Papers

Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally expensive and lack explicit electrostatics, limiting their use for systems governed by long-range interactions and electrical response. Previously, we introduced Latent Ewald Summation (LES), which learns latent atomic charges and long-range electrostatics from density functional theory (DFT) energy and force labels alone. Here, we use LES to extract electrostatics that are latent in foundation models: energies and forces predicted by a teacher model are used to train a lightweight LES-augmented student MLIP, with optional fine-tuning on additional DFT data. The resulting models reduce computational cost while providing access to Born effective charge tensors, and infrared spectra. We benchmark student models distilled from a broad set of foundation MLIPs, including UMA, MACE, Orb, eSEN, GemNet-OC, PET, and EquiformerV2-based models, against experimental infrared spectra for liquid water, concentrated hydrochloric acid, and the anatase TiO2(101)-water interface. Across these systems, electrostatic response can be extracted from most foundation MLIPs. The benchmark further shows that the underlying DFT level and dataset used to train the teacher model play a larger role than architecture in determining electrostatic and spectroscopic accuracy. For the TiO2-water interface, fine-tuning with a modest amount of higher-level DFT data improves structural and infrared predictions. LES-based distillation therefore provides a practical route for converting foundation MLIPs into efficient, electrically responsive models, while also testing the physical fidelity encoded in foundation models.

Motivated by Grand Unification, we study the formation of magnetic monopoles in an SU(3) non-Abelian gauge theory. We find that the number density of magnetic monopoles depends critically on a parameter, $\epsilon$, that controls the abundance and subsequent decay of biased domain walls. For sufficiently small but non-vanishing values of $\epsilon$, very few monopoles and walls survive in our simulations, potentially solving the cosmological monopole over-abundance problem. In addition, the scenario predicts a stochastic gravitational background from biased domain walls and the possibility of magnetically charged black holes.

A (2+1)-dimensional hyperbolic system of four quasi-linear partial differential equations is derived that describes the modulations of lump solutions of the Kadomtsev-Petviashvili I (KPI) equation in the presence of a mean field. The system is then shown to satisfy the necessary conditions for integrability of hydrodynamic chains. Moreover, a suitable reduction of the resulting modulation system is applied to study the interactions between lumps and a rarefaction wave for the mean field. Precise conditions are derived that describe how the lump parameters change as a result of the interaction, and which in particular determine whether the lump is transmitted through or trapped inside the rarefaction wave. The theoretical predictions are compared to direct numerical simulations of the KPI equation, showing excellent agreement.

Microgravity environments provide unique opportunities for ultracold-atom experiments by enabling long interrogation times and reduced acceleration-induced dynamics. However, their realization has largely been restricted to specialized facilities such as drop towers, sounding rockets, and space-based laboratories. Here we realize synthetic microgravity for quantum degenerate gases using optically engineered force landscapes that compensate Earth's gravity to the milli-g level while maintaining continuous confinement of the atomic ensemble. These force landscapes are generated by dynamically painted optical dipole potentials and calibrated in situ through Bloch oscillations in a vertical optical lattice, enabling precise control of the residual acceleration. We use this capability to demonstrate matter-wave beam splitting with arm separations of several hundred microns. We further implement a Bloch-band atom interferometer in which interaction-induced dephasing is strongly suppressed through controlled three-dimensional expansion in the synthetic microgravity potential. This reduction of mean-field effects restores near-$\sqrt{N}$ scaling of interferometric sensitivity for large quantum degenerate ensembles. Our results establish a versatile platform for realizing synthetic microgravity with trapped quantum gases in terrestrial laboratories, bringing the advantages of microgravity experiments to continuously operating systems and opening new opportunities for quantum sensing, matter-wave interferometry, and precision measurements.

The Ring Electron Cooler (REC) is being developed for the Electron-Ion Collider to provide cooling at the top proton energy of 275GeV to reduce emittance growth from intra-beam scattering (IBS) and other effects. Cooling electrons circulate in a 150 MeV storage ring equipped with strong damping wigglers. While these wigglers counteract emittance degradation driven by intra-beam scattering and proton-electron beam-beam scattering, they introduce significant nonlinear dynamics which can affect electron beam lifetime. In this paper we present the nonlinear optimization of the REC, including optimized focusing wiggler fields, chromatic correction, misalignments and orbit correction and space-charge studies. A novel sextupole-like wiggler field configuration is shown to substantially reduce chromatic aberrations compared with quadrupole-like focusing. Transverse and longitudinal dynamic apertures exceeding 5$\sigma$ are achieved in the presence of realistic alignment and BPM errors. Space-charge simulations for a Gaussian beam using the Bassetti-Erskine model show manageable tune spread and limited equilibrium emittance growth. These results demonstrate the feasibility of REC operation under the required cooling parameters.

