Solid state nuclear magnetic resonance (ss-NMR) is one of the most sensitive and popular techniques for structure elucidation in geometrically complex crystalline materials, such as zeolites. Synergistic support from computational modelling is vital to interpret experimental spectra, and relate ss-NMR to atomistic models. Nevertheless, computational predictions are hindered by the high expense of calculating magnetic shielding (MS) and electric field gradient (EFG) tensors from first principles. In this work, we leverage a novel tensorial machine learning approach to train a general model for predicting complete NMR tensors. We demonstrate the utility of the approach for a diverse dataset of zeolitic materials and NMR-active nuclei ($^{27}$Al, $^{29}$Si, $^{17}$O, $^{23}$Na and $^{1}$H), predicting all NMR observables to a high degree of precision. These observables are then translated into predictions of the full $^{27}$Al and $^{29}$Si ss-nMR spectra for the exemplary zeolite RTH. Thus, this work opens a pathway to accurate, high-throughput NMR simulation for large-scale and realistic models of chemically complex zeolites.

Microscopic understanding of liquid properties is essential for advancing a wide range of applications from energy applications such as nuclear reactors and batteries to biomedical applications including drug delivery and microfluidics. However, intrinsic dynamic disorder and lack of structural periodicity in liquids have presented fundamental challenges in developing rigorous microscopic theories of their thermodynamic and dynamic behavior. Recent breakthroughs in computational power and experimental metrologies have driven significant progress in unraveling the complex atomic scale dynamics of liquids. In this Review, we provide a brief historical context of liquid state physics and explore recent advances through theoretical, computational, and experimental approaches. For theoretical and computational approaches, instantaneous normal mode and velocity autocorrelation function calculations are discussed. For experiments, we focus on X-ray and neutron scattering techniques that probe liquid dynamics at the atomic level. Finally, we highlight emerging opportunities and future directions in the study of liquid atomic dynamics.

This paper explores the extent to which the circumgalactic medium (CGM) of Milky Way-like galaxies is located in an extended, ionized, disklike structure. To test this hypothesis, we analyze the spatial and kinematic distributions of different ion species within a sample of MW-like systems in IllustrisTNG. We model commonly observed ions (HI, MgII, SiIV, CIV and OVI) and calculate (1) their angular momentum misalignment from the star-forming disk ($θ$) and (2) the fraction of absorption consistent with galaxy rotation ($f_\mathrm{EWcorot}$). We find that 63% of MgII, 45% of SiIV, 38% of CIV, and 35% of OVI mass along the major axis have kinematics aligned with the galaxy angular momentum axis. We extend this to a mock absorption line survey and quantify $f_\mathrm{EWcorot}$. We find that $f_\mathrm{EWcorot}$(MgII) $\sim80\%$ and $f_\mathrm{EWcorot}$(OVI) $\sim60\%$ at $\sim0.5\ \mathrm{R_{200c}}$, in agreement with recent observational work. We find that in the typical MW analog, there is evidence of cool-warm material in an extended, corotating structure, regardless of whether the angular momentum or observational definition is used. Hence, we expect that the typical MW CGM, especially in the low ions, should be mainly on the plane.

Sodium-ion batteries require anodes that combine high capacity, low operating voltage, fast Na-ion transport, and mechanical stability, which conventional anodes struggle to deliver. Here, we use the SpookyNet machine-learning force field (MLFF) together with all-electron density-functional theory calculations to characterize Na storage in aminobenzene-functionalized Janus graphene (Na$_x$AB) at room-temperature. Simulations across state of charge reveal a three-stage storage mechanism-site-specific adsorption at aminobenzene groups and Na$_n$@AB$_m$ structure formation, followed by interlayer gallery filling-contrasting the multi-stage pore-, graphite-interlayer-, and defect-controlled behavior in hard carbon. This leads to an OCV profile with an extended low-voltage plateau of 0.15 V vs. Na/Na$^{+}$, an estimated gravimetric capacity of $\sim$400 mAh g$^{-1}$, negligible volume change, and Na diffusivities of $\sim10^{-6}$ cm$^{2}$ s$^{-1}$, two to three orders of magnitude higher than in hard carbon. Our results establish Janus aminobenzene-graphene as a promising, structurally defined high-capacity Na-ion anode and illustrate the power of MLFF-based simulations for characterizing electrode materials.

