Papers

Grid-based fluid dynamics solvers routinely struggle with exhaustive meshing demands and ill-posed inverse problems. A practical mesh-free alternative is given by Physics-informed neural networks (PINNs) but, applying them to highly elastic Oldroyd-B fluids results in many training failures. The High Weissenberg Number Problem (HWNP), driven by the exponential stress growth near stagnation points is a major issue, which causes standard PINN optimizers to diverge. To prevent the network from crashing, we apply a Cholesky decomposition to the conformation tensor. This mathematical constraint stabilizes the gradients by guaranteeing a positive-definite stress field. Beyond mathematical stability, the inherent spectral bias of deep learning models can hinder the network from accurately capturing the highly elastic wake structures. Therefore, we used sparse data assimilation to force the model toward the actual physical solution. By anchoring the physics loss with targeted CFD data points and accelerating training via transfer learning, we successfully pushed the network past non-physical local minima. We validated this Cholesky-PINN approach on flow past cylindrical geometries for Reynolds numbers (Re) between 5-25, for the single-cylinder setup. In addition, the relaxation time ({\lambda}) is increased from 0.1 to 0.5 to test the stability of the network. Finally, we scale the framework to a complex 3-cylinder array which proved our constructive solid geometry approach completely bypasses the tedious re-meshing steps of traditional CFD. The combined framework accurately captured sharp viscoelastic wakes, providing a stable computational tool for complex rheological modeling.

The demand for brighter photon beams has driven the development of diffraction-limited storage rings with ultralow electron beam emittance. A complex bend (CB) lattice was recently proposed as an approach for the future NSLS-II upgrade. As a step toward this upgrade, NSLS-II plans to replace two existing electromagnetic dipoles with permanent-magnet CBs, providing the first experimental demonstration of the CB concept in a storage ring. This paper presents the linear optics design, nonlinear optimization, and machine error evaluation of the modified NSLS-II lattice. The optimized lattice provides sufficient dynamic and momentum apertures for off-axis injection and routine operation and can be corrected with the existing NSLS-II correction scheme. Scraper measurements near the planned CB installation location validate the beam aperture requirements used in the CB design.

Many systems in soft and living matter bind through a limited number of bonds per particle: proteins associate via discrete surface patches, nucleic acids form one-to-one contacts, and the phase behaviour of multivalent biomolecules is governed by the number of binding sites they carry. In simulations, valency limits are typically enforced with patchy particles, whose anisotropic potentials require integration of rotational degrees of freedom and combine hard cores with narrow patches, which forces small timesteps and commits the model to a fixed binding-site geometry that is often unknown, flexible or mobile. We introduce the valency-bounding correction (VBC), a many-body modification of generic short-range pairwise potentials that smoothly suppresses attraction once the neighbour count of either interacting particle exceeds a prescribed valency. The correction carries no angular degrees of freedom, applies on top of soft repulsive cores and evaluates in two passes over the neighbour list at the cost of a standard pairwise potential. The VBC drives the coordination number to the prescribed valency with low error, while its cluster statistics depart from Wertheim and Flory-Stockmayer predictions through unrestricted ring formation. A tuned variant exchanges bonded partners through ordinary molecular dynamics, reducing bond lifetimes at high saturation by an order of magnitude. On GPUs the cost of the correction is nearly independent of valency, reaching an almost tenfold advantage over a patchy-particle reference. We illustrate large-scale applications by reproducing the reentrant aggregation of repeat-expanded RNA, and show that the VBC also remedies the Fisher-Ruelle thermodynamic instability of soft-core potentials with attraction. The VBC is available as an open-source GPU plugin for HOOMD-blue.

Physics-informed neural networks (PINNs) struggle on PDEs whose governing physics varies across the domain. We trace this to a structural property of standard coordinate networks: their neural tangent kernel (NTK) is translation-variant and lets training points of large coordinate magnitude disproportionately influence predictions elsewhere, producing long-range coupling and gradient conflict during training. We show analytically and empirically that mixture-of-experts (MoE) architectures with centered, compact-support routers yield a uniformly banded NTK whose kernel-regression weights decay exponentially with distance, localizing the learning. Building on this, we propose Latent-MoE, which interleaves domain-aware MoE blocks within a shared backbone. Unlike FB-PINNs or X-PINNs, which rigidly partition both the domain and the parameters so that the parameters on different subdomains are updated independently, Latent-MoE is designed to preserve the localization benefit of domain-aware routing while allowing capacity to flow across regions through the shared backbone. On standard homogeneous-physics benchmarks Latent-MoE is competitive with established baselines; on benchmarks with multi-stage time-variable physics, where global models and rigid domain decompositions both fall into spurious solutions, it improves over them by more than an order of magnitude, with markedly reduced gradient conflict during training.

