Papers

In this study, we investigate the Jaynes–Cummings model (JCM) using Lie symmetry analysis and conservation-law theory. The dynamics is formulated as a system of partial differential equations by projecting the von Neumann equation onto the atomic degrees of freedom and representing the field mode through its characteristic function. We determined the admitted point and generalized symmetries and constructed invariant solutions by imposing quantum-mechanical constraints. The conventional dressed-state dynamics is recovered, while a second class of solutions with radial dependence expressed through Heun polynomials is obtained for coupled atom–field configurations. We also applied a generating-function methodology to derive the local conservation laws of the JCM differential system in Fourier phase space. In addition to recovering the standard excitation-number constant of motion, we obtained additional conserved currents involving atomic populations, coherence, reduced-state purity, and derivatives of the field characteristic functions. One of these currents yields, at the Fourier-space origin, a balance equation for a combination of atomic purity and coherence whose evolution is driven by atom–field coupling and joint atom–field moments. For globally pure states, the purity contribution provides an indirect connection with the development of atom–field entanglement. The symmetry structure generates generalized symmetries and an infinite hierarchy of conservation laws.

In this work, we present a simple, exact, and fully analytical method for generating light sheets parallel to the propagation direction, with amplitude and phase envelopes structured on demand. We validate the approach theoretically and experimentally by imprinting images onto light sheets, and we compare the theoretical performance with that obtained using an alternative strategy based on arrays of Frozen Waves (FWs). In this context, the proposed method provides a more direct and flexible control of the field envelopes on the light sheets, resulting in higher-fidelity reconstructions than those achieved with FW-based approaches. The method thus offers a versatile framework for structured light-sheet generation, with potential applications in optical manipulation, microscopy, and 3D holographic imaging.

Self-driving laboratories (SDLs), where artificial intelligence proposes subsequent experiments and robotic systems execute them, are rapidly becoming the vanguard of materials discovery. A critical bottleneck, however, lies in seamlessly bridging diverse AI algorithms tailored for specific exploration goals with the heterogeneous robotic hardware found across different laboratories. Here, we present NIMO, an open-source software platform designed to dissolve this barrier through three core paradigms: a modular AI-robot decoupling mediated via simple CSV file exchange, a discrete candidate-pool architecture that seamlessly absorbs domain knowledge, and a unified Python interface pre-loaded with twelve distinct AI algorithms. In this Perspective, we review the operational principles of each algorithm alongside six diverse SDL implementations driven by NIMO, covering electrolyte discovery, organic synthesis, thin-film exploration, fuel-cell process informatics, coffee-ring phase exploration, and legacy liquid-handling automation. One of these also demonstrates NIMO's seamless interoperability with the IvoryOS orchestration framework. To democratize autonomous science, we also introduce a no-code desktop application that enables intuitive, human-in-the-loop exploration for non-programmers. NIMO is freely available at https://github.com/NIMS-DA/nimo, offering a versatile, plug-and-play foundation to accelerate autonomous materials exploration across diverse experimental landscapes.

Adjoint formulations of magnetohydrodynamic (MHD) equilibrium are presented for sensitivity analysis and optimization-based equilibrium calculations. We derive the adjoint of the three-dimensional equilibrium equations under fixed-boundary conditions and analyse its axisymmetric reduction. The reduced operator coincides with the perturbed Grad-Shafranov operator, thereby making explicit the connection between the threedimensional adjoint construction and the axisymmetric adjoint problem. The resulting framework provides a basis for relating adjoint structures in both axisymmetric and fully three-dimensional ideal MHD equilibrium.

