Symmetry-resolved spectroscopies, such as angle-resolved photoemission spectroscopy and polarization-resolved Raman, are central for quantum material characterization, yet remain challenging at nanoscale dimensions and low frequencies. Here, we propose correlated quantum dephasometry, which enables symmetry resolved quantum noise spectroscopy of materials at nanoscale and low ($\sim$MHz) frequencies via correlated dephasing of two spin qubits near materials. Our approach leverages the finite-range spatial structures of nonlocal near-field noise correlations to isolate rotational symmetry of the material response in momentum space beyond single qubit capabilities. We apply our approach to two-dimensional (2D) superconductors, and predict clear fingerprints that discriminate s-, d-, and g-wave symmetry of the superconducting gap. To highlight the generality, we further show that the same framework resolves 2D s-wave antiferromagnets and d-wave altermagnets. Our results establish correlated quantum dephasometry as a nanoscale, low-frequency complement for symmetry-resolved spectroscopy applicable to a broad class of quantum materials.

We propose an accurate thermometry approach for Rydberg atom tweezer arrays combining data from correlation and local susceptibility measurements with a theoretical high-temperature expansion method for dynamic spin correlations. We apply our approach to a recent quantum simulation experiment [Bornet et al., arXiv 2602.14323] realizing an anti-ferromagnetic dipolar spin-1/2 XY model on the Kagome lattice. We obtain T=0.55J and S/N=0.67 ln2 for temperature and entropy respectively, showing that further experimental efforts are required to reach the putative quantum spin liquid regime.

swdatatoolkit is a Python-based scientific software library designed to support the acquisition, preprocessing, and analysis of solar and space weather data. The toolkit consolidates functionality across multiple domains, including data downloading from established heliophysics sources, image preprocessing, edge detection, image texture quantification, magnetic field analysis, and the derivation of higher-level parameters commonly used in solar physics research. Its modular structure reflects the heterogeneous nature of space weather data and enables reproducible, extensible workflows for both exploratory analysis and machine-learning-driven studies. This paper presents an overview of the library's available capabilities, its scientific motivations, and its role in the broader space weather research ecosystem.

Although the spin parameter of dark matter halos is well known to follow a log-normal distribution at fixed epoch, its quantitative redshift evolution - encompassing both the mean and the dispersion - remains only partially explored. Prior studies either lack the mass resolution required to establish reliable evolutionary trends or do not provide analytical relations that enable forward modelling. Using a suite of LCDM N-body simulations with controlled resolution across the redshift range 0 < z < 5, we characterise the evolution of the mean and dispersion of the Peebles (lambda) and Bullock (lambda') definitions of spin. We find a mild but statistically robust linear evolution for ln(lambda) and a non-monotonic trend with a turnover at z ~ 1 - 2 for ln lambda', which we verify are unaffected by mass resolution of choice of halo definition. We provide closed-form fitting functions for these trends that allow modellers to draw physically motivated spin values at any redshift within our range of validity. This is a practical, redshift-dependent alternative to the common assumption of a constant spin distribution, and provides a useful input to semi-empirical and semi-analytic models of galaxy formation.

Since their rediscovery in the 1990s, multiple zeta values have become ubiquitous in many areas of mathematics and physics. Their standard integral and sum representations can usually be traced back to a single source, namely the iterated integrals on the Riemann sphere with three punctures. We refer to such representations as the \emph{linear} geometry of multiple zeta values, since the denominators of the corresponding integrands factor completely into linear terms. However, there also exist equally important and entirely distinct integral representations for multiple zeta values arising in mathematics and physics, in which matrix determinants appear in the denominator of the integrand. We call this the \emph{non-linear} geometry of multiple zeta values. These lectures trace the origins of this non-linear geometry and provide an introductory journey through a range of topics including tropical geometry, the moduli spaces of tropical curves, Feynman integrals in quantum field theory, the general linear group of integer matrices, and the reduction theory of quadratic forms. In doing so, we propose a geometric framework for multiple zeta values based on such non-linear, determinantal representations and set out a number of open questions for future research.

