Statistical Mechanics
Effects of on-site Gaussian disorder in the ferromagnetic Blume Capel model
Kimberly J. Silverman, Daria Lhommedieu, Alykhan Rajan et al. (+3 more)
We conduct a numerical study of a generalization of the Blume-Capel model in which the
crystal field $D_i$, which controls the local vacancy density, is site dependent rather than uniform. We employ Markov Chain Monte Carlo to compute the shifted phase transition line $T_c$ as a function of the width $\sigma$ of a gaussian distribution $P(D_i) \sim \exp( -(D_i - \bar{D})^2/2 \sigma^2)$ at fixed $\bar{D}$. We characterize how the presence of this on-site Gaussian disorder reduces $T_c$ across the second order transition line, and discuss how our results suggest that $T_c$ remains finite even at large values of $\sigma$. We also determine the effects of disorder on the tricritical point of the conventional, uniform, Blume-Capel model, finding that it significantly suppresses the first-order phase transition, eliminating it entirely at large values of $\sigma$.
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All authors Kimberly J. Silverman, Daria Lhommedieu, Alykhan Rajan, Kathryn A. Cooper, Richard T. Scalettar, Eduardo Ibarra-García-Padilla
Mesoscale and Nanoscale Physics
Charge and spin dynamics in spintronic THz emitters from Time-Dependent Density Functional Theory
Ali Kefayati
Spintronic terahertz (THz) emitters based on ferromagnet/heavy-metal
heterostructures provide efficient broadband sources of THz radiation, yet the microscopic relation between ultrafast
spin transport and charge redistribution remains incompletely understood. Here, we employ time-dependent density functional theory to resolve the coupled charge,
spin, and magnetization dynamics of Co/Pt and Co/W
bilayers in real time and space. Despite qualitatively similar interlayer
spin-current dynamics, the two
heterostructures exhibit markedly different interlayer charge transfer: Co/Pt shows a relatively small and partially reversed transfer from Pt to Co, whereas Co/W exhibits a larger and persistent transfer from Co to W. We trace these differences to the material-dependent ground-state electronic structure and the
spin- and orbital-resolved phase space accessible to photoexcited carriers.
Spin-resolved dynamics further reveal coexisting
spin-orbit-mediated redistribution and optically induced intersite
spin transfer, including a delayed minority-
spin transfer from the heavy metal to Co common to both systems. While net electronic
spin angular momentum is transferred between the layers, we find no corresponding net transfer of electronic orbital angular momentum. In Co/W, the persistent redistribution of electronic density from interstitial to atom-centered states additionally provides strong evidence for light-induced electronic localization. These results demonstrate that ultrafast
spin transport does not uniquely determine charge or angular-momentum redistribution in spintronic THz emitters, which instead depends critically on the material-specific electronic phase space.
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Optics
Independent Amplitude and Phase Modulation in Active Transmissive Metasurfaces via Complex Permittivity Tuning
Christopher M. Yi, Ruzan Sokhoyan, Juyoung Kim et al. (+4 more)
Active metasurfaces are a promising platform for spatial light modulators (SLMs) in applications such as holography, beam shaping, and light detection and ranging (
LiDAR), as they enable dynamic control over the amplitude and phase of light at subwavelength scales. Among these, transmissive metasurfaces offer a compact alternative to conventional SLMs, since they allow monolithic integration with chip-scale light sources. While independent amplitude and phase modulation have been previously demonstrated in active reflective metasurfaces, this important milestone has not been achieved in transmission due to the inherent challenges in designing active transmissive metasurfaces. Here, we theoretically demonstrate an active transmissive metasurface that enables independent amplitude and phase modulation in the mid-infrared. The proposed metasurface consists of high-Q cuboid germanium (Ge) Mie resonators integrated with
graphene stripes. Utilizing the intraband and interband transitions of
graphene, electrostatic gating enables modulation of the transmittance from near 0% to 5% at one spectral frequency and achieves 281{\deg} phase-only modulation at another frequency in the same device. To achieve both types of modulation at the same operating wavelength, we use the thermo-optic effect of Ge as a global tuning mechanism to spectrally align the resonant frequencies, enabling both transmittance and phase-only modulation at different base temperatures. The proposed dual-mechanism architecture achieves transmittance modulation efficiency of ~100% and 282{\deg} phase-only modulation. Finally, we demonstrate the efficacy of phase-only modulation by individually addressing each unit
cell to realize a beam-steering device with relative diffraction efficiencies over 90%. Our individually addressable active metasurface opens a route toward compact, dynamically reconfigurable metaphotonic devices.
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All authors Christopher M. Yi, Ruzan Sokhoyan, Juyoung Kim, Sungmin Hong, Jinwoo Song, Harry A. Atwater, Min Seok Jang
Geophysics
Coarse-to-fine multi-resolution hash encoding for implicit full waveform inversion
Linrong Wang, Shaowen Wang, Fan Min et al. (+1 more)
Implicit full waveform inversion (IFWI) reparameterizes the velocity model with a neural network, but it suffers from spectral bias and requires many iterations to converge. Multi-resolution hash encoding uses a learnable feature grid to recover high wavenumber details at multiscales and provides strong local representations. However, activating all resolution levels from the start of the inversion introduces high wavenumber components, which can exacerbate cycle skipping. In this paper, we propose a coarse-to-fine IFWI (C2F-IFWI) that activates the multi-resolution hash levels progressively rather than all at once. Specifically, only a few coarse base levels are kept active at initialization, while the remaining levels are introduced progressively. A frozen hash level contributes no gradient until it is activated, so the coarse levels resolve the long-wavelength background before the finer levels are introduced regardless of the frequency range of the data. A continuous activation index increases with iterations and determines how many levels are active at any point in training. Each newly admitted level is then faded in gradually through a smooth weighting rather than switched on abruptly, which keeps the optimization stable during transition. This scheme allows model capacity to grow synchronously with wavenumber without adding any additional trainable parameters and with negligible computational overhead. Experiments on the synthetic Overthrust and BP 2004 models and on the Viking marine field
dataset show that, compared with the all-resolution hash baseline, the coarse-to-fine scheme converges faster, attains higher accuracy, and is markedly more robust to the choice of initial model and to strong data noise.
