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Physics

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Friday 19 June

Physics and Society

Self-replicating seedbox servers using programmable money

Matei Dogariu and Johan Pouwelse

Centralized content distribution makes availability depend on a single operator's survival and willingness to serve. EternalSeedBox replaces the operator and network with inherited economic parameters: each node is a VPS that seeds media over BitTorrent, holds a Bitcoin wallet, and autonomously decides every twelve hours whether to renew its lease, spawn a child, or sweep its funds to a healthier peer before expiring. A single genesis node seeds the fleet, and every node thereafter is provisioned, funded, and retired autonomously. We validate the design against faithful replicas of both the Bitcoin payment network and the SporeStack VPS marketplace by running the unmodified node code. A lump sum of EUR 10,000 grew the fleet to 33 nodes before capital exhausted at day 153. With simulated income, the fleet held 40–80 live nodes across 510 days, recording 268 births and 190 deaths. A heritable caution trait was introduced to diverge across generations: low-caution lineages reproduced faster during high-income phases, while the survival advantage expected of high-caution lineages during income pauses did not appear, leaving selection in favor of low-caution nodes. The fleet tolerates high node turnover because reproduction depends on any node holding a surplus, not any single node surviving. EternalSeedBox shows that a content distribution network can lease, pay for, and replenish its own hardware without a human operator after genesis, provided income exceeds per-node rent.
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Data Analysis and Statistics · Cross-listed from q-bio.NC

A Tensor Network Framework for Interpretable Graph Analysis of Brain Networks

Domenico Pomarico, Giuseppe Magnifico, Alessandro Grecucci et al. (+10 more)

Identifying robust neurobiological signatures of brain disorders requires machine learning approaches that combine predictive performance with interpretable representations of feature interactions. Here we introduce a quantum-inspired framework based on tensor network machine learning that learns distributed representations of gray-matter features encoded in a Matrix Product State representation, a variational ansatz originally developed for quantum many-body systems. The trained model is then used not only as a classifier but to extract quantum connected correlations between features, which encode higher-order feature interactions and which we map onto a weighted graph. This construction allows us to track, within a single representation, both: (i) the global spectral properties of the network (capturing collective learning dynamics), and (ii) node-level centrality measures (providing interpretable signatures of individual brain regions). Using repeated train-test sampling schemes, we analyze two classification tasks on structural MRI data as examples of complex brain disorders: healthy controls versus schizophrenia and versus bipolar disorder. Node-level analysis identifies a stable set of gray-matter features, most prominently Heschl gyrus, insular cortex, and frontal regions, that act as hubs across multiple centrality measures and across resamplings. These centralities display lower variability across resamplings than Shapley values, supporting the interpretive value of the network representation. The bipolar feature set emerges as a subset of the schizophrenia one, consistent with the hierarchically organized neuroanatomical alterations reported in neuroimaging studies and offering a network-based characterization of this hierarchy.
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All authors Domenico Pomarico, Giuseppe Magnifico, Alessandro Grecucci, Loredana Bellantuono, Jesus M. Cortes, Marianna La Rocca, Alfonso Monaco, Marlis Ontivero-Ortega, Alessandro Scarano, Massimo Stella, Roberto Bellotti, Sebastiano Stramaglia, Nicola Amoroso

Classical Physics

Fermi–Born–Infeld electrodynamics: a nonlinear theory with physical gauge

Renato Vieira dos Santos

We construct a nonlinear extension of Fermi's electrodynamics by incorporating a Born–Infeld structure that depends directly on the four-potential $A_\mu$ rather than on the field strength $F_{\mu\nu}$. The resulting theory, which we call Fermi–Born–Infeld (FBI) electrodynamics, eliminates the $U(1)$ gauge redundancy by elevating the Lorenz gauge to a dynamical condition. The Lagrangian is built from the determinant of a metric-like tensor $g_{\mu\nu} = \eta_{\mu\nu} + 2\kappa\, \partial_{(\mu} A_{\nu)}$, ensuring that the canonical energy–momentum tensor and the spin density remain unique and free of gauge ambiguities. We derive the field equations, which reduce to $\partial_\nu(\sqrt{-g}\, g^{\mu\nu}) = 0$, and show that the Lorenz condition $\partial_\mu A^\mu = 0$ emerges dynamically from retarded boundary conditions and the requirement of a positive-energy spectrum. The nonlinearities modify the propagation of longitudinal modes; we argue, via a Vainshtein-like mechanism, that the nonlinear self-interactions may stabilize the longitudinal mode, opening the possibility of a stable massive scalar photon under extreme field conditions. We also compute the spin density from the Noether current and discuss its properties. The FBI theory preserves the physical gauge of Fermi's original formulation while incorporating the regularization features of Born–Infeld electrodynamics, making it a candidate for describing electromagnetic phenomena in strong-field regimes.
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General Physics

