers the underlying parameter space. Conventional geometric, stochastic, and hybrid placement methods can lead to uneven coverage and redundant templates in higher dimensions. Hybrid methods are generally the most efficient among these, while stochastic methods are simpler to implement, particularly when the parameter-space metric is difficult to compute. In practice, both approaches rely on uniform random sampling, which often requires a large number of proposal points to achieve adequate coverage. We find that stochastic template banks constructed using low-discrepancy sequences achieve comparable recovery fractions while requiring 27.5% fewer proposal points in two dimensions, about 12% fewer in three dimensions and 17% fewer in a four-dimensional eccentric equal-spin toy bank. The final template count is much less sensitive to the proposal sampling, consistent with the metric-volume constraints of the covering problem. We introduce a slab-wise coarse prefilter to accelerate candidate rejection for strongly anisotropic metrics. Applied to the 4D toy bank, it gives a total bank-construction speed-up of about $1.28\times$. The primary benefit of low-discrepancy sampling is therefore a reduction in the size of the initial proposal set, leading to lower memory usage and reduced bookkeeping during bank generation. These results show that low-discrepancy sampling provides a simple improvement to stochastic template-bank generation for current and future gravitational-wave searches.

Timelike geodesics, periodic orbits, and their associated gravitational-wave signatures are examined in the spacetime of a Bonanno–Reuter regular black hole, a geometry arising from Asymptotically Safe Gravity in which a running Newton coupling replaces the central singularity with a de Sitter core. The dimensionless parameter $\alpha/M^2$ completely determines the strong-field dynamics. Increasing $\alpha/M^2$ shifts the marginally bound and innermost stable circular orbits inward, systematically reducing their characteristic radii, angular momenta, and energies; the allowed phase space for bound motion contracts accordingly. Classifying trajectories via the rational frequency ratio $q = w + v/z$ reveals that periodic orbits experience a mild inward contraction, which reduces the energy necessary to sustain a specific topology. Within the numerical kludge framework, we calculate the gravitational-wave polarizations for extreme mass-ratio inspirals. The asymptotically safe correction induces a leftward phase shift that reflects shorter orbital periods, while mildly enhancing peak amplitudes owing to the smaller periastron distances reached in the deep strong-field regime. Waveform sensitivity displays a strong dependence on topology, with high-whirl orbits, which persist longer in the strong-field region near the horizon, showing markedly more pronounced deviations. Unlike environmental effects that inflate orbital scales, intrinsic quantum-gravity modifications generate distinct, observationally detectable signatures for future space-based detectors such as LISA, Taiji, and TianQin.

We study the effect of charge symmetry breaking (CSB) energy density function (EDF) on the mirror displacement energies (MDEs) and charge radius differences of mirror nuclei within the self-consistent Hartree-Fock-Bogolyubov (HFB) model taking Skyrme EDFs, SLy4 and SkM* as the central part of nuclear potential. We introduce the volume and the derivative terms in CSB EDF and calibrate the strength adopting four reference mirror pairs $^{34}$Ar–$^{34}$S, $^{36}$Ca–$^{36}$S, $^{38}$Ca–$^{38}$Ar, and $^{54}$Ni–$^{54}$Fe, for which experimental data of both MDEs and mirror charge-radius differences are available. We introduce a sensitivity matrix which connects two CSB terms to residuals of MDEs and mirror charge-radius differences after subtracting the effect of Coulomb interaction. By using the sensitivity matrix, we found out that the derivative term is important to reproduce both observables in a good accuracy together with the volume term, especially for the residual of mirror charge radii. The optimized CSB EDF are further applied to predict the charge radius differences of mirror pairs, $^{40}$Ti–$^{40}$Ar, $^{42}$Ti–$^{42}$Ca, $^{46}$Cr–$^{46}$Ti, and $^{50}$Fe–$^{50}$Cr. We pointed out also that the CSB effects change neutron skins of mirror proton-rich nuclei at the $10^{-2}$ fm level so that the CSB contributions must be included before charge-radius differences between mirror nuclei are used to extract neutron-skin or symmetry-energy parameters.