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All authors Linrong Wang, Shaowen Wang, Fan Min, Tariq Alkhalifah
Quantum Physics
The stationarity test: a framework for learning quantum many-body systems from their thermal states
Thiago Bergamaschi
The task of learning the
Hamiltonian interactions governing a
quantum system, given samples of its thermal (or 'Gibbs') states, is a foundational question at the intersection of
quantum learning theory and many-body physics. In this paper, we draw connections to the
quantum Gibbs sampling literature to introduce a natural learning
algorithm we call the stationarity test: which simply "guesses" the
Hamiltonian, and measures the rate-of-change of local observables, under the associated detailed-balanced
quantum Markov chain [CKG23]. We leverage the stationarity test to address the following applications: 1. To give the first learning
algorithm for the underlying interaction graph, i.e. structure learning, of
lattice Hamiltonians at all temperatures, given copies of their Gibbs states. 2. To give the first learning
algorithm for the coefficients of a
lattice Hamiltonian, given only copies of its thermal metastable states, modeled as the "local minima" of the free energy. 3. In addition, we present a refinement to the recent area law for thermal metastable states [BCV25], which holds in the thermodynamic limit. Our learning
algorithms are rigorous, time-efficient, and nearly sample-optimal in system size and accuracy. At a technical level, our arguments are based on new approximate locality and convexity properties for the
quantum Fisher information of these Gibbs sampling
algorithms.
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Materials Science
Topo-Spectral Percolation Descriptors for Mechanistic Ion Transport Pathways from Static Crystal Structures
Diptendu Roy, Chiku Parida, Juan María García-Lastra et al. (+1 more)
Ion transport controls the macroscopic performance of solid electrolytes, battery electrodes, ion-selective membranes, electrolysis ceramics, mixed conductors, porous sorbents, and biological channels. In each case, ion transport is determined by structural characteristics, namely the available
diffusion pathways and the specific bottleneck atoms that limit the ionic jumps. These are intrinsic properties of the crystalline framework, whereas temperature, carrier concentration, and correlated motion govern the transport rate. Ab initio molecular dynamics (AIMD) resolves the mechanism directly but is too costly for materials screening, and most surrogate models give only a single transport number. Here we present a calibration-free method for determining the mechanism, based on Topo-Spectral Percolation Descriptors (TSPD). TSPD obtains the transport mechanism from a single static structure in seconds. It constructs a periodic migration network whose edge barriers are computed from physics-based energetics. We then analyze the barrier-threshold
topology of this network and the spectrum of its barrier-weighted graph
Laplacian. To establish its validity for computational solid-state ionics, we test TSPD against AIMD ion densities and
neutron diffraction data for eight materials, including cathodes and solid electrolytes, whose transport ranges from one- to three-dimensional. TSPD reproduces the established mechanism in every case and correctly identifies structures in which
geometric openness does not imply viable ion percolation. The method is most informative for pathway-limited, direction-dependent transport, and it complements full dynamical simulations for strongly cooperative or nearly isotropic transport.
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All authors Diptendu Roy, Chiku Parida, Juan María García-Lastra, Arghya Bhowmik
Quantum Physics
Learned Parallel Bit-Flipping Sequential Belief Propagation Decoding of Quantum LDPC Codes
Mohsen Moradi, Taejoon Kim and Remi A. Chou
Quantum low-density parity-check (QLDPC) codes are promising candidates for low-overhead fault-tolerant
quantum computation, but their practical use requires fast, low-complexity, and reliable decoders. Belief propagation (BP) is attractive because of its local message-passing structure, yet standard flooding BP often suffers from convergence failures on QLDPC codes due to short cycles, degeneracy, and symmetric decoding trajectories. Learned sequential BP improves convergence by using a reinforcement-learning policy to choose variable-node update orders, but a single learned trajectory can still be sensitive to unfavorable local Pauli decisions. We propose a learned parallel bit-flipping sequential BP decoder with a new quaternary score combining syndrome gain, a quantized Pauli log-likelihood penalty, and Q-table lookahead at hypothetical post-flip states. Our decoder first runs learned sequential BP for a fixed number of iterations. If the syndrome is not satisfied, it constructs quaternary bit-flipping candidates, each corresponding to changing the current Pauli decision of one
qubit. The selected candidates initialize independent learned sequential BP continuations from the same decoder state. Since these continuations are independent, they can be executed in parallel, so testing several candidates mainly increases parallel hardware resources rather than sequential decoding latency. Simulations on representative QLDPC codes over the depolarizing channel show that the proposed decoder improves the reliability of learned sequential BP while having a parallel low-latency structure.