A growing population of short-period Neptune-like planets has nonzero eccentricities and, in some cases, large stellar obliquities, suggestive of high-eccentricity migration. Modeling this evolution requires a prescription for tidal dissipation, which in Neptune-like planets may be dominated by rocky or icy cores rather than by extended gaseous envelopes as often assumed for gas giants. We study the coupled orbital and thermal evolution of eccentric Neptune-like planets whose tidal dissipation is controlled by a viscoelastic Maxwell core. We compute the full harmonic tidal response, follow the equilibrium spin state, and evolve the core temperature; the changing thermal state feeds back on the orbit by modifying the viscosity and hence the frequency-dependent tidal response. We find that the coupled evolution is self-regulated: tidal heating initially drives cold, weakly dissipative cores toward efficient dissipation, but the resulting temperature increase lowers the viscosity, shifts the tidal response toward the fluid-like side of the Maxwell peak, and slows the subsequent migration. This feedback drives the system toward a quasi-steady heating-cooling balance, produces long-lived eccentric phases, and weakens the otherwise steep dependence of circularization time on final orbital distance. Thermally regulated tides therefore offer a natural explanation for how observed hot Neptunes can remain eccentric across a broad range of orbital distances, where a single fixed tidal efficiency would either erase eccentricity too efficiently close in or fail to damp it farther out.

Phenomenological non-Hermitian Hamiltonians track selected signatures of complex reservoir dynamics, while post-selected no-jump effective Hamiltonians derived from microscopic open-system theory reveal the underlying system–reservoir physics. We derive such a Hamiltonian for the open Jaynes–Cummings model using a Moore–Penrose normalized $\mathrm{su}(2)$ representation that removes the vacuum-sector singularity and diagonalizes the full Hamiltonian by one operator rotation. Starting from a zero-temperature bosonic reservoir, we obtain a Gorini–Kossakowski–Sudarshan–Lindblad master equation under the Born–Markov approximation with full Bohr-frequency resolution. We use partial Bohr-frequency resolution to build a consistent post-selected no-jump Hamiltonian near exceptional points, where decay rates become comparable to Rabi frequencies and remove the scale separation behind full resolution. The normalized $\mathrm{su}(2)$ form of the resulting non-Hermitian Jaynes–Cummings Hamiltonian reveals the effects of Lamb-shifted detuning, diagonal loss imbalance, and reservoir-modified coupling. Our microscopic exceptional-point analysis recovers the experimentally reported single-excitation exceptional point for unequal independent losses and identifies regimes absent from the standard phenomenological model; for example, equal correlated losses with orthogonal channel phase produce a second-order exceptional point at the same loss-to-coupling ratio in every excitation sector.

The finite width of broad scalar resonances should be taken into account when relating semileptonic $B$-meson transitions to experimentally reconstructable multihadron final states. The quasi-four-body semileptonic decays $\bar B^0\to a_0(1450)^+ (\to\pi \eta)\ell^-\bar\nu_\ell$ with $\ell=(e,\mu,\tau)$ provide a platform for investigating the scalar $a_0(1450)^+$-resonance as the lowest-Fock-state quark-antiquark component. We calculate the first five odd $\xi$-moments of $a_0(1450)$-resonance twist-2 light-cone distribution amplitude (LCDA) using QCDSR within the background-field theory, including vacuum condensates up to dimension six. Based on these moments, two twist-2 LCDA schemes are constructed: a light-cone harmonic oscillator model (S1), whose free parameters are fitted to the first five odd moments, and a truncated Gegenbauer expansion (S2) retaining only $a_{1,3}(\mu)$. The $\bar{B}^0 \to a_0(1450)^+$ transition form factors are then calculated using light-cone sum rules. At large recoil region, we present the TFFs with different scheme. The finite-width effect of $a_0(1450)$-resonance is further incorporated through a two-channel Flatté parametrization including the $\pi \eta$ and $K\bar{K}$ channels. The $\pi \eta$ invariant-mass distribution exhibits a pronounced resonance peak around $s\simeq m_{a_0(1450)}^2$. The spectra of the electron and muon channels are nearly identical, while the $\tau$-channel is significantly suppressed because the larger lepton mass reduces the available phase space. After integration over the full quasi-four-body phase space, the branching fractions are given. We expect that these results will provide useful guidance for future experimental and theoretical studies of $\bar B^0\to a_0(1450)^+ (\to\pi \eta)\ell^-\bar\nu_\ell$ decay and help improve our understanding of the internal structure of $a_0(1450)$.

