Papers

We develop a Bayesian framework for model comparison of second-order Langevin dynamics from position-only trajectories. While approximate increment likelihoods for nonlinear position-only inference have been formulated previously, a unified evidence-based framework for comparing multiple second-order models under positional observation has remained lacking. Here we address this problem by combining exact increment likelihoods for linear Gaussian models with a previously proposed approximate likelihood for nonlinear dynamics. Synthetic-data benchmarks show reliable recovery of the generating model at fine sampling intervals and progressive loss of identifiability under coarse temporal sampling. Application to Dictyostelium discoideum trajectories demonstrates that the statistically supported model depends strongly on temporal resolution. Moreover, the selected models reproduce key statistical properties of the experimental trajectories, providing additional support for the model-comparison results. Our framework therefore offers a practical approach to evidence-based comparison of partially observed stochastic dynamics.

This paper presents a mathematical derivation for three new conserved quantities in the motion of spacecraft on optimal continuous-thrust trajectories in a central gravitational field. The process presented in this paper is rooted in Noether's theorem that connects the point symmetries of a dynamic system with the associated conservation laws of the system. In the approach presented in this paper, the system's Lagrangian is modified to account for the non-conservative control force. Using this generalized Lagrangian, the action functional to be minimized is written. Then, Killing equations are formulated to find the dynamic symmetries for this system. In this paper a process is laid out for how to solve the Killing equations; Noether's theorem is applied to this solution of the Killing equations to write the conserved quantities of the system. Conserved quantities are presented for both the two-dimensional and the three-dimensional trajectories in several different coordinate frames. Numerical simulations and mathematical proofs are used to demonstrate that the computed quantities are conserved.

Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by reduced expression of fragile X mental retardation protein (FMRP), leading to disrupted synaptic plasticity, cortical hyperexcitability, and impaired network synchronization. Electroencephalography (EEG) provides a noninvasive window into these mechanisms and consistently reveals abnormalities in alpha (8 to 12 Hz) and gamma (30 to 100 Hz) oscillations that relate to inhibitory control, sensory processing, and cognition. This paper proposes a multi representation deep learning framework for automated characterization of FXS EEG phenotypes by integrating convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and recurrence plot (RP) analysis. Band limited EEG signals are decomposed into alpha and gamma components and transformed into complementary representations, including temporal feature sequences, time frequency maps, and RP images encoding the nonlinear recurrence structure. CNN modules learn discriminative spatial-spectral and dynamical textures from image based representations, while LSTM modules model temporal modulation of oscillatory activity; a hybrid CNN LSTM architecture jointly captures spatial, temporal, and nonlinear dependencies. Subject-independent evaluation demonstrates that the hybrid model outperforms single modality baselines, with gamma features providing strong discriminative power and alpha gamma integration yielding the best overall performance. These findings support deep learning with nonlinear representations as a scalable approach for EEG biomarker development in FXS, with potential utility for diagnosis, stratification, and treatment monitoring in translational settings.

Photonic integrated circuits (PICs) promise order-of-magnitude reductions in the size, weight and power (SWaP) of optical subsystems for astronomy, planetary and Earth-observation missions, yet no PIC-specific space-qualification standard exists. This paper consolidates the principal NASA and ESA qualification documents that apply, or can be tailored, to astrophotonic PICs — arrayed waveguide gratings, photonic lanterns, fibre Bragg gratings, and integrated beam combiners (ABCD, discrete beam combiners, nullers) for spectrographs and stellar interferometers. A master qualification table lists 19 standard test steps with applicable standards and EU/USA test facilities. Two reference mission profiles — a LEO smallsat demonstrator and an HWO-class Lagrange-2 flagship — yield a tailoring matrix, while a TRL-versus-test-coverage roadmap maps each activity onto the NASA/ESA readiness levels and review gates. A survey of UV/visible/near-infrared platforms relates spectral coverage, maturity and flight heritage, and a radiation-effects summary shows passive silica, Si3N4 and laser-written cores are essentially radiation-tolerant while active III–V and Ge devices carry the hardness burden. The central outcome is a seven-phase qualification template (PIC-SQT) with explicit TRL gates, exact test procedures and mission-class tailoring; we further identify qualification processes relevant to PICs that current standards do not cover, and document 40+ years of optical-fibre flight heritage.

