Papers

Imaginary phonon modes remain a practical bottleneck in computational materials screening because otherwise plausible structures can be locally dynamically unstable under a chosen workflow. Here we present PhononBench-MP40, a spectrum-resolved benchmark dataset of Materials Project-derived crystals for workflow-defined phonon stability. The dataset starts from 47,969 MP40 workflow tasks and provides 46,899 completed records with paired stability labels and local phonopy YAML spectra, including 16,683 Stable records and 30,216 completed-phonon unstable records. A further 1,067 relaxation failures are reported separately rather than merged into the completed phonon denominator. The release centers on the local YAML spectrum: the stability label, the lowest sampled frequency and any threshold-dependent relabeling are derived from that spectrum. The dataset is openly available through Science Data Bank at https://doi.org/10.57760/sciencedb.38735. A companion GitHub repository provides the calculation code and lightweight access utilities. PhononBench-MP40 provides an auditable reference for workflow-defined stability classification, minimum-frequency analysis, threshold studies and failure-aware triage, while keeping the reference workflow, data schema and interpretation boundaries explicit.

The weak cosmic censorship conjecture (\WCCC) and the weak gravity conjecture (\WGC) sit at the heart of how singularities and charged matter are constrained in any consistent theory of quantum gravity. We study both conjectures for Einstein–Maxwell–power–Yang–Mills–AdS (\EMPYM) black holes, a family in which a non-Abelian power-law Yang–Mills field, a Maxwell field, and a negative cosmological constant act together. Working in holographic conformal field theory (CFT) thermodynamics, we treat the central charge $C$ and the CFT volume $\Vol$ as independent variables and obtain an extended first law that carries variations of the Yang–Mills charge and of the nonlinearity exponent $\gamma$. A closed Euler relation follows, and we check it together with the full set of equations of state to machine precision. The perturbation is a charged massive scalar minimally coupled to the Maxwell sector. Its horizon energy and charge fluxes fix the superradiance threshold $\omega<\tilde{q}_s\tilde{\phi}_h$, and the mass–energy relation $\mu_s=\omega$ turns this into the bound $\tilde{q}_s/\mu_s>r_{\min}/\tilde{Q}$. Expanding the metric function $f(r)$ about its minimum for extremal and near-extremal configurations, we find that absorption keeps $f_{\min}$ non-positive, so the horizon survives. The Yang–Mills sector lowers the effective charge-to-mass threshold below the Reissner–Nordström value of unity, while a local stability analysis locates a Davies point in the heat capacity. Across the parameter range examined the \EMPYM\ family respects cosmic censorship under scalar perturbations, with the \WGC\ fixing only the direction of evolution toward or away from extremality.

High performance computers produce extreme-scale data sets that require sampling or compression if they are to be used to their full potential. Existing data compression techniques typically exploit features such as sparsity in the data, homogeneity in the data, or a priori knowledge of what subsets of data are of most interest. Fluid dynamics data in general do not exhibit these features and so are attractive test beds for generic compression techniques that are objective, robust, and tuneable with respect to information lost due to compression. Presented here is a method based on tensor networks, specifically matrix product states or tensor trains, that meets these requirements. The method is demonstrated for compression in one-dimension and is extensible to higher dimensionality. Lossless compression is demonstrated for random Fourier series for sufficiently high bond dimension of the tensor network, with the memory required to store the tensor network scaling directly proportional to the bond dimension. The lossy compression exhibited at lower bond dimension can be well within the relative error of many fluid simulations. The compression algorithm is tested for the time evolution of Burger's equation with excellent results. We additionally demonstrate the capability to perform computations in the compressed form through a tensor network periodic convolution that can be orders of magnitude faster than using fast Fourier transforms and the convolution theorem. In addition to being an attractive method for working with data sets generated by existing computers, the tensor network methods utilised are directly translatable to the emerging paradigm of quantum computing.

While renewable energy sources are the fastest-growing electricity generation technology globally, their competitiveness is still the subject of controversy. This paper presents an electricity system model for investment and dispatch to determine the cost-optimal shares of renewable energy sources. We compute and analyse renewable generation shares in market equilibrium for Germany and Texas, using annual data for 2015 to 2024, and in five-year intervals for 2030 to 2050. Furthermore, we identify the key drivers of the renewable competitiveness and quantify their contribution through parameter variations. Our results show that renewable generation achieves considerable market shares even without subsidies. In Germany, the increase in renewable generation is primarily driven by CO2 pricing, complemented by declining investment costs for renewable technologies. In Texas, solar PV is part of the cost-optimal system, even in the absence of CO2 pricing and despite low natural gas prices.

