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Physics Physics

physics Watch Physics

Thursday 9 July

Optics

Nanosecond Pulsed-Laser Treatment Couples Chloride Removal with Oxide Transformation in Salt-Corroded Carbon Steel

Youichi Ishikawa, Yoshitaka Okuyama and Daishi Fujita

Maintaining carbon steel in marine environments requires surface treatments capable of simultaneously removing corrosion products and chloride contaminants whilst modifying the residual oxide layer. In this study, salt-contaminated SS400 carbon steel was treated using a Q-switched pulsed fibre laser characterized by a full width at half maximum (FWHM) of approximately 150 ns and a decaying tail extending to about 600 ns. The pulse energy was fixed at 1 mJ, and the repetition frequency was varied between 50 and 200 kHz to investigate the effects of cumulative thermal accumulation. Under 100 W (100 kHz) conditions, WDS-EPMA analysis confirmed that Na and Cl levels dropped to near-background values, demonstrating the comprehensive removal of sea-salt-derived contaminants. SEM observations revealed the transition of the porous rust layer into a dense, laser-modified layer, whilst XPS analysis confirmed the suppression of the $\text{Fe}^{3+}$ satellite feature in the $\text{Fe}$ $2p$ spectrum, establishing a distinct phase transformation from a haematite-dominant rust to a protective magnetite-rich scale. These results elucidate a single-step, two-stage mechanism: the high-peak-power leading edge of the pulse drives the ablation of the corrosion layer, whilst the 600 ns trailing tail delivers continuous thermal energy that promotes oxide resolidification and phase transformation. This approach offers a promising, non-contact methodology capable of concurrent decontamination and surface functionalisation in a single processing step.
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Fluid Dynamics

Trapping-loss transition via a saddle-node bifurcation in thermophoretic particle transport driven by a time-periodic vortex

Srikumar Warrier

The transport of inertial particles in unsteady flows is often governed by the competition between multiple migration mechanisms. We investigate the interplay between inertia-induced drift and thermophoretic migration in a time-periodic vortex containing a localized temperature field. Starting from the small-Stokes-number limit of the particle equations of motion, we derive a cycle-averaged radial migration model describing the slow evolution of suspended particles over timescales much longer than the forcing period. The competition between outward inertia-induced drift and inward thermophoretic migration gives rise to stable and unstable fixed points of the reduced radial dynamics, corresponding respectively to particle trapping states and separatrices bounding trapped trajectories. The existence and location of these states are shown to be governed by the dimensionless control parameter $\Pi$, which measures the relative strength of inertia-induced transport to the thermophoretic transport. As $\Pi$ is decreased below a critical value, the stable and unstable fixed points coalesce and disappear, resulting in the loss of particle trapping. Phase portraits, bifurcation diagrams, and local asymptotic analysis demonstrate that trapping is destroyed through a saddle-node bifurcation. The transition is further characterized by the vanishing of the dominant eigenvalue and the associated divergence of the relaxation time, indicative of critical slowing down. Additional calculations employing various other velocity and temperature profiles demonstrate that the trapping-loss mechanism is robust and not specific to a particular profile.
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Atomic Physics · Cross-listed from quant-ph

Quantum linear solvers for quantum chemistry: prospects of exponential quantum advantage

Peniel Bertrand Tsemo, Kenji Sugisaki, Ishita Bhattacharjee et al. (+1 more)

Quantum linear solvers (QLSs) can offer the potential for exponential quantum advantage in solving quantum chemical problems, but its assessment hinges on determining the condition number ($\kappa$) scaling, which itself is computationally challenging. While a recent work applied the Harrow-Hassidim-Lloyd (HHL) algorithm to single-reference linearized coupled cluster equations (SRLCC), the validity of the HHL-SRLCC framework is restricted to weakly correlated regimes. A general treatment requires a formulation that can access strongly correlated regions. We thus begin by extending the QLS-SRLCC framework to its multi-reference form, which is based on the internally contracted multi-reference LCC method (QLS-icMRLCC). We then analyze $\kappa$ scaling using three complementary diagnostics that range from explicit computations to use of indirect structural indicators: (i) direct calculations of $\kappa$, (ii) scaling of the ratio of maximum to minimum diagonal entries of an A matrix, and (iii) structural analyses of the A matrices based on a recently proposed conjecture, which we adapt to the QLS-LCC problem. The three approaches yield consistent predictions, indicating a polylogarithmic $\kappa$ scaling in system size. This finding, when combined with our arguments on sub-linear scaling of sparsity, supports the prospects of exponential advantage using QLSs for the LCC problem. Finally, numerical calculations on potential energy curves of model systems containing up to four atoms recover the ground state energies with errors relative to benchmark classical methods not exceeding 0.009$\%$.
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All authors Peniel Bertrand Tsemo, Kenji Sugisaki, Ishita Bhattacharjee, V. S. Prasannaa

Atomic Physics

Extracting inter-nuclear distances in the oxygen molecule interacting with an XFEL-pulse: a fundamental system for understanding Coulomb explosion imaging

Zixu Wang, Georgios Petros Katsoulis, Giorgio Visentin et al. (+3 more)