Grain boundary stabilization by light interstitials is central to the performance of Ni-based superalloys, yet the thermodynamic mechanisms governing their interactions with substitutional chemistry remain poorly resolved. Here, we use hybrid Monte Carlo molecular dynamics simulations to quantify how boron and carbon modify the thermodynamic, structural, and chemical ordering of grain boundaries in Ni–Cr alloys. By analyzing interfacial state variables, site-resolved segregation spectra, local chemical ordering, and structural evolution, we show that boron and carbon stabilize grain boundaries through complementary pathways. Carbon drives saturation-controlled stabilization by recruiting Cr and conditioning boundary chemistry, while suppressing temperature-driven structural transformations of the boundary. In contrast, boron stabilizes grain boundaries through a selective mechanism that lowers the interfacial grand potential via localized ordering while permitting gradual structural evolution. These effects arise from coupled interactions between interstitial segregation and Cr redistribution, which together regulate site accessibility, chemical competition, and the range of accessible interfacial states. This work provides a thermodynamic framework for grain boundary engineering and suggests design principles for leveraging interstitial-substitutional interactions in alloys.

How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning. Here, we leverage data released through the Machine Intelligence from Cortical Networks (MICrONS) program–a functional connectomics resource spanning multiple areas of mouse visual cortex, in which dense calcium imaging is co-registered with high-resolution electron microscopy reconstruction from the same animal–to build biologically grounded recurrent neural networks. Using neuronal spatial coordinates, anatomical connectivity, and function-derived relationships from nearly 12,000 coregistered excitatory neurons, we initialize recurrent weights and impose communication-aware spatial constraints during learning. Across three cognitive decision-making tasks, networks constrained by cortical structure and function consistently outperform baseline and partially constrained models. Functional weight initialization provides the largest gain, while real spatial embedding yields robust additional improvements across conditions. These biologically grounded networks also develop low-entropy, modular, and small-world organization, and retain strong performance even when recurrence is restricted to positive weights. Together, our results show that the machinery of cortex–its geometry, wiring, and functional structure–can be harnessed as a powerful inductive basis for building recurrent networks that learn more effectively while converging toward key organizational principles of biological computation.

Understanding the structure and evolution of galaxies increasingly benefits from placing the Milky Way (MW), the best-studied stellar system, in an external-galaxy context. To analyse the MW as an extragalactic system, we construct an integrated-light view of it using two complementary approaches: composite stellar populations built from a selection-function-free orbit-superposition solution constrained by APOGEE DR17, and full SKIRT radiative-transfer modelling of a hydrodynamical MW simulation initialised from the same solution. In both cases, the MW data are assembled into mock IFU datacubes and analysed with with full spectral fitting (pPXF). We find, however, that the underlying LOSVD is more complex than can be captured by a Gauss-Hermite parametrisation, as is likely the case in MW-like late-type barred galaxies. For the composite-population mocks, we recover the main large-scale kinematic structures, including the rotation and velocity-dispersion patterns associated with the thin and thick discs and the bar/bulge, together with the mean stellar-population maps. The known disc chemical bimodality in [\alpha/M]-[M/H] cannot be recovered directly from the IFU, regardless of the \alpha-resolution of the SSP templates, but its MW-like spatially varying double-sequence behaviour is recovered across the disc. The broad global star-formation history is reproduced, although artificial bursts, likely driven by the age-metallicity degeneracy, remain even after regularisation. For the radiative-transfer mocks, the main kinematic maps are recovered with moderate accuracy, whereas higher-order moments and stellar-population properties remain difficult to constrain, primarily because of the lower spectral resolution and lower statistical SNR of the datacubes. These results indicate that forward panchromatic modelling remains challenging for high-resolution IFU-like observations.

In this work, we investigate a gauge-invariant formulation of modified gravity (GIMOG) wherein the gravitational interaction emerges dynamically from a scalar field following a first-order phase transition. This framework offers a unified cosmological history: it naturally generates a pre-inflationary phase, smoothly recovers the standard radiation and matter-dominated eras, and accounts for late-time cosmic acceleration without the need for a cosmological constant or dark energy. We evaluate the phenomenological viability of the model by confronting it with observational data across distinct cosmological epochs. At late times, the model is constrained using Pantheon+ Type Ia supernova data. In the early Universe, we impose bounds from Big Bang Nucleosynthesis (BBN), specifically utilizing the primordial $^{4}\mathrm{He}$ abundance. Our analysis reveals a distinct phenomenological tension: while late-time observations favor a stronger effective gravitational coupling, BBN constraints tightly restrict early-universe deviations from general relativity. We demonstrate that reconciling these constraints requires a smooth time variation of the effective gravitational constant, $G$, establishing a clear theoretical target for future precision cosmological tests.