On the centennial of Erdal İnönü's birth, this article reflects on his scientific legacy and his role in shaping modern theoretical physics in Türkiye. We briefly discuss his life, scientific vision, and contributions to academic institutions, and then turn to his most celebrated scientific achievement: the İnönü-Wigner contraction. Through the simple geometric example of a sphere becoming a plane, we present an accessible introduction to this important idea and its significance for modern physics.

PyCBC Live is a low-latency search pipeline that identifies gravitational waves from compact binary coalescences and provides alerts for electromagnetic follow-up. This paper presents improvements to PyCBC Live that were implemented for the fourth observing run (O4) of the LIGO-Virgo-KAGRA network, which operated from May 2023 to November 2025. The ranking statistic was enhanced to incorporate time-dependent background modeling using data quality streams and daily updates of the noise model. Follow-up capabilities were improved through refined probability of astrophysical origin calculations, optimized SNR recovery with reduced computational cost, and a method to incorporate Virgo as a sky-map-only detector. An Early Warning search was implemented to detect binary neutron star and neutron star-black hole systems before merger, providing pre-merger alerts with a pipeline latency of 2.5-3.5 seconds and warning times up to 60 seconds before coalescence. The autogating procedure was extended to apply to the full strain buffer rather than individual analysis segments, improving rejection of loud and rapidly successive glitches. Performance validation using the Mock Data Challenge showed sensitivity improvements of factors of 1.7 to 2.3 for the coincident search depending on source mass at an inverse false alarm rate of 10 years, and factors of 1.3 to 1.7 for the single-detector search. For injections in two-detector time, the O4 configuration identified 1979 of 2495 injections with a decisive SNR greater than 6 at a false alarm rate below one per year (79.3%), compared to 1262 (50.6%) with the O3 configuration. For injections in single-detector time, the O4 configuration identified 218 of 1174 injections (18.6%), compared to 170 (14.5%) with the O3 configuration. The search maintained a median latency of 15.94 seconds from merger to candidate upload.

Smartphone telephoto cameras are approaching a "telephoto physics wall": as pixel pitches shrink toward sub-0.5 micron, the optics remain limited by geometric aberrations, leading to diminishing returns on resolution. Traditional Image Signal Processors (ISPs) cannot eliminate these aberrations, because they operate through local, stage-wise processing with no explicit model of the underlying point spread function (PSF). We demonstrate how a learning-based Neural ISP for image restoration, trained on the underlying degradations, inverts what stage-wise pipelines cannot, turning small-pixel designs into a net advantage. We investigate this through a controlled simulation of a representative telephoto module, evaluating five configurations (0.35–0.75 micron pixel pitch). The aperture is scaled proportionally to keep per-pixel SNR and diffraction spot size fixed, thereby isolating geometric aberration and spatial sampling. While the traditional ISP improves only modestly with smaller pixels, the Neural ISP scales substantially: at 0.35 micron} it reaches 745 cycles/mm MTF50 (vertical), a 2.5–3x resolution improvement over the traditional ISP, and LPIPS improves significantly from 0.244 to 0.151 while traditional results stay comparatively flat. In a low-SNR extension (15 dB per-frame bursts at 0.35 micron), a multi-frame Neural ISP recovers performance close to the bright-light single-frame baseline, whereas a multi-frame traditional ISP shows no meaningful improvement – indicating that traditional pipelines at small pixels are bottlenecked by uncorrected PSF blur rather than by noise. These results point to a design philosophy in which Neural ISPs enable high-resolution telephoto modules by correcting residual optical aberrations rather than requiring increasingly complex optics.