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High Energy Astrophysical Phenomena
Eight Gamma-Ray Bursts Observed with the Fermi Gamma-ray Burst Monitor: Significant Quasi-Thermal Components and Conditional Jet Constraints
Ze-lian Du, Zhao-Yang Peng, Jia-Ming Chen et al. (+2 more)
The composition of gamma-ray burst (GRB) jets remains uncertain. We analyze eight
Fermi Gamma-ray Burst Monitor (GBM) GRBs with reported
redshift information, selected from 126 events by prespecified fluence and spectral criteria. After excluding compound-model DIC evaluations classified as unstable by the posterior-stability tests, a stable added-blackbody (BB) comparison gives $\Delta\mathrm{DIC}>10$ in 203 of 215 accepted bins (60–100% per burst); independently, a multicolor-blackbody plus power-law (mBB+PL) decomposition gives $F_{\rm mBB}/F_{\rm tot}>0.5$ in 214 of 215 bins. The cutoff-power-law (CPL)–mBB correlations ($r=0.83$ and 0.78) exceed the Band–mBB correlations ($r=0.41$ and 0.48), demonstrating model dependence. Time-integrated BB fractions span 0.005–0.311, with six of eight below 0.12. We then apply the non-dissipative photosphere prescriptions of \citet{2013A&A...551A.124H} (H2013) and \citet{2015ApJ...801..103G} (G2015). After excluding boundary or physically inadmissible inversions, the H2013 numbers with $\epsilon_{\rm Th}>0.5$ are 1/2, 1/4, and 2/8 among valid solutions at $R_0=21$, 100, and 1000~km, respectively; the corresponding G2015 numbers with $\sigma_0<1$ are 0/1, 1/4, and 1/8. The large number of invalid solutions at small $R_0$ (six and seven at 21~km for H2013 and G2015) is itself evidence that those assumed radii are incompatible with the adopted inversion for most events. Among bursts having valid solutions at more than one radius, increasing $R_0$ generally lowers $\epsilon_{\rm Th}$ and raises $\sigma_0$. Thus launch radius, efficiency, and spectral decomposition dominate the conditional jet constraints; no universal jet class or unique non-thermal mechanism is inferred.
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All authors Ze-lian Du, Zhao-Yang Peng, Jia-Ming Chen, Yue Yin, Ting Li
Optics
Cross-platform frequency-domain physical neural networks with identical models and parameters
Zichen Xi, Jun Ji, Liyang Jin et al. (+3 more)
Frequency-domain physical neural networks (PNNs) have emerged as a promising analog computing paradigm, achieving a reduced number of devices, enhanced robustness, and high inference accuracy. However, existing demonstrations across various physical domains, such as optics, acoustics, and electronics, are mostly specific to the target devices or platforms, usually requiring hardware-dependent models and post-fabrication parameter tuning. Here, we demonstrate cross-platform implementations of frequency-domain PNN across different wave-based computing platforms using identical, pre-trained parameters. By exploiting shared second-order nonlinear processes, the PNN model is implemented on optical, microwave, and acoustic-wave platforms without the need of fine tuning the parameters. Evaluated on a unified four-class classification task, the frequency-domain PNN achieves high inference accuracies of 97.6% in optics, 98.4% in electronics, and 98.2% in mechanics. Further, we systematically compare related performance metrics across these three distinct physical domains, paving the way to a universal neural network across different physical platforms.
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All authors Zichen Xi, Jun Ji, Liyang Jin, Hsuan-Hao Lu, Wenjie Xiong, Linbo Shao
Optics
Parallax Depth Sectioning and 3D Reconstruction in 4D-STEM
Desheng Ma, Chia-Hao Lee, Zixiao Shi et al. (+2 more)
Three-dimensional (3D) information is encoded in four-dimensional scanning transmission electron microscopy (4D-STEM) through parallax between the virtual images formed at each detector pixel. In this paper we connect the encoding and
retrieval of depth-dependent information in 4D-STEM to two other widely-used 3D imaging methods,
tomography and light-field photography, and derive its 3D phase contrast transfer function. Viewing the 4D data as a collection of angular slices yields a collection of sinograms, providing an intuitive visual representation of the 3D information transfer. Within the small-angle approximation, tilt-corrected bright-field (tcBF) depth sectioning is equivalent to non-iterative tomographic reconstruction. Alternatively, the 4D data also can be mapped to light-field (or plenoptic) imaging, which acquires spatially resolved diffraction data in parallel using an array of lenslets. Adapting a light-field imaging
algorithm, we show that tcBF depth sections can be obtained by extracting two-dimensional slices from a single four-dimensional fast
Fourier transform (4D FFT) of the
dataset, yielding a substantial speedup when reconstructing a large number of depth slices. These differing perspectives distinguish the
geometric aspects of depth resolution from wave-optical effects that are present in phase-contrast imaging. Depth-resolved aberration-corrected bright-field (acBF) imaging yields improved 3D reconstructions compared to tcBF, removing contrast oscillations and reducing the variation in response with depth. We demonstrate acBF depth sectioning using simulated and experimental data and resolve distinct layers in stacked
oxide films and nanoparticle assemblies. These non-iterative volumetric reconstructions from a single 4D-STEM
dataset show promise for improved imaging of thick, weakly-scattering samples and for reconstructing low-dose acquisitions.
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All authors Desheng Ma, Chia-Hao Lee, Zixiao Shi, David A. Muller, Steven E. Zeltmann
Materials Science
Landau-like formalism for the thermal-runaway and filamentation instabilities in switching Mott devices
Aniket Bajaj, Anudeep Tullibilli, Arup Basak et al. (+2 more)
Correlated
oxides can switch to a low-resistance state when exposed to a voltage, making these
insulator-to-metal transition materials promising candidates for memristor technology. Switching in materials such as VO2 occurs because of a thermal-runaway instability that leads to the abrupt formation of a hot current-carrying metallic filament within the
insulator. We study, theoretically and experimentally, this bias-induced switching as a non-linear dynamics stability problem. We construct Landau free energy-like Lyapunov functions to show that both the on-state ("set") and the off-state ("reset") transitions can be described as separate saddle-node bifurcations. Starting from the reaction-
diffusion equation for the heat transport, the abrupt snap-back (reset) to the insulating state is systematically formulated as a mean field Landau-like dynamics with the filament width playing the role of the order parameter. The formulation predicts an abrupt first-order phase transition with a break-off of a finite width filament across the current-controlled reset transition. The formulation is quantitatively compared against our electrical and optical microscopy experiments on VO2 thin film devices. Multi-cycle switching dynamics experiments further reveal the emergence of a stable
stochastic regime following an initial electro-spatial forming phase. Beyond establishing a cross-disciplinary dynamical framework that may be readily compared to instabilities in various reaction-
diffusion scenarios, the work also provides a predictive foundation for the rational design and optimization of Mott-transition-based technologies.