This paper develops temporal-causal unity (TCU), a framework connecting a process-philosophical thesis – time is the ordered unfolding of causal change – to an operational model of cognitive and social dynamics. The framework deliberately separates three claims: an interpretive thesis about becoming, a measurable causal-progress coordinate, and a stochastic network model. Causal progress is defined by $\tau(t)=\int_0^t\lambda(s\mid\mathcal H_s)\,{\rm d}s$, where the nonnegative event intensity $\lambda$ must be specified independently of the outcome. Agents carry an orientation phase and an activation amplitude; weighted interaction, heterogeneous drift, external input, anchoring, and diffusion govern their evolution in $\tau$. First- and second-harmonic order parameters separate consensus from bipolar polarization. For the all-to-all noisy Kuramoto special case with Lorentzian drift width $\Delta$, synchronization begins at the conditional threshold $K_c = 2(\Delta + D)$, not at a universal constant. Reproducible numerical illustrations illustrate (not empirically demonstrate) this threshold, causal-clock curve collapse, and the consensus-polarization distinction. Six historical episodes are treated as scope probes rather than validation data. The paper derives falsifiable hypotheses and an out-of-sample protocol for comparing causal-progress and chronological-time models. TCU is therefore offered as a disciplined bridge between process ontology and complex-systems modeling, not as a replacement for spacetime physics or as an empirically established identity between time and causation.

We derive the form of the one-photon state of the electromagnetic field resulting from spontaneous optical emission of a randomly oriented chiral quantum source, including electric dipole, magnetic dipole, and electric quadrupole contributions to the radiation. We describe how spatial coherence in the emission can be exploited to reveal hidden information about the emitter. This work serves as a prelude and companion piece to our concurrently submitted work \cite{Backlund2026Letter} in which we present the classical and quantum error bounds associated with assigning the handedness of such an emitter.

Kerr resonators generate stable frequency combs in a compact platform with applications in coherent communications, sensing, quantum information processing, and astrophysics. Ultrashort pulses can be generated, moreover, with a wavelength and repetition rate flexibility inaccessible by traditional mode-locked lasers, which is desirable for high peak-power applications including in biomedicine and materials processing. However, for these applications, single-pulse energies will need to be significantly improved beyond the fJ level characteristic of sources today. Here we describe and demonstrate a simple approach for increasing the pulse energy in anomalous dispersion Kerr microresonators. Through scaling laws based on a mean-field cavity model and supported by experimentally-accurate numerical simulations, we show that pulse energy scales strongly with output coupling if supported by sufficient drive power. In strongly over-coupled cavities, femtosecond pulses can be stabilized with pulse energy beyond the pJ level. With this theoretical framework, by pumping a 12 GHz Si3N4 cavity with 30% output coupling with 2.6-ps time-lens generated pulses, we observe stable 74-fs pulses with a record output pulse energy of 6 pJ. High energy Kerr resonators are anticipated to improve performance for current applications and compliment mode locked lasers for high peak power applications.

Rapid pore-volume changes can trigger phase change on timescales shorter than characteristic transport times and may therefore be skipped by conventional nonlinear solves and adaptive time stepping. We present a persistent-variable framework for transport in fractured porous media, introducing specific volume as an independent variable. Starting from a fully coupled system, we derive volume-based models and recover classical pressure-based formulations by eliminating local thermodynamic variables. To resolve abrupt fracture opening, we introduce a nonlinear preconditioner that assumes instantaneous free expansion and resolves the fluid state through an isochoric equilibrium calculation before advancing the transport problem. The preconditioner applies to both volume- and pressure-based models. In the studied fracture-opening cases, unpreconditioned simulations miss transient vaporization, whereas the preconditioned models do not. Across the investigated aperture range, larger openings produce monotonic increases in gas content, expansion-induced cooling, and durations of transients. Thermal effects alter the phase evolution but have otherwise minor influence on the overall transient duration. Within the proof-of-concept setting, fracture opening generates substantial transient pressure reductions, indicating that geomechanical feedback may become important in fully coupled applications. Pressure-enthalpy and volume-temperature formulations recover identical physical solutions but exhibit different nonlinear robustness, with the pressure-enthalpy formulation proving more robust in some recompression-dominated cases. These results show that equilibrium specifications control the numerical properties of the nonlinear problem rather than recovered physical responses, while isochoric preconditioning connects the nonlinear initialization directly to the underlying thermodynamics.