Using analytic theory, radiative particle-in-cell (PIC) simulations, and fluid simulations, we show that relativistic, synchrotron-cooling, collisionless, high-$\beta$ pair plasmas filament into a two-phase medium. This process occurs through the interplay of the synchrotron cooling instability (SCI) with the synchrotron firehose instability (SFHI). One phase has high plasma~$\beta$ and is infested with small-scale firehose fluctuations, which scatter particles and pin the pressure anisotropy to the firehose-marginal level. The other phase has much lower~$\beta$, causing the suppression of firehose modes and thus allowing large pressure anisotropies. We propose a fluid model for this two-phase plasma, which we use to study the linear and nonlinear evolution of the SCI and SFHI, and to predict the emergence time of the two-phase structure.

AlGaN alloys are promising wide and ultra-wide-bandgap semiconductors for next-generation power and RF electronics applications. To realize the full potential of AlGaN based devices, it is important to understand the electron transport to accurately predict device performance and identify material limits under various operating conditions. In this work, the high-field electron transport properties of AlxGa1-xN are investigated using a supercell based full band Monte Carlo method. The supercell approach is employed to explicitly capture the true disorder of the alloy system, enabling a more realistic description of carrier transport. The velocity field characteristics are calculated across a range of Al compositions to evaluate key transport metrics, including peak velocity, saturation velocity, and critical electric field. The role of different scattering mechanisms is studied in detail to understand the high field transport mechanism in the AlGaN alloy system. In addition to steady state transport, transient electron dynamics are examined for various Al fractions to study velocity-overshoot behavior, which is especially important for improving the performance of scaled RF devices. Finally, the temperature dependence of the velocity field characteristics in ultra-wide-bandgap Al0.75Ga0.25N is investigated to assess its transport performance under high temperature conditions. These results provide a detailed understanding of high-field transport in AlGaN alloys and offer guidance for the design of AlGaN-based RF and power electronic devices.

We introduce the Terrain-Weighted Forest Fire Model (TFFM), a lattice model in which fire spreads on a spatially correlated Gaussian height field with the asymmetric bond probability $p_{i\to j}=\mathrm{clip}[e^{-\beta+\gamma(h_j-h_i)},0,1]$, plus an additive wind bias. Simulations on lattices up to $L=8192$ reveal a sharp active-to-inactive transition whose critical suppression threshold $\beta_c$ is even in $\gamma$, decreases with $|\gamma|$, and decreases as the terrain correlation length $\sigma_h$ is reduced: slope asymmetry acts as a suppressant because downhill bonds are penalized and fire stalls at local elevation maxima. For rough terrain and low tree density the fire fails to percolate even at zero suppression. Finite-size scaling on a fine $\beta$ grid at $L=2048$–$8192$ gives a correlation-length exponent $\nu=1.8\pm0.17$ from both the susceptibility peak and the width of the transition, and a front-velocity exponent $\delta=0.34\pm0.03$, identical for smooth and rough terrain; neither matches directed percolation ($\nu_\perp=0.73$, $\nu_\parallel-\nu_\perp\approx0.56$) or isotropic percolation ($\nu=4/3$, $\approx0.18$). The single-seed survival probability at $\beta_c$ is independent of $L$ and decays extremely slowly, with a running exponent falling from $\approx0.09$ to $\approx0.04$, excluding directed percolation and pointing to a survival probability that remains finite at criticality, consistent with the $L$-independent value $P^*\approx0.5$ at which $P_{\rm surv}$ drops to zero. Wind raises $\beta_c$ by a factor of $2$–$4$, produces a sharp onset of downwind fire-scar drift at weak coupling, and, at high terrain coupling, decreases the burned fraction at boundary crossing—a terrain-wind competition effect absent from isotropic bond-disorder models. The model yields fire-risk thresholds and fire-scar signatures comparable to satellite burn-scar data.