Trapped-ion quantum simulators realize programmable spin models through phonon-mediated interactions. For Hamiltonians with noncommuting terms, however, the same phonon bus generates intrinsic spin-phonon errors that strongly distort the target dynamics. Because these errors are governed by the full time history of the spin-dependent phonon motion, they survive standard loop-closing control and limit simulation accuracy. Using a sequence of frame transformations, we isolate the residual error dynamics and show that this intrinsic error can be strongly suppressed while preserving programmable Ising couplings. Full spin-boson simulations of multi-ion chains demonstrate orders-of-magnitude lower error than both constant-drive and conventional loop-closing protocols. These results remove a central precision barrier in trapped-ion analog quantum simulation and enable accurate programmable simulation of noncommuting many-body Hamiltonians and dynamical protocols.

SDE-based generative models, including diffusion models and the Schrödinger bridge, have found broad applications in signal processing tasks such as speech enhancement, image restoration, and time-series generation. This note presents a modeling framework for such models within the context of stochastic thermodynamics. The main results of this note are trajectory-level definitions of work, heat, and entropy production, along with a generalized Jarzynski identity and a second-law-like inequality. The proposed framework extends the original Jarzynski setup to accommodate time-dependent bath temperature and nonconservative driving forces. This thermodynamic perspective may deepen our understanding of diffusion models and the Schrödinger bridge from a nonequilibrium statistical mechanics viewpoint.

Thin wall treatments modify high-speed boundary-layer instability through the pressure they admit or absorb at the wall. This paper develops a unified admittance formulation for such effects on trapped compressible Rayleigh modes. For a simple rigid-wall eigenpair, we prove the spectral sensitivity law \[ c(A)=c_0+KA+\mathcal O(|A|^2), \qquad \delta\sigma=\alpha\Imag(KA)+\mathcal O(|A|^2), \] where \(A\) is the wall admittance and \(K\) is an explicit functional of the rigid-wall eigenfunction. The formula separates wall physics from outer-mode physics and yields a phase criterion for stabilisation. Matched asymptotics show that viscous and thermal wall layers, blind-pore coatings and shallow non-separating roughness all reduce to this same boundary condition, with additive leading admittances. Mach-4.5 computations validate the sensitivity coefficient and demonstrate porous damping, viscous-wall damping and sign-changing reactive roughness effects.

Simulating strongly correlated molecular systems on near-term quantum hardware remains challenging because current hardware offers limited quantum volume and moderate-fidelity qubits. One way to address this challenge is through bootstrap embedding (BE). Bootstrap embedding partitions molecules into smaller fragments that are then embedded in the “bath” of other fragments iteratively. Bootstrap embedding is appealing for quantum simulation because fragmentation reduces the qubit requirements of each fragment problem. In this work, we develop a quantum bootstrap embedding (QBE) workflow that uses variational quantum eigensolver (VQE) fragment solvers and examines the algorithmic choices that determine the success of the overall VQE-QBE procedure. To improve efficiency, we introduce FastAdaptVQE, a sparse-matrix-accelerated form of the adaptive variational quantum eigensolver (ADAPT-VQE) that replaces symbolic commutator evaluation with direct statevector calculations, and MatrixFreeAdaptVQE, a matrix-free extension that removes the sparse-matrix memory bottleneck that arises for larger fragments. We also explore modifying the ADAPT-VQE operator-selection step by replacing the purely greedy choice with a lookahead strategy. Benchmarks on H$_4$, F$_2$, and selected geometries of H$_6$ reach chemical accuracy, within 1 kcal/mol of BE results obtained with a full configuration interaction (FCI) solver. For $n$-butane, density-matching residuals converged, but energy errors remained outside chemical accuracy. These results show that combining QBE with VQE can accurately compute molecular energies, while density convergence alone does not ensure energy accuracy. This work lays the foundation for extending these energy calculations to larger molecular systems and quantum materials on near-term quantum hardware.

Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion. However, Grad-Shafranov equilibrium calculations remain largely device-specific and iterative, limiting their use in latency-constrained control settings. Existing neural approaches can accelerate individual equilibrium predictions, but they do not generally provide reusable models across changing plasma boundaries or tokamak geometries. Here we show that equilibrium reconstruction can be recast as a cross-device operator learning problem. We develop a domain-specific neural operator framework that maps geometry and profile parameters directly to the poloidal flux field, replacing repeated solve-on-demand computation with amortized operator inference. Using the analytically tractable Solov'ev family as a controlled Grad-Shafranov testbed, we generate equilibria across eight geometrically distinct tokamak-like configurations and benchmark five neural operator architectures under four transfer-learning strategies. Single-geometry pretraining gives poor transfer to unseen devices, whereas multi-geometry pretraining enables data-efficient adaptation. The Wavelet Neural Operator gives the strongest cross-geometry performance, reaching mean relative L2 errors below 4% with 100 labelled target equilibria and below 2% with full fine-tuning. The predicted magnetic fields satisfy the divergence-free constraint to numerical precision, and four architectures achieve millisecond or sub-millisecond inference. These results identify neural operator pretraining as a route towards reusable, real-time equilibrium inference across fusion device configurations.

In part I, \cite{Frauenfelder:2026c}, we showed that collisional periodic orbits of twisted Zeeman systems can be detected variationally by a non-local Hamiltonian action functional. In this part II we show that the linearized gradient flow of this non-local functional is a Fredholm operator and prove a non-local elliptic regularity result. These results are obtained with the theory of almost extendability of weak Hessian fields introduced in \cite{Frauenfelder:2025g}.

Discovering the governing equations of a physical system from data is a central goal across the sciences, yet in most experiments only a few states are accessible while the rest stay hidden. Existing approaches treat this partial observability as an obstacle to be removed by first reconstructing the hidden state—a step that is ill-posed under noise and that discards the physical constraints, such as energy conservation, that the true dynamics obey. We show that for conservative (Hamiltonian) systems no reconstruction is needed: projecting the dynamics onto the measured coordinates yields a memory kernel that we prove to be a lossless positive-real rational matrix, whose poles are the hidden natural frequencies and whose positive-semidefinite residues encode the couplings. From this kernel we recover a closed, interpretable governing equation for the observed dynamics—identified from output data alone, passive by construction, and validated by out-of-sample forecasting. Under stronger conditions—an equipartitioned measure with position coupling, or a forced input–output experiment—the bare hidden frequencies and couplings of the underlying Hamiltonian are additionally recoverable. We test the method on linear, nonlinear, and chaotic systems under realistic noise. Because it returns energy-conserving equations of motion from partial measurements, it offers a common tool for problems spanning mechanics, fluid and plasma physics, and beyond.

Assembly theory is an experimental and theoretical framework that introduces a metrological approach to detecting life, with potential applications across diverse substrates. Its two central observables are assembly index and copy number. The assembly index is the minimum number of joining operations required to construct an object from its elementary parts; for molecules, it can be measured using mass spectrometry, infrared spectroscopy, and NMR. Copy number is the abundance of a given distinguishable object within a sample. A key empirical result of the theory is that high assembly index combined with high copy number constitutes a signature that cannot arise abiotically, and this has been validated experimentally in application to molecular biosignatures. The foundational theoretical concept underlying these results is the assembly space, which encodes the causal possibilities determinable from observed objects, with the assembly index the shortest path to them given the physical constraints of a given substrate. Here, we provide a generalized formalism to describe assembly spaces and tools for assembly index approximations. We begin by reviewing the applications of assembly theory across molecules, minerals and atmospheres, and then introduce a general, substrate-independent formal definition of assembly spaces and assembly indices. We develop a unified path hierarchy framework to clarify relationships among the various representations of assembly spaces and assembly paths that appear in the literature on molecular assembly. Finally, we show how formal grammar algorithms can be adapted to efficiently bound assembly index calculations and provide clarification on the utility of such approximations, with the goal to increase the accessibility of tools to explore this emerging area for a broader group of researchers across chemistry, biology, and complexity science.