Quantum-Conditioned Curvatures in Spacetime Surrounding Kerr-Newmann Black Hole

A. Tawfik, Saleh O. Allehabi, M. Ur Rehman et al. (+1 more)

This research examines the possibility whether the curvatures found in conventional General Relativity (GR) are the only existing ones, using both analytical and numerical techniques. To this end, we introduce a thorough investigation of Riemann curvatures in the spacetime surrounding a Kerr-Newmann black hole, which is distinguished by its specific electric charges and rotational dynamics. We apply a geometric quantization ansatz that centers on the quantization of the metric tensor, from which the complete set of field equations can be derived. The conformal transformation of the standard metric tensor upholds all the principles of GR while also extending its applicability to lower (quantum) scales. We recognize two types of Riemann curvatures. In addition to the positive curvatures present in classical GR formulations, we also find significant negative curvatures at lower (quantum) scales. This may indicate quantum sources of gravitation that classical GR does not seem equipped to explore.
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All authors A. Tawfik, Saleh O. Allehabi, M. Ur Rehman, A. A. Alshehri

General Relativity and Quantum Cosmology

Black bounce solutions in a realistic dark matter halo from M60*

Ednaldo L. B. Junior, José Tarciso S. S. Junior, Francisco S. N. Lobo et al. (+5 more)

We formulate a Simpson-Visser black bounce solution embedded in a dark matter halo. The latter is modeled using an empirical density profile calibrated from observations of the elliptical galaxy NGC 4649 (M60), based on imaging from the Hubble Space Telescope, stellar velocity dispersion data, and the dynamics of globular clusters. The resulting spacetime metric, in addition to retaining dependence on the mass parameter $m$, the asymptotic circular velocity $V_c$, and the halo scale radius $a$, also depends on the regularization parameter $q_H$. It reduces to the canonical black bounce solution without a halo in the limit $V_c\to0$ (or $a\to\infty$), and to the Schwarzschild solution with a dark matter halo when $q_H\to0$. We analyze the response of fundamental geometrical and physical quantities in the presence of a halo, such as the event horizon radius, the shadow size, and some curvature invariants. In particular, we show the observational range of the shadow radius, from the imaging of Sagittarius A*, constrains the parameter space of the solution to regular black hole configurations, excluding wormhole scenarios. We study the dynamics of massless particles here through the effective potential and examine thermodynamic properties, highlighting the impact on thermodynamic potentials in terms of entropy. Finally, we extend the analysis to scenarios with electromagnetic fields non-minimally coupled to a phantom scalar field, considering configurations with either purely magnetic or purely electric charge. Our results suggest that the dark matter halo influences both the internal geometry and the observational properties of black bounces, imposing constraints on the solution's parameter space from astrophysical data. This highlights the need to include astrophysical environments in modeling regular black holes and wormholes, offering new tests of gravity in the strong-field regime.
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All authors Ednaldo L. B. Junior, José Tarciso S. S. Junior, Francisco S. N. Lobo, Jorde A. A. Ramos, Manuel E. Rodrigues, Diego Rubiera-Garcia, Luís F. Dias da Silva, Henrique A. Vieira

Materials Science

Real-Space Mapping of Electronic Conductivity in Complex Materials

C. Ugwumadu, D. A. Drabold and R. M. Tutchton

We introduce KuboMap, a real-space representation of electronic conductivity derived from the Kubo-Greenwood formula. KuboMap defines a nonnegative conductivity density whose spatial integral recovers the total conductivity and whose form is guided by Mott's picture of transport through spatially overlapping electronic states. This construction provides a direct map of the transport-active regions of a material. Applied to aluminum, KuboMap recovers an extended metallic conduction network. Applied to amorphous silicon, it distinguishes an insulating defect-free network from a defective structure in which localized near-Fermi states form connected hopping-like pathways. In silicon-oxides, it captures the loss of conduction as increasing oxygen content disrupts Silicon-rich transport networks. KuboMap provides a physically transparent route from Kubo–Greenwood conductivity to real-space transport pathways in complex materials.
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Quantum Physics

Variants of the Quantum Phase Operator for the Harmonic Oscillator

Bogdan D. Djordjevic and Nikolay A. Ivanov

We introduce and study quantum phase operators associated with the Quantum Harmonic Oscillator (QHO). We show that these operators are trace-class perturbations of the Susskind-Glogower operators and examine their mathematical and physical properties. The construction is motivated by the physically relevant two-phase case.
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Materials Science

Interpretable Material Spatial Intelligence for Discovery of Governing Microstructural Features

Mathieu Calvat, Gregory Sparks, Dhruv Anjaria et al. (+9 more)

Many material systems exhibit complex spatial and temporal interactions across multiple length scales and modalities that govern macroscopic behavior. Although Machine Learning (ML) is widely used in materials science to predict this behavior, most approaches still rely on handcrafted descriptors or aggregated representations that overlook spatial organization, limiting insight into governing mechanisms. We introduce Materials Spatial Intelligence (MSI), a framework inspired by spatial intelligence that learns directly from multimodal spatial observations of material systems. MSI encodes high-resolution microstructural and deformation data into shared latent representations that preserve spatial relationships while supporting property prediction, interpretation, and optimization. By combining multimodal representation learning, MSI identifies the key features governing mechanical behavior and property trade-offs in structural alloys. Beyond prediction, MSI enables feature-driven microstructure optimization and mechanism discovery. More broadly, MSI establishes a foundation for applying spatial intelligence to materials science, leveraging interpretable ML systems to accelerate scientific discovery and materiel design
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All authors Mathieu Calvat, Gregory Sparks, Dhruv Anjaria, Chris Bean, Haoren Wang, Paul Gradl, Timothy M. Smith, Allison M. Beese, Gabriel Demeneghi, Kenneth Vecchio, Morad Behandish, J.C. Stinville