Several theoretical and astrophysical problems - including gravitational-wave modeling for extreme mass-ratio inspirals - require accurate time-domain solutions of the spin-weight $s=-2$ Teukolsky equation in Boyer-Lindquist coordinates. Because such simulations are performed on finite computational domains, they typically introduce an artificial outer boundary where nontrivial boundary conditions must be imposed. If these conditions are inaccurate, then spurious reflections and slowly-growing unphysical modes may corrupt long-time evolutions. We develop and implement exact radiation outer boundary conditions for the Bardeen-Press equation (a harmonic moment of the $a=0$ Teukolsky equation), making the artificial boundary transparent at any finite radius. We also construct near-to-far field teleportation kernels that map field data recorded at finite radius $r_1$ to the data reaching $r_2 > r_1$. The possible choice $r_2 = \infty$ corresponds to asymptotic waveform evaluation, that is propagation of the data to future null infinity. We show that both boundary and teleportation kernels are well approximated by exponential sums, with associated error bounds. Implemented in a time-domain solver, our kernel-based boundary conditions eliminate unphysical late-time growth and give the correct late-time decay rates, affording efficient long-duration simulations for waveform modeling and related blackhole perturbation calculations.

Component Mode Synthesis methods, such as the Craig-Bampton (CB) approach, are widely used in structural dynamics due to their modularity and compatibility with substructuring workflows. While highly effective for linear systems, extending these methods to geometrically nonlinear structures remains a significant challenge. In this work, we propose a nonlinear extension of the CB method tailored to such contexts. The approach is based on the construction of a quadratic reduction manifold, derived via perturbation analysis, in which high-frequency fixed-interface modes are statically condensed onto a reduced set of low-frequency modes and interface coordinates. This formulation enables the representation of geometric nonlinear effects without increasing the number of reduced degrees of freedom.The resulting Nonlinear Craig-Bampton (NL-CB) reduced-order model is obtained through Galerkin projection onto the tangent space of the manifold and admits a polynomial structure that is efficient for time integration. The formulation preserves the Lagrangian structure of the underlying finite element model, ensuring consistent energetic behavior and numerical stability.The proposed method is demonstrated on representative nonlinear structural systems of increasing complexity. The results show that the NL-CB model captures the essential nonlinear dynamic response while retaining the modularity and computational efficiency of classical substructuring approaches.

We investigate how multi-band gravitational wave (GW) observations can constrain the uncertainties in the Hubble parameter ($H_0$) using primordial black holes (PBHs) as possible sources. Our framework combines scalar-induced and merger-induced GWs from PBHs, and forecasts on a combination of two future detectors Square Kilometre Array (SKA) and the Einstein Telescope (ET), enabling a multi-band analysis. We perform a statistical forecast of the PBH parameters, $M_{\rm PBH}$ and $f_{\rm PBH}$, using signal-to-noise ratio (SNR) estimates and Fisher matrix analysis. Imposing $\mathrm{SNR} \geq 1$, we identify the accessible PBH parameter space and propagate these uncertainties to estimate the corresponding uncertainties in $H_0$. For $δθ_i/θ_i \leq 0.1$, with $θ_i \equiv M_{\rm PBH}(f_{\rm PBH})$, we find $δH_0 \lesssim 2~{\rm km\,s^{-1}\,Mpc^{-1}}$ in a conservative approach, improving to $δH_0 \lesssim \mathcal{O}(0.1)~{\rm km\,s^{-1}\,Mpc^{-1}}$ for $δθ_i/θ_i \leq 0.01$ for an optimistic approach of precision measurement. The results are further found to be largely insensitive to the fiducial choice of the $H_0$, with only moderate dependence on the PBH collapse efficiency. These findings demonstrate that multi-band GW observations provide an independent and complementary approach to constraining the uncertainties in $H_0$, with the potential to provide a novel, cosmic distance ladder-independent measure of the Hubble parameter.