Efficient fiber-to-chip coupling remains challenging for thin-film lithium niobate (TFLN) devices operating at 1064 nm. Here, we fabricate and characterize an on-chip optical mode converter based on a bilayer inverse taper. The proposed structure demonstrated a coupling loss of 1.9 dB per facet for the TE mode, with a 1 dB bandwidth spanning 1055–1085 nm. The polarization-dependent alignment tolerance was measured along the lateral and vertical directions. Theoretical and simulation analyses indicate that, when using a lensed fiber with a mode field diameter of 2.5 $\mu$m, the coupling loss can be reduced to as low as 0.48 dB per facet. These results demonstrate the feasibility of providing a high-efficiency solution for on-chip optical coupling at 1064 nm.

Head-and-neck (HN) proton therapy is highly sensitive to anatomical change over a 4-to-6-week course, as tumor shrinkage, weight loss, and setup variation can misposition the Bragg peak near critical organs such as the parotids, oral cavity, brainstem, and spinal cord, leading to target underdosing or organ-at-risk overdosing. Online adaptive proton therapy replans on the anatomy of the day, yet standard workflows rely on offline replanning that requires repeated CT acquisition and roughly a week of preparation, adding burden, cost, and delay. We investigate whether a patient's treatment-day anatomy can be predicted before image acquisition by transferring longitudinal change from a population database. We propose a digital-twin framework built on a pretrained foundation-model deformable registration network used without patient-specific training. A first registration aligns a prior patient's planning CT to the target and carries the prior's during-treatment quality assurance CT (QACT) into the target frame; a second registration estimates the prior's planning-to-QACT change, which is then applied to the target's own planning CT to synthesize predicted CTs (pdCTs) with propagated contours. Using 88 HN patients, each with a planning CT and three QACTs, we show that pdCTs better match treatment-day anatomy than the static planning CT. Compared with the planning CT alone, normalized cross-correlation improves by 22.8%, Dice for organs-at-risk by 20.2%, and CT-number error decreases by 23.4%. Gains are largest for patients with major anatomical change and negligible when anatomy is stable. This cross-patient motion transfer leverages the digital-twin concept to anticipate treatment-day anatomy, enabling personalized online adaptive proton therapy without repeated imaging.

Graph codes offer multiple physical representatives of logical observables, while Gottesman-Kitaev-Preskill (GKP) codes retain analog information about bosonic displacement noise. We develop a causal framework that unifies these mechanisms for measurement-based loss tolerance under pure loss followed by quantum-limited amplification. In this framework, each local GKP recovery produces a refreshed logical block, a continuous syndrome record, and a confidence score for the inferred Pauli class. Low-confidence outcomes are deliberately converted into located erasures, so the availability pattern is generated directly from the bosonic data rather than sampled independently. Both accepted and rejected syndromes contribute to a syndrome-resolved posterior over the graph branch, which determines accessible logical representatives and the outgoing logical Pauli frame. We derive decoder-conditioned branch restriction, signed-outcome reconstruction, Pauli-frame updating, recursive concatenation of graph-GKP modules, and syndrome-resolved logical fusion. Numerical simulations across several squeezing levels identify task-dependent loss-tolerance behavior and finite-depth pseudothresholds for square and hexagonal GKP lattices. The resulting graph-GKP interface provides a unified causal control layer for fault-tolerant MBQC, fusion-based computation, and all-photonic repeaters.