We investigate the interaction of O$_{2}$ with an X-ray Free Electron laser (XFEL) pulse of short duration. We consider a photon energy of 570 eV, which allows for the formation of molecular states with up to two core holes. We compute the sum of the final kinetic energies, i.e. the kinetic energy release (KER) of the atomic fragments for different fragmentation channels. We demonstrate that with the knowledge of the potential energy curves the equilibrium inter-nuclear distance of O$_{2}$ is best extracted from the O$^{+}$+O$^{+}$ channel and not from higher-charged channels such as O$^{2+}$+O$^{2+}$. This challenges our current understanding of Coulomb explosion imaging, where from just applying quasi-classical dynamics one expects that the equilibrium inter-nuclear distance of molecules is best extracted from higher-charged fragmentation channels. On the other hand, we show that the KER of the O$^{+}$+O$^{+}$ channel is inconsistent with the one obtained by just calculating the Coulomb repulsion of the atomic fragments resulting from molecular dissociation.
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All authors Zixu Wang, Georgios Petros Katsoulis, Giorgio Visentin, Till Janke, Thomas Pfeifer, Agapi Emmanouilidou

Computational Physics

Learned Committors as Reaction Coordinates for Nucleation Rates

Hubert J. Naguszewski and David Quigley

A central challenge in the analysis of first-order phase transitions is the identification of optimal reaction coordinates. In principle, the committor is the ideal choice; however, its computational cost has historically made it intractable. Here, we train a convolutional neural network ($p_B$-NN) as a proxy for the committor on brute-force committor labels and use it directly as the coordinate of a Markov state model. Applied to magnetisation reversal in the two-dimensional Ising model, $p_B$-NN reproduces brute-force nucleation rates across a range of thermodynamic conditions. The largest geometric cluster size also recovers accurate rates despite providing a poor pointwise predictor of the committor. These results demonstrate that an effective reaction coordinate for nucleation rate calculation must reliably separate the metastable and stable basins, but need not preserve the committor pointwise for every microstate. We stress that this distinction has direct implications for the choice of collective variable in rare-event simulations of nucleation more broadly.
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Instrumentation and Detectors

Development of a Silicon-Based Ultra-Fast X-Ray Beam Size Monitor for SuperKEKB

Matthew Andrew, Riku Nomaru, Gaku Mitsuka et al. (+6 more)

We present the development of a silicon-based ultra-fast X-ray beam size monitor (SiXRM) for SuperKEKB. The system enables, for the first time at SuperKEKB, bunch-by-bunch measurements of the vertical beam size using synchrotron radiation. The detector combines a silicon strip sensor board, amplifier boards, and fast waveform readout electronics. Beam measurements demonstrate clear reconstruction of the bunch structure and X-ray images for individual bunches. The measured beam sizes show good agreement with those obtained from the existing CMOS-based XRM system. The measurement precision is estimated to be better than 6.4 $\mu$m. This system provides a powerful tool for studying beam dynamics and optimizing luminosity in high-luminosity colliders.
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All authors Matthew Andrew, Riku Nomaru, Gaku Mitsuka, Keisuke Yoshihara, Cody Driver, Christopher J. Kenny, Bela Urbschat, Gary Varner, John W. Flanagan

Computational Physics

Parallel simulation of rarefied gas flows on unstructured meshes using the DIG-augmented DSMC method

Tao Huang, Liyan Luo, Hong Deng et al. (+1 more)

While the direct simulation Monte Carlo (DSMC) is a mainstream stochastic particle method for simulating rarefied gas flows, it incurs excessively high computational costs in the near continuum regime. As a hybrid acceleration approach coupling DSMC with macroscopic synthetic equations, the direct intermittent general synthetic iterative scheme (DIG) delivers fast convergence and asymptotic-preserving characteristics, which effectively alleviate the kinetic scale limitations inherent to standard DSMC. In this study, we develop a parallel DIG augmented DSMC solver for three dimensional rarefied gas flow simulations on unstructured meshes. On top of the standard DSMC algorithms for particle transport and collisions, a reliable intermittent coupling framework is constructed to exchange macroscopic flow data between the stochastic DSMC module and deterministic macroscopic synthetic equations. For parallel execution on unstructured grids, we employ a hybrid MPI architecture equipped with ghost cells to enable local particle tracking and batch inter-rank particle migration. A graph partitioning based dynamic load balancing strategy is also integrated to mitigate uneven particle distribution over computational domains. Numerical results demonstrate that the proposed solver achieves satisfactory agreement with the SPARTA DSMC. Leveraging the fast convergence and asymptotic-preserving properties of the DIG method, the required number of spatial cells and statistical sampling steps are drastically decreased, leading to substantial reductions in computational memory and runtime. This work presents an efficient high-performance numerical tool for high-fidelity simulations of rarefied flows over complex geometries. The code is available in the developer repository at the github link.
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All authors Tao Huang, Liyan Luo, Hong Deng, Lei Wu

Classical Physics

An advanced undergraduate derivation of acceleration thermality

Michael R.R. Good

The thermal radioactivity of beta-decay photons, described by a 1D Planck distribution, can be modeled as classical radiation emitted by an accelerated electron. Here, we present the basics of the out-of-equilibrium computation to illustrate acceleration thermality. Suitable for advanced undergraduate calculations, we demonstrate that an exactly soluble non-uniformly accelerated trajectory enables spectral analysis of the emitted photons, facilitates time evolution, and reveals Planckian radiation.
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Chemical Physics