The consequences of a protoplanetary disk collision with a gas stream are being studied using three-dimensional numerical gas-dynamic simulation. The influence of orbital parameters and the stream mass on the accretion activity of the star is examined. It is shown that the orbital inclination and the initial mass of the infalling material are the most influential parameters in determining the accretion rate. The obtained accretion rate dependencies are compared with actual observational data for two FU~Ori type stars. It turns out that not only is the maximum accretion rate consistent with observational estimates, but the behavior of the accretion rate over time is very similar to available long-term light curves.

Recent advances in high-temperature-superconductor technology have made substantially higher toroidal magnetic fields technologically accessible, reopening the design space for compact, high-field tokamak reactors. Because reactor performance projections remain anchored to empirical confinement scalings, the recent update to the ITPA global H-mode confinement database raises an important question: what does the present experimental record and its uncertainty imply for the path to reactor-grade fusion performance? In this work, we revisit confinement extrapolation from an explicitly extrapolation-oriented perspective and, to complement its implications in terms of a direct reactor performance measure, present a cross-machine empirical scaling for fusion power. We systematically search for a minimally complex confinement scaling that optimizes the tradeoff between variance capture and extrapolative robustness. We find that low-order models centered near $N=3$ to $N=4$ optimize this tradeoff, with plasma current, machine size, heating power, and elongation emerging as the dominant engineering levers, together with an empirically inferred confinement penalty associated with metallic walls. Recast in reactor-performance terms, the results indicate that both the fusion triple product and fusion power are governed primarily by plasma current: the triple product scales approximately as $I_p^2$, and the empirical fusion power scaling exhibits a similarly near-quadratic dependence over a survey of the highest performing discharges across several machines. Projecting to reactors, these results suggest that high-field devices with metal walls may require higher plasma current than standard IPB98$(y,2)$-based expectations imply, and that gigawatt-class tokamak performance likely demands operation at $I_p \gtrsim 20\mathrm{MA}$.

We present converged ab initio calculations of short-range neutrinoless double-beta ($0νββ$) decay nuclear matrix elements for the key experimental isotopes $^{76}$Ge, $^{82}$Se, $^{130}$Te and $^{136}$Xe. Starting from different nuclear forces derived from chiral effective field theory, we apply the in-medium similarity renormalization group to obtain an effective valence-space Hamiltonian along with consistently transformed $0νββ$-decay operators. We then obtain a range of values for the matrix elements that is consistent with, but generally smaller than, those from phenomenology. Finally, we combine our results with current limits from $0νββ$-decay searches to obtain constraints for the sterile-neutrino mixing-mass parameter space when considering the inclusion of a fourth sterile neutrino.

The mechanical scaling laws of dealloyed nanoporous metals depart from classical Gibson-Ashby predictions for open-cell foams due to a decreased connectivity in their solid network. However, these scaling relations have been established almost exclusively on nanoporous gold produced by electrochemical dealloying, and it is an outstanding question whether the relations apply to nanoporous networks fabricated by other dealloying methods. Here, we investigate the mechanical response of single-crystalline nanoporous tantalum (np-Ta) produced by liquid metal dealloying (LMD) a TiTa alloy in molten CuBi. Nanoindentation of individual microparticles yields an elastic modulus of 10-30 GPa and a hardness of 0.3-1.1 GPa, both scaling with the solid volume fraction in agreement with Gibson-Ashby predictions. This stiffness-density response of np-Ta departs from previous reports on nanoporous gold and is attributed to enhanced ligament connectivity enabled by the thermodynamics of the CuBi metal bath. Molecular dynamics simulations reveal dislocation-dominated plasticity during indentation of np-Ta, consistent with scanning electron microscopy observations of limited densification beneath the indents, ruling out unusual deformation mechanisms as an origin of the observed scaling. These findings identify solvent chemistry in LMD as a tunable lever for ligament connectivity, and thus for the mechanical response of nanoporous metals.

