Solar and Stellar Astrophysics
Modified electron dispersion relations in degenerate white dwarfs
Edson Otoniel and Iarley P. Lobo
We investigate how modified electron dispersion relations affect the structure of cold white
dwarfs (WDs). The deformation is introduced only in the degenerate electron equation of state, through the energy of a single particle and the group velocity, while the
stellar mass density remains dominated by ions through the relation $\rho\simeq \mu_e m_u n_e$ imposed by charge neutrality. The resulting equations of state are coupled to the standard TOV equations, with no modification of the gravitational field equations. For a baseline composed of carbon and oxygen with $\mu_e=2$, the undeformed limit recovers a maximum mass in the Chandrasekhar scale, validating the normalization of the calculation before the modified cases are considered. The first model of modified dispersion produces only a modest stiffening over the parameter range studied, whereas the logarithmic model depends strongly on the sign of the deformation parameter: negative values increase the pressure and the maximum mass, while positive values soften the sequence. These results show that WDs can isolate the impact of modified electron kinematics on compact
star structure, but the logarithmic branch in particular requires further restrictions from its physical domain, stability conditions, and observational constraints on mass and radius.
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Geophysics · Cross-listed from cs.CV
Fast Fourier Convolutional GAN for 30 m Clear-Sky Land Surface Temperature Gap-Free Reconstruction
Marwa Alfouly, Smajil Halilovic, Nils Bochow et al. (+3 more)
Satellite-derived Land Surface Temperature (LST) provides spatially comprehensive data that ground stations cannot match. However, its utility is frequently limited by severe data gaps due to the presence of clouds. As LST is essential for understanding land-atmosphere interactions, numerous methods have been proposed to address this challenge. Yet, the development of a scalable and adaptable pipeline for generating gap-free LST
datasets and reconstructing cloud-contaminated pixels remains challenging. Moreover, the reconstruction of extensive missing regions in fine-spatial-resolution observations is particularly difficult. To address this challenge, we propose a
Multimodal Fast
Fourier Convolutional GAN for reconstructing cloud-contaminated pixels in fine-resolution (30 m) Landsat imagery to generate gap-free clear-sky LST products. The method leverages Fast
Fourier Convolution to enable a global receptive field across the image, and is guided by a stack of data consisting of satellite observations and Synthetic Aperture Radar (SAR) data. Across all LST
quantiles, the interquartile range of scene-averaged RMSE (computed over reconstructed pixels) is consistently between 0.8 K and 1.8 K. The proposed approach enables the recovery of extensive missing regions, including scenes with more than 70% cloud-induced gaps, while relying on auxiliary data that are readily available at a near-global scale.
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All authors Marwa Alfouly, Smajil Halilovic, Nils Bochow, Thomas Hamacher, Niklas Boers, Konrad Schindler
Mesoscale and Nanoscale Physics
Fourth-order closure obstruction and chiral nonlocality in circular kinetic magnetotransport
P. Shubham Parashar
Closing an angular moment hierarchy at the stress level omits a definite back-action from higher
Fermi-surface harmonics. For a circular two-dimensional
Fermi surface, streaming changes angular momentum by one, so the shortest omitted sequence, $1\!\to\!2\!\to\!3\!\to\!2\!\to\!1$, adds a fourth-order term to the current
eigenvalue, $\Lambda(q)=\gamma_1+\nu q^2-\kappa_4q^4+\cdots$, with $\nu=v_F^2/(4\gamma_2)$ and $\kappa_4=\nu^2/\gamma_3$. We call this missing operator term the fourth-order closure obstruction. Its gradient expansion is controlled when $\nu q^2/\gamma_3\ll1$. Circular
symmetry carries the same coefficient into a radial bi-
Laplacian within each conserved angular-momentum block, and retaining $m=3$ exactly, without a gradient expansion, amplifies higher radial modes monotonically. At zero field, positive collision rates exclude real-wave-number poles and response zeros; an equal-rate tail gives a square-root completion. A
magnetic field makes the coefficient
chiral, produces a
Hall sign reversal, and enhances it when the $m=3$ harmonic is long lived. In the collisionless high-field limit, the complete hierarchy becomes a Bessel pole–zero ladder, while finite closures form rational approximants to it. The result separates a controlled low-gradient coefficient from its geometry- and field-dependent finite-wave-number completion.
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Materials Science
Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives
Xianyuan Liu, Charles Anjah, Benjamin E. Jolly et al. (+9 more)
Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel materials discovery. However, novelty in materials discovery encompasses chemical plausibility, structural distinctiveness, property relevance and experimental realisability, making AI-driven novelty claims difficult to substantiate. We introduce a materials property hierarchy, from intrinsic, composition-determined properties to extrinsic, processing-dependent performance, to clarify deployment constraints and distinguish structural, physical and deployment novelty. This framework motivates an evidence-based view of
multimodal materials data spanning chemical composition, microstructure, processing, and testing and characterisation, showing that current evidence remains concentrated in composition and idealised structure while heterogeneous, under-represented and weakly integrated modalities limit support for physical and deployment novelty. It also highlights the limitations of
benchmarks based mainly on computational labels and proxy novelty criteria. Community-wide standards for data collection, modality
alignment and evidence synthesis are needed to support
multimodal data construction, process-aware
multimodal modelling, feasibility-first generative modelling and deployment-aware benchmarking, so that generative and
multimodal AI can design experimentally realisable materials with defensible scientific and practical novelty.