General Relativity and Quantum Cosmology

Interplay of Holographic and New Agegraphic Dark Energy in Cosmology: A hybrid dark energy model from a generalized length-time cut-off

Sayani Maity, Aritra Sanyal and Prabir Rudra

The spatial and temporal infrared cutoffs are the source of the Holographic dark energy (HDE) and New Agegraphic dark energy (NADE) models, respectively. Inspired by the spacetime unification of space and time in General Relativity, we propose that a combined spacetime cutoff should govern the dark energy density, with HDE and NADE appearing as limiting cases when the spatial or temporal contribution predominates. In this connection, we explore a hybrid model of holographic and new Agegraphic dark energy, where the energy density of the resulting model is a combination of the two models. We consider an interacting and a non-interacting scenario between the hybrid dark energy model and cold dark matter. Cosmological implications of the model is studied via different cosmological parameters like the equation of state parameter, deceleration parameter, statefinder parameter, and Om-diagnostic. A stability check for the model has been performed using the squared speed of sound. Finally the parameter space of the model is constrained using observational data like Hubble data, BAO data and DESI data. We have also checked the Hubble tension for our hybrid model and found it to be substantially low in comparison to other models. From our analysis we see that the constructed hybrid dark energy model can describe the evolution of the universe successfully.
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Quantum Physics

Pauli-Z Moment Matrices in the Variational Quantum Power Method: Goemans-Williamson Rounding, Qubit Locking, and a Correlation-Based Circuit-Cutting Heuristic

Ammar Daskin

The matrix of single- and two-qubit Pauli-Z expectation values of any quantum state is the moment matrix of its computational-basis outcome distribution; it therefore lies in the cut polytope and, in particular, is a feasible point of the degree-2 Sum-of-Squares (Goemans-Williamson, GW) relaxation. Applying GW random-hyperplane rounding to this matrix yields cuts of expected weight at least alpha_GW times the state's energy. This observation and the resulting bound are not new: the bound is the GW inequality applied to the circuit's covariance matrix, and it was stated and used in exactly this form by Martinez, Fawzi and Stilck Franca, who interpret it as showing that a classical surrogate nearly reproduces the circuit and add noisy-circuit guarantees and hardware experiments; relax-and-round of QAOA correlation matrices, with spectral rather than hyperplane rounding, was introduced earlier by Dupont and Sundar. The bound is also not stronger than what direct computational-basis sampling of the state already gives in expectation. In this note we revisit the bound as a post-processing of the two-point data produced by variational algorithms such as QAOA and the Variational Quantum Power Method (VQPM), and give an energy-leakage bound for VQPM qubit locking. We further discuss a heuristic that uses the connected Z-correlation matrix to propose candidate qubit bipartitions for circuit cutting, in the spirit of existing correlation- and mutual-information-based partitioning methods, together with product-state error bounds in terms of quantum mutual information. Pairwise Z-correlations neither certify product structure of the state nor factorization of the underlying unitary, and the error bounds require quantities not available from Z-data alone.
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Quantum Physics

Ultra-Low-Rate Information Reconciliation: Repetition Coding or Dedicated Codes?

Erdem Eray Cil and Laurent Schmalen

We compare repetition-based ultra-low-rate information reconciliation with dedicated ultra-low-rate codes for CV-QKD. Repetition coding offers a favorable performance-complexity trade-off, incurring only a moderate error-rate penalty while reducing decoding complexity by $2\times$, making it attractive for implementation-constrained systems.
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Materials Science

Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages

Krishna Teja Vepa

Machine-learning screens for battery materials are trained and judged almost entirely against computed reference voltages, and those references carry their own systematic errors. We report a case in which this matters quantitatively: our own screening stack (a graph-network voltage screen, a prior-art triage layer, and a local PBE+U bench) fails pre-registered validation against experiment-anchored literature values. Verdict thresholds, failure modes, and the primary metric were committed before analysis. On an operator-audited set of known Na-ion cathodes (n = 6 after one documented exclusion; verdict unchanged at n = 7), the raw held-out mean absolute error was 0.67 V, the pre-registered conservative metric, the upper 95% confidence bound of the cross-validated bias-corrected error, was 1.09 V, and the residual was strongly voltage-dependent (r = -0.94), so no additive calibration is valid. On the two compounds where prediction, database reference, and experiment could all be compared, the Materials Project PBE+U reference sat about 0.54 V below measurement: the reference, not the model, dominated the error. A prior-art screen found at least 70% of the targeted Na substitution space already published. We retire the screen, bound what "verified" means for our DFT ledger, and pre-register a calibration audit of it against four benchmark Li couples.
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General Relativity and Quantum Cosmology

Mirror Symmetry and Double Signature Change

Tevian Dray and Charles Hellaby

The black mirror spacetime proposed by Tzanavaris, Boyle, and Turok [1] connects the two exterior regions of the extended Schwarzschild black hole directly to each other, with no intervening interior region. Using techniques adapted from previous work on signature change, we reexamine the black mirror spacetime as a model of double signature change, and investigate whether there is a surface layer at the horizon, that is, a distributional curvature singularity corresponding to an impulsive gravitational wave. We confirm that the black mirror spacetime does not contain any such singularity, and compare our result with previous claims that the curvature components are analytic. We also discuss the global structure of the black mirror spacetime, and examine what happens to worldlines and curves passing through.
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Statistical Mechanics · Cross-listed from q-fin.CP