This paper presents a method for computing local mean, variance, standard deviation, and effective sample count on incomplete gridded data using boundary-aware spectral operators. The framework combines normalized convolution with explicit boundary-condition modeling: reflective Discrete Cosine Transform (DCT) for non-periodic Cartesian axes and periodic Real Fast Fourier Transform (RFFT) for circular azimuth processing in polar geometry. Stability safeguards (denominator floor, prefill fallback, and variance clamp) are specified for under-supported regions. We evaluate the framework across three targeted scenarios: a Cartesian boundary-condition check demonstrating the mitigation of wrap-around artifacts, a synthetic 3D outlier-identification test, and a real-radar polar application. Results establish bounded, support-aware interpretation of local statistics while preserving a concise reproducibility path through the open-source 'dct\_toolkit' implementation.

We present an amplitude analysis of high-energy polarized photoproduction of $π^-Δ^{++}$ within a Regge exchange framework. A Regge amplitude model incorporating $π$, $ρ$, $b_1$, and $a_2$ trajectory exchanges is fit simultaneously to spin density matrix elements measured by the GlueX experiment at photon energies of $E_γ= 8.2$--$8.8$ GeV and differential cross section data from SLAC. By including SDME data, the fit constrains not only the magnitudes but also the relative phases of the helicity amplitudes. The results confirm the dominance of pion exchange at small momentum transfer, while natural parity exchanges become significant at larger $t$. We analytically continue the $s$-channel amplitude to the $t$-channel, taking care of the kinematical singularities, and isolate the dynamical residues at the meson poles. The extracted $πNΔ$ coupling constant is found to be consistent with the value obtained from the decay width of the $Δ(1232)$. For the $ρNΔ$, $b_1 NΔ$, and $a_2 NΔ$ vertices, first extractions of the relevant coupling constants are provided.

We describe a numerical many-body technique that is based on both tensor networks and quantum Monte Carlo. The variational ansatz is a tensor network that can harvest volume-law entanglement. It is constructed from a tensor train to which one applies a set of non-local operators that force several indices of the tensor train to represent the same physical index, hence its name -- replica tensor train (RTT). From the tensor network toolbox, it inherits the possibility to make linear combinations of these states and apply a certain class of operators. We can therefore find the ground-state of a local Hamiltonian in a purely algebraic way as in standard tensor network algorithms -- i.e. without using gradient descent methods. On the other hand, the volume-law structure forbids calculating physical observables directly. In much the same way as on a quantum computer where one can prepare a state but can only sample it at the end, here we have to use Markov Chain Monte Carlo to compute the observables. We further show that the approach can be extended to build Krylov-subspace ground-state methods within the variational manifold. We illustrate the different algorithms on a two-dimensional spin model with a transverse magnetic field, which can be solved by this approach at low computational cost.

Purpose: Widespread adoption and methodological advancement of Magnetic Resonance Fingerprinting (MRF) are limited by the lack of unified, reproducible implementation frameworks and fragmented open-source tools. To address these barriers, we introduce OpenMRF - a comprehensive Pulseq-based solution - designed to enable consistent, reproducible, and transferable MRF research across vendors, sites, and field strengths. Methods: OpenMRF integrates modular Pulseq-based sequence design, Bloch-simulation-based dictionary creation directly from .seq files, and iterative low-rank subspace reconstruction. The framework was evaluated through digital phantom simulations, a multi-site ISMRM/NIST phantom study on Siemens MRI systems at 0.55 T, 1.5 T, and 3 T as well as GE and United Imaging 3 T platforms, and representative in vivo acquisitions in the liver (0.55 T), myocardium (1.5 T), and brain (3 T). Results: Simulations demonstrated high mapping accuracy in an ISMRM/NIST-like digital phantom, with low-rank reconstruction yielding deviations of 0.03+/-0.32 % (T1) and 0.12+/-1.94 % (T2). The multi-site phantom study yielded relaxation times consistent with reference values at all field strengths, with mean deviations of -0.1+/-2.9 % (T1), -1.5+/-8.7 % (T2), and -4.0+/-7.2 % (T1rho). In vivo acquisitions produced high-quality parameter maps across platforms and field strengths. Conclusion: OpenMRF provides a robust, open-source, end-to-end Pulseq-based solution for MRF that enables reproducible sequence implementation, physics-accurate dictionary simulation, and advanced reconstruction across vendors and field strengths. By providing a unified platform for method development, comparison, and multi-site validation, OpenMRF aims to accelerate reproducible and harmonized quantitative MRI research within the community.

