Papers

We revisit the thermodynamics of the one-dimensional Klein-Gordon (KGO) and Dirac (DO) oscillators within two frameworks of generalized statistics: Beck's asymptotic superstatistics and the recently introduced hyperstatistics. In hyperstatistics, a $\gamma$-distribution of domain Boltzmann factors yields, after Laplace transformation and averaging over a normalisable density $f(\beta)$, the closed-form q-generalized Boltzmann factor $B_q(\varepsilon) = \exp_q(-\langle\beta\rangle\varepsilon)$, independent of $f(\beta)$. We compute the partition function, entropy $S$, and specific heat $C_v$ for both 1D oscillators using excitation energies $\varepsilon_n = E_n - E_0$ to remove the rest-energy shift and enforce third-law behaviour $C_v \to 0$ as $T = 1/\langle\beta\rangle \to 0$. Appropriate degeneracies ($g_n = 1$ for KGO; $g_0 = 1$, $g_n = 2$ for $n \geq 1$ for DO) are applied. Hyperstatistics successfully (i) reproduces the high-temperature Boltzmann limit $C_v \to 2k_B$, (ii) is structurally independent of $f(\beta)$, (iii) avoids the unphysical negative regions of the Beck polynomial bracket, and (iv) systematically distinguishes KGO from DO by capturing the enhanced entropy and sharper specific-heat structure caused by spin-induced degeneracy. The frameworks agree quantitatively for $q - 1 \ll 1$ and $\langle\beta\rangle E \lesssim 2$, but diverge at high temperatures where Beck's polynomial expansion loses validity and the exact hyperstatistical q-exponential remains positive, monotonic, and analytic. Ultimately, hyperstatistics provides a numerically stable and analytically tractable alternative to asymptotic superstatistics for relativistic oscillators, naturally extensible to higher dimensions and external magnetic fields.

Let (\Gamma) be a finite compact connected metric graph and let (A\in L^\infty(\Gamma)) be a real magnetic potential. The magnetic Laplacian (H_A) with standard vertex conditions is defined by the closed quadratic form [ q_A[u]=\sum_e\int_e |(-i\partial_x-A_e)u_e|^2,dx. ] A magnetic Cheeger constant is introduced by adding to the usual boundary term the frustration index of the potential on subgraphs. The first point of the paper is that, on a metric graph, this frustration index is exactly a finite dimensional (\ell^1) flux distance determined by the periods of (A) on cycles. Consequently the Cheeger constant can be written directly in terms of Aharonov Bohm fluxes. We prove a Cheeger type lower bound for the bottom of the spectrum and derive the corresponding explicit lower estimate in terms of the distance of the global cycle flux vector from the integral flux lattice. The estimate also gives (L^2) decay bounds for the magnetic heat semigroup and for the magnetic energy. The constants are not asserted to be sharp; the emphasis is on the flux dependence and on the self contained metric graph formulation.

We derive the vertical chromaticity $\xi_y$ of the Fermilab Muon g-2 storage ring in closed analytic form. Expanding the Hamiltonian as a Taylor polynomial in the dynamical variables and integrating the equations of motion order by order, we obtain the vertical second-order aberrations of the homogeneous magnetic dipole ($\mathtt{DI}$) and the combined-function dipole-and-electrostatic-quadrupole element ($\mathtt{DIQ}$) used in the muon $g{-}2$ ring. Composing the per-element maps over the periodic dispersion orbit yields a closed-form expression for the vertical chromaticity $\xichromy$ of the continuous-ring $\mathtt{DIQ360}$ model, in direct functional analogy with the horizontal result of our earlier work on the same ring (Ref.~\refcite{ChromCPO11}). Comparison against COSY INFINITY differential-algebra computation shows agreement at the $10^{-11}$ level across all three ring models ($\mathtt{DIQ360}$ closed form and the modular $\mathtt{DIEQ\_ON}$, $\mathtt{DIEQ}$ via per-element composition) for muon $g{-}2$ electrostatic-quadrupole (ESQ) voltages $\Vesq \in [10, 26]\,\mathrm{kV}$.