The reconstruction of unknown quantum states via minimal informationally complete measurements (MICs) is a cornerstone of quantum tomography. Although the statistical properties of these measurements are well-understood, their geometric structure has remained elusive. In this work, we establish a correspondence between the class of minimal $s$-tight informationally complete measurements, encompassing, among others, tight IC and morphophoric measurements, and the classical geometry of orthocentric simplices. In particular, we prove a three-way equivalence: a MIC is $s$-tight if and only if its measurement vectors, upon suitable rescaling, form the vertices of an acute orthocentric simplex with the orthocentre at the origin, and such simplices are precisely the homothetically self-dual ones. This geometric manifestation of operational ”tightness” provides a bridge between the physical world and Euclidean geometry. Furthermore, the $s$-tight class is fully characterised by its measurement directions: the angles between them must be obtuse and satisfy a cross-ratio condition. We determine the space of admissible direction configurations: modulo rotations, every such configuration is encoded by a single probability vector, the ”skeleton” of the measurement, together with an orientation class, so that the moduli space of $s$-tight MIC directions is $\Delta^{\circ}_{d+1}\times\{\pm 1\}$. Conversely, every acute orthocentric simplex with the orthocentre at the origin can be anchored in the state space, generating a class of minimal $s$-tight IC measurements that contains exactly one tight IC measurement up to overall rescaling.

Type Ia supernovae provide one of the principal observational probes of late-time cosmic acceleration. Recently, Son et al. [MNRAS 544, 975 (2025)] proposed that correlations between SNe Ia luminosity and progenitor age could introduce a systematic bias in the inferred luminosities, leading to revised distance moduli in the Pantheon+ and DES-SN5YR compilations. Motivated by this possibility, we test whether the age-bias-corrected SNe Ia Hubble diagrams comply with the luminosity-distance relation $d_L=c/H_0\,(1+z)\ln(1+z)$. We find that this one-parameter logarithmic relation provides a high-quality description of both datasets, with the Hubble constant $H_0$ as its sole free parameter. For Pantheon+ and DES separately, the relation yields lower $\chi^2$ values than the two-parameter flat $\Lambda$CDM, with $\Delta\chi^2=12.0$ and $2.1$, respectively. A joint likelihood analysis of both datasets yields $\Delta\chi^2=17.1$, corresponding to $(\Delta\text{AIC},\Delta\text{BIC})=(19.1,25.2)$ in favor of the logarithmic relation. We further find that, after the age-bias correction, neither dataset requires higher-order corrections to the relation, whereas the Einstein-de Sitter cosmology is firmly rejected. These results indicate that the closed-form relation $d_L=c/H_0\,(1+z)\ln(1+z)$ provides a viable one-parameter description of progenitor age-bias-corrected SNe Ia Hubble diagrams. Its asymptotic behavior $d_L\propto z\,\ln z$ naturally accounts for the excess distance moduli observed at high redshift. Should the progenitor age-bias correction be confirmed, the logarithmic relation can be stringently tested using larger samples of SNe Ia within $1\lesssim z\lesssim2$, where it already departs markedly from flat $\Lambda$CDM.

In every dimension three and higher we construct a nonzero function that satisfies the stationary Schrödinger equation with bounded, real-valued potential and decays like $\exp(-c|x|^{4/3})$. This gives a counterexample to the Landis conjecture in dimension three and higher.

Conceptual surveys of multiple choice format have been developed to test the effect of pedagogical interventions on students understanding of physics knowledge. Predominantly, they are administered in pre and posttest settings and analyzed to obtain a performance gain. However, focusing on the correct answers to each question alone ignores the incorrect options which could inform us the stability and coherency of the knowledge structure of students. According to the resource model framework, each specific answer from students could reflect a specific cognitive resource being activated and implemented in the context at hand. Consequently, conceptual surveys could demonstrate how instruction could affect the activation of different cognitive resources of students in a variety of contexts when administered before and after instruction. Guided by the resources framework, we propose a transition matrix analysis to analyze data collected through conceptual surveys to investigate how instruction affects the consistency of cognitive resources activation by students. To provide proof of concept, we demonstrated how to utilize this method by analyzing students responses to a subset of questions from the DIRECT survey in a classroom study.