Precise and autonomous clocks are of fundamental interest and central importance to both foundational studies and practical applications. Here, we construct a blueprint for a quantum clock governed by time-independent interactions. By carefully-engineered coherent transport in dissipative spin chains, we achieve a scaling exponent at the precision-resolution trade-off fundamental bound, bringing this within reach of physically realistic and experimentally accessible systems. We further introduce a sudden-quench protocol that enables repeated operation through a simple initialization and detachment mechanism. Remarkably, the protocol is robust to imprecise detachment timing, implying that high-precision timekeeping can be achieved even when driven by a clock with much lower precision.

Magnetic fields with field strengths between $10^{-17}\,$G and a few Nanogauss are expected to exist today in the intergalactic medium (IGM). Their origin is unknown, but may be of primordial nature, in which case they would have influenced the thermal and ionization history of the IGM as well as the growth of small-scale matter perturbations. In this work, we revisit constraints on Primordial Magnetic fields (PMFs) by consistently accounting for their energy losses through ambipolar diffusion and decaying turbulences from recombination through the epoch of reionization, which progressively reduces the magnetic field strength over time. We implement these effects in ${\tt HyRec}$ and ${\tt exo21cmFAST}$ to model the interplay between PMFs and astrophysical processes up to reionization. Using a neural-network emulator (${\tt NNERO}$), we perform a MCMC analysis that combines late-time probes of the reionization history and galaxy UV luminosity functions. We find that including PMF energy losses significantly relaxes previous bounds, as the reduced field strength suppresses their imprint on observables. Employing a Fisher matrix analysis, we estimate the sensitivity of the 21$\,$cm signal experiment HERA to the PMFs' imprint on intergalactic medium perturbations and show that 21$\,$cm cosmology could significantly improve on current bounds. Our results highlight the importance of modeling PMF evolution self-consistently with the IGM evolution to extract current bounds and future sensitivities.

Using molecular dynamics simulations driven by a machine-learned interatomic potential, we investigate at low to intermediate pressures the $λ$-transition of sulfur, a temperature-induced polymerization. At ambient pressure, we capture the melting of crystalline cyclo-octasulfur into a liquid of molecular rings. Within this liquid, the concentration of non-S$_8$ rings increases with temperature; we show that these molecules act as reactive centers, which eventually trigger polymerization. We reproduce key experimental signatures of the $λ$-transition, including the sharp increase in heat capacity and the pronounced dependence of the transition temperature on the heating rate. Building on this, we reconstruct a phase diagram of polymerization up to intermediate pressures. Our results reveal a moderate decrease of the polymerization temperature with pressure, culminating with its merging with the melting line at a critical point. Beyond this point, we provide direct evidence of polymerization emerging from the crystalline phase. By analyzing temperature-ramp trajectories, we observe the formation of non-S$_8$ rings, open chains, and extended polymeric structures which retain features of the crystalline arrangement; further heating the system leads to disorder taking over through melting. Polymerization is therefore initiated slightly before melting. Altogether, our findings provide a microscopic picture of the $λ$-transition throughout the sulfur phase diagram.