The Hemoglycin polymer of glycine derived from space is terminated by iron and silicon to form a three-dimensional regular lattice with a high density of silicon tetrahedral vertices. Divalent cation bridges bypass vertices to facilitate electron conduction axially from layer to layer of the lattice. In principle, the exact distribution of Mg or Ca ions around each vertex can encode a very large amount of information. In space where the polymer forms in a molecular cloud the current derived from photon input will fade but in-fall hemoglycin in life forms on habitable planets like Earth, could be contained behind lipid electronic barriers of the nervous system in neurons or glia. Energy minimization of the permutations for magnesium or calcium occupancy showed deeper binding for calcium-dominated configurations. A low energy state with two independent magnesium to calcium substitutions appears to be key to the maintenance of long-term chemical modification. This state could be read via a pulse train that induced the matching spatial distribution of calcium to render adjacent insulating lattice regions conductive and provide a completed conduction path, signaling recognition. Writing of information may involve spatial modulation of the Na/Ca exchanger within the lipid membrane.

This thesis investigates the impact of resonant mode-particle interactions on transport and confinement in toroidal fusion plasmas. Using analytical and numerical approaches, we study how intrinsic and externally applied magnetic perturbations affect plasma particles with different kinetic characteristics, focusing on resonant interactions with guiding-center motion. We develop a novel, computationally efficient method based on Action-Angle variables to identify resonance locations and characterize resonances in guiding-center phase space, including the number of islands in resonance chains and the formation of transport barriers. Using the drift center (DC) approximation, we derive analytical expressions for orbital frequencies and the kinetic $q$ factor for large-aspect-ratio (LAR) equilibria. These results provide the conditions for mode-particle resonances, which can strongly influence particle, momentum, and energy transport and, consequently, plasma confinement. The analytical results are validated against numerical simulations, demonstrating an efficient tool for predicting transport barriers and energetic-particle behavior under non-axisymmetric perturbations. We further extend the orbital-frequency analysis from LAR equilibria to numerically reconstructed, realistic equilibria using a computationally efficient, semi-analytical geometrical method applicable to arbitrary unperturbed equilibria. Finally, for the LAR equilibrium, we extend the analysis to time-dependent perturbations and demonstrate the emergence of Arnold diffusion in guiding-center phase space, highlighting the role of the Arnold web in particle transport. Overall, this work advances the understanding of mode-particle resonant interactions and provides computational tools for predicting transport and confinement in realistic fusion configurations.

Fusion reactions can release a substantial fraction, and in aneutronic reactions nearly all, of their energy as the kinetic energy of fusion-product ions. In magnetized plasmas, these energetic ions can form ring distributions in velocity space. Such distributions can drive Dory-Guest-Harris (DGH) electrostatic instabilities, but these self-instabilities require a finite energetic-ion density and can be stabilized by a thermal background. We show that the same background can instead enable a distinct instability mechanism: a cyclotron pole of the minority energetic-ion susceptibility destabilizes a stable ion Bernstein wave (IBW) eigenmode. When the energetic-ion harmonic is distinct from the thermal-ion harmonics, exact root-pole resonance is thresholdless in the ideal collisionless limit. A species-resolved power balance shows that the energetic ions supply the free energy while the thermal plasma receives it. In the LAPD proton–alpha example, $99.96\%$ of the alpha-particle power loss enters the coherent proton response. This self-excited, ion-directed transfer provides a possible linear building block for alpha-particle energy channeling in proton-Boron11 fusion.

Classical crystallography determines orientation and unit-cell geometry from a rotation series collected from a single specimen, whereas serial crystallography acquires one still exposure from each of many randomly oriented microcrystals and must reconstruct a complete dataset by pooling partial measurements across the ensemble. In serial experiments, each frame typically contains only a subset of reflections, and frames with few reliable peaks (the small-N regime) are especially difficult to index blindly, because multiple incorrect lattices or orientations can explain sparse observations. The approach implemented in GLINT addresses this small-N regime by treating the dataset, rather than the individual frame, as the unit of inference. Candidate solutions are generated for many frames, weak but recurrent lattice hypotheses are pooled across the ensemble to identify a shared unit cell, and each frame is then registered against that consensus cell through a known-cell orientation search with lower dimensionality than blind indexing. In benchmark tests, GLINT matched the strongest blind indexer in the comparison on indexing yield at roughly 40x lower per-frame blind-indexing latency against XGANDALF's fastest configuration, and batched known-cell registration ran entirely on one GPU, allowing cell discovery on the fly during serial data collection. On experimental serial data, frames indexed blind by GLINT merged to crystallographic quality (CC* = 0.90), while the method rejected non-crystal images rather than forcing unsupported indexing assignments. These results demonstrate that indexing performance can be evaluated not only by indexing yield and throughput, but also by the quality of the resulting merged crystallographic data.