State-Averaged Density Matrix Embedding Theory for Local Excitations

Zhe-Bin Guan and Hong Jiang

Density matrix embedding theory (DMET) provides an elegant framework in quantum chemistry to describe local properties of chemical systems that allows a high-level method being used to solve an embedded subsystem constructed based on a low-level treatment of the whole system, and therefore achieves a balance between efficiency and accuracy. However, because the embedded subspace in DMET is typically constructed from the mean-field ground state Slater determinant, the resulting bath orbitals inherently favor the ground state, leading to unbalanced descriptions of ground and excited states for local excitations. In this work, we first demonstrate the starting-point dependence of DMET in excitation energy calculations, and then generalize original ground-state based DMET by extending the starting point from the single Slater determinant to state-averaged (SA) complete active space self-consistent field (CASSCF), hence termed as SA-DMDT. In calculations of magnetic anisotropy and excitation energies of transition metal and lanthanide complexes, SA-DMET shows significant improvement in accuracy compared to the single-state DMET. Configuration-averaged Hartree-Fock (CAHF), which is equivalent to SA-CASSCF when all states in the chosen active space are equally averaged, is found to give comparable accuracy as a DMET starting point, thus offering a more efficient choice for state-averaged embedding. Finally, the recently proposed non-orthongal atomic-orbital-based DMET (AO-DMET) is tested on various systems and gives very promising results in all cases. These results establish SA-DMET, especially in combination with AO-DMET and CAHF, as a robust and efficient embedding framework for local excited states in strongly correlated metal complexes.
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Chemical Physics

Charge-partition pathways in strong-field photoionization of carbonyl sulfide monomers and dimers

Chao He, Xinyue Zhang, Cangtao Yin et al. (+2 more)

Strong-field photoionization of molecules and molecular clusters gives rise to a rich variety of fragmentation pathways governed by charge localization and redistribution on ultrafast timescales. Here, we report a velocity-map imaging study of the strong-field photoionization and fragmentation of carbonyl sulfide (OCS) monomers and dimers driven by 150 femtosecond (fs) laser pulses at 775~nm. The images of the total kinetic-energy and angular distributions of the OCS$^{2+}$, S$^+$, and CO$^+$ fragments were interpreted with the help of electronic-structure calculations of the potential energy surfaces for OCS$^+$ and OCS$^{2+}$. We identify distinct dissociation pathways of singly and doubly ionized OCS, including two-body breakup channels of OCS$^+$ into $\mathrm{S}^+ + \mathrm{CO}$ and $\mathrm{CO}^+ + \mathrm{S}$, dissociation of OCS$^{2+}$ into $\mathrm{S}^+ + \mathrm{CO}$$^+$ as well as higher-order three-body fragmentation. In addition, the images of the OCS$^{2+}$ channel exhibit near-zero-momentum components, low-energy isotropic features, and highly anisotropic contributions at high kinetic energies that cannot be explained by monomer ionization alone. Analysis of the KER distributions and angular anisotropies indicates that these features originate from the breakup of multiply charged OCS dimers ((OCS)$_2^{2+}$, (OCS)$_2^{3+}$, and (OCS)$_2^{4+}$) through charge-separation channels. Our results illustrate how dynamic signatures of strong-field fragmentation evolve from intramolecular dissociation in isolated molecules to intermolecular charge separation in weakly bound clusters providing a unified picture of charge-driven dissociation dynamics beyond the single-molecule limit.
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All authors Chao He, Xinyue Zhang, Cangtao Yin, Markus Meuwly, Stefan Willitsch

Optics

Nyquist-Sampled Time-Domain Adjoint FDTD for Memory-Efficient Broadband Nanophotonic Inverse Design

Mingyu Park, Owen D. Miller and Haejun Chung

Adjoint optimization is a cornerstone of broadband nanophotonic inverse design, but conventional time-domain implementations face a severe memory bottleneck because they retain forward-field histories at every finite-difference time-domain (FDTD) time step. Here, we show that this full time-step storage is unnecessary for broadband design objectives because the underlying fields are band-limited. By storing forward fields only at Nyquist intervals and using the resulting sparse fields during the adjoint pass, the proposed method enables on-the-fly gradient accumulation without retaining full forward-field histories. This Nyquist-sampled adjoint FDTD framework preserves the two-simulation scaling of time-domain adjoint optimization while substantially reducing the dominant field-storage. Because the broadband gradient is evaluated directly in the time domain, with no spectral discretization, the per-iteration cost is independent of the number of frequencies—in contrast to frequency-sampled adjoint formulations, whose cost grows with spectral sampling density. Gradient verification confirms that Nyquist sampling reproduces conventional full-storage adjoint gradients with negligible error, whereas undersampling beyond the Nyquist limit produces aliasing-induced gradient degradation. Across four two-dimensional broadband nanophotonic benchmarks and a fully three-dimensional metalens, the method maintains gradient fidelity and optimized device performance while reducing dominant field-storage memory by more than $100\times$ relative to full-history storage in a prototypical example. These results suggest that the principal memory barrier in broadband time-domain adjoint FDTD is not an intrinsic requirement of gradient evaluation but rather a consequence of redundant temporal field storage, thereby opening a practical route to large-scale three-dimensional nanophotonic inverse design.
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Physics and Society · Cross-listed from econ.GN

A Comparative Review of Methods to Create a Composite Index for Sustainable and Inclusive Wellbeing

Ricardo da Silva Vieira, Mario Biggeri, Peter Benczur et al. (+18 more)