The Square Kilometre Array (SKA) is expected to start science operations in 2030 and by that time there could be up to 10$^5$ artificial satellites in Earth's orbit, comprising an increase of an order of magnitude compared to 2024. Most of these new satellites will belong to satellite megaconstellations aimed at providing communication services all over Earth. These satellites create radio frequency interference (RFI) that can impact the observations of modern radio telescopes. In this Letter, we forecast the amount of observing time for which the SKA interferometers will be exposed to satellites, risking RFI contamination. We employed an analytical model and considered two cases of exposure to satellites; (1) satellites that only lie in the main beam and (2) satellites that lie in the main beam or the first sidelobe. We show that for SKA-Low, the exposure is high, with satellites in the beam for 30% of the observation time across half of the frequency range, rising up to 100% below 100 MHz. For SKA-Mid, high frequencies are mostly spared, but observations below 1 GHz could also end up seeing satellites for at least 30% of the time. We conclude that satellites will be unavoidable during SKA observing conditions, risking a strong impact on the RFI environment. This will necessitate a concerted effort to obtain accurate measurements of satellite RFI and to improve our understanding of the impact on various science cases. Finally, new mitigation techniques that are less data-destructive than simple flagging must be introduced.

This paper introduces the Wide-band Asp-Clean (\texttt{WAsp}) algorithm, a novel scale-sensitive image reconstruction method tailored for wide-band imaging applications. This algorithm is particularly beneficial for thermal noise-limited imaging with aperture synthesis telescopes, where joint spatio-frequency modeling of the sky brightness distribution is critical. The \texttt{WAsp} algorithm replaces the use of the MS-Clean algorithm in the MS-MFS algorithm with the {\tt Asp} algorithm \citep{Asp_Clean}, which itself has been improved for both imaging and runtime performance. With the high sensitivity of current and next-generation telescopes, spatio-frequency modeling in a scale-sensitive basis becomes crucial for ensuring that residuals align with the noise model across the frequency band. Although existing wide-band scale-sensitive algorithms have demonstrated superior performance over scale-insensitive counterparts, they often suffer from well-documented deficiencies, leading to significant wide-scale residuals in Stokes-I at low levels and consequently significant relative errors in spectral index maps. The \texttt{WAsp} algorithm addresses these limitations while maintaining computational efficiency. The implementation can be configured to support narrow-band and wide-band scale-sensitive imaging, spectral-cube imaging applications and joint single-dish and interferometer imaging. To demonstrate improved imaging performance, we show comparison with existing algorithms via carefully developed simulations for stress-testing the algorithms. We also present results from its application to real-world wide-band data, underscoring its effectiveness in practical imaging scenarios.

Employing high-vacuum electrospray deposition and scanning tunneling microscopy, we investigated how individual poly(3-hexylthiophene) (P3HT) chains navigated on the periodic energy landscape of a reconstructed Au(111) surface. The resulting polymer conformations were governed by the interplay between the periodically corrugated substrate, in particular the depth and regularity of the modulated surface potential, and thermal energy. On a regularly reconstructed surface, annealing at °C provided sufficient energy for chain segments to overcome energy barriers of the corrugated surface potential landscape, allowing monomers along the chain to experience a strong thermodynamic driving force toward the low-energy valleys on the surface. The adsorbed polymers adopted a state where the polymer conformations were replicating the herringbone pattern. By contrast, on an irregularly reconstructed surface, the correspondingly disordered potential landscape yielded a diverse mix of coiled polymer chains performing a two-dimensional random walk and collapsed chains located in troughs of the energy landscape. Intriguingly, annealing at °C forced polymers to form clusters of many chains. Our results establish that thermal energy and substrate topography represent control parameters for altering polymer conformations, providing a mechanistic framework for rationally designing polymer nanostructures at the molecular level.

