AI for Science

Many fields of physics use quantum Monte Carlo (QMC) simulations to simulate quantum systems in imaginary-time $τ$ and estimate imaginary-time correlation functions (ITCF). However, extracting dynamic $ω$-dependent quantities from ITCFs is a notoriously difficult task, known as analytic continuation (AC), that amounts to solving an exponentially ill-conditioned inverse problem. Within the AC literature, there are competing stochastic and regularized approaches, as well as an emerging collection of works using parameterized models like neural networks. Here we transcend the traditional divides between the communities, introducing the regularized stochastic optimization method (RSOM). This method reformulates AC as a dictionary learning problem, discovering a sparse dictionary to represent the solution. Our approach is motivated by the astounding results dictionary learning has produced in many scientific fields. Remarkably, RSOM discovers a sparse dictionary that maps an ill-conditioned inverse problem to a low-dimensional problem that is well-conditioned. We demonstrate that the method yields competitive results for common synthetic test problems as well as for authentic QMC data from the finite temperature electron gas. This work exposes that a dictionary exists within all stochastic and regularized methods and that dictionary learning provides a new angle of attack for future AC methods.

We present a novel multi-modal optical coherence photoacoustic elastography (OCPE) framework, which combines two imaging modalities, optical coherence tomography (OCT) and photoacoustic tomography (PAT), to enable complementary absorption-scattering measurements for the extraction of quantitative tissue features via quasi-static elastography. For this, we develop a sophisticated hybrid inversion algorithm for merging the complementary information layers contained in both OCT and PAT-based elastography measurements, and perform systematic evaluations to assess the impact of hybrid elastography data on strain and stiffness reconstructions. Studies on a silicone elastomer phantom demonstrate that the combined OCT-PAT approach outperforms single-modality OCT elastography and PAT elastography, yielding higher strain signal-to-noise ratio and improved stiffness estimates. These results establish the advantage of multi-modal complementary imaging and data merging for accurate, high-resolution elastographic strain and stiffness mapping in both scattering and absorbing materials.

The exascale computing era, driven by heterogeneous GPU architectures, requires a fundamental redesign of traditional CFD solvers to fully leverage those heterogeneous systems. The discontinuous Galerkin spectral element method (DGSEM) provides an ideal foundation for this transition due to its high-order accuracy and local computational stencil. This work presents recent advances in the development and application of the architecture-agnostic DGSEM framework GALÆXI by linking hardware optimization, software implementation, and physical validation. The performance of GALÆXI on the AMD MI300A Accelerated Processing Units (APUs) featured on the Hunter supercomputer is analyzed. Specifically, evaluations of the strong and weak scaling performance and the impact of the compute partitioning modes available on the AMD MI300As are performed. Second, the strategy used to integrate the algorithms necessary for wall-modeled large eddy simulations into the GPU-accelerated framework is outlined. Validation of those algorithms is presented in the form of a plane turbulent channel testcase. Finally, the solver is applied to a demanding flow problem in the form of a wall-resolved large eddy simulation of a transonic compressor cascade. The results from this investigation demonstrate the capabilities of GALÆXI to accurately capture complex shock-wave/turbulent boundary-layer interactions.

In fundamental experiments with neutrons, the neutron flux and the neutron storage time in the measuring setup are of primary importance. These quantities can be increased by using a storage device for neutrons generated by a pulsed source. In a storage device with material walls, both parameters are determined by the probabilities of neutron absorption and diffuse scattering upon reflection from the storage walls, as well as by the neutron decay probability. This work considers a neutron measurement method and presents the results of an experimental determination of the probability of diffuse neutron scattering in a wave resonator.