Societal goals need to shift from over-reliance on gross domestic product (GDP) to broader aspects of sustainable and inclusive wellbeing (SIW). However, defining SIW and eventually measuring it with a single number is problematic because it involves many subjective and objective contributors that combine in complex, non-linear ways. Conventional approaches either use linear weighted averages or reduce SIW to subjective wellbeing alone. Neither is sufficient. This paper reviews aggregation methods for SIW against nine conditions derived from needs theory and strong sustainability: limited substitutability, penalisation of imbalances, non-linear transformations, respect for environmental ceilings, respect for lower limits, a formative measurement model, no correlation requirement, distributional sensitivity, cross-border spillovers, and intertemporal aggregation. We compare 13 methods, from simple arithmetic means to penalty-based indices, outranking multicriteria, data envelopment analysis, and insights from ecology, neuroscience, and machine learning. Our illustrative example shows that aggregation choices change significantly country rankings. Compensatory methods create similar rankings. No single method satisfies all nine conditions. We conclude that a future SIW composite indicator will require combining methods across levels: non-linear normalisation, non-compensatory aggregation, and measurement-level choices for inclusiveness and spillovers. This paper provides a step towards the headline aggregated indicator advocated by the UN High-Level Expert Group on Beyond GDP.
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All authors Ricardo da Silva Vieira, Mario Biggeri, Peter Benczur, Robert Costanza, Joseph Eastoe, Tuuli Hirvilammi, Ida Kubiszewski, Matteo Mazziotta, Kenneth Mulder, Taketo Muroya, Kelsey J. OConnor, Francesco Sarracino, Nikos Rigas, Enrico Giovannini, Rutger Hoekstra, Daniel Hopp, Edwin Horlings, Petra Krylova, Michele Melchiorri, Heriberto Tapia, Oscar Smallenbroek

Chemical Physics

Generation and Characterization of Surface-Attached Ultrathin Liquid Sheets for Grazing-Incidence X-ray Scattering

Yibo Wang, Daniel P. DePonte and Adi Natan

Capturing the ultrafast structural dynamics that occur at the solid-liquid interface is key to understanding adsorption, desorption, diffusion, and aggregation processes in catalysis and interfacial chemical reactions. Hard-X-ray scattering in grazing-incidence geometry can, in principle, access interfacial structural changes with angstrom-scale structural sensitivity and ultrafast temporal resolution. However, the long optical paths of the optical pump and hard-X-ray pulses inside the liquid sample pose significant challenges to the temporal resolution, signal-to-noise ratio, and overall stability of such an experimental scheme. Here, we report a method for creating and characterizing ultrathin surface-attached free-flowing liquid sheets, whose submicrometer thickness enables ultrafast temporal resolution and reduces the bulk-liquid scattering contribution. The impinging-jet geometry produces stable micrometer-scale sheets whose morphology depends systematically on incidence angle, jet velocity, and capillary diameter. Gas-assisted shaping using a second capillary further narrows and thins the sheet, producing an extended ultrathin region and reducing the measured minimum thickness below 500~nm for acetonitrile. The resulting platform provides a reproducible, continuously flowing, surface-attached liquid geometry for grazing-incidence scattering experiments.
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Fluid Dynamics

Physics-informed neural networks for shock capturing in inviscid flows around an airfoil

Jiahao Song, Wenbo Cao and Weiwei Zhang

Physics-informed neural networks (PINNs) have shown promise in solving partial differential equations (PDEs). However, shock capturing remains challenging for PINNs, particularly in steady flows around an airfoil, where shocks may not even be captured. In this study, we point out that the steady Euler equations used to construct the loss function impose weak constraints, which make it difficult to correct the continuous function approximation preference of neural networks, causing gradient descent to converge to a smooth local optimum. Based on this insight, we propose to strengthen the physical constraints by reconstructing steady shock capturing as a temporal evolution process governed by the unsteady Euler equations. The temporal evolution law of the flow field guides gradient descent, thereby alleviating its tendency to converge to smooth local optima and ensuring shock formation. We introduce a PDE loss function incorporating a pseudo-time stepping mechanism to obtain the steady state solution without solving the unsteady Euler equations over a long time interval. In addition, we develop a simplified formulation of the Euler equations to reduce the complexity of the PDE loss function, thereby improving shock capturing accuracy. By solving four forward problems involving different flow conditions and geometries, we verify that the proposed method achieves high accuracy in shock capturing while reducing the wall time to one-seventh of that required by existing advanced methods. Moreover, the proposed method demonstrates its capability to solve a parametric problem involving the angle of attack and Mach number without relying on any labeled data.
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Medical Physics · Cross-listed from eess.IV

ConRad: Efficient Conformal Prediction for Radiomics

Matt Y. Cheung, Ashok Veeraraghavan and Guha Balakrishnan

Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines. In practice, these features are often computed from predicted masks, but segmentation models can be overconfident or poorly calibrated, making derived measurements appear more reliable than they are. Conformal prediction (CP) provides distribution-free prediction intervals with finite-sample marginal coverage guarantees, but black-box intervals for segmentation-derived radiomics can be inefficient because they ignore test-time information about image appearance, mask geometry, and segmentation uncertainty. We propose ConRad, a conformal framework for scalar radiomic targets that uses covariates derived from the predicted mask, input image, predicted radiomics, and boundary uncertainty to construct adaptive intervals while maintaining coverage. Across five 2D medical imaging datasets and 171 retained radiomic targets, we show that ConRad improves feature-level efficiency compared to baselines while maintaining near-nominal empirical coverage. Ablation results further indicate that segmentation boundary uncertainty features are the largest contributors to interval efficiency.
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Accelerator Physics

Multi-GeV Electron Combs from a Plasma Wakefield Accelerator

Chaojie Zhang, Douglas Storey, Alexander Knetsch et al. (+16 more)