Rare events govern many important molecular processes but remain difficult to characterize within accessible simulation timescales. This challenge has motivated machine-learning methods to construct low-dimensional collective variables for specific objectives, including state discrimination, slow-mode identification, and committor-function approximation. Here we introduce SelfTICA, a self-supervised framework that uses contrastive learning on time-lagged configurations to learn a latent representation of the slow modes governing relevant transitions. Once learned, this representation is frozen and reused across downstream tasks, including collective variables construction, enhanced sampling, free-energy estimation, and committor learning. Compared with direct slow-mode optimization, SelfTICA improves training stability and provides collective variables even from limited and exploratory trajectories, accelerating rare-event sampling and enabling accurate free-energy estimation. The dynamical information encoded during pretraining also accelerates committor learning and enables characterization of transition-state regions. These results show that contrastive learning of slow dynamical representations provides a common foundation for rare-event sampling and mechanistic analysis.

We propose a heuristic model of underscreening phenomenon in high density Coulomb systems, such as concentrated electrolytes and electron hole conglomerates under ultra high dose rate (UHDR) radiation in biological tissues. It explains the data on screening length $L$ increasing with charge particle concentration and offers additional insights in understanding the conductivity and reduction potential of concentrated electrolytes. Also, it validates our current understanding of the FLASH radiation treatment of tumors (FLASH-RT) perceived as an analogous system. The underlying physics is that mutual binding creates diffusion barriers which suppress the concentration of mobile particles thus increasing the screening length. Also, they slow down the rates of chemical reactions responsible for generation of biologically active radicals which explains the sparing effect observed under UHDR.

We present a performance and portability evaluation of three well-established astrophysical production codes, namely iPIC3D, PLUTO, and OpenGadget3, on a Sophgo SG2044 RISC-V processor (part of the Monte Cimone cluster), with comparisons to AMD EPYC 9554 (x86) and NVIDIA GH200 Grace (ARM) systems. These applications represent memory-bound, compute-bound, and hybrid workloads, respectively. Numerical correctness is verified across all platforms, confirming portability. RISC-V shows consistently lower performance, with slowdowns of about $3-6\times$ relative to x86 and $5-9\times$ relative to ARM. The gap is mainly due to limited memory bandwidth, shared cache constraints, narrower 128-bit vector units, and lower clock frequency, but also less-mature auto-vectorization capability of the GNU compiler suite. Memory-bound kernels are the most affected, where early bandwidth saturation and L2 cache contention reduce scalability at higher thread counts. Hybrid MPI+OpenMP configurations reveal a trade-off between memory contention and communication overhead, with intermediate configurations achieving the best performance. These results suggest that RISC-V is capable of supporting scientific workloads; however, additional improvements in both hardware and compiler technology, particularly in auto-vectorization, are required to achieve competitive performance.

We introduce the orbital-optimized multistate contracted variational quantum eigensolver (oo-MC-VQE) method with spin-adapted operators for the computation of ground and excited states, as well as state-specific and transition properties. The use of spin-adapted operators ensures that the spin symmetry of the reference states is conserved throughout the VQE optimization. In multistate variational approaches, achieving a balanced description of an increasing number of electronic states places growing demands on the expressibility of the underlying ansatz, thereby introducing a fundamental trade-off between accuracy and circuit complexity. We consider the effects of this trade-off explicitly and find that the number of circuit parameters required to obtain accurate results is reported to scale approximately linearly in the number of states. We further present an explicit quantum-circuit implementation of the oo-MC-VQE method and demonstrate its integration with quantum error mitigation techniques. Finally, we execute the method on real quantum devices to compute absorption spectra for two benchmark molecular systems.