The optical binding of gold nanoparticles has conventionally been explored within the Rayleigh limit using dipole approximations. But the field is increasingly focusing on the Mie regime for particles in the 100-500 nm range, where the dipole approximation is insufficient, and a complex landscape of multipolar resonances must be considered. This can be leveraged to engineer more complex forms of optical matter. To this end, we computationally study the optical binding force landscapes experienced by a pair of AuNPs using generalized multiparticle Mie theory. We calculate the total optical binding forces and mechanical trap stiffness values ($dF_i/di$) at the specific resonance wavelengths where the electric dipole, quadrupole, or octupole modes reach their respective scattering peaks and dominate the mechanical response. We demonstrate that the plasmonic mode symmetry greatly influences the spatial distribution of zero-force nodes and the rigidity of the optically bound dimer. By aligning these multipolar phenomena with standard experimental configurations, this work provides a mechanical framework for programmable metafluids and reconfigurable micromachines, bridging the gap between fundamental electrodynamics and reconfigurable nanomanipulation.

Owing to rapid recent progress, ultracold atomic lattice systems for simulating topological phases are now at a pivotal stage, evolving from established paradigms into increasingly versatile and programmable quantum simulators. In this review, we survey recent experimental advances across four major classes of platforms: optical lattices, including optical lattices with laser-assisted tunneling and optical Raman lattices; synthetic lattices in momentum or internal-state space; Floquet-engineered lattices; and optical tweezer arrays, all of which offer distinct capabilities for realizing and probing topological matter. For each class, we highlight representative experimental breakthroughs, the topological models that have been realized, and the advanced detection and characterization techniques employed, emphasizing how these complementary approaches collectively expand the frontier of quantum simulation. We also discuss emerging directions in strongly correlated and nonequilibrium topological phases, and conclude with an outlook on future prospects.

Fault-tolerant quantum computing fundamentally relies on the accurate characterization of circuit-level noise to optimize decoding algorithms. However, extracting complex multi-body error correlations remains challenging. Contemporary greedy inference algorithms can suffer from statistical distortion, discarding true physical mechanisms while introducing many unphysical false positives. Here, we introduce the Correlation-Analysis-based Hypergraph Reconstruction (CAHR) algorithm, a globally consistent framework to invert experimental syndrome statistics directly into discrete physical hypergraphs. By coupling exact algebraic correlation equations with a top-down concurrent-pruning strategy, CAHR recovers the fault topology without false positives for both $d=5$ rotated surface codes and dense 8-body 2D color codes in our benchmark settings. Furthermore, we show that exact continuous parameter extraction in dense codes is limited by a \textit{variance cascade}, where absolute statistical variance accumulates linearly from high- to low-degree mechanisms. This motivates a two-stage inference paradigm: utilizing CAHR to extract the fault topology, followed by continuous probability optimization. This provides a practical approach for characterizing and decoding highly correlated noise in realistic quantum hardware.

We present a quantum-enhanced Markov chain Monte Carlo (QE-MCMC) method to sample turbulent acceleration vectors from a joint target distribution that depends on all three components and height to model the transport and dispersion of massless Lagrangian tracer particles in two turbulent shear flows. A homogeneous shear flow, characterized by a uniform shear rate S, is considered as the starting point. Secondly, a turbulent boundary layer, which forms in both halves of a plane turbulent channel flow at friction Reynolds number Re_tau = 1000, is considered, where the mean shear rate S(y) varies with distance from the wall y. In this hybrid quantum-classical method, the proposal distribution Q for the first of two Metropolis-Hastings sampling substeps is constructed by a parametric quantum circuit. The algorithm generates synthetic tracer particle tracks. The resulting scaling laws for tracer-particle pair dispersion, a central quantity to probe turbulent mixing from a Lagrangian perspective, agree with a stochastic transport model consisting of coupled Langevin equations and with the classical MCMC counterpart. Differently from the classical sampling method, QE-MCMC uses a tempered target distribution. Due to the height dependence of the tracer dynamics in turbulent channel flow, an effective height-weighted spectral gap between the first and second eigenvalue of the Markov-chain transition matrix is introduced. The latter is found to significantly exceed the one of classical MCMC when sampling from a multivariate distribution with cross-correlations at the highest qubit numbers and thus resolutions. Consequently, our results support the applicability of this one-shot algorithm as a generative Lagrangian quantum-computing module, possibly embedded in a complex fluid-flow problem. Our module is found to work reliably for a relatively small number of qubits per spatial dimension of Nq <= 6.