We present a technique for high-precision absolute measurements of gamma-ray production cross sections (n,xg) induced by 14 MeV neutrons. The technique is based on the Associated Particle Imaging (API) method, which tags individual neutrons emitted from a deuterium-tritium source with their associated alpha-particles, enabling coincidence-based suppression of lower-energy neutrons and room background signals, while also providing neutron flux measurements with uncertainties on the order of 1%. This compact, laboratory-scale technique has the potential to address gaps and discrepancies in existing cross section libraries at a fraction of the cost of large-scale dedicated facilities, with direct applications in active neutron interrogation, detector calibration, nuclear fusion science, and Monte Carlo simulations, among others. We demonstrate the technique through proof-of-concept experiments on thin and thick samples of natural Fe (1 mm, 8 mm) and natural C (2 mm, 10 mm). For Fe, gamma-ray production cross sections were measured for the 846.78 and 1238.33 keV transitions from the first and second excited states of 56Fe. For C, the (n,n'g) cross section was measured for the 4438.91 keV transition from the first excited state of 12C. Measurements were performed at 110 and 48 degrees relative to the neutron beam to characterize gamma-ray anisotropy. For the thin samples, the measured cross sections are 723 +/- 70 mb (56Fe, 846.78 keV, 110 deg), 303 +/- 42 mb (56Fe, 1238.33 keV, 110 deg), 755 +/- 106 mb (56Fe, 846.78 keV, 48 deg), 321 +/- 149 mb (56Fe, 1238.33 keV, 48 deg), 141 +/- 16 mb (12C, 4438.91 keV, 110 deg), and 279 +/- 105 mb (12C, 4438.91 keV, 48 deg). Uncertainties are dominated by counting statistics and detector efficiency calibration, both of which can be reduced in future experiments to achieve overall uncertainties of 5% or better.

Electrical transport and magnetic measurements have been made on UTe2 under pressure P up to approximately 16 GPa to determine the superconducting transition temperature Tc vs P phase diagram in the high-pressure tetragonal phase. Superconductivity emerges near 5 GPa, coincident with the orthorhombic to tetragonal phase transition; in the tetragonal phase, Tc reaches a maximum value of approximately 4 K at 6 GPa and then decreases with P and appears to vanish near 18 GPa. Tetragonal UTe2 has a relatively small upper critical field Hc2(0) $\approx$ 1.2 T at 5.3 GPa, smaller than the Pauli paramagnetic limit, and is orbitally limited with a coherence length $\xi$tetra $\approx$ 16.5 nm. This small value of Hc2(0) favors more conventional superconductivity; in contrast, the large values of Hc2(T) for orthorhombic UTe2 exceed the Pauli paramagnetic limit in all three crystallographic directions and have been attributed to unconventional superconductivity, widely believed to involve spin-triplet pairing. The temperature-pressure phase diagram of UTe2 shows a striking dichotomy: a narrow, fragile, unconventional superconducting region in the orthorhombic phase vs a broad, robust, and more conventional superconducting dome in the tetragonal phase. This dichotomy is consistent with the proposal that U-dimers, present (absent) in the orthorhombic (tetragonal) phase, may play a role in spin-triplet superconductivity of orthorhombic UTe2. In the tetragonal phase, the normal-state electrical resistivity $\rho$(T) exhibits metallic behavior with a knee between 200 and 240 K that depends weakly on P and most likely marks the onset of a transition to a magnetically ordered phase that coexists with superconductivity.

Negatively charged boron vacancy ($V_B^-$) centers in hexagonal boron nitride ($h$-BN) have attracted wide-range interests owing to their van der Waals lattice and their potentials for $in$-$situ$ quantum sensing. Here we propose and experimentally demonstrate an all-optical strategy for wide-field magnetometry based on $V_B^-$ centers. This strategy exploits the magnetically sensitive ground-state level anti-crossing (GSLAC) of $V_B^-$ centers, which induces a strong electron spin transition between $m_S = 0$ and $m_S = -1$ states, enabling microwave-free magnetic field measurement. By monitoring the shift of GSLAC feature, the external magnetic field can be precisely determined. Using this technique, we demonstrate all-optical wide-field imaging of near-field DC magnetic field distribution from current-carrying circuits over an area of around 42 $\times$ 21 $\mu$m$^2$. An estimated photon shot-noise-limited sensitivity of 67.1 $\mu$T/$\sqrt{\text{Hz}}$ is achieved for a single pixel, which is an approximately threefold improvement over the ODMR method, along with a spatial resolution of about 1 $\mu$m per pixel. Our approach expands the applicability of $V_B^-$ centers in quantum sensing, paving the way for robust and convenient magnetometry under extreme conditions.