Purpose: The dependence of the long-time (tortuosity) limit of the extra-cellular diffusivity on the intra-cellular volume fraction is of fundamental importance for microstructure modeling. While such dependencies have been explored for the white matter, the tortuosity limit in gray matter is unknown due to complex cell composition and geometry. Here we rationalize and validate numerically the analytical relation between the extra-cellular diffusivity and intra-cellular fractions of cell bodies (somas) and neurites. Methods: The tortuosity relation for extra-cellular diffusivity qualitatively follows from effective medium theory, coarse-grained by diffusion outside somas (spheres) and neurites (cylinders), respectively. This problem is equivalent to finding the overall conductivity in a medium of grains in a matrix, with methodology dating back to the 19th century. We extend the effective medium methodology to populations of impermeable spheres and randomly oriented cylinders with various volume fractions, yielding closed-form expressions corroborated by Monte Carlo simulations. Results: We establish the power-law scaling of the extra-cellular diffusivity with the volume fractions of the extra-soma and extra-neurite spaces. We further evaluate the proposed framework using simulations in realistic tissue geometries. Conclusion: Theory and simulations relate extra-cellular tortuosity to soma and neurite fractions, potentially offering a diffusion MRI protocol design optimized for in vivo assessment of soma size and soma/neurite fractions within clinical scan times. Such in vivo measurements can be used to study development, aging and neurodegenerative disorders.

The growth rate of cosmic structure, parameterized by $f\sigma_8$, is a fundamental test of $\Lambda$CDM and general relativity. Using Type Ia supernova (SN Ia) peculiar velocities in conjunction with galaxy redshift surveys may be one of the most precise pathways to measuring $f\sigma_8$ in the local Universe, yet existing analyses have not quantified its systematic uncertainties. Here, we present an end-to-end simulated $f\sigma_8$ measurement tailored to the Type Ia Supernova Trove from ATLAS in the Nearby Universe (TITAN) survey, using ~2000 simulated SNe Ia at $z < 0.067$. We use the Uchuu $N$-body simulations to generate mock galaxy catalogs and SN Ia simulations with realistic, correlated peculiar velocities, and use these catalogs to reconstruct density fields that replicate the 2M++ redshift survey. Using a modified forward likelihood framework across eight mock realizations, we recover $\langle f\sigma_8 \rangle = 0.429 \pm 0.038$ ($\sigma_{\rm stat} = 0.030$, $\sigma_{\rm sys} = 0.023$), consistent with the Uchuu simulation input $f\sigma_8 = 0.428$ to within 0.1%. The mock-to-mock scatter of 0.031 is consistent with our uncertainties, highlighting the reliability of our error estimates. Our measurement is dominated by the statistical uncertainty, with approximately equal contributions from SN Ia ($\sigma_{\rm sys}^{\rm SN}=0.017$) and density reconstruction ($\sigma_{\rm sys}^{\rm recon}=0.016$) systematic uncertainties. The assumed intrinsic scatter model is the largest single systematic contribution, and a different model choice can shift $f\sigma_8$ to lower values, largely driven by red SNe with a skewed color distribution. Our analysis provides the first systematic uncertainty budget for the "reconstruction-and-scaling" method of measuring $f\sigma_8$ with SNe Ia, and demonstrates that SNe Ia are a competitive probe of the growth of structure in the local Universe.

We present JWST Cycle 2 NIRCam and NIRSpec observations in a quasar field J0226+0302 at z=6.5412 to probe the direct connections between the intergalactic medium (IGM), galaxies, and AGN during reionization. This field was previously observed by the JWST ASPIRE program, detecting eight [OIII]-emitting galaxies at 5.3<z<6.4 with a single NIRCam pointing. Using new NIRCam and NIRSpec observations, we identify 65 additional line-emitting galaxies at 5.3<z<6.4. The IGM-galaxy cross-correlation function shows a ~2-sigma excess IGM transmission at ~10-40 cMpc from galaxies, suggesting a significant contribution from regions traced by star-forming galaxies to the local ionizing background. The IGM-galaxy cross-correlation function in this field is consistent with THESAN simulations for an IGM neutral fraction of 5%-7% and an average galaxy ionizing photon escape fraction f_esc of 6%, although a single field is subject to substantial cosmic variance and cannot constrain these quantities independently. Among 49 line-emitting galaxies observed by NIRSpec, we identify four AGN with broad H-alpha emission lines, resulting in an AGN fraction of (8+/-4)%. From the IGM effective optical depth and the IGM-AGN cross-correlation function, we find that the IGM transmission is higher within 5 cMpc/h of four AGN than around the majority of [OIII] emitters. We interpret the excess transmission as resulting from the local radiation enhancement by the AGN, and estimate f_esc~50%-100%, based on our illustrative model for the IGM-AGN cross-correlation function. Future JWST NIRSpec observations in quasar fields will yield a more constraining IGM-AGN cross-correlation function, providing further insights into the roles of galaxies and AGN in reionization.