The Tropical Atlantic exhibits complex sea surface temperature (SST) variability driven by internal ocean-atmosphere interactions and remote climate forcing. We perform a comparative multifractal analysis of three SST indices, South Atlantic Tropical (SAT), Tropical Southern Atlantic (TSA), and the Tropical Atlantic SST Gradient Index (TASI), using weekly data from 1981 to 2025. Multifractal Detrended Fluctuation Analysis (MFDFA) reveals robust scale-dependent behavior in all indices. TASI displays a substantially broader multifractal spectrum (Delta h about 0.72) than SAT (0.27) and TSA (0.34). Surrogate-data tests show that multifractality in SAT and TSA is mainly explained by linear autocorrelations, whereas TASI contains an additional nonlinear contribution associated with phase correlations. To investigate temporal variability, we introduce a moving-window MFDFA framework that tracks the evolution of multifractal width. Significant reductions are observed during the major 1997-1998 and 2015-2016 El Nino events, indicating a suppression of multiscale variability under extreme Pacific forcing. Lagged correlation analysis reveals a significant negative association with the Oceanic Nino Index at delays of 15-18 months, consistent with known Atlantic-Pacific teleconnections. However, Granger causality and Transfer Entropy tests do not detect significant causal links, suggesting an episodic rather than persistent relationship. Lagged multifractal cross-correlation analysis further reveals scale-dependent inter-basin coupling. These results demonstrate that time-dependent multifractal measures provide a useful framework for characterizing nonlinear Atlantic variability and identify TASI as a dynamically distinct index whose scaling properties contain information not captured by regional SST indices alone.

We experimentally demonstrate that absorption and emission spectra of trivalent lanthanide-doped anisotropic crystals can exhibit a significant magnetic-polarization dependence, which has been largely overlooked in spectroscopic studies to date. Focusing on the uniaxial laser host LiYF4 (YLF) doped with Yb3+, Tm3+, Er3+, and Ho3+, we measure magnetic-polarization-dependent absorption and emission spectra for transitions with strong magnetic-dipole (MD) contributions predicted by theory. Our results reveal that MD-induced spectral anisotropy, i.e., spectral differences for the same electric field orientation but for different magnetic field orientations, is present even in these well-established laser materials. A complete spectroscopic characterization of uniaxial crystals requires three polarizations, including the $α$-polarization, with both the electric field vector E and the magnetic field vector H perpendicular to the c-axis (E $\perp$ c, H $\perp$ c), in addition to the commonly used two polarizations $π$ (E $\parallel$ c, H $\perp$ c) and $σ$ (E $\perp$ c, H $\parallel$ c). We further discuss the observed MD-induced spectral anisotropy and calculated MD branching ratios, the impact of the anisotropy on emission cross-section calculations, and the relevance of our results to other uniaxial and biaxial crystals.

We investigate whether giant fluctuations in a gas can induce corresponding fluctuations on a reactive surface in contact with the gas. Numerical simulations of a minimal heterogeneous catalytic reactor demonstrate that such fluctuations indeed emerge on the surface, with spatial correlations extending over micrometer scales. These fluctuations originate from the dependence of the adsorption rate on the reactant partial pressure. As a result, the surface-coverage structure factor mirrors that of the partial pressure, exhibiting similar enhancement and roll-off behavior across wave numbers.

Electrothermal characterization based on the third or other harmonics of an ac Joule heating current is widely deployed for the thermal analysis of solid conductors and their environment, including solid substrates and fluids. However, a unified theory that bridges heat transfer in two archetypal experimental geometries - suspended vs. substrate-supported conductor - has been missing. Here, we present and validate such a theory that explicitly accounts for finite conductor length, thermal mass, and environmental coupling through a unified thermal transfer function. This framework enables the prediction of voltage responses at all harmonics of the driving current (dc, 1$ω$, 2$ω$, 3$ω$) and the formulation of design principles for the characterization of nanoscale conductors. The conductor length $l$ is the primary parameter controlling the frequency regime at which the conductor's thermal mass dominates the thermal response, with the characteristic frequency $ω_\mathrm{c}=α/l^2$, where $α$ is the conductor's thermal diffusivity - closely related to a criterion previously reported for suspended wires free from environmental coupling. Our unified framework generalizes this result, revealing that sufficiently weak environmental coupling is a necessary condition for $ω_\mathrm{c}$ to govern the onset of thermal-mass-dominated response. Optimization of interfacial thermal resistance and environmental thermal impedance may further improve temperature resolution and facilitate on-substrate implementations.