We challenge the notion that a stoquastic binary governs fundamental computational boundaries in quantum computing and classical simulation of quantum systems. We argue that vanishing geometric phase (VGP), a geometric condition on the Hamiltonian's transition graph, more adequately captures these boundaries. To distinguish VGP from stoquasticity, we construct VGP 3-local Hamiltonians that are formally hard to stoquastize, yet belong to a family admitting polynomial-time recognition of the VGP property. Without constructing a stoquastizing unitary, we prove that the local Hamiltonian problem is $\mathsf{StoqMA}$-complete under the promise that the input Hamiltonian has VGP, and that a frustration-free variant is in $\mathsf{MA}$ under the same promise. We use this result to argue that non-VGP is necessary for any claimed adiabatic advantage justified by escaping the $\mathsf{StoqMA}$ regime. Further, we identify natural settings where the VGP property can be recognized in polynomial time. In contrast, we show that recognition of VGP is $\mathsf{PSPACE}$-complete in general for geometrically local Hamiltonians. Our results show that the computational boundaries $\mathsf{MA} \subseteq \mathsf{StoqMA} \subseteq \mathsf{QMA}$ traditionally attributed to stoquasticity are better understood as boundaries between vanishing and non-vanishing geometric phase structure.

We have analyzed multiwavelength light curves of the very fast nova V1723 Sco, based on our fully self-consistent nova explosion models. The time-stretching method gives the distance modulus in the $V$ band of $(m-M)_V = 15.3\pm 0.2$. Then the absolute $V$ magnitude reaches $M_{V,\rm max}= m_{V, \rm max} - (m-M)_V= 6.77 - 15.3\pm 0.2 = -8.5\pm 0.2$. Using our fully self-consistent nova outburst model combined with the optically thick winds on a $1.25 ~M_\odot$ white dwarf accreted by a mass accretion rate of $\dot{M}_{\rm acc}=1\times 10^{-9} ~M_\odot$ yr$^{-1}$, we successfully reproduce the overall $V$ light curve with a free-free emission model as well as the supersoft X-ray light curve with a blackbody approximation model. The epoch of the first GeV gamma-ray detection is almost coincident with the epoch of our model $V$ peak. This supports the shock formation mechanism that a strong shock arises soon after the optical $V$ maximum far outside the WD photosphere. We conclude that the shocked shell is optically thin.

Materials with negative thermal expansion (NTE) are essential for applications requiring precise control of thermal expansion. Owing to their exceptional chemical tunability, flexible architectures, and low-energy lattice vibrations, metal-organic frameworks (MOFs) represent a rich platform for exploring NTE. However, uncovering the structural motifs that govern NTE across the enormous MOF design space remains experimentally challenging, and large-scale first-principles phonon calculations are computationally prohibitive. Here, we comprehensively evaluate the factors influencing NTE in MOFs by utilizing a high-throughput workflow based on MACE-MP-MOF0, a machine learning interatomic potential fine-tuned for MOFs with near-ab initio accuracy, to construct PhononMOFdb, a database of phonons, inelastic neutron scattering spectra, bulk moduli, and heat capacities for over 12,000 MOFs. High-throughput screening of this database reveals that highly porous cubic topology frameworks with heavier, lower-valent metal nodes favor strong NTE, while linker functionalization provides a practical handle for tuning NTE magnitude and sign without compromising mechanical stability. Experimental validation via high-resolution temperature-dependent synchrotron powder X-ray diffraction on the Ce-UiO-66 MOF and its brominated variants confirms the design recipe and yields volumetric NTE coefficients surpassing current records. This work establishes a data-driven strategy for engineering NTE in MOFs, showing how machine learning-accelerated discovery and targeted experimental validation together unlock predictive materials design.

Magnetron-sputtering inert-gas condensation (MS-IGC) provides a scalable, environmentally friendly vapour-phase synthesis approach for preparing customised nanoparticles (NPs) with bespoke properties via fine-tuning several deposition parameters. However, this high-level control comes with a caveat: the synthesis mechanisms are affected by deposition parameters in complicated ways, often yielding unpredictable outputs. This report details the working mechanism of a typical MS-IGC system, achieving in situ synthesis, size screening, and directional deposition of nanoclusters through the synergistic operation of the three vacuum chambers (condensation, screening, and deposition). The study systematically explores the regulation laws of multiple key process parameters (e.g., inert-gas flows, aggregation length, etc.) on the formation, size distribution, and deposition behaviour of nanoclusters, to rationalise their chosen values toward optimised output. To this end, multiple linear regression analysis was performed to isolate the effect of each deposition parameter and thus quantify its effect on the NP size and size distribution. Our results indicate that parameters that may prolong the nascent NPs' residence inside the condensation chamber (most prominently, the exit nozzle diameter) can positively affect the final NP size. This study expands the understanding of NP formation, enabling improved experimental control and process optimisation.