Detecting and mapping spin noise can reveal spatial variations in surface defect states, catalytic residues, free radicals, and spintronic materials. However, bulk measurements average over spatial heterogeneity, while scanning-probe maps require sequential rastering. Here, we demonstrate a microwave-free magnetic quenching (MQ) method for wide-field spin-noise mapping using nitrogen-vacancy (NV) centres in diamond. The protocol combines continuous LED illumination with a low-frequency, amplitude-modulated magnetic field, avoiding resonant microwave delivery or pulsed optical hardware. Field-induced spin-state mixing reduces NV photoluminescence, providing the primary contrast mechanism. We examine the response as a function of optical power and magnetic modulation amplitude, interpreting the results using a spin-state mixing model. The method is validated with aqueous gadobutrol concentration series, showing trends consistent with optically detected magnetic resonance (ODMR) measurements. We map spin-noise variations across boron nitride and single-walled carbon nanotube samples with differing defect and metallic catalyst contributions, supported by electron microscopy and spectroscopy. Notably, MQ detects a spin-noise response in boron nitride nanotubes that is silent in bulk EPR spectroscopy. This approach provides a practical framework for spatially resolved spin-noise sensing across quantum technologies and advanced functional materials.

The wakes of a lift + cruise aircraft's vertical lift propellers interact with the wing during the transition from hover to forward flight. This study investigates how variations in axial and longitudinal separations between the vertical lift propellers and the wing affect aerodynamic performance during this critical phase. Axial variations were introduced by orienting the motors either upright or inverted at two longitudinal positions relative to the wing. Wind tunnel tests were conducted on the resulting lift + cruise wing configurations over a Reynolds number range of 52,000 to 377,000, using tufts, pressure taps, and load cells to capture aerodynamic behaviour. Results show that the early transition phase (front propeller advance ratios between 0 and 0.25) is particularly sensitive to configuration changes. Rear motor orientation had a pronounced effect: when oriented upright at the shortest longitudinal separation, the active lift propellers increased the lift-drag ratio of the design by approximately 4 units, whereas in the rear motor inverted case the design suffered a 4-point reduction in the lift-drag ratio. Decreasing the longitudinal separation amplified both the beneficial lift contribution of the rear propeller and the generally adverse effects of the front propeller. However, placing the front propeller closer to the wing in the upright orientation enhanced performance at advance ratios between 0.11 and 0.25, due to favourable interactions between the wing and the front propeller's trailing vortices.

Hyperbolic shear polaritons (HShPs) represent a distinctive class of anisotropic polaritonic states that enable directional subwavelength light manipulation through asymmetric momentum-space dispersion, while their control in twisted systems has so far relied mainly on geometric and spectral parameters. Here, we introduce non-Hermitian dissipation as an additional degree of freedom for tuning HShPs in twisted van der Waals heterostructures. In twisted {\alpha}-MoO3/{\alpha}-V2O5 heterostructures with closely matched isofrequency contours but distinct dielectric losses, dissipation modifies the complex hybrid polaritonic states and thereby tunes the resulting momentum-space and real-space asymmetry. Near-field infrared imaging combined with analytical modeling and full-wave simulations reveals the evolution of the shear response with twist angle, excitation frequency, layer thickness, and substrate dielectric environment. The interplay between material dissipation and dielectric screening enables continuous regulation of the shear degree and reversible switching between shear-off and shear-on states. Furthermore, we demonstrate a proof-of-concept binary-state encoding scheme based on dissipation-controlled shear-state switching. Our work identifies non-Hermitian dissipation as an additional control parameter in twisted polaritonic systems, expanding the available design space for reconfigurable mid-infrared nanophotonics.