Ferroelectric materials, with their strong nonlinearities, underpin key technologies across radio-frequency (RF) signal processing, optical communications, and emerging quantum systems. Barium titanate (BTO) is a notable example, combining strong piezoelectric and electro-optic responses. While bulk BTO has been studied for decades, the piezoelectric properties of its recently available thin films, and their behavior at the millikelvin temperatures relevant to quantum hardware, remain largely unexplored. Here, we fabricate and characterize surface acoustic wave (SAW) resonators on thin-film BTO. The measured devices exhibit high electromechanical coupling (k2eff 0.14 at 5.2 GHz) and operate up to 7.8 GHz. From these measurements, combined with finite-element modeling of the multi-domain microstructure, we extract an effective piezoelectric coefficient d33eff of 53 pC/N, comparable to bulk BTO. Exploiting the intrinsic ferroelectricity, we further demonstrate low-voltage switching with a fast (100 ns) response, attractive for reconfigurable RF front-ends and parametric amplifiers. Extending these measurements to millikelvin temperatures, we find that the piezoelectric response persists, with d33eff 19 pC/N, pointing to the potential of BTO for piezoelectric coupling in superconducting quantum circuits. These results position thin-film BTO as a promising piezoelectric platform for both classical and quantum information technologies.

The $δ$ phase of Niobium Nitride (NbN) is a promising superconducting material, which is chemically stable and shares lattice compatibility with conventional III-Nitride semiconductors. Due to a high critical temperature (T$_{c}$) and a high critical (magnetic) field (H$_{c}$), NbN is much-coveted for a diverse set of applications spanning from single photon detectors, and hot-electron bolometers to quantum computing architectures using superconducting circuits. However, synthesizing high-quality epitaxial films of phase pure and stoichiometric $δ$-NbN in a cost-effective manner, is challenging. In this study, we investigate the epitaxial growth of single crystalline $δ$-NbN on TiN-buffered c-sapphire (Al$_{2}$O$_{3}$) substrates by sputtering at room temperature. For these films, we demonstrate a surface-roughness in picometer-scale, the lowest reported till date. The critical temperature (T$_{c}$) of the epitaxial $δ$-NbN films was observed to decrease with the insertion of the TiN buffer layer, tentatively attributable to the leakage of Cooper pairs, due to the proximity effect. TiN and NbN layer behave as a bilayer system, wherein Cooper-pair leakage is facilitated by the absence of any oxide interlayer. Consequently, T$_{c}$ reduces with increasing thickness of the TiN layer.

Thermal runaway remains a critical safety concern in battery systems and other thermally active devices, necessitating reliable methods for predicting the conditions under which thermal runaway may occur. In this work, we develop a finite element framework for assessing thermal stability in systems undergoing transient heat conduction with temperature-dependent internal heat generation. The formulation leads naturally to a generalized eigenvalue problem, wherein the sign of the smallest eigenvalue provides a direct criterion for determining the onset of thermal runaway. This approach enables direct prediction of thermal runaway thresholds in geometrically complicated problems without requiring computationally expensive transient analysis. The proposed methodology is validated through comparison with analytical solutions for a cylindrical Li-ion cell, as well as with a pack of cylindrical cells, demonstrating excellent agreement in both cases. Based on this model, the influence of material properties, boundary conditions, geometric parameters, and spatially varying heat generation on stability limits is examined. A key advantage of this formulation is its effectiveness even for complex geometries where analytical methods become impractical. By providing a systematic and computationally efficient means to identify stability thresholds, the present work offers a practical tool for the thermal design and safety assessment of battery systems.