Plastic pollution has become a critical global challenge, with microplastics pervading ecosystems and entering human food chains. Effectively monitoring this widespread contamination demands rapid, reliable, and portable material identification techniques that often elude conventional Raman and FTIR spectroscopy. Undetected photon spectroscopy within a nonlinear interferometer (NLI) offers a solution, allowing the retrieval of mid-infrared absorption spectra by detecting only near-infrared signal photons using standard silicon-based technology. Here, we demonstrate a highly compact, micro-integrated, thermally-stabilised NLI with a Michelson-like geometry designed for the rapid spectroscopy of plastics. We benchmarked its room-temperature performance, demonstrating a signal-to-noise ratio of 34 with a measurement rate of 100 Hz and a spectral resolution of 6 cm$^{-1}$. We show that we can accurately and rapidly retrieve the characteristic vibrational absorption spectra of common polymers such as polypropylene, polyethene, and polystyrene, without using mid-infrared technology. These results establish our compact module as a promising field-deployable platform for robust, real-time environmental monitoring systems and other mid-infrared spectroscopy applications.

Dense neutrino media in core-collapse supernovae and neutron star mergers can experience collective flavor transformations in the form of neutrino oscillation waves. It was recently reported that the stable fast modes of collective oscillations can be damped through a mechanism similar to the Landau damping of plasma waves. In this work, we show that the actual damping rates of fast oscillation waves are usually very small and vanishes in the pure fast limit. This result does not affect the unstable modes that eventually drive collective neutrino flavor conversions in supernovae and neutron star mergers.

Global entanglement in quantum many-body systems is inherently nonlocal, raising the question of whether it can be inferred from local observations. We investigate this problem in monitored quantum circuits, where projective measurements generate classical records distributed across spacetime. Using graph neural networks (GNNs), we represent individual quantum trajectories as directed spacetime graphs and reconstruct the half-chain entanglement entropy from local measurement data alone. Because information propagates through the network via local message passing, the architecture directly controls the spacetime region over which correlations can be aggregated. By systematically varying this accessible scale -- through network depth and hierarchical spacetime coarse-graining -- we probe how much measurement information is required to reconstruct global entanglement. We find that prediction accuracy improves as the accessible spacetime region grows and that results from different architectures collapse when expressed in terms of an effective spacetime scale combining depth and coarse-graining. These results demonstrate that the information required to reconstruct global entanglement is organized in spacetime scales and show that graph-based learning architectures provide a controlled operational framework for probing how global quantum correlations emerge from local measurement data.

We derive a dressed-state master equation in Lindblad form for two strongly coupled two-level atoms. The resulting decay dynamics are governed by Lindblad operators that couple different dressed states. We show that the eigenvalues and eigenvectors of the Liouvillian can be obtained in a compact form, since each off-diagonal element in the dressed-state basis constitutes an eigenvector. Depending on the interatomic distance and the atomic transition frequency, two distinct time scales emerge. On a short time scale, the system relaxes toward two states, one of which corresponds to a transient, maximally entangled configuration. On a longer time scale, this entangled state gradually decays to the steady state.

Many natural and social science systems are described using probability distributions over elements that are related to each other: for instance, occupations with shared skills or species with similar traits. Standard information theory quantities such as entropies and $f$-divergences treat elements interchangeably and are blind to the similarity structure. We introduce a family of divergences that are sensitive to the geometry of the underlying domain. By virtue of being the Bregman divergences of structure-aware entropies, they provide a framework that retains several advantages of Kullback-Leibler divergence and Shannon entropy. Structure-aware divergences recover planted patterns in a synthetic clustering task that conventional divergences miss and are orders of magnitude faster than optimal transport distances. We demonstrate their applicability in economic geography and ecology, where structure plays an important role. Modelling different notions of occupation relatedness yields qualitatively different regionalisations of their geographic distribution. Our methods also reproduce established insights into functional $β$-diversity in ecology obtained with optimal transport methods.