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All authors Xianyuan Liu, Charles Anjah, Benjamin E. Jolly, Jonathon F. S. Markanday, Joshua Berry, Haolin Wang, Nicola A. Morley, Robert D. J. Oliver, Alexandra J. Ramadan, Delvin Ce Zhang, Katerina A. Christofidou, Haiping Lu
Data Analysis and Statistics · Cross-listed from q-bio.NC
Computer Vision Based Neurology Brain Activity Rejection Architecture and Implementation
Zag ElSayed, Nathan Suer, Grace Westerkamp et al. (+4 more)
The electroencephalogram (
EEG) is a valuable and widely applied tool for investigating
brain disorders and behavioral changes. It offers a minimally restrictive and non-invasive method. However, challenges in using
EEG for cognitive development studies include temporal resolution, signal source localization, and
EEG artifacts. Careful consideration of these factors is essential for informed application of
EEG technology. Independent component analysis (ICA) effectively isolates source generator processes from signals recorded by multiple, adjacent
EEG scalp electrodes. Although ICA decomposition requires manual inspection, selection, and interpretation of independent components (ICs), this process is time consuming and demands expertise. Automated IC classification can achieve sufficient accuracy, expediting large scale
EEG research and enabling near real time applications in conjunction with
brain activity rejection tasks, which are crucial for medical specialists. This study introduces an automated computer
vision based ICA rejection labeling tool compatible with widely used software interfaces like ICLabel and EEGLab. By automating the manual task, the proposed system reduces processing time by 7200 fold and achieves an accuracy of 89.45%.
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All authors Zag ElSayed, Nathan Suer, Grace Westerkamp, Jack Yanchen Liu, Makoto Miyakoshi, Craig Erickson, Ernest Pedapati
Physics and Society
Service Ecosystem Evolution: A Comprehensive Survey from Complex Network Perspectives
Shuiguang Deng, Xingliang Wang, Guochang Li et al. (+5 more)
Digital society increasingly relies on complex service ecosystems, formed by interconnected services from technology
giants. However, the growing scale and intricate dependencies of these ecosystems pose significant challenges to their
evolution, frequently leading to systemic failures during upgrades or restructuring. To address these challenges, current research is shifting from the perspective of single services to the ecosystem. Drawing upon the synthesis and induction of current research, we present a comprehensive survey focused on the study of service ecosystem
evolution, employing a novel three-stage analytical framework. This framework structures the evolutionary lifecycle and provides a systematic way to organize and review existing research, filling the gap caused by the lack of surveys specifically focused on service ecosystem
evolution. Additionally, we pioneer the application of complex network theory to analyze service ecosystem
evolution, providing a novel perspective to capture the inherent connectivity, dynamics, and emergent properties often missed by traditional approaches. Building on our analysis, we point out critical research gaps and propose three specific future directions. We provide a robust theoretical foundation and methodological guidance for understanding and guiding the sustainable
evolution of modern service ecosystems.
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All authors Shuiguang Deng, Xingliang Wang, Guochang Li, Yunkun Wang, Haoran Xu, Chen Zhi, Junxiao Han, Jianwei Yin
History and Philosophy of Physics
Quantum nature of gravity and spacetime: Fundamental insights from the Hoyle-Narlikar theory
Dawood Kothawala
The Hoyle-Narlikar action-at-a-distance formulation for
gravity remains one of the most mathematically rigorous attempts to incorporate Mach principle into physics. A lesser known, but no less significant, is the fact that it is also one of the first in which two point functions play a key role in determining spacetime structure, and hence, are more fundamental than the metric. We argue that these ingredients have direct relevance for modern attempts to understand fundamental, emergent, and informational aspects of
quantum spacetime. The non-local description of
quantum spacetime, with Planck scale $\ell_0$ as a zero-point length, provides a mechanism through which the entire universe can inform local causal structure, `a la Mach, Hoyle, and Narlikar. In this chapter, we highlight certain key aspects of the HN theory and then discuss case studies from modern research that illustrate how the
quantum spacetime might realize a version of the
quantum gravitational Mach principle.