We present R-matrix photoabsorption cross sections for Li-like oxygen O VI and He-like oxygen O VII and examine how plasma broadening modifies them as a function of temperature and density in astrophysical and laboratory high-energy-density (HED) plasma sources. All atomic systems are subject to plasma environment effects, and the propagation of radiation depends on photoabsorption via bound-bound transitions as spectral lines and autoionizing resonances in bound-free photoionization cross sections. We identify and illustrate low and high temperature-density limits for the onset of plasma broadening in O VI and O VII, demonstrating general features and methodology applicable across a broad range of plasma conditions. Calculations are presented along two representative isotherms corresponding to the solar base of convection zone (BCZ) at $T = 1 \times 10^6$ and $2 \times 10^6$ K, and electron densities $N_e = 10^{18}$-$10^{23}$ cm$^{-3}$. Autoionizing resonances progressively dissolve into the continuum with increasing electron density at each isotherm, flattening and merging into the background cross sections at BCZ conditions, at much lower densities than bound-bound line features require. Illustrative examples are given for energy regions containing Rydberg resonance series, including large photoexcitation-of-core (PEC) resonances. Comparisons with previous Opacity Project results reveal that (i) the R-matrix photoionization cross sections cover a much higher energy range where a significantly richer spectrum of autoionizing resonances are present that are not included in OP, and (ii) the corresponding monochromatic opacities show significant quantitative differences. This work is generally applicable to modeling HED plasmas in astrophysics, and to the analysis of transmission spectra in laboratory experiments on inertial confinement fusion (ICF) devices.

Augmented reality eyewear offers a transformative interface poised to reshape human information interaction. In this context, silicon carbide (SiC) offers unique advantages for diffractive waveguides with its high refractive index and excellent thermal conductivity. However, in single-layer full-color displays, existing SiC waveguides generally remain thicker than 0.5 mm to reduce the bounce count of total internal reflection, thereby avoiding severe spatial variations in luminance and color. Here, we demonstrate a 0.35 mm SiC diffractive waveguide with an ultra-lightweight of only 1.98 g. The challenge of spatial non-uniformity is addressed by dual-parameter apodized gratings with continuously varying depth and duty cycle, enabling fine spatial control over local diffraction efficiency. To realize high-throughput production, a parallel gradient transfer method compatible with nanoimprint lithography is introduced, enabling wafer-scale patterning of four lens pairs per 8-inch SiC wafer. Furthermore, magnesium fluoride planarization suppresses grating visibility and achieves a high see-through transmittance of 92%. Optical simulations confirm that this architecture achieves balanced full-color transmission across a 30-degree field of view. This strategy provides a scalable route toward ultra-light and visually unobtrusive glasses, opening new opportunities for practical consumer-grade wearable displays.

Neural quantum states (NQS) have emerged as a powerful framework for simulating non-equilibrium dynamics in strongly correlated quantum systems, offering scalable variational representations of highly entangled states. Yet, accurate NQS simulations have been found to be surprisingly challenging in some physical regimes of limited complexity. Here, we address paradigmatic quench dynamics of a one-dimensional quantum Ising model as a controlled benchmark. Through supervised state reconstruction we establish substantially tighter empirical upper bounds on the required parameter count than previous estimates, ruling out representational limitations as the key obstruction. Instead, we uncover a geometric inflation of small deviations as a hitherto overlooked challenge for accurate solutions of the infinitesimal time-dependent variational principle (TDVP): the dynamical rotation of the kernel of the quantum geometric tensor (QGT) can suddenly lend physical significance to previously irrelevant parameter deviations. The stability of matrix product state solutions of the same TDVP suggests that the non-linearity of the neural network ansatz is the origin of the sensitivity. These results identify QGT-null-space rotation as a geometric diagnostic of sensitive NQS dynamics and as a concrete target for improving TDVP algorithms.

Digital signal processing has become essential in quantum optics, particularly for continuous-variable (CV) information encoding, enabling coherent detection schemes and mitigation of linear impairments. While linear signal processing in double homodyne detection has been studied under the assumption that all post-measurement processing can be backpropagated in the optical domain, a rigorous treatment of the digitization chain, spanning optical, electronic, and sampling stages, is still lacking. In this work, we consider the continuous mode description of double homodyne detection to fully capture the maximum mode-matching coefficient achievable as a function of electronic noise, signal bandwidth, receiver electronics bandwidth, and sampling rate, that is the entry point of digital signal processing. We establish a dimensioning rule that generalizes the Nyquist-Shannon criterion to account for quantum fluctuations of the measured signal, not merely its bandwidth. Analyzing the case of additive white electronic noise, we find numerically that optimal signal-to-noise ratio is achieved when the electronic bandwidth is large enough to pass the signal yet narrower than the sampling rate in order to mitigate noise amplification, yielding a concrete design criterion for coherent detection.