The Quick-Look Pipeline (QLP; Huang et al. 2020, Kunimoto et al. 2021 and references therein) generates light curves for up to 2 million stars every 27.4 days observed by TESS as part of its planet search. As machine learning methods enable deeper searches and scientific priorities shift toward fainter stars, there is a motivation for QLP to perform better at fainter magnitudes. We have adopted the photometry methods employed by the TESS-Gaia Light Curve package (Han & Brandt 2023), which has been shown to have better noise characteristics than the original QLP photometry from 10.5 $<$ $T$ $<$ 13.5. We still perform aperture photometry and deliver 3 apertures, and 3 levels of detrending for all stars brighter than $T$ = 13.5, so the changes should be seamless for external users. This method is implemented as of Sector 94 in QLP light curves and is providing users with higher precision light curves and allows detection of fainter signals in our planet searches.

A search for Higgs boson (\PH) production at high transverse momentum ($p_\mathrm{T}$) in the WW decay channel is presented. The analysis uses proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded by the CMS experiment in 2016$-$2018, corresponding to an integrated luminosity of 138 fb$^{-1}$. The visible decay products of the Higgs boson are reconstructed as a single large-radius jet with one isolated lepton or none (1$\ell$ and 0$\ell$, respectively; $\ell$ = e, $μ$). The \PH-candidate jets are identified using an advanced transformer-based algorithm and are calibrated with the Lund jet plane reweighting technique. The 1$\ell$ channel is further split into gluon fusion, vector boson fusion, and associated production with hadronically decaying vector boson categories, while the 0$\ell$ channel considers all production processes inclusively. The measured cross section times the H $\to$ WW branching fraction relative to the standard model expectation is $μ$ = $-$0.19$^{+0.48}_{-0.46}$, indicating no evidence of a signal above the background. This measurement represents the first dedicated study of highly Lorentz-boosted H $\to$ WW decays, complementing earlier searches for high-$p_\mathrm{T}$ Higgs boson in other decay channels.

In this work, we theoretically propose a broadband asymmetric transmission (AT) device based on periodic Si nanoring arrays embedded in a SiO2 substrate. Results indicate that the device achieves a remarkable broadband AT effect in the near-infrared region (1750-2400 nm), with forward transmissivity exceeding 0.8 (maximum of 0.98), backward transmissivity less than 0.15 (minimum of 0.015) and an isolation ratio (IR) reaching a maximum of 17.8 dB at 2280 nm. Furthermore, the transmissivity spectrum exhibits excellent scalability and tunability through uniform scaling of the structure, allowing the operational band to be tailored across a wide spectral range, from 890 to 3300 nm. This Si-based nanostructure offers a robust and flexible platform for applications in optical isolation, multi-channel sensing, and integrated photonic circuits.

We use the theoretical framework introduced in the companion paper to provide simple formulas as regards the resonance conditions for CBET with smoothed laser beams.Our analytical CBET model with optical smoothing shows that these fusion-critical lasers produce a significantly broader resonance than conventional plane wave models predict. In particular, temporal smoothing, as used in many high energy laser facilities, and flow components normal to the CBET ion acoustic waves, significantly modify the power transfer between smoothed beams. Our model predicts that the energy transfer rate out of resonance is substantially higher with optical smoothing than without, a result that has profound implications for optimizing predicting and interpreting future fusion experiments. We provide a simple criterion which pinpoints the laser and plasma parameters for which laser smoothing impacts CBET. These findings pave the way for experimental investigations in high-energy-density physics and fusion energy.

