Papers

Label-free identification and real-time tracking of biochemical substances became critical for molecular diagnostics and chemical analysis, yet conventional resonant terahertz metasurface sensing relies on a single resonance, limiting spectral selectivity and dynamic capability. Here, we suggest multiresonant membrane metasurfaces and implement them for simultaneous static molecular fingerprint retrieval and dynamic reaction monitoring within a single pixel. We consider a membrane metasurface supporting multiple quasi-bound states in the continuum designed at target frequencies and enabling the tailoring of the field enhancement and frequency-selective interaction with target analytes. As a proof-of-concept, we achieve label-free detection of the dual fingerprint absorption features of pefloxacin at 0.78 THz and 0.99 THz, and real-time tracking of vitamin C oxidation and denaturation under ambient conditions. The kinetic profiles extracted from the THz amplitude evolution show excellent agreement with nonlinear reaction models, demonstrating quantitative biochemical tracking capabilities. Our results establish a versatile and scalable THz photonic platform that unifies static fingerprint identification and dynamic reaction monitoring, paving the way toward integrated on-chip biochemical analytics and multifunctional metasurface sensors.

Epidemics have shaped human history, often with devastating consequences, motivating the development of mathematical models to understand and control their dynamics. Among the many aspects of epidemic behavior, the conditions that lead to epidemic extinction stand out as a central-if not the fundamental-question in epidemic modeling. In this work, we study epidemic extinction in a continuous SIRS (Susceptible-Infected-Recovered-Susceptible) model governed by a system of ordinary differential equations (ODEs). The model includes vaccination as a time-dependent process and considers the reinfection of recovered individuals through waning immunity. We analyze how different parameter regimes – particularly infection, recovery, and immunity loss rates – affect the persistence or extinction of the epidemic. Special attention is given to the limitations of continuous population models, in which the infected fraction can fall below the equivalent of a single individual, leading to nonphysical outcomes such as unrealistically long persistence or artificial secondary peaks. By comparing the continuous SIRS dynamics with expected real-world thresholds for extinction, we highlight the importance of incorporating stochasticity or discrete effects to accurately describe epidemic fade-out.

Twisted bilayer graphene aligned with hexagonal boron nitride (TBG/hBN) hosts rich topological and correlated quantum phases, such as (fractional) Chern insulators, whose character is dictated by the topology of the moiré flat band. This topology is highly sensitive to several material parameters in the continuum model, yet a systematic understanding of their combined influence has been lacking. Here, we present a comprehensive study of topological phase transitions in TBG/hBN by varying the interlayer hopping strengths ($w_0, w_1$) and hBN-induced staggered potential, both with and without the hBN moiré potential. We map out Chern number phase diagrams across a broad, experimentally relevant parameter space, revealing a progressive enrichment of the topological landscape including multiple high-Chern number ($C$ = 3, 4, and 5) states. Each transition is linked to distinct band-inversion mechanisms at generic $C_3$-symmetric k points, high-symmetry momenta, or parabolic touchings, clearly reflecting in the evolution of the Berry curvature. Our results offer theoretical insights that help interpret existing experimental observations, elucidate the mechanisms driving these topological phase transitions and facilitate the exploration of topological states in TBG/hBN and related moiré systems.

Winds and jets are symbiotic when the accretion rate is low, according to black hole accretion theory. Both components are potentially important for active galactic nucleus (AGN) feedback, but previous works typically include only jets with free parameters. We perform hydrodynamical simulations of an isolated elliptical galaxy with both jets and winds included. The key features discriminating our simulations from others are that our simulations resolve the Bondi radius for reliable black hole accretion rate calculation and use parameters from GRMHD simulations. By selectively activating jets and winds, we examine their individual and combined effects. We find that effective AGN feedback, which is capable of generating strong turbulence and subsequently increasing central gas entropy and suppressing cool gas condensation and star formation, occurs only when both jets and winds operate simultaneously. The physical mechanism is the interaction between winds and jets: this interaction produces strong shear at their interface, leading to turbulence via the Kelvin-Helmholtz instability. In contrast, neither jets nor winds alone can generate strong turbulence due to the insufficient shear. The turbulence produced by wind-jet interaction is predominantly solenoidal in nature, giving rise to a broad energy spectrum approximately following a Kolmogorov-like power law and a dissipation rate $\sim 10^{-27}\,\mathrm{erg\,cm^{-3}\,s^{-1}}$ in the interstellar medium, consistent with observations. Our findings highlight the importance of simultaneously considering both jets and winds in studying the effects of AGN feedback in the evolution of elliptical galaxies.