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Cosmology and Nongalactic Astrophysics
A Multimodal Approach to Star–Galaxy Separation using SPHEREx Spectrophotometry and DESI Legacy Survey Imaging
Kendrick Nguyen, Richard M. Feder, Sean Bruton et al. (+1 more)
Stellar contamination is a critical systematic for increasingly precise large-scale structure analyses from ongoing and next-generation surveys. Experiments targeting constraints on local
primordial non-Gaussianity with $\sigma(f_{\rm NL}^{\rm loc}) \sim \mathcal{O}(1)$ demand sub-percent
stellar contamination rates to avoid misidentifying spurious large-scale power induced by
Galactic structure as true cosmological signal. In this work, we explore the use of
multimodal models for
star–
galaxy separation, harnessing the information from both optical broad-band imaging data and SPHEREx near-infrared low-resolution spectrophotometry. The two modalities are integrated using contrastive learning, which projects image- and spectrum-based
embeddings into a shared latent space. We find that classifiers trained on these transformed representations outperform those trained on the original
embeddings and show less performance degradation when simpler classifiers are used. These results suggest that
multimodal alignment organizes the
embedding space along dimensions that are better suited to source classification. The improvement is particularly strong for image-based classification, which we connect to increased predictability of highly-discriminative infrared spectral features from the transformed image
embeddings. Applying
redshift error-based selections and extrapolating to the full SPHEREx footprint, we demonstrate that
stellar contamination can be controlled at the sub-percent level across most of the extragalactic sky, with completeness tradeoffs largely confined to low
redshift. Our work highlights the utility of
multimodal methods for modern
galaxy surveys such as SPHEREx and $\textit{Rubin}$ LSST.
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All authors Kendrick Nguyen, Richard M. Feder, Sean Bruton, Uroš Seljak
Quantum Physics
Component-Level Inverse Design of Transmon Qubits Using Neural Networks
Olivia Seidel, Firas Abouzahr, Abhishek Chakraborty et al. (+11 more)
Designing a superconducting
qubit to realize specific
Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses a layout geometry, simulates it, extracts
circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target
Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate model and evaluate the loss in
Hamiltonian space rather than in layout-parameter space. In validation against a conventional EM solver, 97% of generated designs produce usable geometries, and the inverse-plus-surrogate pipeline reaches mean percent errors of 0.73% for
qubit frequency and 1.58% for anharmonicity, comparable to or below the fabrication and simulation-to-measurement uncertainty expected for academic-process transmon devices of this type. A single pipeline query takes ~60 ms on CPU, versus ~2 min for a conventional EM capacitance extraction on the same hardware, a speedup of approximately 2,000x. Batching minimizes the AI model inference overhead, reducing the runtime to 3.1 microseconds per sample on CPU and 2.6 microseconds per sample on GPU at a batch size of 2048, resulting in speedups of 3.9 x 10^7 and 4.6 x 10^7, respectively, relative to a single conventional CPU EM extraction. Our results indicate that component-level inverse design usefully extends and complements conventional EM simulation, including for small
datasets on the order of 1,000 samples.
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All authors Olivia Seidel, Firas Abouzahr, Abhishek Chakraborty, Sadman Ahmed Shanto, Saikat Das, Daniel Baxter, Jonathan Asaadi, Nicola Pancotti, Haoyu Yang, Brucek Khailany, Sara Sussman, Enectali Figueroa-Feliciano, Eli M Levenson-Falk, Taylor L. Patti
Superconductivity
Geometric Superconducting Diode Effect in an NbN Nanoring
Tianyu Li, Peiyuan Huang, Jiong Li et al. (+14 more)
Superconducting diodes, which exhibit nonreciprocal critical currents, are promising building blocks for low-power cryogenic electronics and superconducting
circuits. Existing superconducting diode platforms commonly rely on Josephson junctions, multilayer
heterostructures,
ferromagnetic elements, gate-difined structures. Here, we demonstrate a geometrically induced superconducting diode effect realized in a structurally minimal, single-materials NbN nanoring, where inversion-
symmetry breaking is introduced solely by the asymmetric geometry. The device exhibits pronounced and polarity-switchable critical-current nonreciprocity. Systematic
magnetic-field and temperature-dependent measurements reveal that, at low fields, the applied
magnetic field redistributes the critical current asymmetrically between opposite bias directions without significantly reducing the overall superconducting current-carrying capability. Moreover, the maximal nonreciprocity and diode efficiency exhibit distinct temperature dependence: the maximal diode efficiency follows the
evolution of the energy gap, whereas the maximal nonreciprocity is more closely associated with the superfluid density. These results establish asymmetric superconducting nanorings as a minimal
geometric platform for studying nonreciprocal superconducting transport and provide a simple design principle for future superconducting electronics.