We present a systematic analysis of high-cadence optical light curves of the non-blazar AGN PKS~0521$-$36 obtained with \textit{TESS} across three sectors: Sectors~5 and~6 (Cycle~1, 30~min cadence) and Sector~32 (Cycle~3, 10~min cadence). The source exhibits moderate variability with $F_\mathrm{var} \approx 0.69$--$1.19\%$, consistent with a mildly beamed jet. The power spectral density (PSD) in all sectors is better described by a bending power-law than a simple power law, with high-frequency slopes $α_1 \approx 2.1$--2.9, indicating red-noise dominated variability. Flux distributions require two-component models, with a double log-normal providing the best description, suggesting the presence of two distinct optical flux states associated with quiescent jet emission and episodic flaring activity. A significant QPO at $P = 2.838 \pm 0.078$~d is detected in Sector~5 at $>99.99\%$ confidence in the Lomb--Scargle periodogram, independently confirmed by WWZ ($2.839 \pm 0.110$~d) and supported at the $3σ$ level by DRW analysis. The signal spans $\sim$9 cycles within the 26.1-day baseline in Sector~5 and is absent in Sectors~6 and~32, indicating a transient feature. The PSD bending frequency ($ν_b \approx 0.308$~d$^{-1}$; $\sim$3.2~d) is consistent with the QPO period, suggesting a common origin. We interpret the oscillation as magnetohydrodynamic kink instabilities in the relativistic jet, consistent with the observed helicoidal structure. A moderate Doppler factor ($δ\approx 5$--10) naturally explains the day-scale periodicity. Together with previously reported $γ$-ray QPOs on longer timescales, this suggests a hierarchical variability structure, and, to the best of our knowledge, provides the first evidence for an optical QPO in a non-blazar AGN with a directly imaged helical jet.

This is the fourth paper in the CayleyPy project, which applies AI methods to the exploration of large graphs. In this work, we suggest the existence of a new discrete version of holographic string dualities for this setup, and discuss their relevance to AI systems and mathematics. Many modern AI tasks -- such as those addressed by GPT-style language models or RL systems -- can be viewed as direct analogues of predicting particle trajectories on graphs. We investigate this problem for a large family of Cayley graphs, for which we show that surprisingly it admits a dual description in terms of discrete strings. We hypothesize that such dualities may extend to a range of AI systems where they can lead to more efficient computational approaches. In particular, string holographic images of states are proposed as natural candidates for data embeddings, motivated by the "complexity = volume" principle in AdS/CFT. For Cayley graphs of the symmetric group S_n, our results indicate that the corresponding dual objects are flat, planar polygons. The diameter of the graph is equal to the number of integer points inside the polygon scaled by n. Vertices of the graph can be mapped holographically to paths inside the polygon, and the usual graph distances correspond to the area under the paths, thus directly realising the "complexity = volume" paradigm. We also find evidence for continuous CFTs and dual strings in the large n limit. We confirm this picture and other aspects of the duality in a large initial set of examples. We also present new datasets (obtained by a combination of ML and conventional tools) which should be instrumental in establishing the duality for more general cases.

Recent advances in large language models (LLMs) have enabled the automation of an increasing number of programming tasks, including code generation for scientific and engineering domains. In rapidly evolving software ecosystems such as quantum software development, where frameworks expose complex abstractions, a central question is how best to incorporate domain knowledge into LLM-based assistants while preserving maintainability as libraries evolve. In this work, we study specialization strategies for Qiskit code generation using the Qiskit-HumanEval benchmark. We compare a parameter-specialized fine-tuned baseline introduced in prior work against a range of recent general-purpose LLMs enhanced with retrieval-augmented generation (RAG) and agent-based inference with execution feedback. Our results show that modern general-purpose LLMs consistently outperform the parameter-specialized baseline. While the fine-tuned model achieves approximately 47% pass@1 on Qiskit-HumanEval, recent general-purpose models reach 60-65% under zero-shot and retrieval-augmented settings, and up to 85% for the strongest evaluated model when combined with iterative execution-feedback agents -representing an improvement of more than 20% over zero-shot general-purpose performance and more than 35% over the parameter-specialized baseline. Agentic execution feedback yields the most consistent improvements, albeit at increased runtime cost, while RAG provides modest and model-dependent gains. These findings indicate that performance gains can be achieved without domain-specific fine-tuning, instead relying on inference-time augmentation, thereby enabling a more flexible and maintainable approach to LLM-assisted quantum software development.