Plasma accelerators now produce GeV-class electron beams with brightness and stability sufficient to drive free-electron lasers. Beyond this, they possess a unique yet largely unexplored capability: shaping the phase space of the beam in situ during injection, on femtosecond or shorter timescales. Here we demonstrate this capability by generating a multi-GeV electron comb comprising more than ten microbunches simultaneously separated in both energy and time. Periodic pinching of the drive beam inside its self-excited plasma wake sequentially injects microbunches via ionization of embedded helium atoms at successive betatron oscillations, while the gently varying plasma density maps each bunchlet to a distinct wake phase, compressing electrons trapped over a ~17 cm region into a comb only micrometers long. Individual microbunches exhibit percent-level energy spreads, energy spacing up to ten percent, and contain several picocoulomb charge. The percent-level spreads and parabolic energy-spacing trend provide experimental evidence for sub-femtosecond microbunch durations and few-femtosecond separations as revealed by beam-loading analysis and confirmed by particle-in-cell simulations. This work demonstrates femtosecond, in-situ phase-space shaping in plasma accelerators, paving the way for electron beams with tailored energy-time structure.
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All authors Chaojie Zhang, Douglas Storey, Alexander Knetsch, Brendan D. O'Shea, Robert Ariniello, Sébastien Corde, Thamine N. Dalichaouch, Claudio Emma, Ole G. Finnerud, Spencer Gessner, Claire Hansel, Valentina Lee, Carl A. Lindstrøm, Michael Litos, Nathan Majernik, Kenneth A. Marsh, Warren B. Mori, Mark J. Hogan, Chan Joshi

Optics

Long-range coupling enabled multiband group-velocity control of topological edge states from slow light to light stopping

Junhao Yang, Jiarui Wang, Jingyu Liu et al. (+2 more)

Topological edge states provide robust optical transport immune to disorder, yet their propagation velocity is usually constrained by the intrinsic band dispersion, limiting dynamic control of topological light transport. We introduce long-range next-nearest-neighbor (NNN) couplings into a Harper–Hofstadter photonic lattice and establish a versatile platform for group-velocity engineering. We demonstrate that the NNN couplings play two distinct roles: the vertical coupling opens a previously closed topological band gap by lifting the degeneracy of bulk bands, while the horizontal coupling reshapes the edge-state dispersion through momentum-dependent corrections, enabling controllable topological slow-light transport. Furthermore, the band-gap Chern numbers associated with different gaps exhibit opposite signs, giving rise to topological edge states with opposite chiralities. Propagation simulations reveal robust unidirectional transport of these counter-chiral edge states with reduced group velocities. By continuously tuning the NNN coupling strength, the group velocity of topological edge modes can be reduced toward zero at specific momenta, resulting in topological light-stopping effects. These results demonstrate that long-range NNN couplings provide an effective mechanism for engineering momentum-dependent topological group velocities and offer new possibilities for robust slow-light devices, optical delay lines, and multiband integrated photonic systems.
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All authors Junhao Yang, Jiarui Wang, Jingyu Liu, Shirong Lin, Xinyuan Qi

Applied Physics · Cross-listed from quant-ph

Robust Quantum Learning through Hamiltonian Reservoir Computing

Youya Xu, Chengyong Yu and Sanjib Ghosh

Quantum learning provides a versatile paradigm for information processing by exploiting the intrinsic representational capacity of high-dimensional Hilbert spaces. Here, we investigate a Hamiltonian-encoding framework for quantum reservoir computing that simultaneously addresses three key challenges in quantum learning: trainability, hardware efficiency, and information stability. In this framework, input data are directly mapped onto a fixed Hamiltonian and transformed into expressive nonlinear features through quantum dynamical evolution. By employing the reservoir-computing paradigm, the approach naturally circumvents the barren plateau problem in quantum learning landscapes. We validate the framework across two complementary platforms: an analog superconducting array processor and a digital gate-based quantum circuit implementation. Despite their fundamentally different realizations, both platforms exhibit comparable representational power and achieve competitive learning performance, establishing a unified framework for cross-platform quantum learning. While both implementations achieve comparable performance, the analog processor may offer a more hardware-efficient realization by bypassing the temporal overhead of gate-based decomposition and thereby making more effective use of finite coherence times, albeit at the expense of universality. Furthermore, we find that finite dissipation suppresses quantum-scrambling-induced instabilities at long evolution times and can enhance learning performance, revealing a constructive role for environmental coupling in stabilizing quantum learning dynamics. Collectively, these results establish Hamiltonian-encoded reservoir computing as a compact, expressive, and hardware-efficient paradigm for quantum learning on current-generation quantum platforms.
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Plasma Physics · Cross-listed from astro-ph.SR

A Transport Theory of Turbulent Coronal Heating in General Geometry

Jonathan Squire, Benjamin D. G. Chandran, Toby Adkins et al. (+3 more)

Magnetic geometry shapes how turbulence transports and dissipates energy in strongly magnetized plasmas. The solar corona, a maze of open and closed flux tubes with sharp transverse gradients, is a prominent example, yet most wave-turbulence models of coronal heating assume symmetric flux tubes or add geometric effects in ad hoc ways. Here we develop a geometry-complete multiscale transport theory for reduced-magnetohydrodynamic turbulence in an arbitrary background field, retaining squashing (magnetic shear), transverse gradients, curvature, and gravity at the same order as standard expansion-driven reflection, and coupling fast, anisotropic fluctuations to slow background evolution through conservation laws. Applied to the corona, it recovers the standard reflection-driven turbulent cascade in smooth regions such as coronal-hole interiors, but predicts that in structured regions geometry-driven channels can dominate: squashing drives reflection even when parallel Alfvén-speed gradients are weak; curvature and non-radial geometry drive compressive heating channels; and waves catalyze the relaxation of velocity shear into heat. The same dynamics drive cross-field transport of mass, composition, momentum, and heat across open-closed interfaces, at rates rivaling the field-parallel supply from the base. These effects bias heating to low altitudes in structured regions, giving a physical basis for the coronal-hole–boundary corrections used in empirical wind-speed predictors. Additionally, the framework's slow-timescale transport equations could be evolved in time, providing a route to a global, geometry-aware model of a structured wave-driven corona and wind. More broadly, the theory provides an energy-consistent account of turbulence, geometry, and transport effects relevant to various astrophysical and terrestrial settings, from magnetospheres and accretion flows to fusion experiments.
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All authors Jonathan Squire, Benjamin D. G. Chandran, Toby Adkins, William A. Clarke, Romain Meyrand, Matthew W. Kunz