Continuous-variable quantum key distribution (CVQKD) using passive state preparation (PSP) offers low-cost, high-rate secure communication. However, the existing PSP-CVQKD scheme with a transmitted local oscillator has high photon leakage noise and poor stability, making it unsuitable for high-loss transmission. In this work, for the first time, we propose and implement a local local oscillator (LLO) CVQKD system using a self-referenced (SR) PSP scheme, and give a theoretical proof of the equivalence of the PSP and GMCS protocol using temporal-mode theory. By employing the novel self-referenced pilot scheme to achieve high-precision time-varying frequency and phase compensation algorithms, we significantly improve the system' s signal-to-noise ratio and stability. The system achieves a record-high asymptotic secret key rate of 10.34 Mbps over a free-space channel with up to 23.5 dB loss, while maintaining low excess noise and robust performance under turbulent conditions. This work establishes the feasibility of SR-LLO CVQKD, providing a practical pathway toward secure, high-rate quantum communication in realistic environments.

Thermal plasma properties play a critical role in plasma simulations and plasma-related applications. However, their strong nonlinear dependence on temperature, pressure, and gas composition makes accurate and efficient evaluation challenging. In this work, an operator learning-based model, termed DeepPropNet, is proposed for fast prediction of thermodynamic and transport properties of thermal plasmas. Two architectures are developed, including a single-property model (S-DeepPropNet) and a Mixture of Experts (MoE)-based multi-property model (MoE-DeepPropNet). The proposed models learn the nonlinear mapping from plasma operating conditions to physical properties based on high-fidelity datasets. The MoE architecture enables efficient multi-property prediction within a unified framework. Predictions are performed for binary SF6-N2 and ternary C4F7N-CO2-O2 mixtures. The results show that the proposed models achieve high accuracy, with relative L2 errors on the order of 10-3 to 10-2, while maintaining strong generalization capability under unseen conditions. The applicability of DeepPropNet is further demonstrated by coupling with finite volume method (FVM) and physics-informed neural networks (PINNs). The results indicate that DeepPropNet provides an efficient and scalable approach for plasma property prediction and plasma simulations.

Deriving governing equations from empirical observations is a longstanding challenge in science. Although artificial intelligence (AI) has demonstrated substantial capabilities in function approximation, the discovery of explainable and extrapolatable equations remains a fundamental limitation of modern AI, posing a central bottleneck for AI-driven scientific discovery. Here, we present machine collective intelligence, a unified paradigm that integrates two fundamental yet distinct traditions in computational intelligence–symbolism and metaheuristics–to enable autonomous and evolutionary discovery of governing equations. It orchestrates multiple reasoning agents to evolve their symbolic hypotheses through coordinated generation, evaluation, critique, and consolidation, enabling scientific discovery beyond single-agent inference. Across scientific systems governed by deterministic, stochastic, or previously uncharacterized dynamics, machine collective intelligence autonomously recovered the underlying governing equations without relying on hand-crafted domain knowledge. Furthermore, the resulting equations reduced extrapolation error by up to six orders of magnitude relative to deep neural networks, while condensing 0.5-1 million model parameters into just 5-40 interpretable parameters. This study marks an important shift in AI toward the autonomous discovery of principled scientific equations.

We demonstrate stable operation of a thin-film lithium tantalate (TFLT) modulator at very high operating temperatures. We show that the electro-optic modulation and bandwidth of the TFLT modulators are not affected by high-temperature operation, and both waveguide and resonant modulators are DC-bias stable even at 120{\deg}C. At higher temperatures, we even observe 10% reduction of the V{\pi} of the modulator. Our results position TFLT modulators as a strong candidate for uncooled operation in co-packaged optics.