Multicomponent colloidal gels can exhibit mechanical responses that depend not only on interaction strengths but also on the temporal pathway by which their networks form. Here, we use particle-based simulations to investigate the steady-shear deformation of binary colloidal gels assembled by sequential gelation with tunable delay time and dominant interspecies attractions. Although varying the gelation delay produces markedly different quiescent morphologies, ranging from well-mixed networks to coarse shell-core structures, steady shear drives the systems toward structurally convergent, mixed states as quantified by cluster, connected-component, and coordination analyses. This structural convergence, however, does not imply rheological equivalence. The transient stress response remains strongly dependent on gelation delay and interspecies attraction strength. For moderate interspecies attractions, increasing delay enhances the stress overshoot, particularly at high shear rates. For stronger interspecies attractions, initially heterogeneous gels exhibit two-step yielding at low shear rates, indicating distinct deformation and restructuring processes. These results show that sequential gelation can imprint a persistent rheological memory in binary colloidal gels, even when shear substantially erases differences in common structural descriptors.

The Gaussian approximation for thermodynamic fluctuations of host magnetization fails for ferromagnetic semiconductors like GdN near their Curie temperature $T_c$, producing unphysical instabilities in the bound magnetic polaron (BMP) free energy. We extend the Dietl-Spalek theory beyond the Gaussian level by incorporating full cubic and quartic anharmonic terms of the Ginzburg-Landau-Wilson functional. We develop two complementary generalizations: (i) a perturbative non-local treatment for finite spin correlation length, and (ii) a non-perturbative resummation of local fluctuations into a closed exponential form. The latter is rigorously justified by a novel polaronic Ginzburg criterion. By including variational optimization of the donor orbital radius, we quantitatively describe magnetic self-trapping. Applied to GdN ($T_c \approx 55$ K), our theory eliminates Gaussian divergences and predicts thermodynamically stable, ferromagnetically ordered BMPs with spontaneous internal spin splitting persisting deep into the paramagnetic phase. For realistic exchange coupling ($J_c = 400$ meV), this robust local ordering survives up to $T^* \approx 155-160$ K. Pronounced orbital contraction signals magnetic self-trapping, with a characteristic kink in the optimal Bohr radius at $T_{char} \approx 79$ K. We reveal a critical exchange threshold ($J_c \approx 330$ meV) triggering cooperative collapse into a highly localized small polaron state, and identify an optimal donor-concentration window near the metal-insulator transition maximizing $T^*$. These results establish a microscopic mechanism for persistent BMP-mediated ferromagnetism well above bulk $T_c$ via polaron percolation, suggesting clear experimental signatures (optical spin splitting, anomalous magnetoresistance) testable in GdN and related compounds.

Data from large physics collider experiments in Quantum Chromodynamics (QCD) research differ fundamentally from the modalities used in modern foundation models. The heterogeneity of detector readouts and their technology dependence require principled curation for cross experiment AI applications. We present a design framework for AI-ready QCD data to define a unified data structure that accommodates heterogeneous detector technologies within a single schema. We apply the design principle to the simulated data of the Barrel Imaging Calorimeter (BIC) in the ePIC detector at the Electron–Ion Collider. The BIC simulation data combines AstroPix silicon pixel imaging layers with Pb/ScFi calorimeter layers across different readout types. We describe the schema specialization, data preparation pipeline, and visualization of the curated AI-ready dataset.

Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightweight error detection can meaningfully improve algorithmic success rates. Existing compilation and error-detection toolchains treat these concerns in isolation, with no principled way to balance detection overhead against success probability under latency constraints. We present an integrated hardware-aware compilation and data-driven quantum error-detection (QED) framework that jointly optimises qubit mapping, SWAP insertion, and syndrome-schedule placement via a noise-weighted cost function and a learned multi-objective scheduler. Simulation experiments on an HPC cluster using GPU-accelerated density-matrix simulation (NVIDIA cuQuantum SDK) across VQE, phase-estimation, and Grover benchmarks, three noise profiles, and circuit sizes of 6-20 qubits (depths 10-160), show that joint co-design raises algorithmic success probability by up to 68 percent (95 percent CI: 60 percent to 76 percent) over SABRE on an 8-qubit VQE instance with post-selection.