In this work, we extend the method of averaged Lagrangian to the study of the general second-order (non-conservative) extended Korteweg–de Vries equation, known as the eKdV equation. Building on the framework introduced in [18], we construct a master (augmented) Lagrangian, modeled on Luke's Lagrangian, that incorporates the governing constraints at the appropriate asymptotic orders via the method of Lagrange multipliers. Averaging the resulting Euler-Lagrange equations in the traveling wave setting yields the existence of a (single) solitary wave solution with a $\operatorname{sech}^2$ profile. Explicit second-order formulas are obtained for the height of the solitary wave, together with the solitary wave velocity and inverse width, in terms of a fixed amplitude parameter. A key feature of the derived expressions is their asymptotic reduction to the classical KdV results when the first-order terms are retained. To assess the robustness and utility of the variational solitonic solutions, the derived formulas are subsequently applied, via the dispersive shock equal amplitude approximation method, to estimate the height and velocity of the leading solitary wave edge of dispersive shock waves governed by the eKdV Riemann problem. Theoretical predictions for the relevant wave parameters in both the eKdV solitary wave and dispersive shock wave problems are compared with direct numerical simulations and found to be in strong agreement.

We investigate the speed at which entanglement can be generated by an interaction parameter encoded in a two-qubit Hamiltonian, quantified by the derivative of concurrence with respect to the coupling parameter. For arbitrary pure two-qubit states evolving under a general nonlocal interaction, we derive a bound relating this entanglement speed to the quantum Fisher information (QFI). Specifically, we show that $|\partial_g C| \le \sqrt{F_Q^{(g)}} \sqrt{1-C^2}$, where $F_Q^{(g)}$ is the QFI associated with estimation of the parameter. This establishes $\sqrt{F_Q}\sqrt{1-C^2}$ as an upper bound on the speed of entanglement generation in parameter space. We further derive the saturation conditions and identify the states and dynamical regimes for which equality is attained. At saturation, concurrence evolves at the maximum rate permitted by the distinguishability of the underlying quantum state. These results reveal a direct connection between quantum metrology and entanglement generation, showing that the same information-theoretic quantity that governs parameter-estimation precision also limits the speed at which entanglement resources can be created.

Generically a quantum system initialized in an unentangled state will, under unitary dynamics, rapidly become entangled, a process closely related to information transport and to thermalization. Disorder can suppress the growth of entanglement and result in memory of initial conditions. In non-interacting systems this arises from localization of single-particle states, the occupancy of which is fixed by the initial condition. In interacting systems similar localized conserved quantities persist, but with the added feature that they are coupled, resulting in entanglement growth which is distinct from both non-interacting localized systems and from generic ergodic systems. The Fermi-Hubbard model has two degrees of freedom per site – charge and spin – and disorder may be present in both of these. We study the growth of entanglement in two scenarios – disorder in charge equal and unequal to that in spin, and determine the distinct contributions of charge and spin degrees of freedom by expanding the Hamiltonian in terms of a set of optimally localized conserved quantities with separate charge and spin character. We find that coupling between charge and spin is significantly weaker than charge-charge and spin-spin coupling. While this decoupling is present in all our results, it is only apparent when the strength of the disorder in the two sectors is different such that there is a separation between the characteristic timescales of the contributions to entanglement made by charge and by spin.

The Earth's mantle has elevated Fe$^{3+}$ relative to those of other rocky bodies, yet the oxidation- and electronic state of iron at extreme pressures is poorly known. We present in-situ energy-domain synchrotron Mössbauer spectra of $^{57}$Fe-enriched silicate glasses at 298 K from 1 bar to 174 GPa in a diamond anvil cell. Glasses were synthesised with Fe$^{3+}$/[Fe$^{3+}$ + Fe$^{2+}$] from 0.02 $\pm$ 0.02 to 1.00 $\pm$ 0.02, as determined by colourimetry. While pure Fe$^{3+}$-basaltic glass shows minimal changes up to 174 GPa, the spectra of Fe$^{2+}$-peridotitic and basaltic glasses are fit by two doublets, D$_1$ and D$_2$. At 1 bar, their relative intensities are $\sim$92 % and $\sim$8 %, respectively, but the integral area ratio, D$_2$/(D$_1$ + D$_2$), reaches 0.65 by 172 GPa. Because this transition is reversible with pressure and no metallic iron is detected, the D$_2$ feature is Fe$^{2+}$ low spin (LS), whereas D$_1$ is Fe$^{2+}$ high spin (HS). Consequently, the Fe$^{3+}$/[Fe$^{3+}$+Fe$^{2+}$] of planetary mantles reach a maximum near $\sim$40 GPa, before decreasing at higher pressures due to the stabilisation of Fe$^{2+}_{\mathrm{{LS}}}$. This peak coincides with estimated core-mantle equilibrium on Earth, implying that its uniquely oxidised mantle and habitable state may result from core formation within a Goldilocks pressure range. Secondary atmospheres are predicted to transition from H$_2$-rich for Moon-sized bodies, to CO-rich for Earth-like planets and H$_2$- and CH$_4$-bearing around super-Earths and sub-Neptunes.