Semi-Analytical Pricing for General Default Intensity Models

Ryan Parker, Mark Stedman and Luca Capriotti

Using the path-integral formalism, we develop an accurate and easy-to-compute semi-analytical approximation for a general class of {default intensity} models. We illustrate the accuracy of the method by presenting results for the Black-Karasinski model for which the proposed approximation provides remarkably accurate results, even in regimes of high volatility and multi-year time horizons. The accuracy and the computational efficiency of the proposed approximation makes it a viable alternative to fully numerical schemes for a variety of applications in econometrics and derivatives pricing, including the computation of XVA for credit products. As a practical example, we consider the pricing of a quanto Credit Default Swap (CDS) under stochastic intensity of default and an FX devaluation model.
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Optics

Residual pump diagnostics of lasing-state absorption in multipass pumped Yb:YAG thin-disk lasers

Hanjin Jo, Jiří Mužík, Pawel Sikocinski et al. (+4 more)

Residual pump power was used as a spatially integrated diagnostic of inversion-dependent absorption in 32-pass pumped Yb:YAG thin-disk lasers. A quasi-three-level rate-equation model was coupled to accumulated pump flux, intracavity signal flux, and an effective ASE-induced depletion term. Two disks were tested under non-lasing and multimode lasing conditions. The analysis reproduced residual pump fractions within 2.4% in non-lasing and 1.8% in multimode operation, and output power within 1.3% relative error. Stimulated emission reduced the steady-state inversion and increased the effective pump-band absorption relative to the non-lasing case, while the absorption remained below the room-temperature small-signal value. The same absorption state was then used to estimate volumetric heat-load trends and to assess pump-pass number and output-coupler transmission for high-power thin-disk scaling.
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All authors Hanjin Jo, Jiří Mužík, Pawel Sikocinski, Michal Chyla, Yoann Levy, Martin Smrž, Tomáš Mocek

Adaptation and Self-Organizing Systems

Van Hemmen interactions in a one-dimensional swarmalator model

Kevin O'Keeffe

We study a one-dimensional swarmalator model with van Hemmen pair disorder in the phase coupling. Pair disorder has two effects. First, it splits the static ring-model states into sync, split, splay, and phase-wave branches organized by the rainbow order parameters $r,s$ and four sign-weighted glass order parameters. Second, because the oscillators move, it creates active macrostates absent from the immobile Kuramoto-van Hemmen model: a bursty active async state and a glassy phase wave with rotating glass order. For balanced sign patterns we derive a six-field reduction, the exact finite-$N$ sync boundary, a closed first split branch with its first spatial destabilization, and an exact antiphase phase-wave branch. The iid sign audits preserve the tested state ordering but shift finite-$N$ thresholds through sample imbalance. The remaining challenge is a nonlinear theory of the two active branches.
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Materials Science

The FAST Framework: Developing a Data-Efficient Machine Learning Potential to Decode Superionic Transition-Induced Thermophysical and Kinetic Anomalies in UO2 under Extreme Conditions

Fengnian Zhuang, Gaosheng Yan, Hong Chen et al. (+4 more)

Uranium dioxide ($UO_2$) serves as the predominant nuclear fuel globally. Despite its widespread application, evaluating its mechanical, thermophysical, and species transport behaviors under extreme accident scenarios remains a formidable challenge for conventional experimental and computational methods. To address this, we develop a versatile machine learning interatomic potential (MLIP) for $UO_2$ by proposing an efficient training strategy, termed the "FAST" (Fine-tuning via Active-learning and Superionic-Targeting) framework. Our "FAST" framework integrates superionic transition-targeted sampling with active learning-enhanced exploration to efficiently construct a highly compact dataset comprising only 500 configurations for fine-tuning a foundation model. By rigorously accounting for the strong correlation of uranium 5f electrons and antiferromagnetic (AFM) ground state during DFT labeling, we train a robust DFT-level neuroevolution potential (NEP) for $UO_2$. We demonstrate that this NEP exhibits superior predictive capability for various physical properties, encompassing mechanical, defect, thermophysical, and ionic diffusion over an extensive temperature range. Moreover, this NEP accurately captures the anomalous thermophysical and kinetic behaviors triggered by superionic transition. Specifically, it reproduces both the $\lambda$-peak in linear thermal expansion coefficient (LTEC) and "non-Arrhenius" anionic diffusion. Crucially, NEP-based simulations elucidate the microscopic origins underlying these anomalies: the pre-melting of oxygen sublattice and resultant kinetic decoupling between U and O ions.
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All authors Fengnian Zhuang, Gaosheng Yan, Hong Chen, Yi Zhang, Wenshan Yu, Minglong Xu, Shengping Shen

Quantum Physics

First-Quantized Relativistic Quantum Simulation with Periodic and Dirichlet Boundary Conditions

Jeongho Bang, Timothy P. Spiller, Kyunghyun Baek et al. (+2 more)