Ion stopping in partially ionized plasmas often receives roughly equal contributions from bound and free electrons, yet bound electron contributions are usually treated at a very coarse level, with the exception of costly time-dependent density functional theory (TD-DFT) simulations. At energies above the Bragg peak, where linear response methods are a good approximation, more accurate treatment is both feasible and critical for predictive modeling. We develop a channel-mixed random phase approximation (cmRPA) dielectric response function that combines Lindhard free response with explicit average-atom bound state transitions. Validating against ambient condition experiments and TD-DFT simulations, we demonstrate excellent agreement for proton stopping near and above the Bragg peak. The computational efficiency of our approach enables systematic exploration of extreme conditions across a wide range of densities and temperatures. We find non-trivial stopping effects from channel mixing between bound and free transitions via RPA, bound-bound stopping contributions at low velocity, and predict significant bound electron contributions to stopping in tungsten at peak compression conditions relevant to inertial confinement fusion experiments. Applying our model to the warm dense matter stopping experiment of Malko et al. (2022), we evaluate whether improved bound-state modeling could resolve the reported theory-experiment discrepancy, finding that within the constraints of linear response theory, bound stopping mismodeling is unlikely to be the source of the observed deficit.

An ordered positive bidiagonal factorization (PBF) is used to develop a spectral and probabilistic theory for Markov transition matrices of arbitrary finite bandwidth. The factors determine two families of mixed-type multiple orthogonal polynomials, an entrywise positive $q\times p$ matrix of measures, and a sequence of elementary death-or-stay and birth-or-stay transitions. This yields Karlin-McGregor formulas for transition probabilities, Green kernels, resolvents, potentials, and first-passage transforms without reversibility or block symmetrizability. Rational stochastic PBFs are characterized by ordered finite-urn experiments. Cyclic reorderings give Darboux intertwinings, while a factor-resolved continued fraction gives the first-return law. Grouping states produces a finite-phase quasi-birth-and-death process and a matrix continued fraction for return time and phase. For bounded continuous-time generators, uniformization preserves the spectral data. For unbounded rates, a conservative generator whose shifted leading truncations all admit scalar PBFs must be tridiagonal; nevertheless, $Q=V(T-I)$ preserves arbitrary fixed bandwidth and separates the embedded chain from the holding rates. The theory is explicit for mixed Piñeiro and Jacobi-like systems. For Piñeiro, the exact PBF region and larger structural-band positivity regions are obtained. Jacobi-like beta-convolution weights admit Gamma-factor cancellations, complete cancellation recovering Piñeiro. For $q\in\{2,3,4\}$, the strict ordering conditions give the only open PBF region, with further lower-dimensional cancellation strata. Rational $(3,2)$ examples provide all factors and the resulting Markov models.

Stellar spectroscopy is a robust technique for determining fundamental stellar parameters such as effective temperature, surface gravity and metallicity. Spectroscopy Made Easy (SME) has long served as a framework for spectral synthesis and parameter inference. In this paper, we introduce webSME, a web-based extension of the Python implementation of SME. webSME integrates enhancements including a non-local thermodynamic equilibrium abundance correction mode, a precomputed grid of synthetic spectra for robust determination of stellar parameters from large wavelength ranges (thousands of Angstroms wide), Markov Chain Monte Carlo sampling for uncertainty estimation, and support for recent reference abundance patterns. It enables efficient and user-friendly analysis of high-resolution spectra, making it suitable for a wide range of applications - from detailed abundance studies to education and outreach. We demonstrate the performance of webSME on synthetic and observed spectra, including benchmark stars, and validate its accuracy against classical SME-based analysis. The platform's ease of access and advanced capabilities position it as a powerful tool in the modern astrophysical toolkit.

This thesis investigates quantum field theoretic approaches to classical gravity beyond General Relativity. Using worldline effective field theory, worldline quantum field theory, and on shell scattering amplitudes, it investigates compact-object dynamics in dark-sector models, scalar tensor gravity, dynamical Chern Simons gravity, and Einstein Maxwell dilaton theory. The thesis derives a range of conservative and radiative observables, including impulses, waveforms, eikonal phases, two-body potentials, and scattering angles, while incorporating effects associated with spin, parity violation, infrared structure, and multi-loop integrals. It also constructs celestial eikonal amplitudes and analyzes their conformal data in gravitational theories beyond General Relativity. Collectively, these results broaden the application of modern quantum field theoretic techniques to gravitational dynamics and contribute to a more detailed understanding of gravity beyond the Einsteinian framework. (Detailed abstract in the thesis.)