The global food system contributes to tens of millions of years of human life lost every year through unhealthy diets, and accounts for roughly a third of greenhouse gas (GHG) emissions. Researchers have thus often sought opportunities to simultaneously redress these large impacts on human health and climate; "dietary shifts" are frequently identified as key to both improving health and reducing land-related GHG emissions. Here we develop a new, spatially-explicit, detailed model of the global food system, and use it to highlight enormous and low-cost opportunities to both improve dietary health and reduce food system emissions. However, we find that health and climate goals are surprisingly independent; for example, reducing red meat intake benefits climate, but may only modestly improve health outcomes. Similarly, there is very large potential both to achieve healthier diets that do not lead to emissions reductions and to reduce emissions without dietary change. Our results highlight both the potential for, and the separability of, food-related policies that advance public health and climate goals.

We investigate Hardy- and Cabello-type logical structures within spatial and temporal extensions of the Frauchiger–Renner (FR) framework, embedding these constructions directly into the FR multi-observer architecture. In the spatial multi-observer scenario, both Hardy and Cabello contradictions arise, with the Cabello construction yielding the stronger violation,$\(\Delta_{\rm Cabello}^{\max}=0.1078\)$, which exceeds the maximal Hardy probability $\(P_{H}^{\max}=\frac{5\sqrt{5}-11}{2}\approx 0.09017\)$. We then develop a sequential temporal FR protocol based on coherent multi-observer measurements performed on a single spin-$\tfrac12$ system. In this temporal setting, the Hardy contradiction disappears identically due to dynamical constraints imposed by sequential state updates, whereas a finite Cabello-type violation survives, \(\Delta_{\rm Cabello}^{\max}\approx 0.0674\). Our results establish a fundamental structural distinction between spatial entanglement and temporal multi-observer correlations in FR-type logical scenarios, and demonstrate that certain observer-independent description failures persist even without spacelike separation.

Quantum error mitigation methods are usually compared by their infinite-shot bias, yet in an experiment the comparison is decided by a finite shot budget, by the instability of the estimator, and by the cost of each shot. We derive finite-shot mean-squared-error laws for virtual distillation (VD) and symmetry verification (SV) whose leading terms predict when one method overtakes the other and whose remainders are bounded with explicit constants. For VD the law contains the statistical bias and denominator instability of the quotient estimator, together with the sample size needed before the quotient can be trusted; for SV it separates the floor left by undetectable errors from the cost of postselection. A first-order decomposition of the noise generator shows which error components each method removes. Two such laws give a tie, a uniform dominance, or a trade-off whose crossing budget scales as $p^{-2}$ or $p^{-1}$ in the noise rate $p$; when the remainder bounds are sharp enough, they turn the leading crossing into a certified interval. In exact white-box QAOA simulations the predicted $p^{-2}$ scaling (fitted exponent $-1.97$) and the sign pattern of all pairwise comparisons ($300/300$ cells) are recovered, one pre-specified all-instance criterion is not met, and the crossings lie $3$–$13\%$ above the leading prediction; a certified interval containing the exact crossing is obtained only in a closed model. Gate-level simulations and runs on two IBM backends show why the ideal VD advantage disappears in the tested instances: interferometric overhead and denominator instability move its window beyond the tested budgets, where SV has the lower mean-squared error.