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All authors Tianyu Li, Peiyuan Huang, Jiong Li, Jiyao Shang, Nuo-Zhou Yang, Wuyue Xu, Wen-Cheng Yue, Yang-Yang Lyu, Chong Li, Yihuang Xiong, Xuecou Tu, Tao Tao, Xiaoqing Jia, Qing-Hu Chen, Huabing Wang, Peiheng Wu, Yong-Lei Wang
High Energy Physics (Phenomenology)
Numerical Backreaction and Finite-Time Energy Transfer in Post-Recombination Magnetogenesis from Ultralight Dark Matter
Aviv David and Robert Brandenberger
We study gauge-field production in a post-recombination magnetogenesis scenario driven by an ultralight pseudoscalar dark matter field $\phi$ coupled to electromagnetism. Previous analytical work has shown that a homogeneous oscillating $\phi$ background can excite gauge field modes through tachyonic and narrow-band resonance channels. Here we follow the coupled
evolution of the homogeneous $\phi$ mode and gauge field modes of both helicities in an expanding background. Our numerical work confirms the results for the spectra of produced gauge particles obtained in the previous approximate analytical treatments. In addition, our study allows us to determine when back-reaction shuts off the resonance. We find that for parameter values for which the tachyonic instability band is open, back-reaction does not shut off the resonance until a fraction ${\cal{F}}$ of order one of the initial dark matter energy density has been transferred to
photons, and this happens on a time scale which is short compared to the Hubble time. In the parameter range in which only the narrow resonance band is open, ${\cal{F}}$ eventually reaches order one, but the time when this happens increases as the effective coupling constant decreases, and therefore ${\cal{F}}$ may remain negligible until the present time.
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Materials Science
First-principles calculation of electron-phonon spectral functions for defects using phonon interpolation
Zoltan Santha and Gergo Thiering
Point defects in wide-band-gap
semiconductors exhibit optical spectra strongly shaped by electron-
phonon coupling, but direct first-principles calculation of the corresponding
phonon sidebands is often limited by the coarse vibrational spectrum of the largest defect supercells accessible by ab-initio electronic structure codes. In this work, we present a
phonon-interpolation method for Huang-Rhys spectral densities and optical lineshape functions on hypercells created by extending the defect-containing supercells on arbitrarily dense
phonon $q$-point grids. The method is based on reconstructing the transition-induced force associated with the optical excitation and using this localized force source to couple the defect transition to a densely sampled vibrational continuum. In this formulation, the local defect physics is obtained from ab initio supercell calculations, while the long-wavelength acoustic modes and the detailed structure of the host
phonon spectrum are recovered by diagonalizing interpolated dynamical
matrices in large hypercells. We demonstrate the method on the negatively charged nitrogen-vacancy centre in diamond between its ground $^{3}A_{2}$ and excited $^{3}E$ states. The transition-force is shown to be strongly localized around the defect, with converged localization measures obtained in a $4\times4\times4$ supercell accessible by density functional theory calculations. We interpolate the electron-
phonon coupling on hypercells up to $32\times32\times32$ corresponding to approximately 17 million atoms, thereby recovering smooth, continuous Huang-Rhys spectral densities with ultrafine spectral resolution. The dominant coupling band is found near 63~meV; the low-energy acoustic contribution follows the expected linear scaling; and we recover the finer van-Hove-related structures in the optical
phonon regime observed in experiments.
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Optics
Emergent Non-Equilibrium Emission Profiles via Luminescent Angle Restriction for Photovoltaic Energy Conversion
Jonathan McTague, Rivi Ratnaweera and Matthew Sheldon
The detailed-balance method determines photovoltaic conversion efficiencies by relating non-
equilibrium converter operation to an
equilibrium reference state. Conventionally, the angular and spectral radiative channels are assumed to be defined by the
equilibrium absorptivity and emissivity, with the
photon chemical potential setting their occupation. The spatial distribution of emission must also be considered when radiative energy is redistributed within a complex nanophotonic converter architecture. Here we analyze this redistribution with a proposed luminescent angle restrictor, in which a flat-plate photovoltaic absorber is coupled to an overlayer with vertically aligned nanorods. The nanorod layer is nearly transparent to near-normal sunlight, while oblique photovoltaic luminescence can be absorbed, re-emitted, and partially returned to the absorber. To calculate the operating emission profile, we develop a Markov-chain transport model with an absorbing-chain formulation that enforces microscopic reversibility for local optical transitions while determining the spatially distributed steady-state emission rates. The model recovers the flat-plate detailed-balance limit and predicts voltage-dependent external emission profiles from the coupled architecture. Under idealized conditions, this non-
equilibrium angular redistribution increases conversion efficiency relative to the conventional flat-plate reference. We further evaluate nanorod photoluminescence
quantum yield, angular restriction, orientational disorder, and Auger recombination to identify practical constraints on the proposed architecture. These results show how detailed-balance limits for photovoltaic structures can depend on internal radiative energy redistribution and the resulting non-
equilibrium emission profile, beyond what can be inferred from the macroscopic
equilibrium optical response alone.