Pakistan experienced an unusually severe flood season between June and December 2025, with cascading impacts on population, infrastructure, and agriculture. Existing operational flood products (e.g., UNOSAT) provide valuable episode-level snapshots but rarely deliver spatially and temporally continuous inundation maps at near-real-time latency within the country. We present a multi-sensor, ensemble-based remote-sensing framework for continuous flood nowcasting in Pakistan that integrates Sentinel-1 SAR, Harmonized Landsat-Sentinel (HLS L30 and S30), MODIS, and VIIRS observations on a harmonized grid in Google Earth Engine. The framework employs a tiered nowcasting ensemble that prioritizes higher-resolution sensors (Sentinel-1 and HLS) and falls back to MODIS and VIIRS when necessary, preserving daily continuity of flood extent at each sensor's native resolution. Applied to the 2025 monsoon period, the system generates near-real-time, spatially consistent inundation maps across Pakistan. As a nowcasting case study, we track the super-flood of 26 August-7 September 2025 day by day, demonstrating the framework's ability to capture the evolving flood footprint in near real time and extend beyond the temporal limits of episodic mapping products. Validation against GloFAS discharge anomalies and precipitation datasets (CHIRPS v3.0, MSWEP) shows strong agreement with observed hydrometeorological conditions. By integrating nowcast outputs with exposure layers (WorldPop, ESA WorldCover, Giga-HOTOSM), the framework enables rapid estimation of affected populations, cropland, and critical infrastructure, supporting timely disaster response and resilience planning in South Asia.

Understanding how vegetation loss alters rainfall remains a major challenge in climate and hydrological science, as deforestation modifies precipitation through heterogeneous, seasonal and nonlinear land-atmosphere feedbacks. Existing models struggle to capture these dynamics: convection is parameterised at coarse scales, tipping behaviour is poorly constrained, and rainfall-deforestation analyses are limited to multi-decadal timescales. Therefore, many approaches resolve correlations rather than causal effects, limiting our ability to anticipate hydrological disruption. Using a neural-network model for hourly rainfall prediction, combined with pathway diagnostics and sensitivity analyses, we examine how vegetation perturbations reorganise rainfall across space, intensity regimes, and timescales under deforestation. We assess whether the model captures physically consistent dependencies linking vegetation, atmospheric state, and precipitation, and whether sustained canopy loss induces threshold behaviour. The model accurately predicts rainfall occurrence and intensity (Spearman = 0.84, F1 = 0.93, ROC-AUC = 0.98) and learns temporally ordered dependencies aligned with ecohydrological theory. Sensitivity analyses reveal rapid, asymmetric responses to vegetation loss: heavy rainfall (20-50 mm/h) declines by up to 7% under sustained deforestation, while light rainfall (0.1-1 mm/h) increases by 4%. Rainfall entropy rises by 1.3%, and dry-season intensity increases by 0.3-0.5% per 0.5% forest-cover loss, with strongest impacts in the north-western Amazon and Andean foothills. Threshold analysis reveals a sharp decline in precipitating area fraction after 2-3 months of sustained vegetation change in sensitive regions. These results demonstrate that data-driven approaches uncover process-relevant land-atmosphere coupling and highlight growing hydrological vulnerability in the Amazon.

After the Large Hadron Collider (LHC) upgrade into High Luminosity LHC (HL-LHC), the instantaneous luminosity is expected to reach values up to 7.5x10^34cm^2/s, causing a harsher radiation environment as well as a significant increase in data rate. The current CMS Tracker detector would not be able to operate under these conditions and it will be replaced by an upgraded version known as Phase-2. In view of the detector upgrade and as part of the design validation process, a Quality Control (QC) test flow has been developed to characterize the first pixel modules prototypes and evaluate their performance. The results of this procedure were the starting point for small design adjustments, especially for the the High Density Interconnect or HDI, the flexible low mass PCB that distributes power and signals to the module and controls the readout through a high speed data transmission channel. This talk includes qualification tests performed on the CMS Phase-2 design to ensure that all the pixel module components satisfy the upgrade specifications, for example in terms of power consumption and leakage current stability. Additionally, stress tests were conducted to probe the limits of the design, demonstrating the robustness and endurance of the module layout. Due to the differing material properties of the HDI copper layers and the silicon sensor and readout chip, temperature gradients induce different thermal expansion and contraction, resulting in mechanical stress on the bump-bond interface. For this reason, among the destructive measurements, dedicated thermal stress tests were carried out to evaluate the bump-bond strength and durability for different bump bonding techniques.