Applied Physics · Cross-listed from cond-mat.mtrl-sci

Interfacial chirality-induced magnetic-field-free switching with high energy efficiency in all-vdW heterostructures

Kai-Xuan Zhang, Suik Cheon, Seungbok Lee et al. (+12 more)

Chirality, a central concept across many scientific disciplines, continues to inspire the discovery of novel physical phenomena. In condensed matter physics, structural chirality - defined by the absence of mirror plane symmetries - has primarily been explored in bulk materials. However, new chiral phenomena can emerge uniquely at the interface, distinct from their bulk counterparts, when a chiral material forms a heterostructure. Here, we demonstrate that all van-der-Waals (vdW) heterostructure composed of the chiral Co1/3TaS2 and the achiral vdW ferromagnet Fe3GeTe2 exhibits two distinct and unconventional spin-orbit torques originating from the interfacial chirality. These torques enable magnetic-field-free switching of perpendicular magnetization with ultralow current density ~ 10^6 A/cm^2 and minimal power dissipation < 10^15 W/m^3. Moreover, by replacing Fe3GeTe2 with a similar vdW ferromagnet, Fe3GaTe2, but of higher Curie temperature, we achieved the magnetic-field-free switching at room temperature in the Fe3GaTe2/Co1/3TaS2 vdW heterostructure. Our findings establish interfacial chirality as a powerful new handle for spintronic control, opening a new pathway to explore chirality-induced phenomena beyond the bulk symmetry constraints - and paving the way toward highly efficient, low-power spintronic devices based on all-vdW heterostructures.
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All authors Kai-Xuan Zhang, Suik Cheon, Seungbok Lee, Joonyoung Choi, Jihoon Keum, Hyuncheol Kim, Yeochan An, Woonghee Cho, Suhan Son, Jingyuan Cui, Pyeongjae Park, Younjung Jo, Jun Sung Kim, Hyun-Woo Lee, Je-Geun Park

Chemical Physics

Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses

Sutanay Choudhury, Anwesha Banerjee, Udishnu Sanyal et al. (+7 more)

Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computationally intensive screening. In complex reactions such as electrochemical carbon dioxide reduction, product selectivity is governed by dynamic interfacial, electrolyte, and potential factors as well as kinetic pathway competition. Conventional descriptor-based machine learning and computational potentials struggle to resolve these mechanistic branch points, primarily relying on static ground-state descriptors or bulk structural correlations rather than end-to-end topological pathway analysis. Here, we show that frontier language models, when strictly constrained to reason over explicit reaction networks, can discover novel catalysts by identifying the physical levers that govern pathway competition. We developed a human-AI co-thinking framework that enforces network invariance to extract testable hypotheses from complex chemical graphs. Applied to CO2 electroreduction, the framework identified ketene desorption and hydroxide capture as the acetate-forming pathway, and predicted a distinct adsorbed CO and CH2 coupling route to ketene. By isolating actionable control levers, specifically local alkalinity, controlled iron incorporation, and restricted interfacial proton-donor accessibility, the framework guided the prospective synthesis of a copper-iron oxide catalyst demonstrating a threefold increase in acetate selectivity over matched Cu-rich baselines. This mechanism-guided reasoning architecture shifts the computational paradigm from retrospective statistical prediction to forward-looking hypothesis generation, providing a broadly applicable blueprint for mechanism-guided materials discovery.
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All authors Sutanay Choudhury, Anwesha Banerjee, Udishnu Sanyal, Jorin Dawidowicz, Chiezugolum Ijeoma Odilinye, Jesun Firoz, Liney Arnadottir, Simone Raugei, Johannes Lercher, Arnab Dutta

Wednesday 8 July

Physics and Society

Cognitive Cells: A Compositional Framework for Populations of Small Language Models

Silvan Ferreira

Recent work on large language models and agentic systems raises a basic question that current practice leaves open: how should artificial cognition be decomposed, measured, and composed? We propose studying multi-agent systems from a fixed unit we call a cognitive cell: a small, frozen language model with bounded memory and a message interface. The methodological commitment, the fixed-cell principle, is to hold this unit constant and vary only the population size, the communication topology, the message bandwidth, and the coordination protocol, so that collective behavior becomes a measurable property of a known device rather than an artifact of per-study engineering. We characterize a single cell by a compact datasheet of measurable parameters, and we ask when replicating and connecting cells improves performance: first we measure how one cell behaves alone, then we replicate it and test when voting, communication, and topology help. Instantiating the framework with small frozen models (1.5 and 3 billion parameters), we report a first round of measurements. Adding cells helps only when their errors are not too correlated. A simple correct/incorrect voting model is a useful but conservative null: real open-ended voting can exceed it, because errors are dispersed across many wrong answers rather than concentrated on one. Popular interactive protocols, namely debate, a shared blackboard, and chain revision, do not beat a matched-cost voting baseline in our setting. Finally, a cell's ability to relay several facts, itself a datasheet quantity, predicts whether a population can solve tasks whose evidence exceeds any single cell's memory. We present these as initial measurements within a broader program on scalable artificial cognition, in which multi-agent architectures appear as the special case of cells autonomous enough to be treated as agents.
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Physics and Society