We study a planar ODE model for the benthic competition between coral, macroalgae, and algal turf on a reef, extending the classical model of Mumby, Hastings, and Edwards by a nonlinear, density-dependent coral mortality that accounts for crowding. The strength of crowding is set by an exponent $\delta>0$ that reshapes the coral nullcline and enriches the bifurcation structure of the system. We establish positive invariance of the biologically relevant region and the absence of periodic orbits, classify the three boundary equilibria together with their local stability, and reduce the coexistence problem to a single scalar equation whose shape, in particular its concavity, controls the number and local stability of the interior equilibria. The grazing intensity $g$ organizes the dynamics through two thresholds $g_0<g_1$ determining the stability of the coral- and macroalgae-dominated states, and a further threshold $g^\star$ at which two interior equilibria collide. We prove that the system undergoes transcritical bifurcations at the boundary equilibria and a saddle-node bifurcation of interior equilibria, and we discuss the implications for coral reef resilience and hysteresis. We complement these results with numerical simulations that illustrate the bifurcation sequence across the grazing regimes.

We derive the dynamic Doppler effects of noninertial observer motion on Dirac spinor fields in 4D spacetime, characterizing how proper acceleration, proper jolt (also known as jerk), observer path curvature, torsion, and hyper-torsion modify the inertial quantum description of free particles. We delineate the impacts of nonlinear observer motion on the amplitude and phase of the Dirac spinor wave field and isolate the spin-induced signatures that have no counterpart in the scalar Klein-Gordon field. For worldlines with constant proper jolt, the wave-field amplitude scales with quadratic rapidity. The complex Frenet-Serret curvature invariants generate both an exponential amplitude factor and a spin-induced phase factor. Spinor structure therefore produces an observable dynamic Doppler signature distinct from any scalar prediction. We analyze the effects of proper jolt and nonlinear rapidity on quantized Dirac fields in the context of acceleration-induced $\beta^+$-decay and derive the instantaneous decay rate, an equivalent of decay rate for non-stationary motions. This work provides new insight for noninertial-proton $\beta^+$-decay and a general framework for testable variable-rapidity Unruh predictions.

Superradiance is an extensive cooperative spontaneous emission phenomenon, exhibited by some atomic collective-spin states. However, distinct initial states differ in their decay dynamics. Dicke states $|j,m\rangle$ with distinct numbers of excitations $n=m+j$, driven by vacuum fluctuations, have their peak emission intensity shifted in time. Rotating Dicke states relative to the decay axis introduces an interesting parity structure that affects the pulse profile and photon correlations. Squeezed-bath prepared states undergo a squeezing-controlled crossover to the rotated Dicke states, making the emission character dependent on the amount of squeezing transferred from light to the atomic state. For semiclassical states with a macroscopic dipole moment, like the atomic coherent state, the emission intensity depends on their polarization. We present detailed results on the superradiant dynamics of a representative selection of states expanded in Dicke states to highlight the initial state as an independent dynamical control parameter. For large-$N$, we are able to predict fairly accurately the pulse profile in each case using the mean-field approximation, an approach based on the Fokker–Planck equation. We also present comparative results on the intensity correlation function, quantify the coherent and incoherent contributions of the emission, and contrast between small and large ensembles.

In the present work we revisit a ponderomotive nonlinearity model used to examine self-trapped laser beams in plasma. Upon briefly considering the exact stationary 1D solutions of the model, we extend considerations to two spatial dimensions where we find both solitonic and vortical structures. The solitary waves localized in both directions are found to be spectrally stable. However, all other structures that we consider in this model, including line solitons – which are homogeneous 2D extensions of 1D solitons – and vortices of topological charge S=1 and S=2 are found to be spectrally unstable. The focal point of our studies then turns to the examination of the collisions of the stable two-dimensional solitary waves for which we map a two-parameter space of soliton speeds and frequencies, in terms of the potential outcomes. While the standard scenarios of merger, inelastic collision leading to separation, separation that leaves behind a localized pulse are all possible, the intriguing outcome that we highlight here is that of a longitudinal collision yielding a transverse spliting of the solitons, either with or without a localized pulse remnant.