Diffractive deep neural networks (D2NNs) promise miniaturized, power-efficient, light-speed optical front-ends for machine vision, yet the most mature demonstrations remain in the terahertz regime, built from readily fabricated millimeter-scale neurons. Translating D2NNs to the visible range, where nearly all vision pipelines operate, was long blamed on the difficulty of fabricating nanoscale neurons; but even after recent advances removed that barrier, visible-range D2NNs matching their terahertz counterparts remain out of reach. We identify the true obstacle as the thin-layer approximation underlying nearly all D2NN training, which treats each diffractive layer as an infinitely thin mask. It fails not because of the short wavelength, as is commonly assumed, but because the low-refractive-index materials (n approximately 1.3-1.5) used at visible wavelengths require relief structures thick enough that intra-layer diffraction and phase accumulation become significant. To overcome this, we introduce a differentiable beam-propagation ($\partial$BPM) layer that models each element as a finite-thickness volume and propagates light through it during training, keeping the fabrication-compatible height map end-to-end trainable without full-wave simulation in the loop. Across MNIST, Fashion-MNIST, and CIFAR-100 classification and imaging, $\partial$BPM training substantially reduces the design-to-device mismatch, and full-wave FDTD validation raises classification accuracy from 50% to 90% without re-optimization. The $\partial$BPM layer thus offers a scalable, physics-aware bridge between efficient optical neural-network optimization and fabrication-consistent diffractive design.

Yttrium Iron Garnet YIG (Y3Fe5O12), is a commonly used material for magnonic devices due to its crystal and chemical structure, which makes the material highly ferromagnetic and enables long-range magnon propagation. Magnonic devices were fabricated by depositing a 390 nm thick thin film of YIG, using low vacuum RF sputtering, on Si substrates with a 240 nm buffer layer of SiO2. Two sets of devices were used to study the effect of the Si/SiO2 interface on the YIG. The first set features patterned hole pairs on the SiO2, which was created using fluorine etching. Patterned samples were used as seed nucleation points to study the crystallization behavior. The second set was a non-patterned Si/SiO2 with YIG deposited uniformly on the top. Post-deposition recrystallization of the YIG film was accomplished in a horizontal furnace under O2 atmosphere, between 750 degrees C and 850 degrees C. By patterning devices with a SiO2 buffer layer, depositing YIG via RF sputtering, and subsequently crystallizing the films in a furnace, we establish a fabrication route toward devices that can be suspended. Although further optimization of stoichiometry is required, achieving precise compositional control would enable the realization of fully suspended and released YIG devices that can be transferred onto alternative substrates.

We extend the classical ideas of the Partial Information Decomposition (PID) to the quantum domain and quantify unique, redundant, and synergistic quantum information. We show that unique information plays the central role in quantum error correction codes: any erasure-correctable subset of encoding qubits must contain zero unique information. Synergistic information between two disjoint subsets of encoding qubits appears when a logical operation is supported on the whole set but not on the subsets separately. In a different application of our PID, we show that redundant quantum information is the crucial feature of the decoherence mechanism proposed by Zurek et al. to explain how the classical world emerges out of the quantum world.

Selecting the active space for multireference electronic-structure calculations is a long-standing bottleneck that often requires expert chemical intuition and costly trial-and-error. We introduce RLEASE (Reinforcement Learning Efficient Active Space Engine), a low-cost method for automatic, geometry-dependent active-space selection. A neural network predicts per-orbital diagnostic scores ($\hat{s}_{1}$) from inexpensive Hartree-Fock orbital descriptors, and a learned threshold partitions orbitals into active and inactive sets. The threshold policy is optimized with proximal policy optimization, using the discrepancy between sc-NEVPT2 energies computed with the selected active space and DMRG reference energies as the reward. After training, the same RLEASE-selected active spaces can be used with multireference perturbation theory or composite coupled-cluster energy estimators. Despite being trained on a small set of molecules and geometries, RLEASE transfers to chemically diverse test systems, producing compact active spaces and competitive potential-energy surfaces relative to established entropy-based selectors. Because deployment requires only inexpensive orbital descriptors and neural-network inference, RLEASE enables high-throughput multireference workflows without molecule-specific retraining or target-system pilot DMRG calculations.