This article constructs a Bessel family of mixed-type multiple orthogonal polynomials for a $q\times p$ matrix weight on the unit circle. Unlike a rank-one product weight, this matrix has generic rank $\min\{q,p\}$ outside a finite subset of the circle. Its reciprocal-Gamma moments recover the multiple Bessel system when $q=1$ and the Bessel-like system of Wolfs when $p=1$. The same matrix is obtained as a scaled Markov-Stieltjes limit of a rank-one Jacobi system, although the interval measures themselves have no finite limit. For balanced near-diagonal indices, explicit formulas are obtained for the mixed $A$ and $B$ polynomial vectors. Their orthogonality and weak normality are proved, and componentwise strong normality is characterized. The components have terminating generalized hypergeometric representations; the $B$ components also admit finite Kampé de Fériet representations and a matrix Rodrigues-type formula. In the one-row reduction, the bivariate representation becomes a generalized hypergeometric polynomial governed by a reflected type-II multiple Hahn polynomial. Finite Gamma-Pochhammer formulas give the near-diagonal and step-line recurrence coefficients. The corresponding banded recurrence matrix has a bidiagonal Christoffel factorization: the lower factors are evaluated from transformed polynomial vectors, while the upper factors are expressed through finite tau determinants. When $q=1$, every Christoffel step remains within the multiple Bessel family, and Gamma-Vandermonde determinants yield the complete factorization.

We investigate the thermodynamic and quantum properties of a magnetoelastic spin-1/2 Heisenberg dimer, where the exchange interaction depends on the dimer displacement. By combining an exact treatment of the spin sector with a harmonic description of the vibrational degree of freedom, we obtain an effective model in which each spin configuration is associated with a distinct vibrational mode, leading to a non-factorizable partition function. We analyze the thermal behavior and identify regimes corresponding to entangled and fully polarized states. Quantum correlations are analyzed through concurrence and local quantum uncertainty, showing that while entanglement is rapidly suppressed by temperature, nonclassical correlations persist over a broader range due to the competition between spin sectors. We further examine the magnetic Fisher information, which provides a measure of the sensitivity of the system to the external magnetic field. Its behavior reveals enhanced response in crossover regions where magnetoelastic effects induce strong redistribution of the level populations. Our results demonstrate that magnetoelastic coupling plays a central role in controlling both quantum correlations and magnetic response, establishing a direct link between entanglement, nonclassical correlations, and thermodynamic sensitivity in coupled spin-dimer systems.

Traditional reaction yield prediction is constrained by 1D quantum descriptors that lack explicit spatial information. To address this gap, a dual-modal Vision Cross-Attention architecture is proposed, fusing tabular physical-organic data with 2D molecular topologies. Notably, it is demonstrated that a generic computer vision backbone processing simple 2D skeletal structures independently outperforms purely quantum-based baselines. By synergizing both modalities, superior predictive accuracy compared to traditional methodologies is achieved by the optimal cross-attention framework (Test RMSE = 5.27%). Through mechanistic probing, active, descriptor-guided spatial querying is observed, effectively offloading macroscopic steric identification to the visual pathway. Furthermore, a dynamic chemical hierarchy is learned by the network to heavily prioritize critical steric bottlenecks, such as the aryl halide. Concurrently, residual skip connections are utilized to protect non-spatial electronic parameters from destructive attenuation during fusion. Collectively, a scalable and highly interpretable blueprint is provided for augmenting physical chemistry with deep visual learning.