Manganese selenide (MnSe) is a promising air-stable two-dimensional magnetic semiconductor for which theory predicts robust ferromagnetism in monolayers with Curie temperatures approaching 250 K. However, the crystallographic phases and magnetic properties of thin-film MnSe grown by scalable methods remain poorly understood. Here, we demonstrate the controllable growth of $α$- and $β$-phase MnSe on C-face sapphire using a three-zone chemical vapor deposition process with elemental Se and ${MnCl_{2}}$ precursors in an $Ar/{H_{2}}$ atmosphere. Using Raman spectroscopy, atomic force microscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy, we show that our process yields phase-pure $α$-MnSe nanorods and $β$-MnSe triangular flakes with lateral sizes up to 20 $μm$ and thicknesses of 15-30 nm. Low-temperature photoluminescence of the $β$-phase films reveals a bandgap of approximately 2.0 eV. Systematic variation of growth parameters shows that precursor vapor pressure, rather than ${H_{2}}$ partial pressure, is the dominant factor controlling lateral flake size. Vibrating-sample magnetometry measurements reveal a $N{é}el$ temperature of 53 K in the $β$-phase films, providing clear evidence of antiferromagnetism in the multilayer regime and establishing MnSe as a tunable platform for 2D spintronic and optoelectronic devices.

Calcium (Ca) insertion into graphite (C) has been considered to require elevated temperatures, and its occurrence at room temperature (RT) has been regarded as highly unlikely. Here, we demonstrate that sodium (Na) catalysis enables the formation of superconducting CaC$_6$ even at RT. In mixtures of Ca, Na, and graphite, the gradual development of superconducting diamagnetism and the emergence of X-ray diffraction peaks confirm the formation of CaC$_6$ during storage at RT. The superconducting transition temperature increases with storage time, and the amount of CaC$_6$ scales proportionally to the square root of storage time. These findings provide new insights into the mechanism of superconductivity and Na-catalyzed formation of CaC$_6$, and highlight the potential of this RT intercalation process for practical applications such as electrode materials in Ca-ion batteries.

Two-dimensional (2D) materials often require specific substrate terminations for epitaxial stabilization, yet the search for suitable templates has largely focused on low-index metal surfaces, which may not provide the optimal conditions for the growth of new phases. Here, we show that crystal-facet engineering on curved Cu surfaces enables the stabilization, within a single preparation step, of two distinct 2D phosphorus phases with different electronic properties. Hexagonal blue phosphorene forms on Cu(111) terraces, whereas a previously unreported skewed-square phosphorus phase is stabilized on Cu(513) facets. By combining complementary microscopy and spectroscopy techniques with theoretical calculations, we determine the structural and electronic properties of this new phase, which displays semiconducting character, in contrast to the metallic behavior of blue phosphorene. The coexistence of these two competing phases gives rise to a metal-to-semiconducting transition of the 2D phosphorus layer over the substrate. Locally, the competition between the two phases gives rise to self-aligned nanoarrays of alternating metallic and semiconducting phosphorus terraces. These results establish crystal-facet engineering as a practical route for discovering and stabilizing emergent 2D material phases on high-index substrates, while also enabling the engineering of nanostructures with tailored electronic properties through a simple and scalable growth process.

Silicon-vacancy (SiV) centers in diamond combine near-infrared emission with solid-state robustness, but their performance hinges on isolating favorable defect charge states. We demonstrate static and dynamic control of ultra-shallow (<15 nm) SiV ensembles in type IIa diamond. By combining low-energy ion implantation with tailored oxygen and hydrogen terminations, we map regimes that maximise the fluorescent SiV- population over dark charge states. We then realize reversible SiV- to SiV0 conversion using aqueous electrolytic gating with sub-200 mV biases and low optical powers. Our results enable low-power electrical control of SiV ensembles for integrated quantum photonics and biologically compatible voltage imaging in the near-infrared.