We introduce the Long-Short-Range Neural Network (LSR-Net), a novel neural operator architecture designed for data-driven forward evolution modeling, and extends it to the prediction of nonlinear fluid dynamics. LSR-Net learns the evolution operator of a dynamical system solely from pairs of initial and future state snapshots, which splits the learnable integral kernel into long-range (LR) and short-range (SR) components within stacked network blocks. While the SR component uses standard convolutions to capture local dynamics, the LR component employs a sum-of-exponentials (SOE) representation. This allows for the efficient computation of global interactions as a trainable Fourier multiplier, reducing computational complexity to $O(n \log n)$ where $n$ is the number of pixels in an input snapshot and requiring only a few parameters per channel. LSR-Net is evaluated on three challenging 2D benchmarks: the coupled Burgers equation, the wave equation with a spatially varying coefficient, and the nonlinear shallow water equation {(SWE)}. Results demonstrate that LSR-Net significantly outperforms the baseline short-range network (SR-Net) as well as FNO and DeepONets in predictive accuracy, achieving substantially lower relative errors by effectively capturing both local fine-scale structures and crucial global pattern interactions.

Evolving networks experience vertex addition and deletion over time via processes which shape their structure and function. In many empirical networks, vertices do not arrive or depart in isolation but instead within groups or cohorts: for example, coauthorship networks grow by the addition of cliques whenever a new paper with multiple authors is published. In this paper, we propose a stochastic network evolution model based on block graphs that evolve through the addition of fully connected subgraphs ($m$-cliques) and the deletion of individual vertices. We derive a master equation governing the time evolution of the joint degree distribution and obtain an exact closed-form solution, valid for an arbitrary vertex deletion rate under uniform attachment, that describes both growing and constant-size networks. From this solution we obtain the marginal degree distributions and the clustering coefficient in closed form and the size of the giant connected component and percolation threshold governing robustness to random vertex failure using age-resolved message passing. Our results generalise existing single-vertex addition-deletion models to clique-based dynamics at arbitrary rates of turnover, and are confirmed by Monte Carlo simulation.

Black-hole thermodynamics has been reproduced with remarkable success from the BFSS matrix model, but the corresponding test in the closely related IKKT matrix model has awaited its recently proposed finite-temperature formulation, within which we investigate this correspondence. Introducing a cubic Myers term, we compute the complete one-loop thermodynamics – free energy, internal energy, entropy, and heat capacity – of a fuzzy $S^2$ background, and compare against the D0 and D2 black holes as the closest available reference points, since no gravitational dual is known for this specific background. At fixed Myers coupling, the leading entropy exhibits an $O(N^3)$ scaling rather than the $O(N^2)$ behavior characteristic of conventional black holes. Holding the physical radius fixed instead yields a genuine $O(N^2)$ scaling, showing that the power of $N$ depends on the large-$N$ prescription and is therefore not by itself an unambiguous test of a black-hole interpretation. We further find no intrinsic Hawking temperature – only a characteristic scale – and a heat capacity that is negative at low temperature but positive at high temperature. The absence of an intrinsic Hawking temperature, tied to the missing gravitational dual rather than to the choice of large-$N$ prescription, persists in either scaling limit. Rather than establishing a black-hole interpretation of the finite-temperature IKKT matrix model, these results provide a quantitative benchmark against which future analytical, numerical, and holographic investigations can be tested.

This expository article serves as a methodical guide, presenting a rigorous mathematical formulation of the D'Alembert-Lagrange principle and the derivation of the Lagrange equations for mechanical systems subject to ideal constraints. Designed for educational purposes, the paper establishes a clear geometric framework for the extended phase space, defining the space of virtual displacements and constraint reactions independently of their specific analytical representations. A central focus of this instructional text is the transition from the general equation of dynamics to the Lagrange equations of the second kind for holonomic systems. Specifically, we demonstrate that the Lagrange equations can be naturally and elegantly derived from considerations of the covariance of the variational derivative under the embedding mapping of the configuration manifold.