Minimum-Distortion Wealth Taxation, I: Information-Theoretic versus Transport-Geometric Optimality on the Proportional Class

Anders G Frøseth

We characterise minimum-distortion wealth taxation under two contrasting normative criteria within a Fokker-Planck framework on log-wealth: the JKO free-energy gap, an information-theoretic measure aligned with the Mirrleesian decision-distortion tradition, and the squared 2-Wasserstein distance from the no-tax distribution at horizon $T$, a transport-geometric measure aligned with the Saez-Zucman distributional-compression tradition. Restricting to the neutrality-preserving (C1)-(C3) schedule class of a companion paper (Froseth 2026), both optima admit closed forms in the two-dimensional design plane parametrised by the corporate-dividend retention $k = (1-\tau_c)(1-\tau_d)$ and the proportional wealth-tax rate $\tau_w$. The JKO optimum partitions the regime axis into three phases as a function of the dimensionless ratio $\rho = \Sigma_0 m_0/\sigma^2$, with $m_0 = \mu - \sigma^2/2$ the geometric mean log-return: a pure wealth-tax phase at low $\rho$, a mixed-instrument phase at intermediate $\rho$, and a pure flow-tax phase at high $\rho$. The $W_2$ optimum, by contrast, is degenerate in this calibration: it pins to the pure flow-tax corner across the whole regime axis. The criterion contrast admits an economically meaningful reading via a bluntness index $B(m_0) = b/(a m_0)$ that measures the wealth-tax channel's mean-displacement-per-revenue overshoot relative to the flow-tax channel; JKO weights $B$ linearly, $W_2$ weights it quadratically, and the two normative traditions correspond to this difference in weighting. Norwegian-flavoured calibrations sit inside the JKO mixed-instrument phase under stock-heavy portfolio volatility but move into the pure flow-tax phase under the realised effective volatility of typical real-estate-heavy households.
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Physics and Society

CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents

Nicolas Bougie, Xiaotong Ye and Narimasa Watanabe

Large-scale urban simulation plays a pivotal role in social science, traffic safety, and transportation policy. Recent work has shown that large language models, when prompted as agents, can generate lifelike daily routines at city scale. Yet these methods typically rely on few-shot prompting, causing agents to reproduce the LLM's behavioral priors rather than the target population. We introduce CityReal, a modular framework for human-aligned urban simulation. CityReal models agents as intention-driven decision makers that pursue coherent mobility and activity plans rather than isolated step-by-step choices. They adapt over time by learning habits and preferences based on experience and constraints. To improve population-level realism, we learn textual adapters for behavior modules that align agent decisions with observed population statistics. Experiments show that CityReal improves alignment with real-world human behavior at both micro and macro levels. Scaling to tens of thousands of agents, it supports analysis of crowd density, place popularity, mobility flows, and well-being under different urban scenarios, offering a scalable testbed for urban simulation and forecasting.
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General Physics

Reliable Weyl Diagnostics for an Inhomogeneous Universe in Numerical Relativity

Hassan Ugail

The electric and magnetic parts of the Weyl tensor provide a geometric diagnostic for describing the local gravitational structure of an inhomogeneous universe, and they allow us to distinguish tidal, gravitomagnetic, radiative, and silent regimes. However, when these diagnostics are computed from numerical relativity data, their interpretation depends on how well the data satisfy the Einstein constraint equations. A small constraint error can create a spurious magnetic signal, especially near a silent regime, and it may therefore be mistaken for genuine radiative structure. In this work, we treat the Hamiltonian and momentum constraint residuals as reliability gates for the Weyl diagnostics. Rather than imposing a fixed tolerance, we measure how a constraint error affects the corresponding Weyl classification, and we then calibrate the tolerance required to achieve a chosen accuracy. Our noise-induced synthetic tests show that the Hamiltonian residual determines the reliability of the electric Weyl diagnostic, whereas the momentum residual determines the reliability of the magnetic Weyl diagnostic. Furthermore, a real numerical relativity initial-data slice confirms that no universal tolerance is adequate. Thus, the calibration measures the slice-level sensitivity of the diagnostics rather than the truncation error of an evolution.
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Geophysics

Observability of Finite-Depth Double-Diffusive Exchange from Sparse Temperature-Salinity Measurements

Sriram P. Kalathoor

Double-diffusive interfaces can support exchange histories that are spatially organized but only sparsely observed. A resolved three-dimensional calculation contains the route by which scalar gradients broaden, remain compact, connect with remote parts of a finite-depth layer, or organize horizontally, whereas field products usually provide profiles, repeated casts, autonomous-float records, or hydrographic sections. We ask which route-relevant features of finite-depth double-diffusive exchange remain observable after that measurement reduction. Four controlled finite-depth exchange histories are treated as known truth fields and sampled with vertical-profile, profile-bundle, vertical-coarsening, Argo-style, and section-like observing formats. The resulting observables are compared with selected Ice-Tethered Profiler, Argo, and CCHDO/GO-SHIP products to place the synthetic measurements in realistic observing contexts. Isolated profiles are weak route identifiers: at the final comparison time, strict case accuracy remains below 0.48, route-family accuracy is about 0.53–0.56 in leave-one-out profile classification, and 22 of 36 profiles are closer to a different-route profile than to a same-route profile under the current feature set. Profile bundles are substantially more informative. In the bundle-enumeration framework, the one-profile late-time route-family baseline is 0.694, while two-, three-, and four-profile bundles reach route-family accuracies of 0.917, 0.961, and 0.994. Coarse vertical sampling can preserve broad route separation while distorting local interface-width estimates, and section-like sampling adds horizontal-scale information only when station spacing resolves the relevant mode. The useful observing unit for hidden finite-depth double-diffusive exchange is therefore an ensemble or section, not an isolated cast.
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Atomic Physics