The Renormalization Group flow connects microscopic to macroscopic descriptions of a system and is therefore typically considered as an initial-value problem. Motivated by situations in which different couplings within a system of Renormalization Group equations are constrained at different scales, we instead consider boundary-value problems in Renormalization Group flows. We find that, unlike initial-value problems which provide $n$ conditions for $n$ couplings, boundary-value problems which provide $n$ conditions for $n$ couplings do not always have a unique solution. When the Jacobian matrix, i.e., the matrix of first derivatives of beta functions, has complex eigenvalues, boundary-value problems may be non-unique. We provide a diagnostic tool for non-uniqueness in systems with many couplings. We also provide two examples with potential relevance for physics, namely within the Standard Model as well as within the Einstein-Hilbert truncation of asymptotically safe quantum gravity.

Scientific machine learning is limited less by model size than by the data it is trained on. Observational data records what happened but not why; template synthetic data has a known generating process but only for the simulator's template, not the case a user faces. We argue a third option is now operationally feasible: instrumented data, in which every datum carries the mechanistic model that produced it, an explicit uncertainty over that model, and an executable family of counterfactuals. Verification-and-validation (V&V) instrumented image-to-simulation pipelines are one realisation: a sensor observation becomes a fully specified, solver-backed simulation with explicit, editable parameters and a propagated aleatoric/epistemic uncertainty. The substrate is case-specific, mechanistically supervised, and supports causal interventions through Pearl's do-operator. Near-term consequences for validation, auditing, and surrogate training span computational biology, climate, materials, fluid mechanics, and medical imaging; a longer-term, falsifiable implication concerns foundation models for scientific reasoning.

Recent experiments have provided evidence for excitonic insulator (EI) states in 1T HfTe2. In this work, we investigate EI states in monolayer, bilayer, trilayer, and bulk 1T HfTe2 using advanced meta generalized gradient approximation (meta GGA) calculations and a model Bethe-Salpeter equation (BSE) approach, together with structural and electronic symmetry breaking analyses. Our results show that both the monolayer and bilayer exhibit negative exciton energies, leading to the spontaneous formation of bound excitons and EI states, whereas the trilayer and bulk display positive exciton energies and do not support EI states. Structural symmetry-breaking calculations show very small in-plane displacements of the Hf atoms from their symmetric positions in the monolayer and multilayers, consistent with experimental observations. Interestingly, electronic symmetry-breaking calculations for the monolayer, performed using a symmetric structure and a hybrid functional, show a pronounced unfolded valence-band feature at the M point and no unfolded conduction-band states near the Fermi level at Gamma, in good agreement with experimental results. Overall, our findings support the existence of EI states in low dimensional 1T HfTe2. The methodology developed here can be readily extended to investigate EI behavior in other related quantum material systems.

When a solid support undergoes crystallization, the advancing amorphous-to-crystalline transformation front separates regions of distinct surface energy, creating a moving interfacial energy boundary. A supported nanoparticle straddling such a boundary experiences an asymmetric particle-substrate interfacial energy environment that constitutes a lateral thermodynamic driving force for migration. Here, using in situ transmission electron microscopy to track Pt nanoparticle motion statistically, paired with time-resolved diffraction and 4D-STEM analysis to characterize support crystallization, we demonstrate that propagating crystallization fronts in amorphous AlO$_x$ thin films actively drag supported Pt nanoparticles over long distances. Temporal correlation between the onsets of support crystallization and rapid particle migration, together with 4D-STEM virtual crystallinity maps, establishes that the front drives particle motion. Phase-field simulations confirm that particle-substrate interfacial energy contrast alone sustains particle drag, and identify curvature gradients along the particle surface as the mechanism by which the advancing front redistributes mass and displaces the particle. These results establish a general mechanism by which any propagating surface-energy boundary on a substrate can act as a deterministic driver of supported nanoparticle transport.

How well turbulence mixes a scalar $\theta$ is governed by the scalar dissipation rate $\chi = 2D |\nabla\theta|^2$, making scalar gradients central to turbulent mixing. We study the structure and amplification of these gradients for passive scalars driven by a uniform mean-gradient in isotropic turbulence, using DNS at grid resolutions up to $8192^3$. The $Re_\lambda$ spans $140-1000$, and $Sc\equiv\nu/D$ spans $1-512$. We analyze joint statistical correlations of velocity and scalar gradients that underlie scalar-gradient amplification. Unconditional statistics reaffirm earlier observations that production of $\chi$ is dominated by nonlinear amplification of scalar gradients by strain-rate. Scalar gradients preferentially align with the most compressive strain eigenvector and remain orthogonal to vorticity, with both trends virtually independent of $Re_\lambda$ and $Sc$. Conditional statistics reveal that this organization becomes dramatically enhanced in regions of intense scalar dissipation: scalar gradient becomes near-perfectly aligned with the most compressive eigendirection and orthogonal to other eigendirections and vorticity. This and visualizations suggest that intense scalar dissipation is organized in sheet-like structures formed in shear layers between vortex tubes, where intense strain also generally resides. However, the effective strain acting along intense scalar gradients is comparatively much weaker, indicating intense scalar dissipation arises primarily from optimal alignments rather than intense strain alone. Molecular diffusion arrests intense scalar-gradient events primarily by redistributing scalar-gradient variance away from intense structures. The contribution from imposed mean-gradient is negligible,but still imprints anisotropy directly onto smallest scales via the strain field. The statistics broadly become universal as $Sc$ and $Re_\lambda$ increases