Let $\mathcal{X}$ be a finite set, let $p$ and $Q$ be strictly positive probability distributions on $\mathcal{X}$, and let $u\in\mathbb{R}^{\mathcal{X}}$ be a log-weight. Define $\Psi_p(u)=\log\sum_x p(x)e^{u(x)}$ and $\pi_{p,u}(x)=p(x)e^{u(x)-\Psi_p(u)}$. The variational free energy $F(Q;p,u)=D(Q\|p)-\mathbb{E}_Q[u]$ satisfies the exact identity $\Psi_p(u)=-F(Q;p,u)+D(Q\|\pi_{p,u})$, whose residual is the canonical divergence of the dually flat simplex. We prove a converse. Suppose $G(Q;p,u)=A(Q,p)+B(Q,u)$ is additively separable and its gap $\Psi_p(u)+G(Q;p,u)$ is nonnegative and vanishes whenever $Q=\pi_{p,u}$. Then $G=F$ and the gap equals $D(Q\|\pi_{p,u})$; $A$ and $B$ are unique up to a $Q$-dependent additive gauge. The gap is not assumed to be a divergence or to depend only on the target, and no continuity, measurability, differentiability, convexity, or membership in a prescribed divergence family is assumed. The proof bounds increments of a corrected accuracy term by centered log-moment-generating costs of order $k^{-2}$ per step. Telescoping $k$ equal exponential tilts gives an $O(k^{-1})$ bound and forces every increment to vanish. Thus target factorization and strictness are conclusions. Counterexamples show that separability, nonnegativity, and tightness are each necessary. Within this class, no $\alpha$-divergence distinct from Kullback–Leibler and no Rényi divergence of order other than one can occur as the residual.

From the catalogs of visible emission lines of galaxies measured by the Subaru Fiber Multi-Object Spectrograph (Subaru-FMOS) and the KECK Multi-Object Spectrograph For InfraRed Exploration (KECK-MOSFIRE), galaxies were classified into Star-Forming Galaxies (SFGs) and Active Galactic Nuclei (AGNs) based on the Baldwin-Phillips-Terlevich (BPT) diagram. In the redshift (z) range 0.8 <= z <= 2.6, z dependence of their number densities was examined, and it was found that SFGs dominate at lower redshifts, whereas AGNs are distributed at higher redshifts. Infrared analyses using the James Webb Space Telescope (JWST) NIRSpec and MIRI for galaxies up to z ~ 6.6 show similar trends, and the number densities of SFGs and AGNs were found to cross at z ~ 1-3. Combined with the fact that the luminosity of galaxies decreases monotonically with the age of the universe, these phenomena can be interpreted by an evolutionary scenario in which SFGs merged to form quasars in the early universe, whose activity weakened and they changed into SFGs later, and SFGs merged again to form Seyferts in the nearby universe. Within this framework, the longstanding issues of "dead quasars" and "downsizing" can be naturally explained.

Disorder fundamentally reshapes how crystalline systems respond to external forces, yet it remains unclear whether disorder drives interacting lattices toward glassy states or instead fragments them into domains separated by mobile interfaces. Here, we investigate vortex motion in superconducting island arrays, where disorder is introduced in a controlled manner by tuning the magnetic field away from commensurate vortex fillings. By driving vortices with an applied current, we observe a two-step depinning transition at incommensurate fillings. Comparison with molecular vortex model simulations shows that this intermediate regime is consistent with domain wall motion in a polycrystalline vortex lattice. While two-step depinning has been explored theoretically in driven periodic systems, direct experimental evidence linking this behavior to interface-dominated vortex motion has been lacking. Our results demonstrate that disordered, interacting vortex systems with strong periodic pinning can favor interface physics over homogeneous glassy dynamics.

In recent years, interest in single-atom catalysts supported on carbon-based structures has grown considerably due to their high catalytic activity and efficient uses of metal atoms. However, the design and characterization of these materials through first-principles calculations are computationally expensive, limiting the exploration of a large number of possible configurations. Here, we developed a framework based on graph neural networks (GNNs) to predict the adsorption energies of transition metals on graphene quantum dots (GQDs). The model was trained using data obtained from density functional theory calculations and achieved an $R^2$ of 0.906 with an MAE of 0.101 eV, while reducing computational cost by roughly six orders of magnitude relative to DFT. This methodology provides an efficient tool for the accelerated screening and rational design of new catalysts based on carbon nanostructures.