We present a large-scale parallel implementation of fully relativistic density functional theory (DFT) for both molecules and periodic solids, using the quasi-four-component (Q4C) method and numeric atom-centered orbital basis sets. Our approach employs a domain decomposition method on nonuniform real-space integration grids, which enables order-N integration of the Q4C Hamiltonian matrix elements using efficient, distributed-memory and compute-parallel real-space operations. Next, we build the Hamiltonian and overlap matrices in a two-dimensional block-cyclic distribution layout. The resulting generalized eigenvalue problems are solved with the massively parallel ELPA eigenvalue solver library. We benchmark memory usage, parallel efficiency, and scalability across multiple MPI tasks and compute nodes. This algorithm extends the reach of fully relativistic DFT simulations for periodic solids, tested up to 3,383 atoms per unit cell (216,628 basis functions) and likely still well below the true reach of the implementation. As a demonstration, we calculate the fully relativistic band structure for a 3,383 atom-per-unit-cell doped hybrid organic-inorganic perovskite, (PEA)2(Pb1-xBix)I4 (PEA=phenethylammonium), showing nearly ideal scalability between 336 and 672 physical CPU cores.

Two-dimensional (2D) material-based phototransistors offer a unique combination of optical sensing, signal amplification, and logic operation within a single device, yet fundamentally suffering from an inherent gain-speed trade-off. Here, we demonstrate a 2D photovoltaic phototransistor that overcomes this limitation using a MoS2/PtSe2 heterojunction to gate a graphene channel. The ultrafast photovoltaic effect in the heterojunction enables charge separation, yielding ultrahigh photoconductive gain (up to 10^8) in graphene channel via interfacial gating. Besides, the response time (below the instrumental resolution of 550 ns) is governed by carrier transit in graphene channel, enabling simultaneous high speed and high gain. Moreover, broadband photodetection from visible to near-infrared is enabled by the optical properties of the MoS2/PtSe2 heterojunction, with the detectivity exceeding 10^11 Jones. These results establish a new paradigm for high-performance 2D phototransistors by harnessing photovoltaic and photogating effects to overcome the classical gain-speed trade-off.

Submillimeter (sub-mm) follow-up of binary neutron star (BNS) mergers provides unique constraints on the early-time energetics and environments of relativistic outflows, capturing the spectral evolution at epochs where centimeter-band emission is often still optically thick or yet to peak. However, the practical scientific yield depends on instrument-specific thresholds, the observing cadence, and the distinct temporal contributions from isotropic ejecta versus beamed relativistic jets. With the upcoming Xue-shan-mu-chang 15-meter SubMillimeter Telescope (XSMT), facility-specific forecasts are needed to test for sustained engine energy injection, as expected for a long-lived magnetar remnant rather than a promptly formed black hole. We present a unified numerical framework that couples engine-driven ejecta dynamics to non-thermal synchrotron emission, accounting for synchrotron self-absorption and deep-Newtonian effects. Adopting fixed 5$\sigma$ (1 h) point-source thresholds of 1.5/2.9/10.2 mJy at 230/345/460 GHz, we construct parameter-space detectability maps and estimate event rates based on current BNS merger-rate priors. For a fiducial local event at 40 Mpc, we find that a magnetar-boosted ejecta afterglow peaks on timescales of weeks to months and remains detectable long enough to allow delayed follow-up, with an expected all-sky rate of $\dot N_{\rm ej} \approx 0.05$–1.7 yr$^{-1}$ at 230 GHz for $f_{\rm mag}=1$; this rate is an upper limit and scales linearly with the long-lived magnetar fraction. Conversely, while relativistic jets produce intense early-time signals, their detection is constrained by narrow beaming and fleeting visibility. Our framework provides a quantitative basis for prioritizing gravitational-wave triggers and maximizing the scientific yield of XSMT in the multi-messenger era.

Gradient-based Bayesian inference methods require efficient access to Jacobian and adjoint-Jacobian operators of high-dimensional forward models. While multigrid solvers provide near-optimal complexity for elliptic partial differential equations, they are rarely available in forms compatible with automatic differentiation (AD). We develop a differentiable multigrid solver for steady-state diffusion-absorption problems and derive its adjoint operations analytically through the full multigrid hierarchy. The resulting solver, DMGS, is implemented in C++ and interfaced with JAX to provide efficient Jacobian-vector and vector-Jacobian products for variational inference in the NIFTy framework. We validate the approach on a 3D inverse problem involving diffuse radiative transfer in tissue, reconstructing an effective radiative source from Monte Carlo-simulated data. The reconstruction reproduces the data at a reduced chi-squared of 1.1 and generalizes to 32 independent validation datasets. Benchmarks against a JAX-native multigrid implementation show comparable runtimes and consistently lower peak memory for the hand-derived adjoint, with modest reverse-mode overhead. These results establish differentiable multigrid solvers as practical building blocks for variational inference in PDE-constrained problems.