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All authors Aniket Bajaj, Anudeep Tullibilli, Arup Basak, Pavan Nukala, Bhavtosh Bansal
Earth and Planetary Astrophysics
Planetary eccentricity enhances inward comet transport but suppresses gentle impacts
Qunfeng Jiang
Comets can supply volatile and prebiotic material to rocky
planets, but survival of fragile molecules depends on atmospheric entry and impact. Compact
planet chains can transfer comets inward at low velocity. We test whether modest planetary eccentricity preserves this route. We follow 64,000 comets from the region between the two outermost
planets of an eight-
planet chain to an Earth-mass
planet on a temperate orbit around a $0.1 M_\odot$
star. A second suite of simulations isolates the last transfer to the innermost
planet across initial
planet eccentricities $e_{p,0}=0$–$0.05$ and separations of 10 and 30 mutual Hill radii; a $\Delta=12$ comparison tests the influence of the near-3:2 period ratio at $\Delta=10$. At $e_{p,0}=0.05$, the fraction of comets intercepted by the outermost
planet falls by 11 per cent and 6 per cent more reach the inner region. The innermost
planet's impact probability changes by less than 3 per cent, while the fraction of impacts below 15 km s$^{-1}$ falls from 25 to 18 per cent. The fraction of all injected comets producing impacts below 15 km s$^{-1}$ consequently decreases by 30 per cent. Measurements of the comet-
planet speed before the final close encounter show that eccentricity preferentially removes the slowest approaches. Applying an impact-chemistry model gives a 31 per cent lower HCN survival-equivalent yield. Modest planetary eccentricity can therefore increase inward transport while reducing the prebiotic material delivered under chemically favourable impact conditions.
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Soft Condensed Matter
Anisotropic medium-range order uncovers dynamic crossovers in glass-forming liquids
Kamlesh Mishra, Rajesh Ganapathy and Walter Kob
On cooling
liquids towards their glass-transition temperature, the dramatic increase of their relaxation times is accompanied by changes in particle dynamics at two distinct temperatures: A high-T crossover, where particles become temporarily caged by their neighbors, and a low-T crossover, where the cage escape mechanism changes. While there is some evidence that the former is associated with a change in local particle arrangement, no structural modification has so far been detected across the low-T crossover, fueling scepticism about the relevance of structure for glassy dynamics. Here, we introduce a novel four-point correlation function which allows to determine a structural length scale characterizing cage anisotropy. Extensive molecular dynamics simulations reveal that this scale, as well as the mean structural length scale of the glass-former, extends into the medium range, i.e., significantly exceeds the particle size. Strikingly, the difference between these two scales - a measure of the degree of cage anisotropy - peaks at both crossover temperatures. We discuss how the presence of these peaks enables understanding the nature of the change in the microscopic transport mechanism at the two temperatures, thereby linking both dynamical crossovers to a single structural observable, the anisotropic medium-range order. This fundamental insight demonstrates that structure, extending well-beyond the local cage, is an essential ingredient for understanding the relaxation dynamics of deeply supercooled
liquids.
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Instrumentation and Methods for Astrophysics
Comparing Optimized Systematic Error Correction Methods on Selected TESS Light Curves
David Rapetti, Jon Jenkins, Joseph Twicken et al. (+2 more)
The correction of systematic errors in TESS light curves is crucial for all astrophysical analyses employing these observations. Here we present a data analysis and a software package, SysCoCoPy, to investigate and directly compare the performance of the Presearch Data Conditioning (PDC) correcting method from the Science Processing Operations Center (SPOC) pipeline and three correctors developed by the community. We incorporate these three correctors based on their implementations in Lightkurve and are particularly interested in the ability of the four correctors to remove scattered light contamination from the Earth and the Moon, which is a key systematic for TESS. We implemented these correctors in SysCoCoPy with a framework that allows an automatic optimization of their parameters to scale the analysis towards increasingly larger samples. SysCoCoPy provides qualitative and quantitative products for the comparison of individual cases as well as statistical results for selected samples. We currently find that while our automatic parameter optimization provides a significant number of successful scattered-light corrections for two of the correctors with a design that favors this purpose, an statistical analysis of our largest sample indicates that PDC is presently more robust, with a larger overall success level of the metrics used.
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All authors David Rapetti, Jon Jenkins, Joseph Twicken, Douglas Caldwell, Jeffrey Smith
Quantum Physics
Function-like pseudorandom unitaries generate pseudorandom quantum processes
Zitai Xu, Rahul Arvind, Kishor Bharti et al. (+5 more)
Haar-random
unitaries provide a canonical model of generic random
quantum evolution, but they typically have exponential description and
circuit complexity. Although pseudorandom
unitaries efficiently emulate a single Haar-random
unitary, many tasks require an entire reusable family of independently random-looking operations. We introduce pseudorandom function-like
unitaries (PRFU), which generate such a family indexed by public labels using only a single short key. We distinguish classical- and coherent-label access and establish security under adaptive
quantum queries. For classical labels, we provide a generic construction from a post-
quantum pseudorandom function and a pseudorandom
unitary. For coherent labels, we use an indexed path-recording framework to analyze interference across labels and prove security of a construction based on
quantum-secure function and permutation primitives. We further show that PRFUs generate pseudorandom channels and
quantum combs with private
memory, which are secure against adaptive interventions and concurrent sessions. Combining PRFUs with
unitary gluing yields a one-key family of pseudorandom
unitaries whose supported register widths can be chosen after key generation. Further applications include nonce-resolved
quantum authentication, coherently masked QRAM queries, and efficient emulation of random multi-time dynamics. Our results extend
quantum pseudorandomness from individual operations to efficiently generated families of random-looking
quantum dynamics.