Theoretical ab initio Evolution of Satellite Intensity near Threshold for Cu K-shell transitions

Daniel Pinheiro, Gonçalo Baptista, César Godinho et al. (+9 more)

In this work, we have investigated the evolution of satellite intensity near the ionization threshold for Cu K-shell transitions through theoretical methods. Employing standard state-of-the-art ab initio methods, we have calculated all Cu K-shell transitions and simulated the full K$\alpha_1$ and K$\alpha_2$ spectrum where all transition parameters, as well as shake probabilities were determined theoretically. Through these calculations we show that standard state-of-the-art ab initio methods achieve good agreement with experiment and enable us to simulate the intensity evolution near ionization thresholds within a good margin of error. Below-threshold satellite intensity was found to originate from resonant 1s$\rightarrow$3d and 1s$\rightarrow$4p excitations in Cu(I) and Cu(II) oxide phases respectively, which were included in our simulations.
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All authors Daniel Pinheiro, Gonçalo Baptista, César Godinho, André Fernandes, Jorge Machado, Pedro Amaro, Nancy Paul, Martino Trassinelli, Miguel Avillez, Paul Indelicato, José Paulo Santos, Mauro Guerra

Accelerator Physics

TeV Electron Beams from Plasma Acceleration via Regenerative Cascading

Chaojie Zhang and Chan Joshi

Plasma accelerators sustain gradients orders of magnitude higher than conventional radiofrequency machines, but most proposed paths to TeV energies still require tens of stages, each demanding sub-micrometer alignment, femtosecond synchronization, and precise matching of the accelerating trailing bunch. Here we introduce plasma wakefield acceleration via regenerative cascading, in which each stage self-injects a fresh trailing electron bunch and the accelerated trailing bunch becomes the driver for the next stage. This approach has several advantages: energy multiplication instead of addition; automatic alignment, synchronization, and matching of the trailing bunch to the wake; and trailing bunch brightness reset in each stage. Particle-in-cell simulations show the generation of a 1.1 TeV electron beam with ~0.3% rms energy spread and 0.12 nC charge from a two-stage, sub-kilometer plasma accelerator driven by a 45 GeV, 100 nC beam. The low energy spread is achieved via dynamic beam loading in the evolving wake of the post-depletion driver that acts as a built-in energy dechirper.
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Computational Physics

A Puck-informed mode-resolved phase-field fatigue framework for unidirectional composites

Aamir Dean

Fatigue fracture in unidirectional fibre-reinforced composites is strongly mode dependent: transverse and off-axis cycling is governed by matrix and inter-fibre mechanisms, whereas fibre-aligned cycling activates a longitudinal channel with a higher fracture-energy scale and a different crack topology. Single-damage-variable models can fit global stiffness loss but cannot identify the active mechanism. This work proposes a Puck-informed, mode-resolved phase-field fatigue framework with separate channels for fibre-dominated and matrix/inter-fibre fatigue. Each channel has its own fatigue history, threshold, and resistance-degradation law. Fatigue does not directly degrade elastic stiffness; it lowers the fracture resistance of the active channel, while the corresponding phase field controls stiffness loss and crack-path evolution. The formulation is implemented in Abaqus/Standard using a compact UMAT-UEL architecture with one orthotropic mechanical routine and two scalar phase-field layers. Using one fixed IM7/8552 material and fatigue card, the model is verified through one-element tests, parameter sweeps, and centred-notch and open-hole tension cases at 0, 45, and 90 degrees under monotonic and cyclic loading. Without orientation- or geometry-specific tuning, the framework reproduces transverse matrix/inter-fibre cracking at 90 degrees, off-axis cracking at 45 degrees, and longitudinal matrix splitting with delayed fibre activation at 0 degrees. The fatigue lives follow the expected ordering: 45- and 90-degree cases fail within about 1,000 cycles, while 0-degree cases run out to 200,000 cycles without fibre cracking. Additional load, hole-size, mesh, length-scale, and cycle-block studies confirm consistent crack modes and converged trends. The study is a numerical verification and cross-geometry consistency assessment, not a calibrated experimental life-prediction claim.
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Atmospheric and Oceanic Physics

Orthogonality of coastal trapped waves

Jörn Callies

Coastal trapped wave modes are shown to be orthogonal in the sense that they make independent contributions to the energy of the wave field. The hydrostatic Boussinesq dynamics on an $f$-plane, linearized around a state of rest, are formulated as a (generalized) Schrödinger equation, which exposes the Hermitian structure of the wave operator that implies the orthogonality of eigenmodes. This formulation, which becomes particularly simple in weak form, is parlayed into a finite-element discretization that preserves the symmetries of the original problem and therefore the energy conservation and orthogonality of modes. The geostrophic momentum approximation, under which the orthogonality of modes has been recognized previously, is reprised and discussed in the present context to emphasize that low-frequency coastal trapped waves are edge waves. Their dynamics are governed by boundary potential-vorticity anomalies, both Bretherton-type contributions familiar from the quasi-geostrophic equations and lateral contributions that are important for steep slopes and coastal walls.
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