The canonical and path integral quantization for non-linear electrodynamics in the presence of an external electromagnetic (EM) field are proposed in this work. The EM background field is introduced expanding a general lagrangian of a generic non-linear electrodynamics around the background for small fluctuations of the propagating fields, where we consider up to the quadratic terms in the propagating fields. As consequence, we obtain an electrodynamics linearized by the presence of the external EM field. Thereby, we study the canonical quantization calculating the energy for the ground state of the linearized EM field in terms of an external magnetic field. The microcausality of the model also is discussed through the Pauli-Jordan function. We also define a generating functional for the linearized EM field, in which, in the Coulomb gauge, the Green function of the model is obtained, and we can construct the perturbative formalism like in standard quantum field theory (QFT). As application of the perturbation theory, the effective potential at one loop is calculated for the linearized EM field coupled to a complex scalar field. We apply the results in the case of the Modified Maxwell ED.

The conducting bar sliding on rails through a uniform magnetic field is a standard textbook illustration of Faraday's law, almost always solved assuming the magnetic field produced by the induced current is negligible. We extend this classic problem by retaining the self-induced field: modelling the circuit as a rectangular loop of round wire of radius $d$, we compute in closed form its geometry-dependent self-inductance $L(x,l)$ and its gradient $dL/dx$ from the Biot–Savart law, including the flux inside the wire and at the corners. The bar then obeys coupled mechanical–electrical equations of motion containing, besides the familiar braking force $-B_0lI$, the inductance-gradient force $\tfrac{1}{2}I^2\,dL/dx$ familiar from electromagnetic launchers. In the absence of resistance the total energy $\tfrac12Mv^2+\tfrac12LI^2$ is exactly conserved; with resistance the system becomes an electromechanical damped oscillator that, in an appropriate regime, maps onto a series resistor–inductor–capacitor (RLC) circuit with equivalent capacitance $C_{eq}=M/(l^2B_0^2)$, the bar's momentum playing the role of the capacitor charge. Numerical integration of the full equations confirms these analytic approximations in their respective regimes and locates the crossover between over-damped and under-damped behaviour.

We develop a symplectic formulation of the Lagrangian Radon transform based on the transitive action of the symplectic group on the Lagrangian Grassmannian. This geometric framework naturally extends the classical notion of the marginals of the Wigner transform, viewed as elementary tomograms, to arbitrary Lagrangian subspaces. The resulting generalized tomography provides a unified treatment of position and momentum measurements related by symplectic rotations. As applications, we revisit the Pauli reconstruction problem and establish reconstruction formulas for density operators from generalized Lagrangian tomograms.

In the present study, we propose a consistent and bound-preserving finite-volume WENO scheme that satisfies the requirements of consistency, conservation, equilibrium, and bound preservation for compressible multiphase flows with the Phase-Field mechanism. The proposed WENO scheme is developed based on a new calculation of WENO weights determined by relative smoothness between stencils and on a coupled reconstruction of the masses and volume fractions. Consistency of reduction and volume fraction summation to unity are considered during the development so that fictitious phases, local voids, or overfilling are not produced numerically when there are $N$ ($N \geqslant 1$) different immiscible phases. The proposed WENO scheme is applied to the consistent and conservative Phase-Field method with adaptive mesh refinement enabled. Various benchmark compressible two- and $N$-phase flows are performed to verify the properties of the proposed WENO scheme as well as its variant with the consistent limiter. We finally demonstrate the capability of the proposed WENO scheme in shock-induced cavity collapse and shock-vessel-bubble interaction problems, with discussion of the necessity of bound preservation for high-order schemes and comparison of different compressible multiphase flow models.