Hyperspectral photoluminescence (PL) maps of moiré transition-metal dichalcogenide heterobilayers encode rich information about the underlying disorder, but extracting it is hampered by the ambiguity of assigning individual spectral peaks. We show that the spatial correlations among a set of simple, peak-decomposition-free spectral descriptors – such as the centroid energy, the dominant-peak energy, and a sharp-line fraction – provide a peak-decomposition-free route to infer features of that disorder. Different descriptors act as filters that select different components of a multi-scale disorder landscape – a smooth, micron-correlated background and a dense set of localized traps – and therefore acquire different spatial correlation lengths. The central prediction is a correlation-length hierarchy, $\xi(E_{\rm cent}) \ge \xi(E_{\rm dom})$, which we derive by splitting the dominant-peak energy into a smooth background part and a short-range trap-switching fluctuation. The same picture explains the measured inter-descriptor correlations, including the near-perfect anticorrelation $\rho_{\rm S}(\Delta E_{\rm cd}, R_{\rm HL}) \approx -0.978$, which we show to be a robust geometric trend for spectra dominated by a common emission-envelope asymmetry. Benchmarked against phenomenological simulations, Hamiltonian diagonalization, and the measured MoSe$_2$/WSe$_2$ descriptor correlations, the framework turns descriptor maps into a quantitative, peak-decomposition-free probe of slow disorder and local traps in moiré excitons and, more broadly, in disordered semiconductor emitters.

Beam intercepting devices rely on cooling systems to effectively dissipate the thermal energy generated during the impact of a high-energy beam. Regardless of the device's size, integrating the cooling system is a complex task, particularly when the resulting device is only a few centimetres in size, as is the case with the positron source target for the Future Circular Collider at CERN, where the current design consists of a tungsten core with two embedded tantalum cooling tubes. Due to the reduced dimensions of the chosen tantalum tubes (OD6.35xID4.35 mm), the selected manufacturing method is compression bending. The present study develops and evaluates a numerical model to manufacture the required elbow. The methodology is divided in four steps: i) minium allowable bending radius calculation, ii) material constitutive law validation, iii) prediction of the resulting distortion due to ovalization and iv) experimental validation via (non) destructive methods. The results indicate that a minimum bending radius of 10 mm is suitable for manufacturing the elbow. The distortion caused by ovalization is within +-0.5 mm, resulting in an important deviation respect to the nominal geometry. The numerical model was successfully validated experimentally. The micrographies performed in the cross-section of the tantalum tube before and after plastic bending confirm the integrity of the elbow. Additionally, an empirical expression is proposed to estimate the yield stress of pure tantalum based on Vickers hardness measurements. The proposed numerical model is capable to predict the ovalization along the resulting elbow, offering a viable alternative to define the cooling tube geometry. This study provides a methodology to determine the minimum bending radius for thick walled tubes to be used with compression bending and can be applied for the cooling system design of other high-performance devices

In this work, we investigate the phenomenological consequences of neutrino and muon interactions with matter. In our studies, we focused in phenomenological predictions for two experiments: FASER and IceCube. FASER is a detector located at the LHC that measures neutrinos produced in proton-proton collisions. A new version of FASER, FASER2, has been proposed to operate in the Forward Physics Facility during the high-luminosity regime of the LHC. The intense flux of tau neutrinos expected at FASER2 motivated us to study the polarization effects of the tau produced in charged current interactions. Our results show that the produced taus will not be completely polarized. Among the Standard Model particles, only neutrinos and muons produced in proton-proton collisions at the LHC can reach FASER. In our study, we show that muon-initiated events can reveal interesting nucleon properties, such as nuclear effects and the existence of an intrinsic charm. The high number of events induced by neutrinos at FASER motivated us to study rare processes in neutrino interaction, such as the neutrino trident. Our results indicate that the neutrino trident process can be observed at FASER2. We have also studied muon trident at the LHC, and we showed that tau pair production can be observed for the first time in this reaction. In contrast to neutrinos detected at the LHC, the neutrinos observed at IceCube come from natural sources, being mainly atmospheric and astrophysical neutrinos. IceCube is capable of observing neutrinos across a wide energy spectrum, ranging from a few GeV to beyond PeV. We show that the study of these events can contribute to our understanding of the structure of target hadrons, as well as the search for physics effects beyond the Standard Model in the propagation of these neutrinos in the universe until they reach the Earth.