Altermagnetism defines a third fundamental class of collinear magnetic order, featuring compensated magnetic moments with antiparallel spin alignment, yet lifted Kramers degeneracy without the need for relativistic spin-orbit coupling. Its ability to host spin-polarized electronic bands and unconventional chiral magnons makes it a promising platform for functional materials. However, experimental verification has proven challenging; while circular dichroism in resonant inelastic X-ray scattering (RIXS) has been suggested as a signature of chiral magnons, it remains controversial whether this effect is an intrinsic property of altermagnetism or an artifact of experimental geometry. In this work, we resolve this debate and provide unambiguous experimental evidence of $d$-wave altermagnetism in the strongly correlated Lieb-lattice magnet La$_2$O$_3$Mn$_2$Se$_2$. The RIXS spectra exhibit a $d$-wave-symmetry circular dichroism in the magnetic excitations that vanishes in the paramagnetic phase. Through RIXS-operator symmetry analysis and exact-diagonalization calculations, we prove that the observed dichroism is a direct consequence of altermagnetic symmetry constraints, independent of magnon branch splitting. Our results provide definitive evidence for the experimental realization of $d$-wave altermagnetism in La$_2$O$_3$Mn$_2$Se$_2$ and establish circularly polarized RIXS as a highly symmetry-sensitive spectroscopic framework for detecting magnetic phases that evade conventional probes.

Collinear antiferromagnets with broken time-reversal symmetry have emerged as a fertile platform for spintronics. Using a general tight-binding model and first-principles calculations, we show that strain engineering provides a simple route to control magnetic phase transition and activate transverse responses in representative altermagnets RuO$_2$ and MnF$_2$. For pristine RuO$_2$ and MnF$_2$ with Néel vector $\mathbf{n}\parallel$ [001], symmetry constrains the off-diagonal elements of the Hall conductivity tensor to vanish, thereby forbidding anomalous transport and magneto-optical responses. Shear strain applied along the $ac$ direction preserves the spin symmetry relating the two spin-opposite magnetic sublattices and therefore maintains the altermagnetic phase. By contrast, shear strain applied along the $ab$ direction breaks this spin symmetry and drives a transition from an altermagnetic phase to a partially compensated ferrimagnetic phase in metallic RuO$_2$ and to a fully compensated ferrimagnetic phase in semiconducting MnF$_2$. In addition, the lowered symmetry enables finite anomalous Hall, anomalous Nernst, and anomalous thermal Hall conductivities, as well as magneto-optical rotation angles, which are prohibited in the pristine systems. These responses exhibit a clear strain dependence and become progressively stronger as the strain amplitude increases. Our results establish strain engineering as an effective route to manipulate magnetic phases and functional responses in unconventional antiferromagnets, thereby expanding opportunities for antiferromagnetic spintronics and magneto-optical applications.

Twisting layered materials produces moiré superlattices, but prescribed twist angles are usually obtained by demanding assembly procedures. Here we show that montmorillonite, an abundant swelling clay, forms tunable moiré superlattices naturally. Focal-series high-resolution transmission electron microscopy, geometric phase analysis, and molecular dynamics simulation reveal that its apparent rotational disorder is biased toward low-angle misorientations inherited from discrete hydration states. Multilayer stacks preferentially adopt twists near 1-2°, 4°, and 10°, producing long-wavelength moirés without long-range rotational order. We define this kinetically trapped state as a moiré glass, distinct from featureless turbostratic stacking. Simulations indicate that lattice-charge disorder stabilizes the angular preferences, whereas charge ordering promotes random stacking. Hydration screens interlayer interactions and lubricates twist, while dehydration arrests the resulting configurations in discrete steps. These results establish dynamic hydration as a macroscopic handle for programming twist in layered matter.