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All authors Zitai Xu, Rahul Arvind, Kishor Bharti, Tobias Haug, Dax Enshan Koh, Wai-Keong Mok, Andrew Tanggara, Alexey V. Gorshkov
Materials Science
Train for Accuracy, Execute at Scale: Architecture-Preserving Inference for Equivariant Atomistic Foundation Models
Lei Fu, Zihui Feng, Yongheng Li et al. (+8 more)
Equivariant atomistic foundation models provide broadly transferable interatomic potentials trained against
quantum-mechanical reference data, but their repeated execution at simulation scale remains computationally and
memory intensive. We present Symmetrix-XL, an inference engine that scales pretrained MACE checkpoints without retraining,
distillation, or modification of their learned weights. It combines streamed-edge execution to avoid graph-wide materialization of expanded edge intermediates, model-specialized code generation to compile checkpoint-specific operators, and tiled execution to reuse a bounded device-
memory workspace. On complete LAMMPS-step
benchmarks, Symmetrix-XL reduces inference time by 3.1-5.0 times relative to ML-IAP + cuEquivariance across tested A100 and RTX 5090 workloads. On a single A100 80 GB GPU, the tested MACE-OMAT-0 capacity boundary increases from 24,565 atoms to 11.24 million atoms. This substantially lowers the hardware threshold for simulations that would otherwise require spatial decomposition across many GPUs and compute nodes. The same backend weak-scales to 703 million atoms on 64 A800 GPUs at 93.8 percent efficiency. Energy, force, stress, molecular-dynamics stability, and Matbench Discovery evaluations reproduce reference MACE behavior within measured tolerances. Case studies spanning solid-state, interfacial, and reactive systems demonstrate the increased capacity in realistic workflows. These results establish post-training execution as a scaling axis complementary to model redesign, compression, and distributed scale-out, and show that the practical accuracy-cost frontier depends on both model architecture and inference execution.
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All authors Lei Fu, Zihui Feng, Yongheng Li, Hongwei Du, Xin He, Junyi Wu, Kejie Bao, Yueyu Zhang, Zeyu Deng, Ziheng Lu, Bonan Zhu
Geophysics
GeoFWI3D: Large-scale 3D Velocity Model Dataset for Deep Learning-assisted Seismic Imaging
Sujith Swaminadhan, Yiran Shen, Chao Li et al. (+11 more)
We introduce GeoFWI3D, a large-scale open-source
benchmark dataset of geologically plausible 3D subsurface models designed to accelerate deep learning (DL) assisted seismic imaging and full waveform inversion (FWI). FWI is a physics-driven, wave-equation-based optimization technique used in seismic imaging to estimate the subsurface properties. However, traditional FWI is highly non-linear, non-unique, and ill-posed. The iterative process often converges to local minima when the starting model is insufficiently accurate. Most importantly, 3D FWI is prohibitively expensive. With the advent of DL, a direct mapping between the shot gathers and subsurface properties can be established by training a network on realistic models. The primary bottleneck for such approaches is the lack of large-scale, realistic training
datasets. GeoFWI3D addresses this gap with 10,000 geologically diverse velocity models at 96x96x96 resolution, spanning four structural complexity classes: pure stratigraphy, faulted networks, salt diapirism, and complex coupled fault-salt systems. Each model is accompanied by co-registered multi-modal labels including compressional velocity (Vp), zero-offset seismic reflectivity, relative geologic time (RGT), and semantic fault/salt masks. To facilitate systematic evaluation, we introduce
benchmark tasks covering fault detection, joint salt body
segmentation and chronostratigraphy prediction, FWI, wavefield and traveltime surrogates using neural operators, and generative modeling with a 3D
diffusion model. We present baseline results for each task to establish reference performance metrics for future users. The
dataset is publicly available under Creative Commons Attribution 4.0 International.
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All authors Sujith Swaminadhan, Yiran Shen, Chao Li, Kai Gao, Ting Chen, Sergey Fomel, Tolulope Agbaje, Yang Cui, Liuqing Yang, Jaewook Lee, Robin Dommisse, Umair bin Waheed, Mrinal K. Sen, Yangkang Chen
Solar and Stellar Astrophysics
1RXS J174320.1-042953: another polar with a red-shifted absorption component in emission line wings
Diego H. González-Buitrago, Ma. T. García-Díaz, Sergey Zharikov et al. (+5 more)
We present the results of a spectroscopic and multi-band photometric study of the
magnetic cataclysmic variable 1RXS J174320.1-042953. Multi-band photometry confirms a periodic modulation with an orbital period of $P_{\rm orb}=0.0864 \pm 0.0001$ days. Using the estimated secondary mass of $\sim0.15\,\mathrm{M_\odot}$ and, the X-ray-based white
dwarf mass of $\sim0.75\,\mathrm{M_\odot}$, we obtain values of $q\sim0.2$ and a system inclination of $47^\circ\pm10^\circ$. The optical spectra of 1RXS J174320.1-042953 are dominated by strong, complex, asymmetric, and highly variable single-peaked emission lines of the Balmer series, \ion{He}{i}, and \ion{He}{ii}, which are characteristic of polar-type
magnetic cataclysmic variables. Doppler
tomography reveals no evidence of a Keplerian
accretion disc, supporting the classification of the system as a polar. The H$\beta$ and \ion{He}{ii} emission lines can be decomposed into at least two distinct components. The low-velocity components have semi-amplitudes of approximately 170 km s$^{-1}$ (H$\beta$) and 147 km s$^{-1}$ (\ion{He}{ii}), while the high-velocity components reach about 317 km s$^{-1}$ and 460 km s$^{-1}$, respectively. The low-velocity component is likely associated with the irradiated side of the secondary
star facing the white
dwarf and/or the vicinity of the L$_1$ point, whereas the high-velocity component is related to the
accretion-stream structure. A redshifted absorption component in the emission-line wings is detected at orbital phases near $\phi \approx 0.0$, reaching velocities up to $\sim1400$ km s$^{-1}$ and likely produced by
accretion-stream material crossing the line of sight.