Atomic-scale phase-field modeling extends the phase-field framework down to the level of individual atoms by treating the probability density of atomic vibrations as a field variable and constructing a corresponding free-energy functional from this atomic field together with interatomic potentials. In this way, the framework has the potential to visualize local thermodynamic states with atomic-level resolution, just as conventional phase-field modeling has served as a computational microscope for free energy, stress, and related quantities at mesoscopic scales. However, existing formulations mainly assume Gaussian probability distributions for atomic vibrations, which limits their applicability to more complex and heterogeneous systems such as surfaces. In this work, we generalize the atomic-scale phase-field methodology by extending the free-energy functional to include non-Gaussian fluctuations. We apply this approach to monolayer SnTe in the NVT ensemble and show that the predicted equilibrium polarization is in better agreement with molecular dynamics simulations and that the ferroelectric-to-paraelectric phase transition is successfully reproduced. Furthermore, by decomposing the entropy on a per-atom basis, we visualize atomically resolved maps of local entropy and find that Sn atoms contribute more strongly to the entropy than Te atoms at high temperature, which is a driving factor of the ferroelectric-to-paraelectric phase transition. These results broaden the applicability of phase-field approaches to a wider range of atomic systems and suggest a route toward an atom-resolved theory of phase transitions based on high-resolution thermodynamics.

This study numerically investigates the interaction between an isolated vortex gust and a freely-flying airfoil, introducing a theoretical framework for interpreting the coupled lift and heave response. This complex and coupled dynamics is important for modern light-weight aircraft where gusts may easily perturb the wing, generating transient changes in trajectory and attitude. Here, the freely-flying airfoil is modeled with a single degree-of-freedom in heave, and is impacted by an isolated vortex gust generated upstream. Computational results demonstrate that the freely-flying airfoil reaches a maximum heave displacement after vortex impingement and subsequently rebounds with a comparable magnitude. The lift coefficient is then modeled by augmenting the lift from a corresponding stationary airfoil interaction with motion induced contributions associated with the induced angle of attack and added-mass. A comparison of the modeled lift with the simulation data confirms that the dynamics of the airfoil before impingement is dominated by these two terms, however the rebound after impingement is only partially explained by the model since it is also influenced by the gust-induced vortex shedding. Comparisons across various parameters show that the pre-impingement motion depends primarily on vortex rotation direction, whereas the post-impingement and induced shedding patterns vary with respect to angle of attack and vortex transverse position. With the lift coefficient of the corresponding stationary airfoil interaction as an input, the model can successfully predict the heave trajectory, thus providing a mechanism to assess the dynamic motion of an airfoil from experimental/computational data of gusts interacting with fixed airfoils.

Establishing quantitative relationships among mix design, raw material properties, curing conditions, and performance remains a long-standing challenge in cementitious materials, particularly for alkali-activated materials with variable precursor and activator chemistry. Here, we curated the largest literature-derived alkali-activated slag (AAS) dataset to date, comprising over 3100 compressive strength records, 155 chemically distinct ground granulated blast-furnace slags (GGBSs), and 24 attributes incorporating precursor chemistry, fineness, and reactivity. Multiple machine learning (ML) algorithms were benchmarked across progressively enriched feature scenarios, demonstrating that integrating GGBS compositions, fineness, curing conditions, and specimen geometry improves predictive performance. The average metal oxide dissociation energy (AMODE), a physically interpretable representation of precursor reactivity, provides a compact alternative descriptor to explicit oxide compositions while enabling comparable predictive performance. Model interpretation revealed physically consistent trends from heterogeneous data, including non-monotonic effects of Na2O dosage and silicate modulus, reduced predicted strength at higher water content and larger specimen size, and coupled oxide-level effects more coherently represented by AMODE than by individual oxide contents. Statistically constrained design space exploration reveals reactivity-dependent trade-offs among strength, embodied CO2 emissions, and cost. The design maps identify high-strength regions with substantially lower CO2 emissions than OPC-based references at similar cost. Overall, this work demonstrates how reactivity-informed ML can extract physically meaningful trends from heterogeneous AAS data and guide source-dependent binder design. The curated dataset is publicly accessible to support advances in cement and concrete research.