Nanoscale three-dimensional localization across large biological volumes remains an outstanding challenge in optical microscopy, with existing approaches typically limited by imaging speed, volumetric field of view and localization precision when required simultaneously. Here, we overcome these limitations by combining a twin Airy engineered point spread function with two-photon light sheet fluorescence microscopy, enabling nanoscale localization throughout large volumetric fields of view. Our framework explicitly incorporates the broadband fluorescence emission characteristic of biological fluorophores, ensuring accurate localization under realistic imaging conditions. We achieve localization precisions of $<20$~nm laterally and 42~nm axially over volumes measuring 295~$μ$m x 330~$μ$m x 100~$μ$m , with a projected path to sub-10-nm localization in millimeter-scale specimens. Experiments in fluorescent bead phantoms and live mammalian oocytes confirm robust performance in both controlled and biologically complex environments. These results establish a scalable strategy for localization-based super-resolution imaging across biologically relevant volumes, bridging the gap between nanoscale precision and large-scale volumetric microscopy.

Tin vacancy (SnV) centers in diamond are promising solid state qubits for integrated quantum photonics. Here, we fabricate and characterize a diamond on Al2O3 dual taper waveguide structure containing SnV centers, demonstrating optical coupling between the diamond nanobeam and the underlying Al2O3 waveguide. The devices are realized using a bilayer fabrication approach compatible with wafer scale lithography. Clear guided SnV- emission is observed in all optically active devices, indicating effective optical coupling in the integrated structure. These results demonstrate a scalable fabrication approach toward integrating diamond color centers with photonic waveguides.

To simulate the relaxation of an entire 2D cellular network, this study proposes a symmetric relaxation method for both inner and marginal vertices. The relaxations of these two types of vertices are determined by the central angle symmetry of associated cells and the angle symmetry at each vertex, but with different major considerations. Trimmed Voronoi networks with varying irregularity are used as initial networks for the relaxation simulation. In particular, we propose a regular hexagon disordering method to generate Voronoi networks and find that the inner cells of networks with an irregularity value of one exhibit a conserved edge number distribution, as found in other 2D cellular networks. Simulation results agree with the von Neumann-Mullins law for both inner and marginal cells, and a modified equation including a geometric correction term significantly improves prediction quality. The Aboav-Weaire law and Lewis law are also reproduced, with the latter showing that relaxed cells tend to approach the ellipses' maximum inscribed polygons. Analysis of edge length, interior angle, and shape index reveals that symmetric relaxation inhibits T1 (neighbour exchange) topological transitions by reducing short edges while increasing area disparity among neighbouring cells. The findings suggest that T1 events may be triggered when force disequilibrium overcomes the stabilising effect of symmetric relaxation, providing a possible mechanistic explanation for T1 in 2D foams.

Differential Interference Contrast (DIC) microscopy and chiral analysis are two imaging techniques that measure the birefringence, i.e., the phase difference introduced by a sample on two orthogonal polarizations. Conventional approaches employ Gaussian beams and infer birefringence from polarization changes, resulting in phase-estimation sensitivities that depend on the unknown phase. We demonstrate here a new type of birefringence detector. It makes use of a vector vortex beam, a type of structured light endowed with optical modes that carry opposite orbital angular momentum (OAM). Using quantum estimation theory tools, we demonstrate that the sensitivity of phase estimation is independent of the value of the unknown phase, and can be even better, in principle, than the conventional approach. We experimentally validate the proposed scheme, demonstrating the potential of structured light for robust and uniform birefringence sensing.

We realize an injector device that supplies warm gas directly into a cryogenic environment. This injector has several advantageous features, including robustness, rigidity, simple installation, and excellent thermal isolation between a hot ($\sim$300 K) copper fill line and a cold ($<$3 K) cryogenic buffer gas cell. Less than 200 mW heat load on the cell is observed in realistic conditions of a molecular precision measurement experiment. A polyamide-imide (PAI) tube is the essential design feature. The fill line is epoxied to one end of the tube while the other end of the tube is connected to the cell via a slip-fit onto a brass nipple, realizing a complete vacuum-tight seal. PAI contracts on the brass nipple when cooled, forming a cryogenic leak-tight seal. The injector is easily (de-)mountable and rigid, with no significant displacement of the fill line relative to the cell observed during cooldown to 4 K. We characterize injector performance by flowing into the cell $\text{SF}_6$ through the hot fill line and cold $\text{He}$ buffer gas through a separate cryogenic fill line while laser ablating a barium-containing target. This produces cold BaF free radicals, detected using absorption spectroscopy. This injector design will be employed to laser cool radium-containing molecules, such as $\text{RaF}$ and $\text{RaOH}$, where leak-tight delivery of $\text{SF}_6$ and $\text{H}_2\text{O}$ reagents into a cryogenic buffer gas cell is required for scientific and safety reasons. These molecules are of particular interest for the study of symmetry-violating nuclear properties and searches for physics beyond the Standard Model.