The turbulence driving parameter ($b \equiv \sigma_{\rho/\langle \rho \rangle}/\mathcal{M}$; the ratio of the density to velocity fluctuations) is widely used to infer the dominant mode of energy injection in interstellar turbulence. Numerical simulations of turbulence using Fourier Space Driving (FSD) establish a mapping from $b\approx 1/3$ for purely solenoidal to $b\approx 1$ for purely compressive driving. We test the robustness of this calibration by comparing FSD against Point Source Driving (PSD), which stochastically injects radial momentum at random locations mimicking supernovae. Using isothermal hydrodynamic simulations in a periodic box with AthenaK, we run a suite of carefully curated simulations to match Mach numbers between the two driving methods and compare morphology, probability density functions, and power spectra of density and velocity. Despite injecting purely compressive motions, the PSD models yield $b=0.33$ to $0.49$, values that the FSD calibration would associate with more solenoidal driving. With mass-weighted mean Mach number, excluding high-velocity bubble interiors, $b_M=0.74$ to $0.79$ still does not recover the expected $b\approx 1$ for volume-filling, purely compressive driving. More broadly, the PSD models show density and velocity statistics closer to solenoidal and compressive FSD models, respectively, and exhibit unique features, including non-Gaussian velocity tails and a positive density-Mach number correlation at high densities. Within the FSD framework itself, varying the forcing correlation time changes $b$ by a factor of more than 3 for compressive driving. These results demonstrate that $b$ is degenerate with both the spatial locality and the temporal correlation of the driving, limiting its utility as a standalone diagnostic of the energy injection mode.

Wavefront synthesis is a central objective in many applications of programmable metasurfaces (PMs), ranging from electromagnetic holography and computational imaging to massive backscatter communications. Yet, fundamental limits on the ability of a given real-world PM prototype to synthesize a desired output wavefront remain largely unknown. Here, we derive prototype-aware and electromagnetically consistent bounds on target-wavefront synthesis in reconfigurable MIMO wave systems whose programmability stems from tunable lumped elements. Our approach combines multiport network theory (MNT), experimentally estimated proxy MNT parameters, and semidefinite relaxation. We account for relevant practical aspects of typical real-world PMs, such as mutual coupling, binary programmability, and lossy tunable loads. We derive bounds on strength-agnostic wavefront-synthesis fidelity, shape-agnostic target-mode strength, and the strength–fidelity Pareto frontier using two complementary threshold sweeps. We evaluate these bounds for four experimental MIMO systems whose transfer functions are parametrized by a reconfigurable intelligent surface (RIS), involving up to 100 1-bit-programmable elements and radio environments ranging from rich scattering to free space. Our bounds yield practical insights such as the identification of unattainable performance regions and the close-to-optimality certification of certain optimization outcomes. Comparisons with feasible discrete-optimization benchmarks show that the bounds can often be closely approached in practice, indicating tightness. While demonstrated with a RIS prototype, our methodology applies broadly to lumped-element-reconfigurable wave systems, including dynamic metasurface antennas. Altogether, this work contributes to the development of a prototype-aware electromagnetic information theory for reconfigurable wave systems.

We consider system identification for discovering parameterized operators in governing partial differential equations (PDEs) from noisy spatiotemporal data. Building on variational system identification (VSI), which identifies PDEs through Galerkin weak-form residuals, we develop a Bayesian VSI (B-VSI) framework for operator selection, parameter estimation, and uncertainty quantification. The central idea is to define the likelihood directly in weak-form residual space by propagating observation uncertainty through the weak-form residual map. The resulting likelihood captures heteroscedastic and correlated residual errors while avoiding repeated forward PDE solves during inference. For efficient computation, we use lagged-covariance updates that yield generalized least-squares estimates and conjugate posterior approximations when applicable, together with gradient-based and particle-based methods for more general priors and posterior structures. Model-form uncertainty is handled through sequential operator elimination guided by a residual-space Bayesian information criterion. We demonstrate the framework on state-linear and nonlinear PDEs, including the Fokker–Planck equation and a two-field Cahn–Hilliard equation. The results show that B-VSI accurately recovers active operators and coefficients from noisy data, improves robustness relative to classical VSI, and provides posterior uncertainty estimates for coefficients and derived physical quantities.