In this work, we present a methodology for first-quantized relativistic quantum simulation on one-dimensional finite domains under the two boundary conditions most commonly used in lattice models: periodic boundary conditions (PBC) and Dirichlet boundary conditions (DBC). Starting from the positive-energy relativistic kinetic operator, we construct weakly relativistic lattice Hamiltonians whose leading correction requires the boundary-consistent discretized momentum moments $\langle \hat{P}^{2}\rangle$ and $\langle \hat{P}^{4}\rangle$. These moments are reconstructed in the PBC Hamiltonian from moments of a unitary cyclic translation while the DBC Hamiltonian uses the open-chain finite-difference. In a qubit-register implementation, it can be evaluated as the corresponding cyclic translation estimator plus boundary-local terms that remove the unphysical wrap-around link. The resulting energy-estimation workflow uses translation measurements for the kinetic terms, a small number of endpoints and near-endpoints overlap probabilities for DBC, and position-basis sampling for diagonal potentials. We valdate the framework of the relativistic quantum simulation in various benchmark potentials such as no potential and a cosine potential for PBC as well as an infinite square well and a harmonic potential for DBC, with finite-shot sampling tests. These benchmarks show good agreement between the estimator reconstruction and direct matrix evaluation while separating the finite-grid discretization, weak-relativistic truncation, and measurement errors.
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All authors Jeongho Bang, Timothy P. Spiller, Kyunghyun Baek, Kyoungho Cho, Jaewoo Joo

Mathematical Physics · Cross-listed from math.AP

Geometric Structures of Pseudo-Sonic Curves in Self-Similar Solutions of the Euler Equations for Potential Flow

Gui-Qiang G. Chen, Mikhail Feldman, Xin Gao et al. (+1 more)

We are concerned with the geometric structures of pseudo-sonic curves in two-dimensional self-similar solutions for the Euler equations for potential flow, allowing for non-uniform supersonic states. Mathematically, the governing second-order potential flow equation is of mixed hyperbolic-elliptic type, with degeneracy occurring along the pseudo-sonic curve. In this paper, we develop rigorous analytical approaches to analyze the geometric structures of pseudo-sonic curves in such self-similar solutions. We first show that the pseudo-sonic curve is necessarily a circle if the pseudo-velocity at each point is a normal to the curve. We then analyze the general case in which the pseudo-velocity on the pseudo-sonic point is not a normal to the curve, and study the geometric properties of streamlines in a neighborhood of the pseudo-sonic curve. Next, we establish two theorems that provide sufficient conditions ensuring that the pseudo-velocity at a pseudo-sonic point is normal to the curve, under natural assumptions on the local behavior of the solution. These results yield a precise characterization of the geometry of pseudo-sonic curves. Finally, we apply the developed theory to the shock reflection-diffraction problem with non-uniform incoming flow. We prove that the pseudo-sonic curve must be an arc if the solution is a $C^2$-small perturbation, either in the pseudo-supersonic or pseudo-subsonic region, of a solution with uniform incoming flow. In particular, the density and velocity must be constant, corresponding to the radius and the center of the pseudo-sonic arc, respectively. Moreover, we prove that the solution is $C^{2,\alpha}$-regular in the pseudo-subsonic region up to the sonic arc (except at point $P_1$). The techniques and ideas developed in this paper are expected to be applicable to other nonlinear problems involving similar mixed-type degeneracies.
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All authors Gui-Qiang G. Chen, Mikhail Feldman, Xin Gao, Wei Xiang

High Energy Physics (Experiment) · Cross-listed from cs.LG

What Do Lorentz-Equivariant Jet Taggers Learn?

Jay Agarwal, Siddharth Khare and Dhruv Kumar

We study what Lorentz-equivariant jet taggers learn internally, using equivariance tests, linear probes and grade ablations across five models including L-GATr, L-GATr-slim and LLoCa-T. Linear probes show that equivariant models suppress frame-dependent pseudorapidity to zero while encoding jet mass and N-subjettiness strongly. Grade ablations on L-GATr reveal that bivector channels are negligible for top-quark tagging while vector-like channels are dominant but seed variable, consistent with the network exploiting multiple representational pathways. These results characterize which physical features and algebraic grade structures carry discriminative information in equivariant taggers and may inform future development of such models.
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Geophysics

Bayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network

Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie et al. (+1 more)

Accurate 3D seismic velocity modeling through seismic travel-time tomography using both active- and passive-source data provides critical underpinning models for seismicity monitoring and hazard assessment. Because travel-time tomography is an inherently ill-posed inverse problem, UQ of the estimated models using Bayesian methods is also important for reliable downstream interpretations and analyses. However, Bayesian inference for 3D tomography based on conventional grid-based representations faces the “curse of dimensionality” and severe computational bottlenecks. Consequently, rigorous Bayesian UQ for margin-wide 3D travel-time tomography has remained largely unexplored. In this study, we propose a meshless 3D Bayesian travel-time tomography method that combines PINNs with a neural representation of the velocity structure, enabling tractable and data-efficient Bayesian inference through function-space particle-based variational inference. To efficiently integrate passive-source data into the Bayesian estimation of the velocity structure, we conduct analytical marginalization treating uncertain source parameters as nuisance parameters, with passive-source relocation carried out in post-processing. We validated the capability of our approach for 3D problems through synthetic experiments. Furthermore, we applied the method to a real-world dataset from marine active-source surveys and natural earthquakes off the Kii Peninsula, Nankai Trough. Our probabilistic 3D ensemble successfully resolves key geological features and provides data-consistent uncertainty maps. The posterior mean hypocenters shifted mainly in the vertical direction by 10-15 km, consistent with a previous relocation result. Finally, the neural representation drastically reduces storage requirements for the entire ensemble velocity model, highlighting the scalability and data efficiency of the proposed framework.
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All authors Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie, Dan Bassett