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All authors Diego H. González-Buitrago, Ma. T. García-Díaz, Sergey Zharikov, Gagik Tovmassian, A. Castro, E. J. Kotze, Yair Krongold, C. A. Negrete
Quantum Physics
Device-Independent Conference Keys from Parity-Extended Games
Suvradip Chakraborty, Ronak Ramachandran and Aniruddha Sen
Device-independent conference key agreement (DI-CKA) lets a group of parties establish a shared secret key from untrusted
quantum devices, with security certified by non-locality. Existing DI-CKA protocols are each built around a single Bell inequality, typically a multiparty variant of the CHSH game. DI-QKD protocols, in contrast, have been built from a much richer landscape of non-local games, and it has remained unclear how to carry this landscape over to the conference setting. We introduce $\textit{Parity-$G$ games}$, which extend any two-player game $G$ to $N$ players, for every $N$, provided $G$ has an optimal strategy in which one player measures Pauli observables. The extension preserves the
quantum and classical values of $G$, and the security of the resulting $N$-party protocol follows from an analysis of the two-player game alone. Our framework recovers the Parity-CHSH game of Ribeiro, Murta and Wehner (Phys. Rev. A, 2018) as a special case. Applied to the Mermin–Peres Magic Square Game, it yields a new $N$-player pseudo-telepathy game, the $\textit{Parity Magic Square Game}$, which ideal devices win in every round. We use it to construct the $\textit{first}$ DI-CKA protocol based on a pseudo-telepathy game. We prove the protocol secure against coherent attacks. It produces up to two key bits per round, and at low noise its key rate exceeds that of the DI-CKA protocol based on the Parity-CHSH game.
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Quantum Physics
Exact $T$-counts of CCZ layers from an isotropy bound
Arul Rhik Mazumder
The $T$-count is a dominant cost of fault-tolerant Clifford$+T$ computation. We prove that a layer of $m$ disjoint CCZ gates, the diagonal core of a parallel Toffoli layer, needs exactly $6m+1$ $T$ gates in every Hadamard-free Clifford$+T$
circuit with clean ancillas. Campbell and Howard gave the matching construction, and to our knowledge this is the first proof that it is optimal for general $m$. The proof rests on a novel isotropy bound. When a gate's phase
polynomial has only cubic terms, the vectors recording which $T$ gates touch each
qubit span a self-orthogonal subspace over $\mathbb{F}_2$, and this forces the $T$-count to be at least twice its dimension. This argument gives a lower bound, computable by Gaussian elimination, on the $T$-count of every diagonal gate in the third level of the Clifford hierarchy. It is never below the stabilizer nullity $\nu$ and reaches $2\nu+1$ on non-Clifford cubic gates. No bound that also holds for
circuits with measurement and feedforward can do this, since such
circuits implement CCZ ($\nu=3$) with four $T$ gates, while the floor gives seven. It also recovers Campbell and Howard's count $4m+3$ for a fan-out of Toffolis. On the output of PyZX's TODD-based optimizer, relative to its Hadamard placement, it certifies $193$ of $311$ phase-
polynomial blocks $T$-optimal, against $113$ for nullity, including $37$ of the $72$ blocks too large for exhaustive search.
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Superconductivity
Highly anisotropic collective modes of altermagnetic superconductors with Bogoliubov Fermi surfaces
Huaisong Zhao, Peng Zou, Xia-Ji Liu et al. (+1 more)
We investigate collective modes in charge-neutral altermagnetic
superconductors with $d$-wave
spin-split bands, focusing on the impact of emergent Bogoliubov
Fermi surfaces. In the absence of a
magnetic field, the BCS state supports collective modes that inherit the underlying $d$-wave
symmetry, resulting in pronounced momentum-space anisotropy. The broken time-reversal
symmetry further couples these modes to
spin-density fluctuations, allowing their signatures to emerge in the
spin dynamical structure factor. Upon applying a
magnetic field, Bogoliubov
Fermi surfaces emerge and qualitatively reshape the collective-mode dynamics through gapless particle-hole excitations and strongly anisotropic Landau damping. The damping is strongest along the momentum-space diagonal, where it can suppress the low-energy
phonon mode at small momenta. Remarkably, the suppressed
phonon is accompanied by the emergence of an in-gap structure in the
Higgs mode and an associated pronounced enhancement of the
spin response. These results establish Bogoliubov
Fermi surfaces as a mechanism for generating highly anisotropic collective-mode dynamics and identify
spin response as a sensitive probe of in-gap
Higgs excitations in altermagnetic
superconductors.
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All authors Huaisong Zhao, Peng Zou, Xia-Ji Liu, Hui Hu
Chaotic Dynamics
The Effect of Gait Stability Based on Two Types of Impact Strategies for Two-Link Walking and Brachiating Robots
Alan Estrada Flores and Nelson Rosa Jr
In this paper, we explore the impulsive dynamics common to single-joint, two-link models of walking and brachiating gaits with respect to slope and switching time. In particular, we investigate how the stability of a gait and bifurcations encountered within a family of gaits change under time-based and state-based switching of the impulsive dynamics.