In his 1972 book Science At the Crossroads, Helbert Dingle attacked the consistency of special relativity through a fallacious argument championed by the crank community even to this day. Dingle's affair is a curious chapter in the history of physics and, more generally, science. We briefly review Dingle's case from a historical and didactic perspective.

Groundwater in the Densu Basin is increasingly threatened by heavy metal contamination, but conventional methods fail to capture the statistical complexity and spatial heterogeneity of pollution indicators. A key challenge is modelling the Heavy Metal Pollution Index (HPI), which is typically skewed and affected by correlated contaminants, leading to biased predictions without transformation. This study develops a predictive framework integrating response transformations with nested cross-validated ensemble machine learning. Three transformations (raw, log, and Gaussian copula) were applied to HPI and evaluated across six learners: support vector regression (SVM), $k$-nearest neighbours (k-NN), CART, Elastic Net, kernel ridge regression, and a stacked Lasso ensemble. Raw-scale models produced deceptively high fits (Elastic Net and stacked ensemble $R^2 \approx 1.0$), suggesting over-optimism. The log transformation stabilised variance (SVM: $R^2 = 0.93$, RMSE $= 0.18$; k-NN: $R^2 = 0.92$, RMSE $= 0.20$). The Gaussian copula gave the most reliable results: stacked ensemble $R^2 = 0.96$ (RMSE $= 0.19$), with other learners maintaining high accuracy. Copula-based models improved residuals and produced spatially plausible maps. DBSCAN clustering revealed Fe and Mn as primary HPI contributors, consistent with regional hydrogeochemistry. Limitations include reliance on random (not spatial) cross-validation and basin-specific scope. Future work should explore spatial validation and other geological settings. Overall, distribution-aware ensembles with clustering diagnostics offer robust, interpretable assessments of groundwater contamination.

Entropic cosmology with the Rényi entropy of the apparent horizon $S_R=(1/\alpha)\ln(1+\alpha S_{BH})$, where $S_{BH}$ is the Bekenstein–Hawking entropy, is studied. By virtue of the thermodynamics-gravity correspondence a model of dark energy is investigated. The generalised Friedmann equations for the Friedmann–Lemaître–Robertson–Walker spatially flat universe with the barotropic matter fluid are obtained. We compute the dark energy density $\rho_D$, pressure $p_D$ and the deceleration parameter $q$ of the universe. At some model parameters the normalized density parameter of the matter $\Omega_{m0}\approx 0.315$ and the deceleration parameter $q_0\approx -0.535$ for the current epoch, which are in the agreement with the Planck data, are found. Making use of the thermodynamics-gravity correspondence, we describe the late time of the universe acceleration. The entropic cosmology considered is equivalent to cosmology based on the teleparallel gravity with the definite function $F(T)$. The Hubble parameters are in approximate agreement (within $5$ percents) with the observational Hubble data for redshifts $0.07\leq z \leq 1.75$ at the entropy parameter $\alpha\approx 0.305~GH_0^2$.

Topological indices are numerical invariants derived from molecular graphs and play an important role in characterizing chemical compounds and predicting their properties. Among the earliest descriptors are the classical Zagreb indices introduced by Gutman and Trinajstić in 1972. A more recent development is the hyper-Zagreb index ($HM$), defined as $HM(G)=\sum_{v_i v_j\in E(G)}(d_i+d_j)^2$, where $d_i$ denotes the degree of vertex $v_i$. In 2023, Hayat et al. posed an open problem concerning bounds on the $HM$ index under fixed vertex-connectivity or edge-connectivity, along with the characterization of the corresponding extremal graphs. In this work, the problem is resolved by determining the extremal graphs that maximize $HM$ index under these constraints. The investigation is further extended to several additional extremal problems, including graphs with a given number of leaves, chromatic number, and independence number. The associated extremal graphs are identified in each case. In addition, the chemical relevance of $HM$ is examined through QSPR studies. Finally, the conclusion is presented.