IRAS 17449+2320 is the first identified magnetic B[e] star. It belongs to a subgroup of peculiar hot B-type stars, called FS~CMa stars. presenting the B[e] phenomenon, i.e., permitted and forbidden emission lines, and infrared excess as a result of the large amount of gas and dust surrounding the central star. The origin of this circumstellar material is unclear, with binarity being the most accepted scenario. However, the presence of a magnetic field in IRAS 17449+2320 points to the merger origin. In this paper, we confirm and characterize the magnetic field of this object, and constrain its rotation and binarity. We used a sample of 12 spectropolarimetric observations taken with ESPaDOnS at the CFHT. We obtained the least squares deconvolution (LSD) profile of the Stokes $V$ parameter of each spectropolarimetric observation. From the LSD profiles, we measured the longitudinal magnetic field (B$_z$), and obtained the magnetic field modulus ($|B|$) from the Zeeman splitting of some metallic lines of the spectra. We calculated a Lomb-Scargle periodogram over B$_z$ to obtain the rotational period of this star. B$_z$ presents a sinusoidal variation with a stable period of 36.11 days, with values ranging from 3000 G to -1000 G. $|B|$ measurements range from $\sim$4400~G to $\sim$6000~G, and it shows a second order term periodic variation. The stable sinusoidal variation of B$_z$ implies an approximately dipolar surface field topology. IRAS~17449+2320 is the first magnetic B[e] star detected. It presents Zeeman splitting in individual lines and consistent Stokes $V$ profiles. We see no evidence of binarity in this object, with stable radial velocities and no detectable lines of a secondary star in the spectrum. We consider the possibility that this star is the result of a merger, which could have generated the strong magnetic field and the large amount of circumstellar material.

The quantum speed limit establishes a bound on the minimal time required for a quantum system to evolve from a given initial state to a final state. When the mean energy variance is fixed this limitation is captured by the Mandelstam–Tamm bound. The fastest quantum evolution saturating this bound follows a geodesic arc connecting the two states. Such geodesics in the manifold of quantum states are explicitly known when the states are pure (Fubini-Study geodesics) and when they are mixed and given by faithful density matrices (Bures geodesics). In this article we obtain the explicit form of the Bures geodesic arcs joining two non-faithful density matrices, which may have different ranks. For pure states one recovers the Fubini-Study geodesics. A necessary and sufficient condition for the uniqueness of the shortest geodesic arc is given. When the condition is not fulfilled there are infinitely many such arcs, all having length equal to the arccos Bures distance between the two states, in analogy with the arcs of great circles connecting the two poles of a sphere. We discuss the implications of our results for the quantum speed limit.

Kagome metals with the formula RM6X6 (R = rare-earth, M = 3d transition metal, and X = Sn/Ge) provide a rich platform for exploring magnetic and electronic phenomena, with tunable properties enabled by the combination of rare-earth elements and transition metals. In this study, we report the structural, electrical and magnetic properties of Kagome metal (NdxGd1-x)V6Sn6. We demonstrate that substituting lighter Nd atoms at the Gd site tunes the complex magnetic ground state of GdV6Sn6 into a ferromagnetic-like one. Moreover, the isotropic magnetization of GdV6Sn6 becomes anisotropic, with the c-axis emerging as the easy axis. Transport measurements reveal a strong coupling between magnetism and electronic properties, with negative magnetoresistance observed at low magnetic fields in all compositions. In addition, a fourfold anisotropy component tends to emerge in end compounds at higher magnetic fields. These findings highlight the role of rare-earth substitution in tuning magnetic anisotropy and magneto-transport behaviour in RM6X6 compounds, featuring a non-magnetic Kagome layer, with potential implications for spintronic and topological applications.

We settle the projectedness problem for the compactified unpunctured supermoduli stack in every genus at least two. In genus two, the odd component is split, whereas the even component is non-projected. In every genus $g\geq 3$, both compactified parity components are non-projected.

Young compact clusters of massive stars contain dozens of O-, B- and WR-type stars with fast powerful winds in a small $\sim$ pc radius core. The particle acceleration by ensembles of shocks accompanied with long-wavelength compressions and rarefactions in the turbulent environment of young massive star clusters (YMSCs) is an alternative to the standard paradigm of Galactic cosmic ray acceleration on supernova shocks. In recent years, the topic has been of great interest due to the fact that modern gamma- and X-ray observatories have detected the radiation from YMSCs, which indicates particle acceleration processes in these objects. We study particle propagation and acceleration in a YMSC with the help of 3D magnetohydrodynamic (MHD) modeling using an open source PLUTO code. The code allows modeling of the turbulent environment of YMSCs and obtaining crucial for particle acceleration values of velocity, density, and magnetic field inside the cluster core. The Particle module implemented in PLUTO allows solving the equations of motion for test charged particles together with MHD equations for the medium. We obtained that protons acceleration up to hundreds of TeV takes place in the cluster core near the termination shocks of O-stars, which are surrounded by shocks of their neighbour stars. We also modeled an interesting case of a young supernova remnant expanding inside the cluster core. In this case a very fast acceleration takes place: particle energies $\gtrsim$ 100 TeV can be obtained in $\lesssim$ 100 years. The particle spectra and spatial distribution are discussed.