Direct dark matter detection experiments require large-scale photon sensing arrays with hundreds of thousands of synchronized readout channels. Silicon photomultipliers (SiPMs) have emerged as a leading candidate for these detectors due to their high integration density, low bias voltage, and superior radiopurity. However, existing digital SiPM readout architectures struggle to simultaneously preserve nanosecond-level temporal resolution for sparse scintillation events and sustain data integrity during high-intensity photon bursts. We propose DarkFlow, a hierarchical digital SiPM architecture that features local data aggregation, compact relative-time encoding, consumer-driven backpressure, and occupancy-aware eDRAM burst buffering within a unified dataflow framework. We show that DarkFlow maintains ultra-low packet loss at billion-photon event rates, where conventional architectures can exceed 80% data loss. Besides, the occupancy-aware refresh achieves a 2.14x improvement in effective refresh rate over conventional global refresh. Hardware evaluation in GlobalFoundries 22nm node confirms that the digital readout datapath accounts for less than 0.86% of the detector area and complies with the strict power budget in liquid argon environments.

The DART impact on Dimorphos produced a large population of low-velocity ejecta (v < vesc), likely containing most of the excavated mass, whose early fate remains poorly constrained. We investigate the first 22 h evolution of ejecta launched at 1-9 cm/s and assess how orbital dynamics and surface transport after first contact shape the surface distribution of the re-accreted ejecta blanket. We use RAVEL, a model that couples three-dimensional orbital dynamics in the Didymos-Dimorphos system with surface transport on a digital terrain model, including detachment, re-impact, rebound, and frictional sliding. Re-accretion is rapid and asymmetric: more than 99% of the re-accreted mass returns to Dimorphos within 5 h. The slowest ejecta remain concentrated near the DART crater and dominate the primary ejecta blanket, whereas faster particles undergo orbital transport and preferentially populate antipodal and trailing regions. Surface motion strongly modifies the first-contact pattern, and the DART-derived rough terrain model produces elongated, topographically channelled depositional streaks dominated by the slowest ejecta, giving rise to a ray-like pattern. These results provide testable predictions for ESA's Hera mission and link ejecta-blanket morphology to orbital dynamics, re-accretion, and surface topography.

Characterizing the Marine Atmospheric Boundary Layer (MABL) requires understanding the coupling between ocean waves and the turbulent atmospheric boundary layer above them. This coupling controls momentum exchange between the atmosphere and the ocean; it is of practical importance in the global climate, flow of ocean currents, ocean engineering, and offshore wind energy. Computational study of the MABL is complex because it must resolve the coupled physics of waves and turbulence over a wide range of spatial and temporal scales. This study expands on approaches for representing dynamic, local waves in Large Eddy Simulations (LES) of the MABL by developing a subfilter wave drag model to be local and scale-invariant. It explores the effects of different wave parameters (significant wave height and peak frequency of the wave energy spectrum) on the resulting momentum flux beyond monotonic relationships between surface stress through friction velocity $u_\ast$ and wind velocity above the surface $U_{10}$. Results are compared to field data and in a discussion on how representation of the MABL and associated momentum flux need to account for both wind and wave effects.