The effect of active region parameters on the evolution of solar axial dipole is well understood. However, their effect on the solar equatorial dipole has not been studied in detail. Understanding the development of the equatorial dipole is important as it drives the interplanetary magnetic field on intracyclic timescales (~ 1yr). We study how the latitude, tilt angle, and the polarity separation affect the evolution of solar equatorial dipole. We use dipole flux transport (DFT), a matrix implementation of surface flux transport (SFT) model, to simulate the dipole evolution of synthetic bipolar magnetic regions (BMRs) with a wide range of tilt angles and latitudes. We also study the evolution of active regions in HMI SHARP database and their associated BMRs. We quantify the dipole evolution by means of the maximum and mean amplification and the growth and decay times of the equatorial dipole. Tilt angle controls the amplification of the equatorial dipole, with larger tilt angles leading to stronger equatorial dipole. Latitude controls the timescale at which the equatorial dipole grows and decays. Low-latitude regions produce the longest living equatorial dipole. We find that only 31% of anti-Joy regions experience amplification of equatorial dipole, compared with 96 of regularly tilted active regions. Latitudes and tilt angles of active regions can have considerable effect on the solar equatorial dipole. Systematic changes in their properties could increase/decrease the strength of the equatorial dipole relative to the solar activity, which could lead to similar relative change in geomagnetic activity. We suggest that large low-latitude coronal holes and relatively high geomagnetic activity in the declining phase of solar cycle 20, could be related to emergence of unusually low-latitude active regions that produced strong and persistent equatorial dipole.

The linear Breit-Wheeler (LBW) process ($\gamma+\gamma\rightarrow e^{-}+e^{+}$) is a fundamental prediction of quantum electrodynamics, but yet to be observed under laboratory conditions using real photons. In recent years, a few experimental schemes utilizing high-intense ($\sim10^{22}$W/cm$^2$) laser-plasma interactions to observe the LBW process have been proposed. However, a high level of signal-to-noise-ratio are expected in these schemes, hindering the first-ever experimental detection of the LBW process by real photons. In this paper, we present a simple experimental setup which could enhance the expected positron signals by 2-3 orders of magnitude compared to previously proposed schemes, reaching the level of $10^{6}$MeV$^{-1}$str$^{-1}$. Moreover, such high positron signal is achieve in the direction opposite to the laser propagation, where a significantly quieter background is expected compared to the previously focused direction of laser propagation. The key to achieve this result is a newly discovered self-organized positron reorienting and pinching mechanism, enabled by the in-situ strong plasma fields from the laser-plasma interaction.

The primary challenge of rocket propulsion is the burden of accelerating the spacecraft's own fuel. Light sails leave the propellant at home, with the achievable speed set by the sail area, the thermal tolerance of its material, and the power of the driving array. In GL15 we showed that leakage from a microwave array driving such a sail between habitable worlds produces Jansky-level radio transients lasting tens of seconds at $100$~pc. We take that leak to optical and near-infrared, where Fresnel matching would shrink the aperture to $\sim\!20$–$100$~m at $1\,\mu{\rm m}$, but the intensity on that aperture is $2\times10^{8}\,{\rm W\,m^{-2}}$, five orders above published directed-energy loadings. Spreading $1.5$~TW at $\sim20\,{\rm kW\,m^{-2}}$ is a $\sim10$~km phased array, pushing the emitters; Lubin's $10^{3}\,{\rm W\,m^{-2}}$ loading is a $40$~km array, comfortable but more expensive. A tenth-wave delay on $40$~m tiles sized so one tile still covers the sail leaks $0.054$ of the power ($80$~GW) into a halo. The typical Galactic detection is a single $\sim30$~s peak at $m_{\rm AB}\simeq19.4$ in F146 at $8$~kpc; a pair $87$~s apart, about one in five, is the confirmation test. Roman's Galactic Bulge Time Domain Survey reaches $109$~kpc at $8\sigma$ on a faint host, so a pointed flash of that halo is visible from anywhere in the Galaxy if the beam points at us. For $10$–$40$~km optical beamers held to $\sim\lambda/10$ at $v_{\rm max}\gtrsim250\,{\rm km\,s^{-1}}$, $N_{\rm det}=1$ wants $\Gamma_{6{\rm h}}\simeq16$ and a null search limits $\Gamma_{6{\rm h}}\lesssim49$, a factor-of-three window in launch rate. The real threshold is set by PSF-coincident stellar and instrumental transients. If intensity-limited optical beamers are commonly employed in our galaxy, this activity could be revealed by Roman, Rubin and Euclid at no additional observing cost.