Beta-gallium oxide (\beta-Ga2O3) holds enormous potential for medium voltage range power electronic applications. This work reports VBr > 10 kV/Ron,sp = 43 m\Omega*cm2 class edge terminated vertical heterojunction diodes (HJDs) with e-beam/sputtered nickel oxide (NiOx) stack on epitaxial (011) \beta-Ga2O3. The power figure of merit (PFOM) of the HJD exceeds 2.3 GW/cm2. The extracted parallel plane breakdown field is > 5.3 MV/cm, which is the highest reported electric field for thick (011) \beta-Ga2O3 epitaxial drift layer.

We present dm-PhiSNet, a physically constrained PhiSNet-based equivariant model that predicts one-electron reduced density matrices (1-RDMs) directly from molecular geometries in an atomic-orbital (AO) basis for accelerated self-consistent field (SCF) workflows. Training follows a two-stage schedule with progressively introduced physically motivated objectives, and the resulting predictions are refined by a lightweight analytic block. This block enforces electron-number conservation, drives the 1-RDM toward generalized idempotency in the AO metric, and regularizes the occupation spectrum of the Löwdin-orthogonalized density. Across six closed-shell systems – H$_2$O, CH$_4$, NH$_3$, HF, ethanol, and NO$_3^-$ – the refined 1-RDMs provide SCF initial guesses that substantially reduce iteration steps by 49–81% relative to standard initializations. Beyond SCF acceleration, the learned 1-RDMs yield accurate one-shot total energies and Hellmann–Feynman atomic forces without force supervision, indicating that the model captures chemically meaningful electronic structure. These results demonstrate that combining equivariant learning with analytic constraint enforcement provides a simple, general route to solver-ready density-matrix initializations and accelerated SCF workflows.

If sufficient training data are available, neural networks are attractive for representing missing physics in simulations, such as sub-grid scales in the coarse-mesh particle-turbulence system we consider. Physical constraints are known to both increase performance and reduce the need for data; we use the complete physics represented in the discretized governing equations as a constraint. Two-way coupled particles in two-dimensional turbulence provide a sufficiently complex system to assess effectiveness for various training data, all constructed from well-resolved simulations, in cases intentionally degraded to assess robustness. Surprisingly, using the full space-time data actually hinders model effectiveness. Instead, training that targets only spectra – hence, neglecting phase information – provides better closures, which is related to the well-known success of non-dissipative discretizations for simulating turbulence. It is found that some of the missing physics that lead to preferential particle concentration errors are fundamentally stochastic on the coarse mesh and therefore uncorrectable by the basic approach; a learning formulation is introduced for a Langevin-type closure to correct this. Most importantly, training just for particle kinetic energy – without any direct input from the flow field – also yields effective sub-grid-scale stress models. This holds true even if noise is added to the particle data, if only a sub-sample of particles are used, or if only one component of the particle velocity is used. In sum, these results show a path for inferring sub-grid-scale physics based just on particle data from experiments.

X-ray Thomson scattering (XRTS) is a common diagnostic used in the warm dense matter (WDM) regime to estimate plasma parameters like density, temperature and charge state. Experimental analysis typically relies on a forward model to obtain estimates for these parameters, as the measured spectrum is a convolution of the dynamic structure factor (DSF) and the source-instrument function. The Chihara decomposition, where the spectrum is separated into contributions from bound and free electrons, is commonly used to estimate DSFs in the WDM regime, as it allows for the fast calculation of DSFs and therefore can easily be applied in a large-scale parameter optimization. Due to the limited availability of XRTS codes, in this work we present the “X-ray Diagnostics, Analysis, Verification and Exploration“ (xDAVE) code, designed to quickly estimate DSFs using the Chihara decomposition and analyse experimental spectra. The code is validated by re-analysing an experiment with isochorically heated beryllium at the OMEGA Laser Facility. In addition, we demonstrate the applicability of the code to plan experiments and predict scattering spectra through the coupling to a ray-tracing code. Lastly, the importance of accounting for the energy-dependence of spectrometer instrument functions is demonstrated by comparing ray-tracing simulations to the standard convolution for strongly compressed Beryllium shots at the National Ignition Facility similar to previously published results.