The study of the large-scale distribution of galaxies provides essential information for testing the standard cosmological model, namely the $\Lambda$CDM paradigm. This scenario is based upon two foundations: General Relativity as the theory of gravity, and the Cosmological Principle, which states that the Universe is statistically homogeneous and isotropic on large scales – so that we can measure distances and ages in the Universe assuming the FLRW metric. In this work, we perform a test of the Cosmological Principle by probing the angular homogeneity scale, $\theta_H$, using the state-of-the-art observational data of Luminous Red Galaxies (LRGs) from the Dark Energy Spectroscopic Instrument Data Release 1 (DESI DR1). Our analysis is performed exclusively in two dimensions, across narrow redshift ranges inside a larger redshift sample of $0.4 < z < 1.1$, in two different surveyed regions of the sky (North and South Galactic Caps), as we want to minimize a priori dependences on an underlying cosmological model. We obtain that such a scale is indeed identified in all redshift ranges, and that they are consistent with mock simulations assuming the $\Lambda$CDM model. Moreover, our results are in great agreement with previous measurements using Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 16 (SDSS-IV eBOSS DR16), as well as between the north and south galactic caps of the DESI DR1 survey. These findings help underpinning statistical isotropy and homogeneity of the Universe as a physically valid hypothesis in light of upcoming stage-IV redshift surveys, hence are consistent with one of the fundamental pillars of the standard cosmological model.

Unambiguous state discrimination (USD) measurements are attractive because outcomes are either marked as conclusive (i.e., error free) or inconclusive (i.e., erased). We study affine filtering measurements, a structured variant of USD for decoding classical linear codes over pure-state classical-quantum channels, where a conclusive outcome identifies an affine subspace containing the transmitted codeword and an inconclusive outcome is treated as an erasure. For a group-covariant indexing of pure-state codewords, we show that the optimal design of affine filtering measurements is a semidefinite program that can be reduced to a linear program via character-based diagonalization. We use the resulting measurement to build a quantum decoding framework for local codes, and we demonstrate (via simulations on regular LDPC codes from Gallager ensembles using single parity check local constraints) that affine filtering based decoding can outperform symbol-wise USD and symbol-wise pretty good measurement based decoding methods on i.i.d. pure-state channels. In an independent and concurrent work, Buzet and Chailloux study similar fine-grained USD measurements for symmetric families of states. Their focus is on the code-agnostic setting whereas our focus is on code-aware constructions and decoding.

Ultrasound Localization Microscopy (ULM) enables microscopic imaging of the cerebral microvasculature in vivo, but relies on a multi-stage processing pipeline in which acquisition settings and reconstruction processes strongly influence the final output. Existing public datasets remain sparse, restricting rigorous evaluation and slowing progress in algorithm development, including emerging machine-learning approaches, which by design require large quantities of data to be robust and reliable. We introduce ULMShare, an open-access dataset of 99 whole-brain transcranial ULM acquisitions from 61 healthy mice (36 females, 22 males, 3 unknown; mean age: $8.2 \pm 5.5$ weeks; mean weight: $17.7 \pm 4.2$ g), for a total of 30TB of raw data. The dataset spans three experimental procedures, multiple injection and anesthesia protocols, two ultrasound probes, and different imaging planes and orientations. Each acquisition includes raw ultrasonic data, detailed metadata, an illustrative reconstruction and the corresponding microbubble trajectories. Alongside the data, we report vascular saturation, Fourier Ring Correlation, and track-length statistics, plus expert visual gradings. ULMShare provides a broad, standardized and publicly available resource for method development, validation, and benchmarking. The full dataset is available on the Federated Research Data Repository and additional resources are hosted on the ULMShare Github repository.