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Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Toward Mechanistic Interpretability of an AI Foundation Model Fine-Tuned for Atmospheric Chemistry
Jason Y. Hu, Ivan Higuera-Mendieta, Patrick Obin Sturm et al. (+1 more)
Weather
forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than conventional chemical transport models. These FMs are typically trained on reanalysis data and generate forecasts through autoregressive rollout. They do not explicitly represent governing physical or chemical processes. Therefore, high forecast skill does not reveal whether a model has learned physical mechanisms or exploits statistical regularities in its training data. Here, we present the first study of what a FM fine-tuned for atmospheric chemistry has learned by examining Microsoft's Aurora model. We impose controlled chemical perturbations on its forecasts and test them against known photochemical relationships. We then examine the internal representations that generate these forecasts. We find that Aurora captures a first-order ozone response to reactive nitrogen but does not enforce the chemical constraints that a process-based model encodes. It generates chemically inconsistent combinations of related species and relaxes localized emission features such as wildfire plumes toward background. Internally, its representations remain largely organized around the meteorology inherited during pretraining, with little structure specific to chemistry. Using sparse autoencoders, we identify internal components that causally control the chemical forecast but do not map cleanly onto individual atmospheric processes. This work provides a framework for testing whether AI
forecasting systems learn atmospheric chemistry from reanalysis data. As these models are increasingly positioned to inform environmental policy decisions, we argue that composition forecasts should also be judged by their internal mechanisms rather than by
benchmark skill alone.
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All authors Jason Y. Hu, Ivan Higuera-Mendieta, Patrick Obin Sturm, Makoto M. Kelp
High Energy Physics (Theory)
Flux Vacua Near the Boundary of Large Complex Structure
Aman Chauhan, Michele Cicoli, Anshuman Maharana et al. (+1 more)
Three-form fluxes generate a potential for the complex structure
moduli in type IIB string compactifications on Calabi-Yau threefolds. In the large complex structure patch, the potential consists of a perturbative contribution and a convergent series of instanton corrections. We present examples of
vacua near the boundary of large complex structure, where the minimum of the potential is essentially determined by the perturbative contribution and the first instanton correction, while the effect of all higher instantons is negligible. This phenomenon occurs when fluxes are such that the magnitude of the perturbative potential in certain directions in
moduli space is of the same size as the first instanton contribution. Analysing an ensemble of flux
vacua, we find that this phenomenon is statistically quite common. We also discover more subtle phenomena where instanton terms affect the multiplicity of the solutions and induce monodromy shifts.
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All authors Aman Chauhan, Michele Cicoli, Anshuman Maharana, Pellegrino Piantadosi
Instrumentation and Methods for Astrophysics
The High-Energy Light Isotope eXperiment (HELIX) Instrument
P. S. Allison, M. Baiocchi, J. J. Beatty et al. (+27 more)
The HELIX detector is a balloon-borne instrument designed to measure the flux of light (Z < 11)
cosmic-ray isotopes. In this paper we describe the initial configuration of HELIX, optimized for measurements in the energy range from approximately 0.2 GeV per nucleon to beyond 3 GeV per nucleon. In addition to a spectrometer, which is based on a one-tesla superconducting magnet and a drift-chamber tracker, HELIX employs scintillator-based time-of-flight counters and a ring-imaging Cherenkov detector, allowing event-by-event reconstruction of primary particle mass, charge, and
magnetic rigidity.
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All authors P. S. Allison, M. Baiocchi, J. J. Beatty, L. Beaufore, D. H. Calderon, Y. Chen, S. Coutu, E. Ellingwood, D. Fuehne, N. Green, D. Hanna, H. B. Jeon, B. Kunkler, M. Lang, R. Mbarek, K. McBride, C. E. McGrath, I. Mognet, J. Musser, S. Nutter, S. O'Brien, N. Park, T. Rosin, K. Sakai, M. Tabata, G. Tarle, G. Visser, S. P. Wakely, I. G. Wisher, M. Yu
Optics
Digitally Programmable Photochromic Hydrogel Contact Lenses as Light-Adaptive Artificial Irises
Asad Nauman, Tristen Moncada, Guli Gulinihali et al. (+3 more)
Excessive exposure to ultraviolet (UV) radiation is associated with a range of ocular pathologies, motivating the development of soft optical devices that can dynamically regulate incident light. In the human eye, this adaptive optical functionality is performed by the iris, which modulates pupil size to control retinal irradiance in response to ambient illumination. Here we present a photochromic contact lens based artificial iris that mimics this biological light-adaptation mechanism through reversible, spatially programmable modulation of optical transmission with intrinsic UV blocking. Photochromic dyes are embedded within a biocompatible hydrogel
matrix, while the cross-linked network is patterned using a digital micromirror device (DMD)based grayscale UV lithography to encode controlled radial gradients in dye switching. This approach generates iris-like attenuation profiles that emulate pupil-dependent light regulation while enabling customizable iris geometries and transmission patterns. The resulting lenses exhibit rapid and reversible UV-induced darkening with position-dependent kinetics, enabling continuous modulation of transmitted light. The photoresponse remains stable over repeated activation cycles without measurable fatigue. The patterned lenses maintain mechanical stability, controlled swelling, and wettability suitable for contact lens applications. This platform combines programmable photochromism and hydrogel optics to enable light-adaptive lenses that mimic key functions of the human iris.