Network theory can be fruitfully used to describe quantum coherence in physical systems. To that purpose we introduce persistent currents in signed directed networks by interpreting the signed magnetic Laplacian as an effective Hamiltonian and the associated edge phases as a discrete gauge field. In a canonical ensemble, persistent currents arise as thermodynamic responses to variations of gauge-invariant fluxes. We show that these fluxes are naturally defined on the cycle space of the network, and that the resulting currents are constrained to the divergence-free subspace and decompose onto independent cycles. This formulation provides a direct generalization of persistent currents from rings and lattices to arbitrary topologies. Detection of persistent currents provides a signature of the quantum phase coherence supported by the network, and a direct signature of the geometry of its cycle space. Such a mapping, not only allows a practical way to deal with quantum coherence for a variety of situations in the field of quantum technologies, but it also allows a physical interpretation of the importance of the Laplacian operator in graph theory, linking its role to the one of Hamiltonian (i.e. a tight-binding one) in physical systems. To test the power of the method, we construct a signed directed network that reproduces the Hofstadter butterfly spectrum.

Synchrotron beamlines differ in hardware, technique, and workflow, making customized control interfaces necessary; bespoke per-beamline graphical user interfaces (GUIs) do not scale well, one-size-fits-all facility software forces compromises that leave most of the interface unused, and even recent component-library approaches keep per-scientist tweaks on a developer's queue. We present Lightfall, a control platform designed for facility-wide use, whose API-first architecture exposes every panel, device, and scan plan through a single uniform addressable interface. An embedded language-model agent drives experiments through that interface, from a single move-and-read to a Gaussian-process-driven autonomous scan, while beamline staff extend the interface during operation via skills: plugin modules the agent invokes to compose and modify panels in the running application. The result is a closed development loop: a beamline scientist authors a panel change in natural language, the agent emits and applies it, and the commit lands in the beamline's plugin repository as a side effect. The per-iteration cost of a scientist-driven change is then fixed in the scientist's own time rather than in developer hours the facility must supply. Lightfall is in testing at the COSMIC-Scattering beamline at the Advanced Light Source.

The cylindrical snap-fit is a ubiquitous fastening method that is both simple to manufacture and assemble, and yet secure. It consists of a partial cylindrical shell that `snaps' onto a cylindrical object. We build on previous work to describe the mechanics of the cylindrical snap-fit as a naturally curved thin elastic shell placed atop a rigid cylinder; we investigate the shell's behaviour when subject to a point force pushing it onto or pulling it off the cylinder. We classify the possible contact regimes according to whether the shell has a nonzero lifting capacity. We term situations with lifting capacity `grip-fits' and show that this includes both the snap-fit and a `stick-fit' regime, which allows lifting despite not having the characteristic `snap'. Regimes without lifting capacity are also characterized for completeness. We show that the different regimes may be characterized entirely by the shell/cylinder geometry and the coefficient of friction. We then consider different metrics for the lifting performance in the grip-fit regime. Our analysis reveals the trade-offs between assembly force, disassembly force, lifting force, and clamping force, providing design principles for secure lifting, easy detachment, and safe handling of fragile objects.

We use first-principles molecular dynamics simulations to study the deformation and breaking of neutron star crusts. When simulating with strain rates several orders of magnitude slower than prior work, we find a new regime of steady plastic flow beyond the breaking point that is independent of the initial crystal structure. Polycrystals exhibit a robust transition from linear elasticity to perfect plastic flow at shear strains of $\epsilon = 0.05$, while monocrystals break at $\epsilon = 0.11$ and then flow plastically. The universal post-break plasticity may arise because the crystal self-consistently assumes a defect density to accommodate the imposed strain rate. If broken crusts can re-anneal to large crystal sizes, crust breaking may repeat with implications for magnetar bursts and flares.