We present PDE-Agents, a multi-agent ecosystem that automates the full lifecycle of partial differential equation (PDE) and finite element method (FEM) simulations through natural-language interaction. Three large language model agents, Simulation, Analytics, and Database, are orchestrated by a LangGraph supervisor and run locally using Qwen3-Coder-Next and Llama 4 Scout on dual NVIDIA RTX PRO 6000 Blackwell GPUs. The model-agnostic architecture is validated across two generations of open-source LLMs. A GraphRAG knowledge base using Neo4j and 768-dimensional vector embeddings provides material properties, failure patterns, and prior run lineage. We report seven contributions: (i) verification and validation showing second-order spatial convergence, O(h^2), for three heat-equation benchmarks; (ii) a 50-task ablation with a frozen knowledge graph comparing KG On, KG Off, and KG Smart, where KG Smart achieves 100% success and the highest output quality, with physics score 0.933 versus 0.853 and material property fidelity (MPF) 0.926 versus 0.796 for KG Off; (iii) a novel-material study using three fictional materials known only to the knowledge graph, where KG Smart reaches MPF = 1.00 versus 0.34 without the graph; (iv) failure analysis tracing KG On's three failures to budget exhaustion and timeout, identifying warm-start injection as the main reliability factor; (v) an adaptive framework selecting retrieval mode per task; (vi) production metrics from 1,369 runs showing 97.8% overall success and 85.4% first-try success; and (vii) a 100-task knowledge-graph growth study showing an 8.8% MPF gain on hard tasks while easy and novel tasks remain at ceiling. All code, models, and evaluation artifacts are openly released. These results show that integration pattern, rather than knowledge content alone, determines whether GraphRAG helps or hinders LLM agents.

Platinum silicide (PtSi) thin films are promising for silicon-based superconducting quantum devices due to their compatibility with CMOS fabrication, air stability, and superconducting transition temperature near 1 K. We report a systematic study of PtSi phase formation, microstructure, and interface quality as a function of annealing temperature and duration, characterizing films using grazing-incidence X-ray diffraction, X-ray reflectivity, and electrical transport measurements. Phase-pure PtSi forms within minutes by rapid thermal processing at 600 {\deg}C and is stable under extended annealing, while 30 s anneals across 300-600 {\deg}C yield equivalent film quality with consistent microstructure and superconducting properties. X-ray reflectivity reveals that interfacial roughening is an intrinsic consequence of the Pt2Si-to-PtSi conversion step rather than a result of elevated temperature or prolonged annealing. These results establish a robust processing window for PtSi formation in silicon-based superconducting device fabrication flows.

Parker Solar Probe (PSP) observations in the young solar wind reveal new properties of both plasma particle velocity distributions (VDs) and associated electromagnetic (EM) wave fluctuations. The quasilinear (QL) kinetic theory of plasma wave instabilities has recently shown that new hammerhead (HH) proton distributions can be generated by the relaxation of proton beams through the instabilities of right-handed (RH) polarized waves. Such RH waves have indeed been reported in association with HH distributions. In this paper, new results from hybrid simulations of proton-beam-plasma systems with properties typical of those observed to excite EM-RH wave instabilities are presented. From the long-term evolution of these systems, it is found that beam relaxation is driven by instabilities and growing wave fluctuations, leading to HH-type features in the velocity distributions. The production of these features, as well as their prominence, depends on the magnetic power of the waves generated by the instabilities and, therefore, implicitly on the available free energy, quantified by the plasma beta parameter and the relative beam drift. The simulation results capture the self-consistent evolution of the instabilities and their nonlinear development. Linear theory, together with simulations, helps identify the nature of the unstable modes and the plasma conditions under which they arise. The good agreement with quasi-linear (QL) theory further indicates that it can serve as a computationally efficient complementary framework for interpreting the associated wave-particle interactions.

Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials. However, these methods are computationally demanding and can exhibit localized numerical instabilities or convergence failures that are difficult to detect within high-throughput workflows. We introduce an agent-guided multi-fidelity framework for correcting GW-Bethe-Salpeter excited-state landscapes in strained MoS2-WS2 bilayers. Across stacking registries, strain branches and reciprocal-space samplings, the workflow identifies spike-like excursions, near-zero-gap collapse and cross-fidelity inconsistencies associated with fragile long-wavelength dielectric screening. A structural agent evaluates calculations by assigning confidence weights and selectively using a small number of high-accuracy reference points. Machine learning models then transfer information across related systems and apply Gaussian process corrections to recover improved quasiparticle gaps and exciton binding energies, with calibrated uncertainty estimates. The approach corrects numerically induced artifacts without erasing physical strain dependence and substantially improves agreement with higher-fidelity references relative to a no-agent baseline. These results show that reliable surrogate learning for excited-state materials requires explicit diagnosis of numerical fragility, not direct interpolation of raw first-principles data points. The proposed framework is readily transferable to other optoelectronic nanomaterials characterized by strong quantum confinement, such as quantum dots, nanoribbons, layered two-dimensional semiconductors, and hybrid perovskite nanostructures.

Public-key cryptosystems eliminate the requirement for pre-shared secret keys by enabling encryption with a publicly disclosed key and decryption with a corresponding private key. In this article we generalize the public-key cryptosystems to ternary algebraic structures, with particular attention to ElGamal as a representative family. We introduce the necessary algebraic background for nonderived ternary structures, including special elements, ternary group rings, and a matrix ternarization procedure that maps binary rings and group rings to antidiagonal symbolic matrices closed under ternary multiplication. Building on these foundations, we formulate a ternary analogue of the ElGamal three-step protocol (key generation, ephemeral encryption, and decryption via querelements) and derive explicit ternary power and querelement formulas that enable correct decryption. Concrete instantiations and numerical examples over a ternary fraction field, a matrix-ternarized finite group ring, and a finite \((6,3)\)-ring (field) validate the construction and illustrate admissible word-length quantization and cycle behaviour of ternary powers. The ternary framework highlights two practical advantages: richer algebraic structure (querelements replace binary inverses) that increases algebraic complexity for attackers, and higher information density (matrix ternarization transfers paired/plaintext vectors). Formal hardness assumptions, optimized parameter choices, and comprehensive security and performance analyses remain necessary future work.

The determination of nuclear charge radii is crucial for understanding the internal structure of nuclei and their fundamental interactions. A persistent discrepancy, not only in the measured proton charge radius but also in the light nucleus charge radius, between electron scattering and muonic spectroscopy has fueled ongoing debates in nuclear and particle physics. Using this discrepancy, we revisit the role of one of the proposed solutions, namely the use of Lorentz-boosted nuclear form factors to find a subtle connection between the boost and the cluster structure of nuclei. By applying two distinct relativistic formalisms, namely the Licht-Pagnamenta and Mitra-Kumari approaches, we systematically analyze corrections to the moments of the density distributions in hadrons and nuclei. Our results demonstrate that boosting the form factors from the Breit to the rest frame of the nucleus not only assists in reconciling the spectroscopic and scattering measurements but also provides a method to infer on the quark and nucleon cluster configurations within nuclei.

We determine the optimal parameters for thermal neutron-induced fission of $^{233,235}$U and $^{239,241}$Pu in the complete event fission model $\mathtt{FREYA}$. First, we revisit and, in most cases, improve the prior values determined for spontaneous fission using a genetic algorithm. Our optimization procedure is then applied to thermal neutron-induced fission, replacing the empirically-chosen parameters previously employed in $\mathtt{FREYA}$. Finally, the optimized parameter values are used to make predictions for spontaneous and thermal neutron-induced fission observables not included in the fits. This work represents the first step in a broader program to study neutron-induced fission as a function of incident neutron energy, to systematically improve the performance of $\mathtt{FREYA}$, capturing any energy-dependent trends of the physics-based $\mathtt{FREYA}$ parameters.

Von Neumann's Hilbert-space formalism of quantum mechanics constitutes a logico-physical theory of observed or measured reality. Imposing the logical constraint of Booleanity, essential for objectively shareable descriptions among observers, reveals the physical meaning of classicality inherently embedded within the formalism itself. Starting from this consideration, the present work reformulates the quantum-classical transition via Hilbert-space classical mechanics (HCM), grounding classicality not in the dynamical limit ($\hbar \to 0$), but in the logical constraint of Booleanity (i.e., the mutual commutativity of preparable states). Within this state-centric framework, applying the Einstein-Podolsky-Rosen (EPR) criterion alongside locality and measurement independence reduces standard quantum mechanics to the HCM model. Thus, the EPR argument reveals not quantum incompleteness, but the implicit classical boundaries of its own premises. To resolve this impasse, we articulate a nuanced quantum ontology grounded in a fundamental structural bipartition between the observational environment and the observed object, which accommodates three categorical distinctions: ontic, processional, and tropos-existential. Building on this, we propose a criterion of objective reality wherein descriptive objectivity is treated as merely a sufficient condition for physical reality. This addresses the historical Bohr-Einstein ambiguity, enabling the quantum formalism to ontologically unify objective measured phenomena and non-objective observed interference within a context-dependent framework.