Understanding how swine farms are interconnected, directly and indirectly, is essential to characterizing infectious disease transmission. This study aimed to describe the connectivity of swine farms across 11 network types, including vehicle movements (i.e., trucks and trailers), animal movements, and distance-based farm-to-farm contacts, to identify links among production types and farms likely to be consistently characterized as super-spreaders. Truck and trailer movement networks were the most densely connected, particularly for feed transport, showing connectivity levels between 98.7% and 99.7% higher than those of pig movement and distance-based networks. These networks also exhibited the highest degree and frequency of connections between farms, while the aggregated truck network, which included all truck types, showed the greatest potential to act as a bridge connecting farms. Finisher farms were highly interconnected with other farm types across all networks. Sow farms were frequently reached by other farm types, especially through feed truck movements, representing up to 8.7% of these links. We demonstrated that in vehicle movements and proximity networks, finisher farms played a major role as super-spreaders. When comparing the top 50 farms ranked by super-spreader score in each network, vehicle-based networks showed the highest similarity, with up to 89% of top-ranked farms shared between vehicle networks. In contrast, pig movement and distance-based networks identified largely distinct sets of top-ranked farms, sharing at most 4% and 8%, respectively, with other contact networks. Overall, each network exhibited a distinct connectivity structure, resulting in different sets of high-risk farms, particularly regarding potential transmission to breeding farms. These findings support the integration of multiple transmission pathways into disease surveillance.

In this work, we describe the development of track- and energy-reconstruc-tion algorithms for atypical Time Projection Chambers (TPCs) that will be used at the Institute of Experimental and Applied Physics, Czech Technical University in Prague, to search for the anomalous internal pair creation reported by the ATOMKI group. These chambers operate with an inhomogeneous toroidal magnetic field oriented orthogonally to the electric field; we therefore refer to them as Orthogonal-Field TPCs (OFTPCs). Although this configuration distorts the drift of ionization electrons and complicates the resulting electron and positron trajectories, it also offers several practical advantages. We present the most effective of several tested approaches, which employs a simulated ionization-electron drift map for track reconstruction and a Runge--Kutta-based fit for energy reconstruction. Using simulations, we demonstrate that -- under idealized conditions, namely an ideal charge readout with no amplification and no noise and with known initial track positions and directions -- it is possible to achieve a fitted Gaussian width (sigma) better than 1\% in relative energy for both electrons and positrons, after applying corrections for systematic effects that depend on the track parameters.

Structural ensemble refinement is widely used to integrate molecular simulations with experimental measurements. While most applications focus on the maximum-a-posteriori (MAP) ensemble, Bayesian sampling of the posterior distribution can provide uncertainty estimates and posterior averages for arbitrary observables. A notable step in this direction was introduced by the Bayesian Energy Landscape Tilting (BELT) framework, where sampling is performed on a family of maximum-entropy ensembles parametrized by Lagrange multipliers. Here, we show that Bayesian sampling in this setting requires an explicit choice of ensemble-counting measure. In particular, the flat measure in Lagrange-multiplier space used in the original BELT formulation leads to a posterior distribution that is formally non-normalizable for finite reference trajectories. We propose the Jeffreys measure as an invariant ensemble-counting prescription, restoring normalizability in the finite-sample situations considered here, and providing a consistent definition of posterior averages. Using both an analytically tractable Gaussian model and maximum-entropy refinement of RNA oligomer simulations, we compare different ensemble-counting measures and show that they can significantly affect Bayesian estimates. The resulting methodology has been implemented in the \texttt{MDRefine} software package.