The May 2024 solar superstorm highlighted the vulnerability of rapidly expanding low Earth orbit (LEO) satellite networks to severe space weather events. To systematically evaluate LEO network resilience, we introduce an open-source tool, CosmicDancePro. It enables a comprehensive analysis of the effects of solar storms in the LEO satellite network. It integrates real-world multimodal datasets, including space weather measurements from several satellites, upper-atmospheric density conditions from data-driven and high-fidelity physics-based models, and LEO satellite trajectory and LEO network measurement traces to quantify orbital decay driven by enhanced atmospheric density and network connectivity degradation. We utilize CosmicDancePro to analyze the Starlink constellation's behavior during two recent major solar storms. First, we identify the specific fleet management strategies Starlink adopts during the May 2024 solar superstorm and how they differ from its regular orbit-correction strategy. Second, we identify the mechanisms driving the previously unexplained 'W'-shaped altitude variation pattern across orbital planes of LEO constellations. Finally, our network-layer analysis quantifies the connectivity degradation during these storms, revealing transient disruptions that include repetitive short-lived outages, reconfiguration latency spikes above 500 ms, up to 60% increase in uplink loss, distorted diurnal latency patterns, and a 10+ Mbps drop in end-user data rates during storm peaks.

We use geometric Landau analysis to determine the singularity structure of four-point, one-cycle negative geometries in $\mathcal{N}=4$ super-Yang-Mills theory, which represent certain contributions to the logarithm of the four-point amplitude or equivalently the normalized quadrangular Wilson loop with a Lagrangian insertion. By analyzing the relevant Landau diagrams recursively, we prove that this quantity has singularities only at $z=-1,0$ and $\infty$ to all loop orders. This represents a first step towards obtaining a non-perturbative resummation for this quantity at next-to-leading order in the expansion over cycles.

A nonzero neutrino magnetic moment arises already in the minimally extended Standard Model with right-handed massive Dirac neutrinos. The well-known consequence of the neutrino magnetic moment is the phenomenon of neutrino spin oscillations in a magnetic field. It can manifest itself not only as a lack in the flux of active cosmic neutrinos arriving on Earth but also as characteristic features in low-energy neutrino elastic scattering processes. Following our approach developed earlier, in this work we study the influence of arbitrary spin-flavor state of incoming neutrino on low-energy neutrino scattering off different particles in a detector. We demonstrate that superposition of left- and right-handed helicity neutrino states gives rise to an azimuthal asymmetry in the angular distribution of the recoil momenta. We present numerical calculations for elastic neutrino scattering on electrons, protons and 40Ar and 132Xe nuclei, demonstrating the azimuthal-asymmetry effect. Our results indicate the unique potential of directional neutrino detection to observe the neutrino spin oscillations.

Hydrophobic eutectic solvents (HES) are emerging as sustainable alternatives to conventional organic solvents for the extraction of molecular pollutants from water. Yet, their selectivity remains poorly understood, hindering the predictive design of eutectic solvents beyond empirical success. Here, we present a multiscale strategy to rationalize and predict solute partitioning in HES. Focusing on bisphenol A (BPA) in trioctylphosphine oxide (TOPO):menthol as a prototypical system, we combine monophasic and biphasic molecular dynamics with quantum energy decomposition of dominant solvation motifs. Our methodology captures the experimentally measured BPA spontaneous migration and thermodynamic stabilization in the HES phase but also identifies the microscopic origin of selectivity: cooperative hydrogen bonding couples to strong dispersion and polarization in the hydrophobic eutectic microenvironment. The robustness of our workflow paves the way for the predictive in-silico screening and design of HES formulations for green and sustainable applications.

We present 3D kilonova radiative transfer simulations for a series of binary neutron star merger models. The masses of the neutron stars are varied as well as the total mass of the system and two different equations of state were used (SFHO and DD2), producing a range in dynamical ejecta masses and elemental abundance patterns. In this paper, we focus on the bolometric light curves and spectra in the polar direction for comparison with observations of the kilonova AT2017gfo. We calculate line-by-line opacities and include new calibrated lanthanide atomic data. All of the simulated spectra show strong features from Sr II, La III, Gd III and Ce III, which appear to correspond to features identified in AT2017gfo, although the simulated features are generally more blueshifted. The models with the lowest lanthanide fraction in the polar direction also show a Y II feature. Ce III, Ce II, Nd III and Nd II play an important role in shaping the spectral continuum. While the bolometric luminosities in the polar direction vary with the ejecta mass of each model, we find only little sensitivity of the spectral properties to the merger configuration. Our study demonstrates that dynamical ejecta alone can reproduce (although at earlier times) many spectral properties of AT2017gfo, suggesting dynamical ejecta may have a strong impact on the early spectral evolution. However, future simulations are needed to also elucidate the role of other ejecta components for shaping the kilonova spectrum.