Fibre optic sensing, such as distributed acoustic sensing (DAS), has become a widespread technology for geophysical studies. To process the large-scale datasets produced by DAS, several machine learning methods have been proposed. However, without standardization of data and models, these methods lack comparability and interoperability. This introduces a gap between model developers and practitioners analyzing DAS data and inhibits adoption of deep learning for DAS. To address these limitations, here we present SeisBench DAS, an extension to the SeisBench library for machine learning in seismology. SeisBench DAS defines standard formats for DAS benchmark datasets, including standardised metadata and labels, and DAS models. It builds on the xdas framework for data ingestion and virtual array handling, and on PyTorch for reading and applying the machine learning models. Importantly, SeisBench provides an engine to efficiently apply deep learning models to diverse formats of DAS data, bridging the gap between model developers and practitioners. SeisBench DAS is designed as an open and extensible framework, allowing to easily incorporate future developments in deep learning for DAS.

In this article, we study the flow induced by crystallization of sodium chloride in sessile droplets. We measure flow velocities on the order of 1 $\mathrm{cm.s^{-1}} during crystallization, two orders of magnitude higher than those reported in previous studies. We show that this discrepancy is due to surface contaminants in earlier experiments. Numerical simulations reveal that in the clean case, the flow is driven by Marangoni stresses, whereas it is density- driven in the contaminated case. Consequently, droplet cleanliness significantly influences the flow which, in turn, determines the growth rate of crystals.

Accurate prediction of high-performance liquid chromatography (HPLC) retention times (RTs) across diverse molecules and chromatographic methods remains challenging because experimental training data cover only a limited region of chemical and method spaces. Here, we develop FUSE-RT (Foundation model Unifying Simulation and Experimental supervision for Retention Time), a multitask foundation model that integrates RT data from 179 chromatographic methods and adapts to unseen molecules and methods using limited target-domain data. To extend transferability beyond experimental coverage, we introduce simulation-to-real (Sim2Real) transfer learning, in which molecular representations learned from large-scale computational data are transferred to experimental RT prediction. Specifically, we use PolyOmics, comprising 39 properties for approximately 21,400 molecules generated by molecular dynamics and density-functional theory calculations, as auxiliary supervision. We evaluate generalization under molecular, method, and joint molecular–method distribution shifts. Simulation-derived supervision substantially improves transfer beyond the experimental molecular domain, particularly under pronounced coverage gaps and few-shot adaptation. Moreover, RT-prediction error decreases systematically with increasing simulation-data size, following a significant power-law relationship. These results establish Sim2Real transfer as a scalable strategy for extending RT prediction beyond the finite coverage of experimental chromatographic data.

Rare-event search experiments continue to extend their sensitivities to unprecedented levels. This requires increasingly small backgrounds, and shielding schemes that can suppress external backgrounds by several orders of magnitude. Moreover, detailed simulations are required to attain a good understanding of these experimental backgrounds. Studies have suggested, however, that simulations and computing-related tasks contribute approximately 10% to the average particle physicist's carbon footprint. With backgrounds frequently below 0.01 counts per kg of target per keV of energy, simulations require more computing resources; growing a rare-event researcher's computing-related carbon footprint. Event biasing is often applied in shielding simulations as a remedy, yet there is a lack of systematic guidance on how to maximally benefit from them. An optimisation study for the importance-splitting biasing technique is discussed, focused on balancing statistical precision with simulation CPU-time, and how this can benefit the average researcher's carbon footprint.