I study the impact of mixed contrarians on the opinion dynamics of an heterogenous population with conformists using Galam Majority Model. Activation of contrarians is a function of the ratio majority/minority in the local groups of discussion. Restricting the group size to 3, two types of contrarians are included in respective proportions $c_{3,0}$ for configurations with ratio 3 to 0 and $c_{2,1}$ for ratio 2 to 1. I then derive the explicit update Equation and obtained analytically the fixed points, their stability, and the resulting full two-dimensional landscape of the dynamics of opinion. Setting $c_{3,0} =c_{2,1} = c$ recovers the original results obtained with uniform contrarians. The findings allow for considering a wide spectrum of new disruptive strategies to secure either a majority/minority ending ensuring the opinion having the larger initial support to win, or a single attractor dynamics at fifty/fifty, which implies a random winner regardless of initial supports.

The chemical bond is a central organizing concept in chemistry, yet it is absent from the molecular Hamiltonian and no "bond operator" exists. Bonding is therefore not a primitive physical entity but a derived descriptor emerging from the quantum state. The logical consequences of this observation are revisited. Statements such as "bonding stabilizes structure" when taken literally risk circular reasoning (petitio principii), whereby bonding is inferred from a stationary structure and then invoked as its cause. The same caution applies to concepts such as steric repulsion, which is also a derived descriptor. Bonding accompanies stable or metastable states and correlates with their properties without constituting their cause. Illustrative examples are drawn from QTAIM, non-covalent interaction (NCI) approach, protein structure, and hydrogen-hydrogen bonding. Causation, language, and the autonomy of chemistry are also briefly discussed. The aim is not at all to diminish the role of bonding, but to place it at the correct logical level, that is, as a powerful, state-dependent descriptor that organizes, classifies, and predicts chemical behavior without serving as its fundamental cause.

On-chip silicon photonic coupled cavity arrays (CCA) are a promising platform for quantum simulators, with access to high Quality (Q) factor resonators, tunability, and foundry compatibility. Furthermore, scalable two-dimensional (2D) silicon photonic CCAs allow for simulation of rich physical phenomena via Hamiltonian engineering. However, complete reconstruction of the Hamiltonian is limited by access to cavities in the bulk, with current approaches relying on imaging scattered light from bulk resonators. These approaches often require additional scatterers to be built in, limiting scalability, while also being hampered by imaging technology in the near-infrared range. Instead of these approaches, Hamiltonian tomography algorithms that require homodyne boundary measurements have been demonstrated in literature, however measurements of complex scattering measurements along a CCA boundary have not been shown. Here, we experimentally demonstrate an on-chip homodyne measurement setup along a single boundary of a $3\times 3$ silicon photonic racetrack resonator array and reconstruct the system's edge scattering matrix.

Ocean turbulence plays a key role in shaping large-scale circulation, heat uptake, and biogeochemical processes. The kinetic energy (KE) wavenumber spectrum is a fundamental diagnostic, quantifying how KE is distributed across spatial scales. The second-order structure function – computed from velocity differences between spatially separated observations – provides a complementary measure, but unlike the KE spectrum, it reflects a non-local, weighted integral of KE over all scales. Analytic relationships link the two metrics, permitting forward and inverse transformations between them. However, recovering the KE spectrum from the structure function via the inverse relationship is highly sensitive to sampling limitations and numerical discretization errors. Here we propose a regularized approach in which the spectrum is assumed to consist of a finite number of segments with distinct slopes and amplitudes, and the inversion is formulated as an optimization problem. The approach is first validated in an idealized setting; for a number of idealized KE spectra with prescribed sets of spectral slopes and amplitudes, the corresponding structure functions are computed by numerically evaluating the forward relationship. These structure functions are then used to determine the underlying parameters using our proposed approach, which shows that we are able to perfectly recover the parameters and consequently the KE spectra. The method is further evaluated on high-resolution ocean model output, where it reconstructs the underlying spectra well even in the presence of noise. Finally, we apply the method to surface drifter observations (GLAD and LASER experiments). The results show that the framework enables estimation of the KE spectrum from sparse Lagrangian data, extending spectral diagnostics beyond gridded Eulerian measurements.