In the context of inertial confinement fusion (ICF) experiments, spatial and temporal laser beam smoothing techniques are used to control the beams propagation in hohlraum plasmas. Currently, spatial and temporal smoothing are either neglected or not properly taken into account in the inline cross beam energy transfer (CBET) models included in the hydrodynamic codes dedicated to the design of these experiments. In some cases, which we will highlight in this study, this simplification leads to important errors in the power transfer of importance for the implosion symmetry of the capsule, either in the direct or indirect drive ICF configurations. In a recent study [A. Oudin et \textit{al}., Phys. Plasmas \textbf{32}, 042706 (2025)], we demonstrated the necessity of accounting for spatial smoothing when modeling CBET, provided that the beams do not have the same wavelength. This work presents a linear kinetic model compared with Hera paraxial fluid simulations and compared with the Smilei particle-in-cell code, demonstrating the important influence of smoothing by spectral dispersion on CBET. Moreover, we demonstrate the importance of accounting for the plasma velocity profile, the beam modulation bandwidth, and the spectral dispersion to better predict the power exchanged between the beams. Additionally, we reveal a strong sensitivity of this power transfer to the synchronization of the phase modulators.

The thermodynamic critical field $B_{\rm c}$ provides direct access to the superconducting condensation energy, yet its pressure dependence has been studied much less extensively than that of the transition temperature. Here, muon-spin-rotation/relaxation measurements of the thermodynamic critical field $B_{\rm c}$ of elemental Pb under hydrostatic pressure up to $\simeq2.3$ GPa are reported. From the magnetic-field distribution in the intermediate state, $B_{\rm c}(T)$ is determined and $B_{\rm c}(0)$ is extracted at different pressures. In combination with previously reported high-pressure data for $B_{\rm c}$ and $T_{\rm c}$, it is shown that the pressure dependence of $B_{\rm c}(0)$ follows that of the superconducting gap $Δ(0)$ more closely than that of the transition temperature $T_{\rm c}$. At higher pressures, the logarithmic pressure derivatives of $B_{\rm c}(0)$ and $T_{\rm c}$ are found to converge, indicating that the coupling strengths ratio $α=Δ(0)/k_{\rm B}T_{\rm c}$ becomes nearly pressure independent. This behavior is interpreted as thermodynamic evidence for a pressure-driven crossover from strong- to weak-coupling superconductivity in Pb.

Optical skyrmions are members of the emerging topological branch of solid-state physics and photonics, allowing for control over topological light textures through light-matter interactions. However, in nanophotonics their practical application has been severely limited by high inherent losses in plasmonic materials, resulting in the lack of tunability between different topological properties. Here, we utilize the strong dispersion of silicon carbide thin films to realize highly confined surface phonon-polariton skyrmion lattices, which we image via near-field microscopy. We experimentally demonstrate topological tuning between bubble- and Néel-type skyrmions, a unique advantage that polar dielectrics offer over most existing approaches. Changing the excitation wavelength by only 10% switches the skyrmion type, revealed by examination of the skyrmion number density contrast. Analysis of domain wall size and steepness in analogy to magnetic materials also confirms this transition. Our results are a starting point to investigate other topological features in phononic systems such as merons, skyrmion bags, and other complex structured light fields. Furthermore, strong light-matter hybridization and nonlinear effects owing to anharmonicity of the phonons may be observed in the future, possibly leading towards the discovery of polaritonic skyrmion-skyrmion interactions and hence applications in topology-based information processing.

Previous experimental reports of long-range interactions in ionic liquids (ILs) stand in contradiction with theoretical predictions and numerical simulations. To provide insights into the literature discrepancies regarding the experimental ranges of electrostatic screening, claimed with orders of magnitude larger, the interactions between pairs of mica and borosilicate surfaces confining ILs are investigated by two complementary advanced Surface Force Apparatuses. Regardless of differences in confinement geometries (crossed-cylinders, sphere-flat), radii of curvature (cm-mm), and measurement techniques (stepwise vs continuous approach), two ever present force regimes are evidenced. At small surface separations, oscillatory forces reflect IL structuration and layering, while outside this gap, the interaction is monotonic repulsive. In both regimes the spatial extent and force magnitude depend critically on motion conditions, as demonstrated by achieving velocities as low as 9 pm/s with equilibration times up to 90 s. At large separations, fast surface displacements generate long-range interactions (over tens of ion size) creating the illusion of anomalous underscreening, whereas increasingly slow ones shrink both magnitude and range of the repulsion with decay-lengths converging ultimately to a screening length consistent with Poisson-Boltzmann theory with finite ion sizes. The transition from apparent long-range to short-range screening unfolds over nearly two orders of magnitude in time, revealing slow relaxation dynamics reminiscent of aging phenomena. These findings definitely resolve a decade-old controversy on force measurements and reveal rich out-of-equilibrium dynamics. The hydrodynamic contribution to the net force is admittedly crucial to be reduced especially when relaxations span decades in time, but approaching thermodynamic equilibrium during measurements proves essential.