The continuous demand for improved batteries motivates the discovery and advancement of materials with improved transport. Ionic diffusivity is the relevant material property where its measurement depends on accurate assessment of the active material length-scale, generally from the mass-specific surface area. In this perspective, we argue for renewed focus on diffusivity comparisons. A procedural review of 303 recent open-access publications about battery material development revealed two aspects: (1) 49% of publications support structure-property transport claims using diffusivity values and (2) of those reporting diffusivity values, 15% clearly stated that the length scale was measured after grinding-alone or stated that grinding was not used at all. Diffusivity assessment rationally requires length scale measurement after grinding (grind-measure), rather than the reverse. A range of measurement methods are compared, including SEM, BET, and SAXS as well as the resulting apparent diffusivities. Common errors and pitfalls of each of these approaches are described. As an example, datasets are presented for TiNb2O7 (TNO1) and Ti2Nb10O29 (TNO2) made from sol-gel (SOL) and solid state (SS) techniques using rigorous quantitative measurements to separately compare material diffusivities and galvanostatic performance. Here, the SOL samples had shorter length-scales and lower diffusion coefficients. Galvanostatic cell measurements, however, revealed that the shorter length-scales more than compensated for the lower diffusivities with better overall high-rate capacity retention. This example shows how cell level metrics often differ from underlying diffusivities. We argue that materials development needs renewed focus on property measurements like diffusivity where best-practices are important to derive meaningful insights towards structure-property relationships.

The growing use of generative AI tools among students raises an important pedagogical question: how can AI be structured to promote active learning rather than passive answer-seeking? This study introduces the Physics AI-Replication (P-A.I.R.) framework, in which students identify challenging problems, use AI to explain underlying concepts and solutions, generate similar problems, and practice independently before reviewing answers. Survey data from 39 undergraduate students in algebra-based physics indicate that nearly all participants reported improved conceptual understanding following engagement with P-A.I.R. across the semester, and self-confidence scores were consistently above the scale midpoint (mean = 7.03; range 5-9). A strong association between conceptual clarity and replication helpfulness (Spearman r = 0.582, p < 0.001) supports the framework's design. Qualitative findings highlight conceptual clarification and structured problem-solving. These results suggest that P-A.I.R. offers a practical approach for integrating AI into physics learning.

Pump-probe reflectivity reveals coherent acoustic oscillations at 33 and 8.2 GHz in a Ba(Fe$_{0.92}$Co$_{0.08}$)$_2$As$_2$ thin film. The acoustic response was analyzed using a modified logistic-function model, suggesting a temporal redistribution of coherent acoustic energy consistent with anharmonic decay of the higher-frequency mode into the lower-frequency mode.

We have studied the shear viscosity, bulk viscosity and associated observables of baryon asymmetric hot QCD medium subjected to weak momentum anisotropy induced by the asymptotic expansion of the medium during the early stages of ultrarelativistic heavy-ion collisions. The shear and bulk viscosities are evaluated by solving the relativistic Boltzmann transport equation within the relaxation time approximation in a kinetic theory framework. We have further analysed the Prandtl number, the Reynolds number, specific shear viscosity, and specific bulk viscosity, characterizing the sound attenuation, flow properties, fluidity, and conformal behavior, respectively. The interactions among the constituent partons are incorporated through the anisotropic quasiparticle distribution functions, with the medium characterized by finite temperature, momentum anisotropy and baryon asymmetry. Our analysis shows that both shear and bulk viscosities decrease with increasing anisotropy in both baryonless and baryon asymmetric media. In contrast, finite baryon asymmetry enhances both viscosities compared with their baryonless counterparts. The effects of expansion-induced anisotropy remain pronounced in both the baryonless and baryon asymmetric media, leading to substantial modifications of the associated viscous observables. These features are attributed primarily to the squeezing of the parton distribution functions caused by expansion-induced momentum anisotropy, together with the resulting modification of the partonic dispersion relations. Our results demonstrate that the expansion-induced anisotropy significantly influences the sound attenuation, flow characteristics, fluidity and conformal behavior of baryon asymmetric hot QCD medium, providing new insights into the viscous properties of the medium created in ultrarelativistic heavy-ion collisions.