We demonstrate a micro-Fabry-Pérot cavity array through laser etching of high-surface-quality mirrors onto a single fused silica substrate. A cavity finesse of $4750\pm 200$ was achieved with a simple array design with $500~\mu m$ cavity length, $100~\mu m$ diameter micromirrors and $300~\mu m$ transverse separation. Arrays with up to 12 cavities were simultaneously tested for single mode operation, and absolute frequency measurements correlated strongly with the etched depth as measured by profilometry. Simultaneous measurements of the absolute resonant frequency for neighboring cavities showed a factor of 5 common-mode cavity drift reduction. Arrays of such cavities can be employed in chip-scale cavity QED networks (current cooperativity estimates are at the border of strong coupling for $^{87}$Rb atoms, $C=1$) as well as for precise laser stabilization at nearby wavelengths on a chip.

Orbital data centers are being evaluated as solar-powered compute constellations and relay-integrated processing platforms. Their feasibility is not set by orbital solar flux alone, but by simultaneous closure of photovoltaic generation, eclipse recharge, radiative heat rejection, sustained space-to-ground communications, utilization, replacement cadence, and delivered compute-years over finite mission life. This paper derives necessary cluster-level competitiveness conditions using delivered information-technology (IT) electrical power $P_{\rm IT}$, deployed mass per delivered IT power $m_{\rm kW}$ in kg/kW, communication intensity $\Gamma=D_{\rm sg}/E_{\rm IT}$, sustained communication ceiling $\Gamma_{\max}$, effective utilization $U_{\rm eff}$, and lifetime penalty $\Pi_{\rm life}$. For a representative $P_{\rm IT}$=1 MW high-sunlight anchor, the base case gives beginning-of-life photovoltaic area $A^{\rm BOL}_{\rm PV}=5.64 \times 10^3 {\rm m}^2$, radiator area $A_{\rm rad}=2.50 \times 10^3 {\rm m^2}$, and 29.4 kg/kW for photovoltaic, storage, and radiator mass; fixed spacecraft mass raises the total to 34-59 kg/kW. At m_kW ~ 40 kg/kW, a terrestrial infrastructure benchmark of 10-40 k\$/kW allows only 250-1000 \$/kg for the combined launch and spacecraft-build cost before space-to-ground communications, operations, utilization, and lifetime terms are included. That allowance is 3.4-13.5 times below the current public Falcon 9 dedicated low-Earth-orbit launch-price benchmark alone, before spacecraft build is included. Space-native preprocessing and communications-integrated edge compute are credible early regimes; terrestrial-user general compute closes only for low Earth-coupled communication intensity, high effective utilization, long delivered lifetime, and very low combined launch-plus-build cost.

Recent advances in occupancy extrapolation (OE) show that potential of orbital-occupation based energy functions can describe electronic excitations. Here, the OE method in the particle-hole channel is extended to an effective quasiparticle Hamiltonian, enabling a multi-configurational description beyond single-determinant OE and $\Delta$SCF. The method performs comparably to the Bethe-Salpeter equation for valence singlet and charge-transfer excitations, and better for valence triplet and Rydberg states, supporting its accuracy and broad applicability.