A general scheme of evolution from autocatalytic processes to socio-technological system is discussed from the perspective of maximum energy dissipation principle. The scheme treats the emergence of biological systems as an initial stage in the acceleration of global free energy dissipation. The sequential emergence of qualitatively new levels of organisation (biological cell, multicellular organism, social system) has been proposed, the levels are suggested as the results of cooperation (symbiosis) of the dissipative systems at the previous levels. The cooperation/symbiosis provides sufficient complexity for development of the next, essentially a new level of energy dissipation, leading to a new form of free energy utilization and a new form of information mapping. From a thermodynamic perspective, every qualitatively new level of biological organisation provides an additional step to increase the rate of energy dissipation from qualitatively new sources, essentially widening the number of these sources involved in the utilization.

A common formalization of homeostasis is the free energy principle, a framework that defines a set of desired observation values, or critical states, that the agent should reach or remain close to. Under the free energy principle, an agent should act to maximize the probability of receiving the desired observations. Here we revisit the common approach of solving the problem of maximizing the log probability of the desired observations by maximizing a variational lower bound, the so-called negative free energy. We show that, instead, an approach directly maximizing that probability under the agent's policy is better suited to, and provides a better solution for, the original homeostatic control problem. This is done using hidden Markov model (HMM) control by allowing the policy to act over hidden states or noisy versions thereof while trying to maximize the probability of repeatedly having the desired observations. HMM control largely improves performance over the variational, or free energy, approach. We also show that the optimal policy is strictly deterministic, while the variational approach leads to a stochastic policy approximation. We finally provide a homeostatic reinterpretation of the maximum occupancy principle -a principle proposing that agents ought to maximize the occupancy of action-state path space -by defining homeostatic states as any states that do not immediately entail the termination or death of the agent.

In this work, we introduced a positive viscosity coefficient into Newtonian dynamics to explore the rotational properties of disk galaxies. Our results show that the rotational behaviors of disk galaxies predicted by this modified Newtonian dynamics share notable similarities with those from the well-known MOND model. Specifically, the modified framework naturally recovers the deep-MOND limit postulated in MOND. Furthermore, we derived an explicit interpolation function relating centripetal acceleration to Newtonian acceleration-a relation that is purely empirical within the MOND model. We also identify key differences from MOND: the critical acceleration a0, regarded as a universal constant in MOND, actually varies with galactic mass and increases for more massive galaxies. Additionally, driven by the anti-dissipation effect of the viscosity coefficient, disk galaxies undergo extremely slow radial expansion over cosmic time, analogous to the expansion of the Universe. But regrettably, the fundamental physical origin of the viscosity coefficient introduced here remains unclear for us. But relevant hints can be found in existing literature, pointing to a promising direction for our future research.

We numerically investigate the long-time intra-cavity-field dynamics of a periodically pump- modulated pair of coherently coupled Kerr micro-ring resonators. Along a detuning scanning, fixed-phase stroboscopic trajectories change from recurrent regular state to states with positive maximum-Lyapunov growth. Long observation windows near the transition, where finite-time estimates converge slowly. The low- and high-detuning states both retain multi-mode Kerr-comb spectra, while their cycle-to-cycle field organization changes much more strongly. Spatiotemporal intensities, adjacent-cycle profiles, and full-field recurrence measures show a loss of one-period recurrence together with changes in the inter-resonator phase relation, coherent exchange, and dispersive intensity flow. We linearize the coupled equations around the long-time field and examine the growth of an infinitesimal perturbation. The resulting tangent-energy balance shows that the loss contribution changes little along the scanning, whereas the Kerr contribution increases and exceeds the loss magnitude in the high-detuning states with positive Lyapunov growth. A separate dimensional correspondence relates the normalized working range to Si3N4 micro-ring. These results provide a field-resolved description of the long-time dynamical change and its associated perturbation growth.

This research proposes a model-free inverse approach for identifying the nonlinear anisotropic single-valued magnetic constitutive characteristics of soft magnetic materials from boundary measurements. Using the harmonic approximation, the internal magnetic flux density B and magnetic field strength H are independently reconstructed from the measured boundary data, thus avoiding the limitation of using a predefined parametric B–H mapping function. Subsequently, the reconstructed fields are spatially averaged along selected collection lines to recover the B–H characteristics of the principal directions. Numerical results demonstrate high reconstruction accuracy for unperturbed data and good robustness against Gaussian perturbations of the boundary measurements, with the overall mean error remaining below 5% for a 10% noise level. These results indicate that under the harmonic-field assumptions, the field-based reconstruction provides a simple and computationally efficient framework for non-invasive identification of nonlinear anisotropic magnetic constitutive characteristics.