Fluid Dynamics

Physics-Preserving Latent Compression for Zero-Shot Resolution Transfer in 3D Turbulence

Yilong Dai, Yiming Sun, Yiheng Chen et al. (+4 more)

High-resolution turbulence modeling is essential for scientific computing, but remains constrained by the cost of direct numerical simulation and the scarcity of full-resolution data. Existing scientific compressors reduce storage but typically operate on per-frame representations, whereas learned compressors yield compact latents that are often resolution-dependent and weakly aligned with the physics of turbulence. This raises the need for a compression framework that reduces data size, preserves physical diagnostics, and transfers from low-resolution training fields to high-resolution test fields without retraining. In this paper, we propose Physics-Preserving Latent Compression (PPLC), a patch-local latent compressor for three-dimensional turbulence. Motivated by inertial-range scale similarity, PPLC treats fixed-size patches as transferable units and applies a shared variational autoencoder independently of the global grid size. It combines exact mean preservation, zero-mean fluctuation encoding, an invertible Haar wavelet front-end, shift-consistency regularization, and overlap-aware reconstruction. Instantiated on forced isotropic turbulence, PPLC is trained only on stride-downsampled 256^3 fields and transfers zero-shot to 1024^3 fields. Experiments show that PPLC improves the balance between reconstruction accuracy and physical fidelity over classical and learned baselines, keeping diagnostics such as dissipation, enstrophy, energy spectra, and incompressibility closer to the ground truth. Beyond turbulence compression, PPLC offers a general strategy for physics-preserving latent representations that support data-efficient scientific surrogate modeling.
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All authors Yilong Dai, Yiming Sun, Yiheng Chen, Ziyi Wang, Shengyu Chen, Xiaowei Jia, Runlong Yu

Astrophysics of Galaxies

Euclid Quick Data Release (Q1): The geometry of dark matter halos from extragalactic streams

Euclid Collaboration: N. Starkman, J. Nibauer, S. Pearson et al. (+294 more)

Wide-field surveys like Euclid mark a new era of extragalactic stellar stream studies. With a large number of streams, it is now possible to constrain the dark matter halos of galaxies in a cosmological volume and draw comparisons to theoretical expectations for the geometry of dark matter halos. This study combines Euclid imaging with visual detection and segmentation annotations to analyse streams. We use projected stream morphologies to constrain the shape and centre-of-mass position (CoM) of each host galaxy's potential, jointly probing baryonic and dark matter distributions. These inferences complement weak lensing methods, with sensitivity to halo profile and geometry on sub-virial scales. The method enables both stacked, population-level constraints on halo flattening and CoM position, and constraints on these quantities for individual halos. We also present a novel method for transforming segmentation maps of stellar streams into smooth, curvature-preserving tracks optimised for fast and robust dynamical inference. This approach enables rapid modelling of stream morphology, supports a statistically rigorous combination of constraints across multiple streams within a single galaxy, and enables joint inference across galactic hosts. From our study of 13 galaxies with prominent tidal streams, we find agreement with spherical halos, albeit a mild preference for flattening with $q = 0.95^{+0.05}_{-0.10}$ at 68% confidence. This is promising early agreement with $\Lambda$CDM predictions. With thousands more discovered streams expected across \Euclid's mission, our programme will enable precise measurements of halo shapes and CoM positions across large samples and redshifts, offering constraints on the geometry of dark matter halos.
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All authors Euclid Collaboration: N. Starkman, J. Nibauer, S. Pearson, S. Wu, M. Walmsley, L. Necib, J. Bovy, F. R. Marleau, E. Sola, D. Scott, B. Altieri, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, M. Bolzonella, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, A. Costille, F. Courbin, H. M. Courtois, M. Cropper, H. Degaudenzi, G. De Lucia, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, L. Gabarra, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, M. Huertas-Company, K. Jahnke, M. Jhabvala, B. Joachimi, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, M. Magliocchetti, G. Mainetti, O. Mansutti, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, N. Mauri, E. Medinaceli, S. Mei, M. Melchior, M. Meneghetti, E. Merlin, G. Meylan, P. Monaco, A. Mora, M. Moresco, C. Moretti, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S.-M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, A. Pezzotta, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, I. Risso, E. Romelli, M. Roncarelli, C. Rosset, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, P. Schneider, A. Secroun, E. Sihvola, P. Simon, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespí, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, E. A. Valentijn, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, M. Ballardini, E. Bozzo, C. Burigana, R. Cabanac, M. Calabrese, A. Cappi, T. Castro, J. A. Escartin Vigo, J. García-Bellido, J. Macias-Perez, R. Maoli, J. Martín-Fleitas, R. B. Metcalf, M. Pöntinen, V. Scottez, M. Sereno, M. Tenti, M. Tucci, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, G. Angora, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, L. Bazzanini, D. Bertacca, M. Bethermin, F. Beutler, A. Blanchard, L. Blot, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, F. Cogato, A. R. Cooray, O. Cucciati, F. De Paolis, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, J. M. Diego, P.-A. Duc, V. Duret, M. Y. Elkhashab, A. Enia, Y. Fang, A. Finoguenov, A. Franco, K. Ganga, T. Gasparetto, R. Gavazzi, E. Gaztanaga, F. Giacomini, F. Gianotti, E. J. Gonzalez, G. Gozaliasl, A. Gruppuso, M. Guidi, C. M. Gutierrez, A. Hall, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, L. K. Hunt, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, J. Kim, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, M. Lembo, F. Lepori, G. Leroy, J. Lesgourgues, T. I. Liaudat, S. J. Liu, A. Loureiro, G. Maggio, A. Manjón-García, F. Mannucci, C. J. A. P. Martins, L. Maurin, M. Miluzio, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, D. Paoletti, F. Passalacqua, K. Paterson, L. Patrizii, C. Pattison, A. Pisani, D. Potter, G. W. Pratt, S. Quai, M. Radovich, K. Rojas, W. Roster, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, F. Shankar, J. Stadel, K. Tanidis, F. Tarsitano, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Urbano, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, S. Vinciguerra, N. A. Walton, J. R. Weaver, A. H. Wright