Quantum anomalies arise when symmetries of a classical theory cannot be preserved upon quantization, leading to unconventional topological responses. A prominent example is the parity anomaly of a single two-dimensional Dirac fermion, which enforces a half-quantized Hall response. Anomaly inflow mechanism allows this effect to be observed at the surfaces of three-dimensional topological insulators, however, its realization in a genuinely two-dimensional system has remained elusive. Here we report the observation of a parity-anomalous Hall response at the critical point of a quantum Hall topological phase transition in a synthetic two-dimensional system of ultracold dysprosium atoms. By coupling a continuous spatial dimension to a finite synthetic dimension encoded in atomic spin states, we engineer tunable Chern bands with C = 0 and 1. At the transition, the bulk gap closes at a single Dirac point, where we observe a robust half-quantized Hall drift despite strong non-adiabatic excitations. We show that this response originates from the global structure of the band topology, is protected by an emergent parity symmetry at criticality, and disappears when parity is explicitly broken. Our work establishes synthetic quantum systems as a powerful platform to probe quantum anomalies and their interplay with topology and non-equilibrium dynamics.

Mass loss from massive stars located in the part of the Hertzsprung-Russell diagram (HRD) where we find luminous blue variables (LBVs) is profoundly important for stellar evolution yet poorly understood. We use time-dependent radiation-hydrodynamic (RHD) simulations to examine the atmosphere and wind properties of such massive stars, computing 2D and 1D RHD models of the coupled envelopes, atmospheres, and wind outflows, tuned to this region in the HRD. Our unified simulations start deep in the stellar envelope (well below T ~ 200 kK) and include the outflowing wind, accounting for line-driving, radiative enthalpy, and photon tiring. Mass-loss rates, wind speeds, and the radiative luminosity at the photosphere are emergent properties in the simulations. A grid of models is created by slightly increasing the stellar energy at the lower boundary. This results in a natural transition from very turbulent atmospheres with line-driven winds to effectively stationary super-Eddington massive outflows. Our sub-Eddington models are essentially blue hypergiant stars with very variable surfaces, effective mass-loss rates $\dot{M} \sim 2 - 5 \times 10^{-5}$ $M_{\odot}$/year, and wind speeds $v_{\infty} \sim 200 - 300$ km/s, resembling quiescent LBVs like P Cygni. The super-Eddington models have optically thick wind envelopes and extremely inflated yellow surfaces (Teff ~ 5000 K), $\dot{M} \sim 0.1 - 1$ $M_{\odot}$/year, and $v_{\infty} \sim 400 - 500$ km/s, resembling a massive star during a great eruption like eta Carinae's. Our models naturally reproduce the overall characteristic stellar and wind parameters inferred for massive stars in their quiescent LBV and yellow giant eruptive phases. It remains an open question whether the energy increase needed to trigger a giant eruption can be obtained solely by the internal evolution of the star itself or if it requires an external energy source.