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All authors Asad Nauman, Tristen Moncada, Guli Gulinihali, Muhammad Waleed Khalid, Yeshaiahu Fainman, Abdoulaye Ndao
Chemical Physics
Importance-Sampling Estimation of Gaussian Molecular Shape Overlap: Exact Union Volumes and Confidence-Bounded Virtual Screening
Egor I. Tuzharov, Alexandra V. Bochenkova and Yury Maximov
Gaussian descriptions of molecular shape underpin 3D shape-based virtual screening, but existing methods evaluate Gaussian overlap analytically. The widely used first-order approximation is fast but systematically overestimates overlap, whereas the exact molecular volume requires a combinatorial inclusion-exclusion expansion. We introduce the first
stochastic estimator of Gaussian shape overlap: an unbiased Monte Carlo method that importance-samples directly from a molecule's Gaussian mixture. The
estimator reproduces analytic overlap without bias and extends to the exact union volume of all inclusion-exclusion orders with O(N) cost per sample. On drug-like molecules, the union
estimator matches high-resolution grid quadrature with a mean relative error of 0.07 percent, while the first-order approximation overestimates the true union volume by 3.4x on average. The
estimator provides analytic standard errors, enabling confidence-bounded screening that reduces sampling by 94 percent while preserving ranking. Implemented in JAX, it is fully differentiable and supports gradient-based rigid
alignment on CPU, GPU, and TPU. On the DUD-E and LIT-PCBA
benchmarks, the method achieves shape-only enrichment comparable to existing single-conformer approaches while additionally providing unbiased absolute volumes and uncertainty estimates.
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High Energy Physics (Phenomenology)
Spin Structure of the Nucleon: Overview
Yoshitaka Hatta
I present a pedagogical review of the decomposition of the
proton spin. Both the Jaffe-Manohar and Ji decompositions are discussed. Particular emphasis is placed on the
quark and
gluon orbital angular momenta, including their gauge invariant definitions, small-$x$ behavior and connection to experimental observables.
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Quantum Physics
Suppressing errors in analog logical rotation gates via balanced fusion
Sam McArdle, Alexander M. Dalzell and Fernando G.S.L. Brandão
There have been a number of recent proposals to use analog logical rotations in early fault-tolerant
quantum algorithms. Existing proposals implement a logical rotation by angle $\phi$, with error $O(p\phi)$, where $p$ is the physical error rate. While this is not fault-tolerant, if $\phi$ is sufficiently small the logical error rate can be suppressed. In this work, we introduce and analyze the `balanced fusion' technique for improving the error scaling of analog logical rotations to $O(p \phi^{1.5})$, without having to resort to fallback synthesis. Balanced fusion is enabled by an improved analysis of the accumulation of coherent error terms in analog logical rotations. Our techniques improve the viability of analog logical rotations for small rotation angles as an alternative to cultivation-powered rotation synthesis.
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Solar and Stellar Astrophysics
The Effects of Magnetic Accretion on the Spatial Extent of White Dwarf Pollution
Dang Pham, Aster G. Taylor and Tim Cunningham
Many white
dwarfs are polluted by metals, which are generally understood to be the accreted remnants of a planetary system. Modeling these systems typically assumes that the metal concentration is homogeneous throughout the white
dwarf's atmosphere. However, the
magnetic fields of a white
dwarf may affect the
accretion geometry of the white
dwarf via magnetospheric
accretion. Convection in the white
dwarf's photosphere will then transport the metals across the surface, with a structure set by the relative sinking versus spreading timescales. In this work, we construct models for the
accretion geometry, subsequent spreading, and observed pollution of
magnetic white
dwarfs. We show that the
magnetic fields will initially concentrate the pollution into a narrow region of the white
dwarf's surface. The relative spreading and sinking timescales determine whether the metals become uniformly distributed or remain confined to localized patches. If the
magnetic field and
spin poles are misaligned, then patchy white
dwarfs exhibit periodically variable pollution signatures, which enable constraints on the patch area. We explore this model as a possible explanation for the recent detection of periodically variable pollution signatures in
magnetic white
dwarfs. Finally, we also demonstrate that the concentration of material due to the
magnetic field may lead to systematic underestimates of the mass
accretion rate onto these objects.
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General Relativity and Quantum Cosmology
Thermodynamics of boosted Schwarzschild black holes
Glenn Barnich
The
equilibrium thermodynamics of boosted Schwarzschild black holes is worked out in a concise way and shown to agree with expectations from relativistic thermodynamics.