Schrödinger cat states - quantum superpositions of classically or macroscopically distinct states - constitute a powerful resource for quantum computing, enhanced metrology, and probing coherence on large scales. Encoding such states in the phase space of an oscillator requires a nonlinearity, typically inherited from an auxiliary degree of freedom such as atomic spin or a Josephson junction. Neutral atoms trapped in reconfigurable optical tweezer arrays - a leading platform for quantum science and computing - provide an intrinsic nonlinearity via the motion of a single atom in a tightly focused trap. However, this self-Kerr mechanism has not previously been exploited for cat-state generation, and remains largely unexplored as a resource for motional-state control. Here we realize Schrödinger cat states in the quantized motion of a single neutral atom trapped in an optical tweezer. By modulating the depth and position, we demonstrate parity control of both Kerr-cat and Fock states alongside tunable nonlinearity, establishing a spin- and species-independent framework for controlling motion. We further show that the cat-state encoding is intrinsically robust against trap-frequency fluctuations that otherwise limit the fidelity of direct Fock-state transitions. These results establish Kerr-based control of neutral-atom motion as a new paradigm for cat-state and bosonic-state engineering in optical tweezers, providing a route toward quantum-error-correcting codes such as grid states, and toward quantum-enhanced sensing with arrays of non-Gaussian states.

The dynamically driven double degenerate double detonation (D$^6$) model has emerged as a promising progenitor scenario for Type Ia supernovae. In this model, a carbon-oxygen white dwarf (WD) in a close double WD binary undergoes a double-detonation triggered by dynamical mass transfer from its companion. The mass transfer stream directly impacts the surface of the primary WD, potentially igniting a helium detonation in the surface layer. The resulting shock converges in the core, triggering a carbon detonation that ultimately unbinds the star. While previous studies have demonstrated the viability of this mechanism, the conditions under which the helium shell ignites remain uncertain. We perform two-dimensional simulations using FLASH to study helium detonation ignition driven by stream impact on a carbon-oxygen WD with a realistic, unmixed composition profile left at the end of the previous helium-shell burning phase before the WD was formed. We model WDs with masses of 0.8, 0.9, and 1.0 $,M_\odot$ and vary stream properties for each case. We find that stream impact leads to helium ignition in all cases, with ignition occurring at varying distances from the impact point. Ignition consistently occurs near the core-shell transition, highlighting the importance of a realistic WD profile. However, the ignition mechanism differs between models and can arise from interactions of neighboring hot, mixed regions rather than a single point. Ultimately, all models but one sustain a propagating detonation capable of traversing the WD surface further reinforcing the viability of the D$^6$ model as a progenitor scenario for Type Ia supernovae.

In recent years, chiral quantum optics has emerged as an active research area due to the promising applications in quantum information processing as well as nonlinear optics. We present results on the properties of the incoherent component of the transmitted light after transmons chirally coupled to a waveguide. The well-known resonance fluorescence of one chiral transmon resembles nonlinear quantum optics as it can convert the coherent light, with photons having spatially extensive coherence and Poisson distribution, into a field with spatially localized coherence and bunching photon statistics. However, another chiral transmon can undo the effect of the first transmon regardless of the Rabi frequency under the idealization involved in this work. A full wavefunction calculation shows that, in the weak driving limit, this incoherent light mainly comes from two-photon processes - one chiral transmon converts two independent photons into a photon pair with opposite momentum shift. In addition to the driving through the waveguide, a local driving with independently tunable amplitude and phase can address each transmon individually. The interplay between the waveguide driving and local driving modulates the contribution of the incoherent transmission and hence enables the engineering of the quantum statistics of the transmitted field, with tunable $g^{(2)}(0)$ spanning anti-bunched, coherent, and strongly bunched regimes.