Two different neutron sources, based on $^{235}$U-fission neutrons with different average energies, provide integral benchmark data for validation of $\gamma$-ray production data in the evaluated nuclear data libraries corresponding to fast-neutron-induced inelastic-neutron scattering reactions. Firstly, we consider the IRT-M Research Reactor, formerly located at the Nuclear Research Institute just outside of Baghdad, Iraq, to demonstrate the validation methodology using the associated $\gamma$-ray data in the Evaluated Nuclear Data File, version VIII.0 (ENDF/B-VIII.0), for several $\gamma$-ray transitions over a wide range of nuclides including $^{28}$Si, $^{32}$S, $^{56}$Fe, and $^{186}$W. Using the characterized neutron flux of the Baghdad IRT-M Reactor, we find flux-weighted cross-sections deduced using the ENDF/B-VIII.0 $\gamma$-ray data to be in good agreement with the integral measurements performed at the Baghdad Research Reactor in addition to the corresponding results of different reaction-model calculations, {\tt CoH$_{3}$} and EMPIRE. Given the excitation thresholds for the $\gamma$-ray transitions involved in this investigation, these observations lend further support to the characterization of the IRT-M flux in the fast-neutron energy region $0.862 \leq E_{n} \leq 5.0$ MeV. The additional detail devoted to the IRT-M source reflects the broader scope of the validation work carried out at that facility. A second neutron source considered for this validation work is the Forschungsreaktor Münich (FRM-II), Garching, Germany. Again, the flux-weighted $\gamma$-ray data from ENDF/B-VIII.0 for $^{56}$Fe compare well to the integral FRM-II measurement and reaction-model calculations.

We prove that the map sending the diffusion profile to the solution of a time-changed Parisi PDE evaluated at time-space $(0,0)$ is concave. This result strengthens the raywise concavity result proven by Auffinger and Chen (2016). As an application, for the balanced multispecies Ising spin glasses, the lower bound of Bates and Sohn (2025) matches the Hopf-type upper bound given by the Hamilton–Jacobi framework developed by Mourrat, Chen and Xia.

We present the formal statistical validation of TOI-7701.01 (TIC 122522333), a sub-Saturn transiting companion candidate orbiting a bright F-type subgiant host star ($V=11.09$). Initially detected via an automated machine-learning transit survey by Salinas et al. (2025), the planetary nature of this signal remained unclear. We implement an independent vetting and statistical pipeline leveraging multi-sector space kinematics and the triceratops Bayesian framework to compute false positive scenarios. A key methodological finding is the structural convergence of the companion's physical scale; despite utilizing un-detrended Simple Aperture Photometry (SAP) with a raw depth of $2417$ ppm to preserve field dilution metrics, the triceratops MCMC engine naturally converges on a physical radius of $7.86\,R_\oplus$ under the dominant Target Planet scenario ($\mathcal{P}_{\rm TP} = 78.1\%$). This independent statistical derivation matches the geometric radius of $8.07\,R_\oplus$ inferred directly from the instrumentally corrected Pre-Search Data Conditioning (PDCSAP) data. By aggregating our multi-iteration ensemble to suppress MCMC stochasticity, we derive a robust global False Positive Probability ($\mathrm{FPP} = 0.00191$) and a Nearby False Positive Probability ($\mathrm{NFPP} < 10^{-6}$), firmly validating the companion well below the rigorous $1.5\%$ threshold established for this Bayesian framework. While the derived $7.8\text{--}8.1\,R_\oplus$ radius places the object at the boundary between giant planets and low-mass brown dwarfs, such a radius is highly atypical for a brown dwarf, pointing to a planetary nature. We report TOI-7701.01 as a validated companion and encourage prompt radial velocity characterization.

We investigate the propagation of cylindrical waves in a square network of acoustic waveguides. We establish, both theoretically and experimentally, the anisotropic dispersion relation governing wave propagation in the network, and demonstrate excellent agreement between experimental measurements and theoretical predictions. Owing to this anisotropic band structure, each propagation direction exhibits distinct dispersive properties. Consequently, the network supports anisotropic cylindrical waves at both low- and high-amplitudes, with waveforms that vary markedly with direction: from nearly dispersionless pulses to Airy-like wave packets in the linear regime, and from sharp shock-like fronts to smooth solitary-like profiles in the nonlinear regime. The theoretical results are further corroborated by numerical simulations based on the two-dimensional Westervelt equation.