Mesoscale and Nanoscale Physics

Solid-state transcapacitor, a new gain element for logic, memory and interconnects

Amrita Mathuriya, Roza Kotlyar, Neal Reynolds et al. (+9 more)

Today's transistors dictate the voltage and charge scales for both logic and memory. While AI systems are recognized to be limited by memory energy, the dominant share of the energy is expended in the intrachip interconnects whose voltage and charge scales are set by transistors. The energy scaling challenges of transistors can be attributed to simultaneously meeting high current density, high current/impedance modulation, and the inability to lower voltages. Hence, a new logic element that lowers the voltage and charge needs is a priority, not only for lowering logic power but also memory access power. Here, we propose a novel 3-terminal logic element for low energy computing, a solid-state transcapacitor (TCAP). A TCAP is a solid state displacement current modulator realized by a gate which controls the charge-voltage relationship of the channel. Unlike transistors, TCAPs eliminate the dissipative transport current, are not bound by the Boltzmann current modulation limit, and operate with displacement currents limited only by the polarization response and contact resistance. Hence, TCAP circuits may simultaneously overcome the voltage, current density, and current modulation limits of CMOS. We describe a solid state TCAP using a piezoelectric transcapacitor in which a gate-controlled stressor modulates the capacitance of a polar channel via electromechanical coupling. This device achieves inversion and gain, essential for logic, and is functionally equivalent to a 1T-1C memory cell, enabling dense memory. Using voltage scaling, capacitive energy recovery, and high polarization densities of polar materials, the logic based on TCAP offers a pathway to 100 fold lower energy consumption with a delay comparable to ultimately scaled CMOS devices. This approach provides a new potential pathway for low-energy computing beyond the limits of transistors using electro-mechanics and multiferroics.
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All authors Amrita Mathuriya, Roza Kotlyar, Neal Reynolds, Rafael Rios, Alan Kalitsov, Peter B. Meisenheimer, James Clarkson, Noriyuki Sato, Tanay Gosavi, Ramamoorthy Ramesh, Dmitri E. Nikonov, Sasikanth Manipatruni

Earth and Planetary Astrophysics

Radiatively Controlled Thermal Stability of High-Altitude Clouds in Exoplanetary Atmospheres

Taro M. Shefferson-Nagata and Kazumasa Ohno

One of the most striking findings of exoplanetary science is the ubiquity of clouds. A conventional approach to infer the cloud compositions is to utilize the thermochemical equilibrium model assuming the same temperature shared with clouds and ambient gases; however, this assumption is actually not always valid, especially in the tenuous upper atmospheres where particle-gas collisions are infrequent. In this study, we investigate the radiative equilibrium temperatures of exoplanetary clouds to assess the thermal stability of aerosols in the low atmospheric pressure limit. For eight cloud-forming condensates (KCl, ZnS, Na$_2$S, MnS, SiO$_2$, Mg$_2$SiO$_4$, Fe, and Al$_2$O$_3$), we solve the energy balance between stellar radiative heating and infrared cooling to calculate the particle temperature, which is then compared with their condensation temperatures. We find three composition-dependent groups in the particle-temperature behavior, and show that silicate condensates (SiO$_2$ and Mg$_2$SiO$_4$) maintain a cool enough temperature in a wide range of stellar and planetary conditions. Conversely, sulfide condensates (ZnS, Na$_2$S, and MnS) are readily heated to their sublimation temperatures even on temperate planets. WASP-17b and HD 189733b fall within the thermodynamically stable regime for SiO$_2$ clouds, consistent with recent JWST observations. We also examine the vertical profiles of cloud temperatures on WASP-17b, showing that Fe clouds cannot exist on the dayside, and Al$_2$O$_3$ clouds can exist only in a confined region, even though atmospheric temperature appears to allow the formation of these clouds. This study provides novel insights on cloud compositions in upper exoplanetary atmospheres, testable by upcoming atmospheric surveys of JWST and Ariel.
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Quantum Physics