In this work we extrapolate the separatrix operational space (SepOS) projections to SPARC and introduce detachment access criteria, thus formulating the combined power exhaust constrained SepOS (i.e., PE-SepOS) to evaluate integrated power exhaust solutions at scale. Through the interpretation of SPARC SOLPS-ITER datasets and foundational work already demonstrated in experiments we formulate an as simple as possible description of the SOL net power and momentum losses in dissipative regimes to link the main power exhaust quantities with the SepOS parameters. Through this framework, we demonstrate the utility of a normalized PE-SepOS framework in identifying accessible operational points for given exhaustible Psep requirements. In applying the PE-SepOS framework to project the SPARC operational space, we find inherent trade-offs, namely: i) accessing high impurity radiation scenarios leads to pronounced reductions in ne,sep (e.g., 50% reductions at 2% Ne concentration) as a consequence of power limitation, and ii) given present understanding of access criteria to the quasi-continuous exhaust regime (QCE), a compromise between high radiative fraction and high density/neutral pressure is required for QCE access at sufficiently high density, high alpha_t conditions, with the divertor dissipative regime transitioning to pronounced detachment. Taking advantage of universal trends enabling projections of density and impurity seeding scans, the PE-SepOS thus provides a framework for mapping out the edge plasma operational space in a scalable manner, subject to validation during early SPARC operations with suitable divertor and edge plasma observables.

Multi-physics simulations are essential for understanding and monitoring intricate subsurface processes such as CO2 storage. Their computational demands call for surrogate models and, for unstructured meshes, Graph Neural Networks (GNNs) are natural candidates. The main bottleneck in developing them is generating and managing the large, physically consistent simulation datasets required for training. To address this challenge, we present Agents4GEOS, an AI-agent framework built on the Model Context Protocol (MCP) that provides 52 domain-aware tools for natural-language-driven workflows with GEOS, an open-source multi-physics simulator. The agent facilitates input-file creation, mesh inspection, fluid-property computation, and result post-processing. Through human-curated skills and fresh-context subagents coordinated by an orchestrator, the system executes complex workflows, evaluates simulation outputs, diagnoses issues, and suggests improvements, grounding every quantity in actual computation. By automating routine tasks, Agents4GEOS allows domain experts to focus on the most challenging aspects of their work.

Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout. This separation reduces reliance on repeated quantum parameter updates and avoids the barren plateaus associated with variational circuit training. Its computational power is often attributed to the exponentially large Hilbert space of the quantum system. However, the memory, nonlinearity, and expressivity that determine what a reservoir can actually compute depend jointly on the input encoding, quantum evolution, observables, measurement, and readout, not on Hilbert space dimension alone. On hardware, these capabilities are further constrained by finite sampling, hardware noise, measurement backaction, and the cost of estimating observables, so a large state space alone does not guarantee useful computation. In this survey, we develop a common system model that connects these components and use it to organize QRC foundations, computational properties, reservoir architectures, operating protocols, and physical implementations. We examine spin, photonic, superconducting, bosonic, neutral atom, and other analog platforms, together with applications, software and high performance computing support, benchmarking, and reproducibility. The analysis distinguishes hardware demonstrations from simulations and identifies the assumptions and resources that govern comparisons across implementations. Current results do not establish a broad quantum advantage over well matched classical reservoirs. We therefore specify the resource accounting, benchmark standards, and theoretical criteria needed to evaluate claims of quantum advantage.

As they orbit within the Galaxy, stars swim through a vast population of interstellar objects (ISOs). In this paper, we use N-body simulations to show that a fraction of ISOs within $\sim$1 pc of the Sun (its tidal radius) may be captured during the flyby of another star – a mechanism that requires no planets. Capture is most efficient when the impulse imparted by the flyby is comparable to the escape speed at the widest stable orbit, which is roughly 0.1 km/s for the Sun. ISO capture is dominated by the few highest-impulse stellar flybys, typically involving relatively slow encounters with massive stars. Most ISOs are captured in the outer parts of the Oort cloud, with semimajor axes greater than $\sim$50,000 au. Using Monte Carlo simulations, we show that the Sun underwent only a small number of ISO-capturing flybys in its history (median [mean] of 1 [1.7]). Using the Ōtautahi-Oxford population model, we estimate that a few times $\sim$$10^{4}$ `Oumuamua-sized ISOs were likely captured by the Sun. This only represents a $\sim$$10^{-8\pm1}$ contribution to the total Oort cloud population, yet it contains roughly as many present-day captured ISOs as Jupiter-assisted capture provides. Given that flybys are unavoidable in the Galactic field, most stars should host sparse Oort clouds populated with ISOs captured during stellar flybys. Massive stars are both the main drivers of capture when they fly by a given star, and more efficient at capturing ISOs around themselves than low-mass stars.