Drug delivery to the brain is limited by the blood-brain barrier (BBB). We developed a capacitive micromachined ultrasonic transducer (CMUT)-based transcranial focused ultrasound system capable of both delivering therapy via BBB opening and monitoring microbubble activity across a broad frequency range. The performance of the geometrically focused half-ring array consisting of five transmitters and one receiving element was first assessed through simulations and in-vitro acoustic measurements with microbubbles. Use of phase-inversion (PI) during transmission effectively suppressed CMUT-generated harmonics and enhanced broadband detection of microbubble emissions. In rats, the same system achieved spatially localized BBB opening, confirmed by T1-weighted magnetic resonance imaging. BBB permeability mapping using dynamic contrast-enhanced magnetic resonance imaging (Ktrans) scaled with pressure. Time-resolved acoustic spectra captured microbubble arrival and decay kinetics, and 7-20dB enhancement in the effective dynamic range is observed with PI processing of acoustic emission signals. Together, these findings establish an integrated CMUT platform for combined therapeutic and sensing applications for BBB opening in small animal models, providing a foundation for future real-time, frequency-agile, closed-loop control of ultrasound-mediated drug delivery to the brain.

Dark matter and baryons acquire a relative velocity after decoupling in the early Universe. Baryons are gravitationally unstable only above their Jeans scale, while cold dark matter (CDM) is unstable on all scales. We show for the first time that their relative drift triggers a resonant gravitational instability that drives sound waves in baryons. When the projected DM drift is subsonic, the stable oscillatory branch of baryons resonates with the Doppler-shifted DM mode, producing exponentially growing perturbations whose growth rates exceed the intrinsic CDM growth rate. The instability peaks below the baryon Jeans scale and, in baryon-dominated environments, opens a window of complete stability between the Jeans scale and the resonance. Supersonic drift suppresses growth, as previously noted. The resonant coupling also transfers momentum between the species, creating a non-viscous, collisionless drag. We derive an accurate analytical approximation for the growth rate at resonance and show that the associated timescales range from years to tens of millions of years across diverse environments -- planets, protoplanetary disks, stars, molecular clouds, galaxies, and galaxy clusters -- typically much shorter than their ages. In an expanding FLRW universe, the instability enhances baryon density perturbations at different redshifts for appropriately oriented modes while suppressing the growth of those aligned with the DM stream. The universe thus sings across all scales, and this resonant mechanism provides the means to listen: it offers a novel probe of dark matter through its seismic imprint on astrophysical objects and may explain long-standing puzzles such as the persistence of spiral arms and the heating of the intracluster medium in galaxy clusters.

Near-term hardware is constrained by high error rates, small qubit counts, and relatively low output fidelity, making the execution of large, high performance quantum circuits difficult. Circuit partitioning (or circuit cutting) has emerged as a promising approach to circumvent these limitations by decomposing circuits into smaller subcircuits at two-qubit interaction points. However, it remains unclear which classes of circuits benefit the most from partitioning and under what hardware conditions it is most effective. In this work, we evaluate the suitability of quantum circuits for partitioning from three perspectives: improving fidelity, enabling distributed execution, and scaling to larger circuit sizes. Specifically, we compare uncut circuit execution against two circuit partitioning approaches: Qiskit's automatic cut finding technique and a custom performance optimized circuit cutting method. We also measure these across GHZ, QFT, brickwork, and random quantum circuits ranging from 4 to 14 qubits, using mean absolute error of expectation values and overall output fidelity. Our results show that partitioning benefits larger, highly interconnected circuits, with our custom method reducing error by up to 55\% and improve fidelity for GHZ circuits, but degrading performance for brickwork circuits at larger scales.