Deep learning offers a powerful approach to quantum many-body problems via neural network wavefunctions, but their optimization remains a severe bottleneck. Existing optimization methods, including natural gradient descent and stochastic reconfiguration, suffer from spectral gap-dependent convergence that limits their effectiveness on systems fraught with competing orders and nearly degenerate ground states, such as frustrated magnets and strongly correlated electron materials. Here, we introduce Projected Inverse Iteration (PII) by re-framing the ground-state search as an eigenvalue problem. PII achieves rapid, gap-insensitive convergence while preserving the favorable polynomial computational scaling of stochastic reconfiguration. Demonstrated on challenging two-dimensional spin systems, including the highly frustrated $J_1$-$J_2$ model, PII outperforms standard optimization techniques and presents a promising algorithmic strategy for discovering complex quantum states in the presence of small spectral gaps. More broadly, PII can be interpreted as a novel natural gradient method tailored for eigenvalue problems, opening up its application to related challenges within deep learning.

We investigate motility-induced phase separation in a lattice gas of self-propelled particles with hard-core exclusion, where an internal director biases particle hopping along the lattice coordination directions while undergoing rotational diffusion, together with a thermal-like translational diffusion. Rather than employing stochastic simulations, we adopt a master-equation formalism within a general mean-field approximation. By linearizing the mean-field master equation around the homogeneous stationary state and applying Bloch's theorem, the stability analysis is reduced to a $z$-dimensional tight-binding eigenvalue problem. A perturbation expansion in the wavenumber near $\vk = 0$ then yields the spinodal surface in closed analytical form for six Bravais lattices: linear, square, hexagonal, simple cubic, body-centered cubic, and face-centered cubic. The influence of lattice geometry is shown to enter exclusively through a single coefficient $\mathcal{A}$ which we evaluate exactly for each case. We further show that translational diffusion smooths the interface between the dense and dilute phases. Finally, we determine the rotational probability currents associated with the inhomogeneous stationary states, a distinctive signature of the broken detailed balance underlying active-system dynamics.

Liquid-crystal spatial light modulators (SLMs) are widely used for programmable wavefront control but are typically operated as phase-only devices, limiting applications that require independent amplitude and phase shaping. Here we demonstrate full complex-field modulation using a single phase-only SLM by implementing two sequential modulation planes on different regions of the same device. The phase retardance introduced by the first SLM region is converted into amplitude modulation by a polarizer placed in the beam path, while the second region compensates the associated phase offset and imposes the required phase distribution. The field from the first region is imaged onto the second, enabling complex-field synthesis without a second modulator. We validate the approach by generating Bessel–Gaussian beams, helical-phase fields, and arbitrary focal-plane intensity patterns. This single-SLM platform provides a compact route to programmable complex wavefront engineering for structured illumination, holography, and electron–light interaction experiments.

Nonspherical gas bubble dynamics in viscoelastic materials influence the stress transmission and energy dissipation of their surroundings and are difficult to predict. Their accurate prediction is essential in applications ranging from biomedical procedures to high-strain-rate rheological measurements. However, existing models do not sufficiently capture the nonspherical rotational dynamics. We formulate and superpose a rotational contribution to the perturbed deformation with a potential contribution. Linearised forward and inverse coordinate maps are formulated based on the deformation field which are used to compute velocities, accelerations, and stresses. The addition of the rotational degree of freedom satisfies the momentum balance equations and stress continuity at the bubble surface. The material surrounding the bubble is modelled with a Kelvin-Voigt constitutive model with Newtonian viscosity and quadratic strain-stiffening neo-Hookean elasticity. The model agrees with previous viscous fluids models when elastic effects are neglected and radial oscillations are small. When viscous effects are small relative to elastic, shear waves radiate from the bubble surface into the material. The resulting strain energy is delocalised and increases damping of the perturbation amplitude in time relative to potential-based models. We show agreement between the stability of the shape modes with previous ultrasound forced experiments and temporal evolution of different shape modes with previous laser-induced cavitation experimental data.