The oxygen evolution reaction (OER) on perovskite oxides is controlled by the interplay of dopant chemistry, defect charge states, and surface segregation, yet these factors are rarely treated on equal footing. Using first-principles density functional theory, we investigate how Fe dopants (\FeTi{}) and oxygen vacancies (\VO{}) in different charge states affect the OER on TiO$_2$-terminated \ce{SrTiO3}(001). We combine charge-dependent defect formation energies, segregation energies, and charge transition levels with OER free-energy profiles obtained in the computational hydrogen electrode framework. Neutral \FeTix{} preserves near-pristine activity, with overpotentials of $0.43$–$0.48$~V compared to $0.45$~V for the pristine surface, whereas the reduced states \FeTip{} and \FeTipp{} raise the overpotential to as much as $1.35$~V when intermediates bind to Ti sites adjacent to surface Fe. Oxygen vacancies segregate to the surface across the entire band gap ($\Delta E_\mathrm{seg} = -0.50$ to $-0.80$~eV) but do not improve the activity: \VOx{} and \VOp{} overstabilize oxygenated intermediates ($\eta$ up to $2.13$~V), and only \VOpp{} retains a balanced pathway ($\eta = 0.45$~V in the bulk-like region). Because the stable charge state and the segregation tendency of each defect are set by the Fermi level, the OER overpotential itself becomes a Fermi-level-dependent quantity. These results establish Fermi-level engineering as a framework for assessing and tuning defect-mediated OER activity in perovskite oxides.

This paper is the final part in a series devoted to the development of numerically stable and efficient algorithms, together with multi-precision Fortran implementations, for computing Bessel functions with real orders and complex arguments. Parts~I and~II presented stable and efficient algorithms for the modified Bessel functions I_nu(z) and K_nu(z); here we treat Bessel functions J_nu(z) and Y_nu(z). The proposed algorithms support complex arguments for both positive and negative real orders and are implemented in native double and quadruple precision. Quadruple precision substantially increases dynamic range and accuracy (by roughly an order of magnitude in reliably computable |nu| and |z|), thereby extending applicability to problems requiring 20-30 digits. Comprehensive accuracy and performance comparisons are carried out against both the widely used Algorithm~644, restricted to double-precision arithmetic, and the more recent Algorithm~912, which supports both double- and quad-precision arithmetic as well as complex orders. In double precision, the present implementation consistently outperforms Algorithm~644, achieving execution times of approximately 35-67% for J_nu(z) and 44-72% for Y_nu(z), while also producing reliable results where Algorithm~644 fails. In comparison with Algorithm~912, the present algorithm achieves comparable accuracy in double-precision computations and significantly higher accuracy in quad-precision calculations. At the same time, it requires only a small fraction of the computational cost (from a few thousandths to a few hundredths) of the time taken by Algorithm~912, depending on the precision and parameter regime. Furthermore, unlike Algorithm~912, whose applicability is restricted to a limited region of the (Re(nu),z) plane, the present algorithm remains stable and accurate over the full tested domain for which reliable reference values are available.

Spectral encoding enables single-shot measurements of ultrafast transients by mapping temporal information onto the spectrum of a chirped probe. This encoding allows dynamics to be recorded that are beyond the response limits of conventional electronic detectors. However, because the measurements record only spectral intensity, the phase of encoded signals is lost, and dispersion in the detection process introduces waveform distortions that complicate reconstruction and quantitative interpretation of spectra. In single-shot terahertz time-domain spectroscopy (THz-TDS), these distortions manifest as a tradeoff between temporal resolution and the measurement window of signals and can produce spectral null frequencies that limit the recoverable THz bandwidth. To address this challenge, a Bayesian inversion framework is developed to recover the underlying waveform from the squared spectral observable by inferring the THz field, the modulation coefficient, and a low-dimensional empirical parameterization of the probe spectrum jointly, while a Gaussian process prior regularizes the waveform. The framework is validated using single-shot THz-TDS experiments spanning two probe spectral profiles and three chirp conditions with $\alpha$ ranging from 14.5 to 40 ps$^{-2}$. Across all cases, the inversion reconstructs both the time-domain waveform and spectral null frequency structure within the credible interval of a delay-line reference measurement. These results establish a pathway to eliminate penalties that are associated with the detection process in spectral encoding methods without adding additional optics or alignment complexity.

In this study, we analyze the choice of local unitary cluster Jastrow (LUCJ) ansatz initialization and sensitivity of the sample-based quantum diagonalization (SQD) algorithm within the quantum-centric supercomputing (QCSC) framework. We examine six initialization strategies, including those based on coupled-cluster singles and doubles (CCSD), Møller-Plesset second-order perturbation theory (MP2), data-driven coupled-cluster (DDCC), and trivial (zeroes and random) initializations, across twelve molecular systems and three basis sets (STO-3G, cc-pVDZ, and aug-cc-pVDZ). We find that while the mean absolute percentage errors (MAPEs) between the alternative and CCSD-initialized t2-amplitudes span many orders of magnitude, the resulting SQD energies are largely insensitive to this variation. In particular, most initializations recover energies within chemical accuracy (+/-1.6 mEh) of the CCSD reference, with convergence improving as the basis set size increases. Notably, random initialization achieves performance competitive with CCSD across all basis sets, while zeroes initialization, despite having smaller deviations from CCSD, yields the worst energy agreement. Our results highlight that the proximity to the CCSD initialization is not a reliable predictor of the quality of electronic energies. These findings establish that configuration recovery within SQD, rather than circuit initialization, is the dominant factor governing energy accuracy, and suggest that computationally cheaper initialization strategies are viable alternatives to CCSD for QCSC workflows