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Materials Science
HVPE Homoepitaxy of $\beta$-Ga$_2$O$_3$: High-Temperature Growth and Orientation-Dependent Surface Evolution
Sourav Sarker, Saleh Ahmed Khan, Ahmed Ibreljic et al. (+2 more)
In this work, we systematically investigate homoepitaxial $\beta$-Ga$_2$O$_3$ growth by halide vapor phase epitaxy (HVPE) on (010), (001), and ($\bar{2}$01) native substrates over a broad growth parameter space, extending the growth temperature as high as 1150 $^\circ$C. The growth kinetics exhibit strong dependence on precursor supply, temperature, reactor pressure, and Ga-source-to-substrate distance. Increasing the HCl flow from 80 to 300 sccm enhances the growth rate by up to seven-fold, while increasing the temperature from 1050 to 1150 $^\circ$C nearly doubles the deposition rate. At 1150 $^\circ$C, atmospheric-pressure growth yields rates more than 2.5 times higher than those at 100 Torr, while reducing the O$_2$ flow to 5 sccm further increases the growth rate to 14.3 $\mu$m/h, producing films up to 42.9 $\mu$m thick in 3 h. Notably, phase-pure homoepitaxial layers with good crystalline quality are obtained across all three orientations, with low rocking-curve full width at half maximum (FWHM) values of 63.4, 66.6, and 42.8 arcsec for the (010), (001), and ($\bar{2}$01) epilayers, respectively. The surface morphology exhibits a pronounced crystallographic dependence and evolves strongly with the HVPE growth conditions. In particular, the ($\bar{2}$01) surface develops well-defined step-and-terrace morphology with local RMS roughness as low as 0.17 nm. These results extend $\beta$-Ga$_2$O$_3$ HVPE homoepitaxy into a higher-temperature regime and demonstrate a broad process window for high-rate growth of thick, high-quality epilayers with strongly orientation-dependent surface
evolution.
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All authors Sourav Sarker, Saleh Ahmed Khan, Ahmed Ibreljic, Pronoy Kanti Das, Anhar Bhuiyan
Physics and Society · Cross-listed from cs.AI
Evaluating LLM-Generated Preference Distributions
Fan Huang, Minsuk Kim, C. Tyler Diggans et al. (+1 more)
Large
Language Models (LLMs) are increasingly used as probabilistic generators for simulation, synthetic data generation, and decision support in settings where real-world data are unavailable. Yet, the structure and reliability of the distributions they produce remain understudied. Here, we systematically analyze LLM-generated distributions of preferences for air travel, restaurants, and consumer products. Encouragingly, all models considered in our analysis exhibit self-coherence, with the most probable outcomes stabilizing rapidly under repeated sampling. At the same time, we observe substantial discordance across both model families and scales, with little consensus even among their most probable outcomes. These patterns hold across nine open-weight models, three choice domains, and show robustness under temperature changes, greedy decoding, and perturbations of
prompt and ordering. Our findings indicate that outcomes are influenced more by the choice of model than by the wording of the
prompt, challenging the common assumption that sufficiently capable LLMs produce similar preference distributions when used as stand-ins for survey respondents.
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All authors Fan Huang, Minsuk Kim, C. Tyler Diggans, Filippo Radicchi
Cosmology and Nongalactic Astrophysics
Weak-Lensing Shear Response for Photometric Redshift-Based Tomographic Binning
Xiangchong Li, Tianqing Zhang, Rachel Mandelbaum et al. (+1 more)
Dividing source
galaxies into tomographic
redshift bins is a cornerstone of modern weak gravitational
lensing analyses, enabling measurements of the growth of
cosmic structure and the nature of dark energy. In practice, these tomographic bins are defined using photometric
redshift (photo-$z$) estimates. However, correlations between photo-$z$ estimates and weak
lensing shear can introduce
redshift-dependent selection biases in the measured shear signal; if left uncorrected, these biases distort the inferred amplitude and
redshift evolution of the
lensing signal, and in turn bias the measurement of the growth of
cosmic structure across
cosmic time. In this paper, we extend the analytical self-calibration for shear measurement (AnaCal) framework to account for photo-$z$-based selection biases in tomographic weak
lensing analyses by propagating shear responses through the selection process. This approach eliminates the need for external image simulations to calibrate this correction. As a first sanity check on real data, we validate the photo-$z$ estimates derived from AnaCal fluxes on the Rubin Observatory Data Preview 1
dataset, and find that they reach photo-$z$ quality comparable to, and at high
redshift slightly better than, the standard LSST estimates. We then validate the shear calibration on LSST-like image simulations with blending at the expected LSST Y10 depth, with two representative photo-$z$
algorithms – a template-fitting method and a machine-learning method – and show that the multiplicative shear bias induced by photo-$z$ selection remains within the LSST ten-year requirement $|m| < 3\times 10^{-3}$ across all five tomographic bins for both
algorithms. These results establish AnaCal as a self-consistent pipeline for tomographic weak
lensing science in upcoming LSST analyses.
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All authors Xiangchong Li, Tianqing Zhang, Rachel Mandelbaum, the LSST Dark Energy Science Collaboration
High Energy Physics (Experiment)
Search for dark matter produced in association with a low-multiplicity jet in proton-proton collisions at $\sqrt{s}$ = 13 TeV
CMS Collaboration
A search for dark matter particles is presented using events with large missing transverse momentum and a single energetic jet containing few constituents. The analysis uses
proton-
proton collision data recorded by the CMS experiment at a center-of-mass energy of 13 TeV. This corresponds to an integrated luminosity of 138 fb$^{-1}$, recorded during the 2016$-$2018 LHC data-taking period. This is the first search at the LHC to employ the low-multiplicity jet signature. Supervised machine learning is used to maximize sensitivity to potential signals in this challenging final state. No excess of events over the standard model background prediction is observed. The results are used to set upper limits on the dark matter production cross section at 95% confidence level (CL) within simplified models featuring vector and axial-vector mediators. For a dark matter particle mass of approximately 150 (550) GeV, mediator masses up to 4250 (3500) GeV are excluded at 95% CL.