Galaxies frequently interact with nearby systems, a process that can significantly alter their morphology and star formation activity. However, spectroscopic studies of their faint and diffuse remnants require very long exposure times and often exceed the limited field of view of integral field units (IFUs). On the other hand, broad-band imaging can have a much wider field of view, but lacks the spectral resolution to identify key spectral features, restricting accurate constraints on stellar population properties. With its 54 narrow-band filters in the optical and wide coverage (planned 8000 square degrees), J-PAS fills this gap. In this case study, we examine PGC 3087775, a massive galaxy at z = 0.046179 (~ 201 Mpc) in the later stages of a major merger in the J-PAS early data release. Photometry was validated with MaNGA IFU data (for the central part). Stellar population properties was derived using both J-PAS and SDSS photometry. SDSS indicates a metal-rich population with an extended star formation history (SFH) and elevated star formation rates. J-PAS instead points to a less metal-rich population with moderate extinction and a more rapid SFH, consistent with a quenched stellar population. The average Dn(4000) index of the tidal features is 1.24, suggesting that it was a non-dry merger and a fourfold improvement in the precision of stellar mass and Dn (4000) was found with J-PAS. We also assessed two heuristic methods for estimating the mass-to-light ratio from SDSS filters and found that they overestimate the stellar mass in this galaxy by 0.5 dex and 0.4 dex relative to SED fitting results from J-PAS and SDSS, respectively. Future work will extend this analysis to a larger sample of merging galaxies and evolution of the stellar populations of such structures across the nearby Universe to unprecedented detail. This project is fully reproducible, through Maneage (commit 0f0d7e2).

We investigate s-wave kaon condensation in dense matter and neutron stars within the updated Chiral Mean Field model with an improved meson description (mCMF), which incorporates dynamically generated in-medium meson masses arising from explicit chiral symmetry breaking and vector-meson self-interactions. In contrast to conventional relativistic mean-field descriptions with constant meson masses, the mCMF framework introduces a self-consistent feedback between the meson sector and the dense-matter equations of motion. The kaon dispersion relation is derived from the nonlinear SU(3) Lagrangian, including the Weinberg-Tomozawa interaction and additional baryon-pseudoscalar couplings, and the onset of condensation is determined under conditions of charge neutrality and $β$ equilibrium. Our calculations include the full baryon octet together with electrons and muons at zero temperature. We analyze the impact of hyperons, muons, and kaon condensation on the equation of state, on neutron-star mass--radius relations, and neutron-star thermal evolution, and examine the sensitivity of the onset density and stellar properties to variations in the nucleon--kaon scattering length and to different model vector parameters and vector self-interactions. We find that $K^{-}$ condensation sets in between $n \sim (2-8)\, n_0$ (in units of nuclear saturation density) and leads to a moderate to strong softening (in one case, a slight stiffening of the equation of state), depending on the interplay of kaons and hyperons, while remaining compatible with current $2\,M_\odot$ and small-radius neutron-star observational constraints and producing distinguishable behavior in the neutron-star cooling. This work provides an improved and thermodynamically consistent framework for studying exotic degrees of freedom in neutron-star matter.

Recently, a class of spin chains known as ``free fermions in disguise'' (FFD) has been discovered, which possess hidden free-fermion spectra even though they are not solvable via the standard Jordan-Wigner transformation. In this work, we extend this FFD framework to open quantum systems governed by the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) equation. We establish a general class of exactly solvable open quantum systems within the FFD framework: if the Liouvillian frustration graph is claw-free and has a simplicial clique, the Liouvillian possesses a hidden free-fermion spectrum. In particular, the (even-hole, claw)-free condition automatically guarantees this, enabling exact computation of the Liouvillian gap and an infinite-temperature autocorrelation function. Our results provide the first realization of the FFD mechanism in open quantum systems.

We expand upon our paper (Rice and Yeates, 2021) which introduced `Outflow Fields': alternatives to the widely-used potential field source surface (PFSS) extrapolations of the Sun's coronal magnetic field which take into account the effect of the solar Wind. We showed that our fields have several advantages over PFSS, namely more accurate measurements of the Open Solar Flux (OSF) relative to observations, more realistic streamer shapes and less dependence on the arbitrary source-surface height. In this paper we seek to quantify these improvements. This includes comparison of magnetic field line angles with eclipse photography, an improved solar wind solution model and the introduction of data from a wider range of observations. We use these comparisons to determine the optimum parameters for our model using an evolutionary algorithm, in addition to the creation of synthetic eclipse images. We find that our Outflow Fields can accurately capture the overall topology of the magnetic field, and reduce the well-known discrepancy with in-situ magnetic field measurements by a significant margin relative to PFSS. Specifically, over the period between 2000 and 2022 for a typical source-surface height we find that optimized Outflow fields reduce this discrepancy from around 45% to 24% while also matching the field line topology seen during eclipse photography. Our model is presented for wider use by the community as a new python package "outflowpy".