Property testing of distributions is a central topic in information theory, learning theory, and statistics. While quantum algorithms are known to offer significant speedups for property testing of a single distribution or a pair of distributions, it is unclear whether quantum algorithms provide speedups for property testing of $m$ ($m\geq 3$) distributions. This work focuses on quantum algorithms for testing whether $m$ distributions have similar means or are $\epsilon$-far from mean similarity under two models. In the query model, the algorithm can choose which distribution to sample from, whereas in the sampling model, the distributions are selected uniformly. We design quantum algorithms with complexities $\tilde{O}(1/\epsilon)$ (the $\tilde{O}$ notation hides poly-logarithmic factors) and $\tilde{O}(\sqrt{m}/\epsilon)$ in the query and sampling models, respectively, achieving quadratic speedups over the classical counterparts. We further establish quantum lower bounds of $\Omega\left(1/\epsilon\right)$ and $\Omega\rbra{m^{1/3}+\frac{m^{1/4}}{\epsilon}}$ for the query model and the sampling model, demonstrating the optimality of our quantum algorithms in terms of the dependence on $\epsilon$ up to logarithmic factors.

The cosmological dynamics of generalized holographic Ricci dark energy (GHRDE) of Xu type, embedded within power-law $f(\mathcal{T})=\alpha\mathcal{T}+\beta\mathcal{T}^{m}$ teleparallel gravity and driven by the Hubble-parameter-dependent deceleration law $q=b-\nu/H$, are investigated. The deceleration law yields an exact closed-form Hubble function $H(z)$ that interpolates between an early decelerating phase and an asymptotic de~Sitter regime, enabling all cosmological diagnostics to be expressed as explicit analytical functions of redshift. The present-day deceleration parameter $q_{0}=-0.60$ and transition redshift $z_{t}=1.0$ are consistent with current observational estimates. The GHRDE equation-of-state parameter $\omega_{G,0}=-0.81$ lies within the quintessence band, and the effective equation of state evolves monotonically toward $-1$ without phantom crossing. The geometrical statefinder pair locates the model in the quintessence region of the $r$–$s$ plane, clearly displaced from the $\Lambda$CDM fixed point. A phase-space analysis reveals a stable-node de~Sitter attractor with strictly negative eigenvalues, guaranteeing robust late-time acceleration for generic initial conditions. The null, weak, and dominant energy conditions are satisfied throughout the redshift range considered, while the strong energy condition is violated at low redshift, as expected for an accelerating Universe. An observational constraint methodology combining cosmic chronometer data, Pantheon$+$ supernovae, and baryon acoustic oscillation measurements is outlined as the natural continuation of this work.

The Wide-field Spectroscopic Telescope (WST) is a proposed 12 m wide-field spectroscopic facility combining several multi-object spectrographs. It requires a yet unprecedented number of detectors. In this paper we present conceptual architecture for the detector controller and infrastructure required to operate a large number of detectors, potentially applicable to WST, focusing on the system-level, power distribution, and the associated data handling. We also consider how these elements may evolve over the expected development timeline of such a facility. Motivated by the scale of the problem, we outline a possible distributed detector-controller architecture, based on modular units placed close to the detectors and networked backend electronics.

Universal quantum computation requires single-qubit control together with at least one entangling two-qubit gate. CNOT and CROT gates are two famous examples of such gates, wherein the state of one qubit (control) dictates the transformation applied to the other (target). By using a simple derivation motivated by symmetry, we show that current device architectures of semiconductor-based quantum dot devices prevent efficient implementation of a CNOT and other asymmetric control-target gates via Heisenberg exchange, Coulomb repulsion, or other interaction that is invariant under spin exchange. Guided by this general principle, we propose a heterogeneous blueprint of double quantum dot devices that enables efficient implementation of the CNOT by breaking the spin exchange symmetry. Crucially, our numerical simulations predict that this novel device blueprint can enable a single-pulse fault-tolerant 100-ns CNOT gate in isotopically purified silicon.