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Biological Physics
Selection rules for the harmonic spectroscopy of animal decisions
Mohammad Salahshour
Spectroscopy reads structure by sending a structured probe into a system and measuring what comes back. Here we apply this principle to animal decision-making. Harmonic Theory casts choice as motion on an angular landscape over heading, whose
Fourier components form an animal's decision spectrum. We show that the arrangement of cues enters that landscape as a structure factor, so it factors like a diffraction amplitude, and
symmetry imposes selection rules: a p-fold cue array annihilates every harmonic that is not a multiple of p, and sweeping two cues apart draws a notch through each harmonic. Cue geometry becomes a tunable probe: the experimenter sets which harmonics an experiment can see. We read these spectra from public recordings of flies, schooling fish, larval zebrafish, mice and a swimming alga, and from the fly's neural compass, where symmetric scenes suppress the forbidden harmonics and breaking that
symmetry restores them. Measuring the compass form factor favours the theory's smooth bump over its square one, and raising cue contrast narrows that bump in every fly. A width fitted to one harmonic then predicts the next. Cue geometry is a spectrometer for decisions, and
symmetry sets what it can see.
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Cosmology and Nongalactic Astrophysics
Effects of Poisson Isocurvature Perturbations on Induced Gravitational Waves
Zi-An Zhou, Cheng-Jun Fang and Zong-Kuan Guo
A sudden transition from an early matter-dominated era to a radiation-dominated era provides a representative mechanism for enhancing induced gravitational waves (GWs). In the
primordial black hole (PBH) evaporation scenario, discrete PBH number-density fluctuations naturally generate Poisson isocurvature perturbations. By evaluating the GWs induced by these perturbations through the sudden evaporation transition, we show how the full hierarchy of intrinsic non-Gaussian statistics of a Poisson isocurvature source enters the induced GW spectra. We find that the tensor power spectrum retains only the two-point correlations of the scalar source, while higher-order correlations first appear in the tensor bispectrum. We also find a nontrivial scale dependence in the relative amplitudes of different bispectrum configurations, which may provide additional information for distinguishing the discrete PBH scenario from other induced-GW mechanisms.
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Materials Science
Layer-by-layer growth of highly aligned MoS$_2$ nanoribbon arrays
Kaichi Yamamoto, Pablo Solís-Fernández, Haiming Sun et al. (+10 more)
One-dimensional transition metal dichalcogenides (TMD) nanoribbons (NRs) offer a promising route to aggressive channel-width scaling in nanoscale transistors. However, controlling their layer number, a key determinant of device performance, has remained elusive. Here, we demonstrate a chemical vapor deposition (CVD) approach for layer-by-layer growth of MoS$_2$ NRs on a low-
symmetry sapphire surface. This approach enables the oriented growth of single-crystalline
bilayer NRs with a 2H stacking configuration, while maintaining widths below 20 nm. Increasing the MoO$_3$ precursor supply was found to be crucial for promoting the growth of the second and subsequent layers. By controlling the growth time and precursor supply, the layer number of MoS$_2$ NRs can be increased up to four layers. This method can also be extended to the growth of vertical MoS$_2$/WS$_2$ hetero-NRs. The average carrier mobility and current density of
bilayer NR field-effect transistors (FETs) are two- and three-times higher than those of monolayer counterparts, respectively, and a maximum mobility of 86.7 cm$^2$V$^{-1}$s$^{-1}$ was attained with a
bilayer NR-FET. These results highlight layer number as a key parameter for optimizing NR device performance. Our thickness-controlled growth strategy provides a general route towards TMD NR-based transistors with enhanced scalability and performance.
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All authors Kaichi Yamamoto, Pablo Solís-Fernández, Haiming Sun, Yanlin Gao, Yeri Lee, Yushan Tseng, Aika Uchida, Masahiro Hara, Sunmin Ryu, Yung-Chang Lin, Susumu Okada, Kazu Suenaga, Hiroki Ago
Physics and Society
Community-Centers Identify Robust Biomarkers in High-Dimensional, Low-Sample-Size Gene Expression Data
Ruiqi Li, Te Bai, Renaud Lambiotte et al. (+4 more)
High-dimensional, low-sample-size bulk gene expression data poses a fundamental challenge in transcriptomics, which typically includes tens of thousands of genes but relatively few samples, leading to overfitting and unstable selection for key features as biomarkers. We propose a regression by community centers (RCC) framework tailored for such data. RCC converts gene expression data into a feature proximity network and leverages the phase transition of the
giant connected component to identify a critical distance threshold that yields a sparse yet maximally informative network representation, indicated by a minimum normalized shortest compression length. Intuitively, at the criticality, the network balances fragmentation and over-connectivity, allowing meaningful gene-gene associations communities to emerge while filtering noises, such that community-centers capture the most informative and non-redundant signals. These representative genes are then fed to a simple ordinary least squares (OLS) model for downstream predictions. Across five bulk gene expression
datasets, RCC displays exceptional prediction performance, clearly outperforming
benchmark methods, remains robust to missing data and noises, and does not rely on external biological knowledge. These results suggest that network-based representations provide an effective and general framework for high-dimensional, low-sample-size prediction tasks beyond transcriptomics and illustrate how network-science ideas can support robust learning in data-scarce regimes.
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All authors Ruiqi Li, Te Bai, Renaud Lambiotte, Orr Levy, Paul Expert, Daqing Li, Shlomo Havlin