We investigate the causes of discrepancies in meridional-circulation measurements derived from the helioseismic observations by the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) and the Global Oscillation Network Group (GONG). Using contemporaneous Dopplergrams from both instruments that are processed consistently, we measure relative oscillatory phase shifts at identical solar locations and analyze north-south acoustic travel-time shifts on both sides of the solar central meridian. Our analysis reveals a persistent area of phase-shift anomalies in the northwestern quadrant of the solar disk, whose magnitude increases over the analysis period from 2010 through 2024. After removing the axisymmetric component, the phase-shift maps display a deceasing trend from the northeastern to the southwestern quadrant, which can be misinterpreted as flows in helioseismic analyses. The travel-time measurements also show significant inconsistencies on the eastern and western sides of the central meridian for both instruments, although a close agreement between both sides is expected. These findings indicate that both SDO/HMI and GONG carry systematic artifacts affecting meridional-circulation measurements, and that the time-varying phase anomalies and eastern-western asymmetry pose major challenges for their accurate characterization and correction.

The hydrodynamic wall has traditionally been considered a featureless object, whose only role is to provide a boundary for fluid flow. Yet, there is now ample evidence that at nanometer scales, liquid flows are sensitive to the wall's internal – in particular, electronic – degrees of freedom. Here, after reviewing the experimental evidence for nanoscale liquid-electron couplings, we present the theoretical advances that have allowed for their quantitative understanding. We discuss how a quantum description of the liquid-solid interface reveals the influence of electron dynamics on classical fluid transport, in the form of the fluctuation-induced quantum friction effect. Quantum friction is at the root of liquid-electron coupled transport phenomena, that may be combined into a hydro-electronic transport matrix. We present analytical formulas for the hydro-electronic transport coefficients, that allow for their quantitative estimation in practical cases; we further outline the potential consequences of coupled liquid-electron transport for the water-energy nexus. Fluctuation-induced and quantum effects at liquid-solid interfaces represent an emerging interface between fluid dynamics and condensed-matter physics, and a largely uncharted territory for both theory and experiment.

We present a simple, rule-based filter for SETI target selection that flags stars unlikely to host complex life and produces an audit-ready exclusion catalog. Using seven stellar parameters, including age, metallicity, and multiplicity, the model excludes roughly half of a 1.74 million-star Gaia DR3 sample, retaining 777,835 high-priority targets, mainly G and K dwarfs. Age and metallicity dominate the rejections. Importantly, using Gaia's age upper bounds instead of point estimates saves 355,086 stars from exclusion. A comparison of empirical and synthetic proxies shows that while the overall exclusion rate is robust, individual target assignments change significantly; for instance, the commonly used RUWE indicator flags 2.7x more binaries than Gaia's own non-single-star flag. Cross-matching with the Breakthrough Listen target list reveals a 56.5% exclusion rate, highlighting the complementary nature of habitability-driven and proximity-driven surveys. The catalog, pipeline, and a generalized community tool are publicly available.

Temperate sub-Neptune and terrestrial exoplanets could contain large inventories of water in various phases, such as water-dominated atmospheres or even oceans. Observations have shown that many exoplanets, including water worlds, likely contain photochemically-generated hazes. Haze particles are a key source of organic matter and may impact the evolution or origin of life; their optical properties are imperative for interpreting observations through theoretical atmospheric modeling. Modelers have thus far assumed haze optical properties that may not represent hazes under sub-Neptune and terrestrial atmospheric conditions. Often orbiting close to M-dwarf stars, these planets receive large amounts of radiation, especially during flaring events, which may accelerate atmospheric escape and affect atmospheric compositions. Here, we present optical constants of experimentally-generated sub-Neptune haze analogs before and after UV irradiation across a broad wavelength range (0.5 to 8 {\mu}m). We find that UV-irradiation alters haze optical constants which become generally more absorbing in this wavelength range, which we hypothesize is due to our sample containing more oxygen-rich absorbing bands post irradiation. We use Virga and PICASO to simulate transmission spectra of potentially hazy water-dominated planets GJ 1214b and LHS 1140b, accounting for irradiated haze layers in their atmospheres. For our GJ 1214b CH4-rich haze modeled case, we see a difference in the N-H feature at 2.6 {\mu}m in the resulting transmission spectrum between irradiated and unaltered haze that should be observable within current JWST capabilities. Broadly, we demonstrate the importance of using more representative optical constants, as they have an impact on current and future atmospheric composition interpretations.