General Relativity and Quantum Cosmology · Cross-listed from math.DG
The Penrose conjecture for initial data sets satisfying a $2$-convexity condition
Conghan Dong
Let $(M^3, g, \mathbf{k})$ be a smooth, connected, asymptotically flat initial data set with connected outermost past apparent horizon $\Sigma $. We prove the Penrose
conjecture, namely that $m_{\mathrm{ADM}}(g) \geq \sqrt{\frac{|\Sigma |}{16 \pi }} $, under the assumptions of the dominant energy condition and the $2$-convexity condition that the sum of the two smallest
eigenvalues of $\mathbf{k}$ is nonnegative. The main tool is the $\sigma $-inverse mean
curvature flow, together with a monotonicity formula developed in \cite{Dong26SigmaIMCF}.
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Mathematical Physics
High-resolution decomposition of canonical Hamiltonian systems
Kaja Krhac and Frederic P. Schuller
We identify the topological and dynamical conditions for a
Hamiltonian system to possess port-
Hamiltonian reformulations with increasing resolution of structural detail that remains hidden in the
Hamiltonian theory. We find that the principally attainable resolution grows exponentially with the number of factor
manifolds into which the configuration space of a canonical
Hamiltonian system decomposes and is tempered only by degeneracies of the original
Hamiltonian.
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Physics Education
Interactive and accessible quantum key distribution modules for introducing students to quantum science and engineering
John M. Donohue, Silas Ifeanyi, Andrew Chisholm et al. (+11 more)
Teaching key concepts in
quantum information science and technology benefits from hands-on activities that can engage students at all levels, especially high-school and undergraduate students. In this paper, we report on an educational kit for student exploration of
quantum key distribution (QKD) via optical analogies that provides an affordable and highly interactive role-playing experience for students with minimal abstraction and direct connections to applications, key
quantum concepts like superposition and measurement uncertainty, and student's pre-existing knowledge like light polarization. Manual and automated versions of the kit are detailed, as well as feedback from workshops with high-school students, undergraduates, and educators.
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All authors John M. Donohue, Silas Ifeanyi, Andrew Chisholm, Julien Côté, Quazell Cunningham, Jamiel Nasser, Aaron Xayvongsa, Evangeline Dryburgh, Fiona Dang, Gabriel Ghrayeb, Ian Jinzo Macpherson, Fareed Rasheed, Andi Zhao, Simarjeet S. Saini
Solar and Stellar Astrophysics
An extensive grid of DARWIN models for M-type AGB stars II. Effects of pulsation periods on wind properties
Emelie Siderud, Kjell Eriksson, Susanne Höfner et al. (+1 more)
Mass loss from asymptotic
giant branch (AGB)
stars is the result of a complex interplay between pulsation, atmospheric dynamics,
dust formation, and radiative acceleration. Pulsation periods are a key input in dynamical atmosphere and wind models, and different prescriptions for assigning periods based on
stellar parameters may lead to systematic differences in the predicted wind properties. To better constrain this critical parameter, we investigated how the choice of pulsation period affects the wind properties of dynamical atmosphere and wind models of M-type AGB
stars by comparing models based on an empirical period-luminosity (P-L) relation with corresponding ones that adopt a period-mean density relation derived from 3D pulsation models. We analysed two grids of DARWIN models that cover a range of current
stellar masses, luminosities, and effective temperatures. For each grid, pulsation periods were assigned using either the P-L relation or the period-mean density relation, allowing for a direct comparison of the resulting dynamical structures and wind properties for pairs of models differing by period only. Independent of the adopted period prescription, the time-averaged wind properties correlate strongly with $L_\star/M_\star$. The pulsation period affects the atmospheric dynamics through changes in the relative timing of shock propagation and
dust formation, which affect both wind formation and the resulting wind properties. Shorter periods favour the onset of a wind, and models differing only in pulsation period can exhibit significantly different wind properties. The period-mean density relation provides a physically motivated alternative to the empirical P-L relation by accounting for
stellar parameters beyond luminosity, and enables a more direct comparison between DARWIN models and observed Mira variables.
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All authors Emelie Siderud, Kjell Eriksson, Susanne Höfner, Arief Ahmad
Materials Science
Analytical Forces from the Bethe-Salpeter Equation for Large-Scale Excited-State Relaxation
Yu Jin, Victor Wen-zhe Yu, Marco Govoni et al. (+1 more)
We present an efficient plane-wave implementation of analytical
nuclear forces for electronic excited states described by the Bethe-Salpeter equation (BSE). The formulation combines density-
matrix perturbation theory with a Lagrangian approach, and avoids both explicit empty-state summations and the response calculations for each atomic displacement, required by conventional approaches based on density functional perturbation theory. Together with GPU acceleration, these advances make BSE forces calculations tractable for solid-state systems containing hundreds of atoms. We demonstrate the method on two point defects with distinct dielectric environments: the nitrogen-vacancy center in diamond, where BSE and time-dependent density functional theory (TDDFT) yield consistent excited-state relaxations, and the carbon-dimer defect in two-dimensional hexagonal boron nitride, where the screened electron-hole interaction included in the BSE stabilizes the localized defect excitation and corrects the relaxation pattern predicted by semilocal TDDFT. These results establish a scalable framework for BSE-level studies of excited-state relaxation and vibronic coupling in heterogeneous condensed systems.