Success-driven social learning, in which individuals preferentially adopt the ideas and methods that appear most successful, is a foundational principle of collective behavior across systems ranging from ant colonies to scientific communities. But science is a particular kind of collective search – one in which the quality of an explanation is itself difficult to assess. Is success bias adaptive in this setting? In agent-based simulations of collective theory building, we find that it is not. Scientists in our model systematically overestimate the quality of their own theories, creating an illusion of understanding: a persistent gap between perceived and actual performance. Success bias amplifies this illusion; communities that favor apparently successful theories explore a narrower range of possibilities, efficiently filtering out poor explanations but failing to discover better ones. This effect intensifies with problem complexity, as scientists in more complex environments become increasingly unable to assess how well their theories actually perform. Most strikingly, when agents optimize their social behavior to maximize the perceived success of their theories, they paradoxically undermine their actual performance, and produce levels of inequality that mirror those found in real scientific communities.

The detection of CO2 on the Jovian satellite Europa by Galileo NIMS and recent mapping of the leading side by JWST has revealed that it is most concentrated in geologically young terrains, and its v3 asymmetric stretch appears as a spectral doublet centered at 4.25 and 4.27 um. Since crystalline CO2 is unstable at Europan surface conditions, this observation implies an active source and a trapping medium, which may be separate. To this end, several hypotheses have been proposed, but no laboratory work has successfully reproduced the spectral features of CO2 on Europa so far. Radiolyzed carbonates have also been discussed as plausible precursors and host materials for CO2, though their role has not been experimentally validated in a Europa-like environment. Here, we report the first laboratory experiments investigating CO2 production from carbonate salts exposed to 10 keV electron irradiation at 50, 100, and 120 K in ultrahigh vacuum. Using diffuse reflectance FTIR spectroscopy, we observe the emergence, growth, and saturation of an absorption doublet centered near 4.25 and 4.27 um, consistent with the CO2 v3 band. Postirradiation thermal desorption studies using residual gas analysis reveal that the radiolytically formed CO2 is stable at temperatures beyond Europa's surface. This work provides the first experimental evidence that low-energy electron irradiation of carbonates in cryogenic, vacuum conditions can produce and retain CO2, and suggests that carbonates can serve as endogenous reservoirs of CO2 on irradiated icy bodies in the outer solar system.

The standard architecture for a high-peak-power femtosecond laser is chirped pulse amplification using diffraction gratings for compression; the damage threshold of the compression gratings limits current lasers to multi-petawatt peak power. Plasma gratings have orders-of-magnitude higher damage tolerance than conventional optics, so plasma gratings with sufficiently high optical quality could allow the construction of ultra-high-power femtosecond lasers. Here, we present experimental measurements of the angular dispersion, angular bandwidth, and diffraction angles of ionization-based plasma transmission gratings and show that both the dispersive and the diffractive properties of these gratings are in close agreement with optical theory and simulations. Gratings with a period of 10.2 microns are found to have an angular dispersion of approximately 0.005 degrees/nm. The dispersion and bandwidth of these gratings suggest plausible designs for a plasma-grating-based compressor and indicate a pathway to compact lasers with petawatt to exawatt peak power.

We propose an accelerated computational fluid dynamics framework based on a hybrid Fourier Neural Operator-Lattice Boltzmann Method (FNO-LBM) for steady and unsteady weakly compressible flows. FNO-based initialization significantly accelerates LBM in reaching steady-states of porous media flows across all macroscopic fields, achieving up to 70% speed-up in convergence of density and more than 40% of pressure-drop while preserving the final steady-state accuracy. Simulations of unsteady flows can be accelerated by hybrid coupling strategies that employ FNO rollouts embedded into LBM time advancement in a way of super-time-stepping. Global and time-resolved error metrics across 100 trajectories for generic 2D flows demonstrate that hybridization consistently improves accuracy and stabilizes long-horizon rollouts. Best efficiency is achieved for a lightweight 2.6M-parameter FNO, which diverges under pure autoregressive rollout but achieves 96-99.8% error reduction under hybrid coupling, matching the predictive capability of a much more expensive 11.2M-parameter model. The hybrid framework enhances predictive fidelity, suppresses error accumulation, and enables small and cheap surrogate models to operate effectively within the same error regime as larger surrogates. These results demonstrate that hybrid neural-operator coupling achieves robust and computationally efficient accelerated LBM while maintaining physically consistent flow evolution.