This paper is devoted to study the spin-spinor formalism to deal with amplitudes for massive particles. After presenting the basic formulae and conventions, we evaluate in detail the amplitudes for two specific examples, namely the decay W -> l \nu_l and the reaction e+ e- -> mu+ mu-. For each case, we display the amplitudes using charts that represent the flow of spin/helicities from the initial to the final particles, which can be used to calculate the total squared amplitude. One can also study the symmetries of the process by comparing different branches along the charts, which are related by those symmetries. Finally, we study the massless limit of the massive theory, employing the concept of Little Group Contraction (LGC), which was used first by Inonu and Wigner to derive the algebra of the Little Group (LG) for the massless case as the limit of the massive one.

Supraparticles (SPs) are assemblies of colloidal particles whose properties can be tuned by modifying the chemistry, shape, and size of the colloidal particles as well as their arrangement in the SP. SPs with internal porosity are of particular interest for catalysis, photonics, and adsorption applications because of their high surface area and tunable pore size distribution. SPs are often fabricated by droplet drying, and the nonequilibrium nature of drying processes may provide an additional handle to control particle arrangement within the SP. Here, we use mesoscale particle-based simulations to explore the drying-induced assembly of SPs made from rod-shaped and spherical colloidal particles. We selectively remove one type of particle after drying and characterize the structure of the resulting porous SP. We find that the remaining particles form connected networks for most compositions, with rods percolating at lower volume fractions than spheres. Most of the resulting void volume forms a single contiguous space whose surface area closely follows the total surface area of the remaining component. The pore-size distribution, however, depends strongly on sphere size and on the removed component, reflecting differences in sphere-clustering and rod-bundling before removal. This work provides new insight into how particle size and shape, as well as processing conditions, might be used to manipulate porosity in SPs.

We introduce a Mori–Zwanzig graph neural network (MZ–GNN) framework for learning reduced-order Lagrangian dynamics of tracer particles in homogeneous isotropic turbulence. The model represents particle acceleration as a finite-memory expansion over present and delayed particle-neighborhood graphs, with each memory contribution parameterized by an equivariant message-passing graph neural network. By construction, the architecture respects the relevant physical symmetries of the problem, including permutation equivariance, Galilean invariance, and equivariance under rotations and reflections. Trained on direct numerical simulation data, the model is rolled out autoregressively and evaluated on observables that are not imposed during training. We show that memory is essential for recovering the intermittent, heavy-tailed acceleration statistics, and that the learned dynamics accurately reproduce single-particle dispersion, pair-dispersion statistics, and four-particle tetrad geometry. Our results establish a physically structured, scalable route to data-driven multi-particle simulation of turbulent transport, and a template for learning reduced dynamics of correlated, symmetry-rich particle systems.

Capturing stylized facts and their evolution is essential for understanding the impact of climate change on complex environmental systems. In this work, we investigate the spectral properties of correlation matrices constructed from sea surface temperature data, employing tools from random matrix theory to identify signatures of global warming and long-term climate variability. By constructing yearly ensembles of correlation matrices, we analyze the evolution of both the eigenvalue density and the statistical behavior of the largest eigenvalue. Our results reveal significant departures from the universal behavior expected for random correlations. In particular, the empirical spectra systematically deviate from the Marchenko–Pastur law, indicating the presence of strong collective correlations in ocean temperature dynamics. Moreover, we find that the average largest eigenvalue exhibits a pronounced increase over time, closely following the rise in global mean ocean temperature. The distribution of the largest eigenvalue is found to be approximately Gaussian rather than Tracy–Widom, suggesting that the system lies outside the standard universal regime associated with weakly correlated Wishart ensembles. Together with conventional statistical indicators, these spectral signatures consistently reflect the progressive modification of the ocean-temperature correlation structure. Our findings demonstrate that spectral observables derived from correlation matrices, particularly the largest eigenvalue and its fluctuations, provide a sensitive framework for characterizing climate dynamics and detecting emerging signatures of long-term climate change.