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All authors Yu Jin, Victor Wen-zhe Yu, Marco Govoni, Giulia Galli
Strongly Correlated Electrons
Exact Nagaoka-to-spiral transition in the doped infinite-U triangular lattice
Yang Zhang and Cristian D. Batista
We study the single-hole infinite-$U$ Hubbard model on the triangular
lattice with nearest- and next-nearest-neighbor hoppings $t_1$ and $t_2$. A frustrating $t_2$ destabilizes Nagaoka ferromagnetism through a Lifshitz transition to a long-wavelength coplanar spiral at $t_{2,c}/t_1=-0.182$. We determine the critical point analytically by reducing the many-body problem to an effective two-body hole–
magnon scattering problem in which the hard-core hole–
magnon constraint is treated exactly. Numerical calculations confirm the spiral ground state and reveal a coherent
spin polaron with quasiparticle weight $Z\simeq0.92$ at the Lifshitz-shifted momentum $\mathbf{K}=-\mathbf{Q}^*$. These results establish the first
quantum instability of the triangular-
lattice Nagaoka ferromagnet and provide the
magnetic baseline for
magnon-mediated pairing at finite doping.
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Astrophysics of Galaxies
Hierarchical Bayesian inference with compositional score modeling for stellar streams
Giuseppe Viterbo, Jonas Arruda and Tobias Buck
Context:
Stellar streams trace the gravitational potential of the Milky Way over a wide range of Galactocentric radii. Since different streams sample different regions of the
Galaxy, combining several of them can constrain the global mass distribution more tightly than modeling any single stream in isolation. Most of the existing multi-stream analyses rely on likelihood-based methods that require a new inference run whenever additional streams or kinematic measurements become available. Aims: We aim to infer the Milky Way potential from multiple
stellar streams combined with an additional constraint through the
Galactic circular velocity curve within a single hierarchical framework. Methods: We model the problem hierarchically, separating parameters that are common to all streams from parameters that are specific to each progenitor. We train score-based neural posterior
estimators on a library of simulated streams and combine information from different streams through compositional score modeling as a post-training step. Results: Tests on independent simulations show that the inferred posteriors are well calibrated and accurate. Combining several streams reduces the uncertainties on the global potential parameters relative to single-stream analyses. Applied to
Gaia data, the model favours a mildly oblate dark matter
halo with axis ratio $q_{NFW} = 0.77$, scale radius $a_{NFW} = 9.3$ kpc, a disk mass of $4.3 \times 10^{10} M_\odot$, and a local dark matter density $\rho_{NFW,\odot} = 0.01153 M_\odot pc^{-3}$ consistent with recent stream-based studies. Conclusions: This hierarchical framework provides a practical way to combine information from multiple
stellar streams without repeating the full inference procedure for each new
dataset. The method is adaptable to new
datasets, like future
Gaia DR4, or new spectroscopic surveys, with minimal computational cost.
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Cosmology and Nongalactic Astrophysics
Probing the baryonic–dark matter connection in galaxy clusters using X-rays with gated recurrent unit neural networks
Asif Iqbal, Subhabrata Majumdar, Weiguang Cui et al. (+3 more)
Accurate cluster mass measurements are crucial for cosmology, yet conventional hydrostatic
equilibrium (HSE) methods can suffer from systematic biases, particularly in dynamically disturbed systems. We present a gated recurrent unit (GRU) based deep learning framework for predicting three-dimensional mass profiles of
galaxy clusters from spherically averaged intra-cluster medium (ICM) radial profiles. By treating ICM profiles as sequential data, the GRU captures radial dependencies and naturally handles profiles with different radial samplings. We train and validate the model using high-resolution hydrodynamical simulations from The Three Hundred Project, achieving unbiased mass predictions with a typical 1$\sigma$ scatter of $\sim$5% over most of the cluster region, significantly improving upon HSE estimates. The model provides radius-dependent uncertainty estimates and remains robust against variations in data quality and cluster morphology. When trained jointly on independent simulation suites (GIZMO-SIMBA and GADGET-X), it successfully generalises across both simulations. Feature importance analysis shows that enclosed gas mass is the dominant predictor, with pressure and temperature providing additional information on the radial mass distribution. We further apply the GRU model to X-ray observations of the REXCESS and X-COP cluster samples from XMM-Newton and compare the inferred mass profiles with HSE estimates. The GRU and HSE mass estimates show close agreement for the lower-mass REXCESS sample, while HSE masses are systematically lower than the GRU predictions for the higher-mass X-COP sample, an offset consistent with hydrostatic mass bias. This work provides a data-driven framework for cluster mass inference that bridges simulations and observations and can be extended to multi-wavelength
datasets, including Sunyaev-Zel'dovich and optical observations.
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All authors Asif Iqbal, Subhabrata Majumdar, Weiguang Cui, Elena Rasia, Gabriel W. Pratt, Daniel de Andres