The basic and effective reproduction numbers are widely used metrics for characterizing the dynamics of infectious disease epidemics. However, the interpretation of these numbers is based on the assumption of homogeneous mixing and may not hold in real-world populations where the contact patterns deviate from that assumption. In this paper, we present a network-based framework to compare reproduction numbers in populations with and without spatial structure, while other parameters of the disease remain fixed. Using this framework, we show that in homogeneously mixed populations, in the absence of external interventions, the effective reproduction number decreases exponentially as the susceptible population declines. In contrast, in spatially structured populations, the basic reproduction number is smaller, and the effective reproduction number initially decreases faster but eventually converges to unity. We show that the reproduction number is determined by the level of competition between infectious nodes, which is governed by the network structure. Our results suggest that without knowledge of the network structure, reproduction numbers may not be informative for parameterizing the contagiousness of the disease or predicting the behavior of epidemic spreading.

Charge transport in superconducting junctions at finite voltages is governed by Andreev reflections, including multiple Andreev reflections, which are processes that enable multiple charge transfer, a hallmark that shot noise can directly quantify. Since the effective charge extracted from shot noise measurements varies with the transparency of the junction, systematic control of transparency is essential but experimentally challenging. Here, we present shot noise scanning tunneling microscopy measurements enabled by a newly developed amplifier, allowing access to different transparency regimes. We perform shot noise measurements on Pb(111) with tunable transparency at 2.2 K and observe that the shot noise evolves from a single electron tunneling regime to multiple charge transfer regime as transparency increases. Our results are quantitatively consistent with theoretical simulations of Andreev reflections and multiple Andreev reflections for a single-channel system. These results establish junction transparency as the key parameter governing the evolution of charge transport and demonstrate that noise-STM is a powerful platform for investigating microscopic charge transport mechanisms with controlled junction transparency at the atomic scale.

Manganese(II) mobilised by mining activity poses a persistent water-quality challenge, yet the mechanisms by which low-cost sorbents, such as biochar, sequester Mn(II) remain poorly resolved. This study identifies the specific chemical drivers of Mn(II) sequestration by combining fixed-bed column and batch experiments with atomistic molecular dynamics simulations. Oilseed rape straw biochars, produced at 350\textdegree C, 550\textdegree C, and 700\textdegree C, removed 20-50% of dissolved Mn from acidic influent (pH 4, 5 ppm). High-temperature biochar achieved the greatest removal ($\sim$50%) and rapidly increased effluent pH to 9, triggering alkaline precipitation. Conversely, lower-temperature biochars removed 20-30% of Mn while maintaining a near-neutral pH (7-7.5). Enhanced \ce{K+} release in these systems indicates significant cation exchange and non-precipitative pathways. Molecular simulations confirmed that while neutral surfaces show weak Mn(II) association, deprotonated sites drive strong adsorption through inner-sphere complexation ($\sim$50% removal) and outer-sphere association ($\sim$10%). These results establish a mechanistic framework to distinguish between precipitation-led and surface-complexation-led removal. By providing specific chemical criteria for Mn-targeted sequestration, this work enables the rational design of engineered biochars for sustainable water remediation.

Ansatz selection is a key factor in the performance of variational quantum algorithms (VQAs). While much of the state-of-the-art still relies on heuristic choices, an inadequate circuit structure can compromise both the expressive power and the trainability of the resulting model. Recent results have also established theoretical connections between expressibility and the onset of barren plateaus, highlighting the need for systematic criteria for ansatz selection. In this work, the ansatz is treated as a design feature to be optimized rather than a fixed block, and a design space exploration (DSE) is performed over a diverse set of parametrized quantum circuits (PQCs). Three complementary metrics -- expressibility, trainability, and resource cost -- are evaluated and used to analyze the trade-offs that emerge across different PQCs. Beyond identifying Pareto-optimal candidates, this multi-objective perspective helps clarify the interplay between these metrics and contributes quantitative evidence toward understanding the expressibility--trainability tension in variational circuits.