Common causes for quantum identical particles

A. Hovhannisyan, S. Weigert and A. E. Allahverdyan

Violations of Bell's inequalities imply that joint probabilities generated by non-commutative measurements on two (non-identical) quantum particles do not have a single common cause. But joint probabilities generated for such non-identical particles via commutative measurements do have non-trivial common cause variables. We focus on commutative measurements and consider two identical quantum particles, whose density matrices and observables (hermitian operators) are necessarily permutation-symmetric. It is natural to demand that the common cause describing joint probabilities is also permutation symmetric, i.e., it acts symmetrically on both particles. Looking at various ways of defining joint probabilities from the same measurement data, we conclude that either symmetric common causes need not exist (i.e., that the particles can be hiddenly distinguishable), or that symmetric screening variables exist, but they are trivial, i.e., no single common cause can explain all single-measurement correlations.
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Quantum Physics

Dynamical Decoupling using Universal Optimal Tracking

Amit Devra, Emanuel Malvetti, Niklas J. Glaser et al. (+7 more)

Dynamical decoupling (DD) is a widely used and resource-efficient technique for error suppression, but conventional DD relies on periodically repeating a short pulse block to refocus the qubit state during idle periods. Imperfections in this block cause residual errors to accumulate, ultimately degrading state recovery over long idle times. Here, we introduce a universal optimal tracking approach that extends the original tracking concept to a fully state-independent setting for designing DD sequences. By monitoring the qubit's evolution at predefined waypoints during optimization, the method dynamically compensates residual errors while preserving regular refocusing. Experimental demonstrations on a superconducting-qubit platform confirm the suppression of error accumulation under static control imperfections, in agreement with numerical predictions. Complementary simulations further show that optimal-tracking-based sequences maintain strong performance under time-dependent noise. These results establish optimal tracking as a practical and hardware-agnostic approach to designing short, robust DD sequences suitable for noisy quantum devices.
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All authors Amit Devra, Emanuel Malvetti, Niklas J. Glaser, Abhishek Agarwal, Ivan Rungger, Santana Lujan, Max Werninghaus, Stefan Filipp, Léo Van Damme, Steffen J. Glaser

Quantum Physics

Feedback-Controlled Magnon-Atom Entanglement and Photon Statistics

Yiping Lu, Yulong Hu, Zhuzheng Han et al. (+1 more)

Quantum systems face inherent challenges in achieving precise control, and solving the Schrödinger equation is often intractable for complex hybrid platforms. Here, for the first time, we introduce a magnon into a feedback-controlled quantum system. To solve the dynamics numerically and efficiently, we employ a Long Short-Term Memory network, a machine learning approach, to propagate the probability amplitudes to the steady state. By applying coherent feedback, we effectively stabilize the intracavity state. Our results reveal that the photon-photon correlation function and the concurrence, a measure of magnon-atom entanglement, exhibit periodic oscillations with the cavity-mirror distance, and that feedback significantly enhances both antibunching and bunching when the detuning is varied. These findings not only demonstrate the power of artificial intelligence in quantum dynamics simulation, but also open a promising route for on-demand quantum state engineering in hybrid magnonic systems, with potential applications in quantum networks and quantum information processing.
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All authors Yiping Lu, Yulong Hu, Zhuzheng Han, Shirong Lin

Instrumentation and Methods for Astrophysics · Cross-listed from cs.RO

RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers

Jon Reifschneider

The number of planned lunar micro-rovers is increasing, yet few open, benchmarked tools exist to explore trade-offs in their design. Rover sizing is a highly coupled problem with a feedback loop between vehicle mass and mobility: a heavier rover sinks deeper into loose regolith, deeper sinkage increases power demand, more power requires a larger array, and the array adds mass. We present RoverDevKit, an open-source tradespace exploration toolkit with an evaluator that couples a closed-form wheel-soil model with bottom-up mass, solar-power, thermal-survival, and traverse models. The evaluator is fast enough to serve as the fitness function of a multi-objective genetic algorithm (NSGA-II): a search comprising 3,000 mission evaluations takes under a minute on a single laptop core. We use it to map trade-offs among distance traveled, vehicle mass, and slope capability on mare, polar, highland, and crater-rim missions. The limiting factor varies by mission: energy storage at high latitude, slope traction on loose highland regolith, and traverse range on mare and crater-rim terrain. The wheel-soil model reproduces measured single-wheel drawbar pull from two independent datasets with accuracy consistent with the 20-30% error typically reported for this class of model, and the mass model predicts published 5-50 kg rover masses with median absolute percentage error of 10.5%. A comparison with five published rover designs in the same mass class shows that they lie near the predicted Pareto front.
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Quantum Physics · Cross-listed from eess.IV

Configurable Algorithms for Histopathologic Cancer Detection on Quantum Hardware

Nandika Goyal, Glen Uehara and Andreas Spanias

Histopathologic cancer detection is challenging due to tissue variability, staining differences, and subtle visual distinctions between disease classes. We propose two quantum algorithms for this task: a configurable dual-gradient CSWAP circuit (DG-CSWAP) that computes multi-directional edge responses in a single execution via per-pixel local Ry encoding, and a hardware-efficient destructive swap circuit (DG-DST) natively matched to quantum processing unit (QPU) gate sets at substantially lower circuit complexity. We prove algebraic equivalence between DG-CSWAP and DG-DST, enabling a two-circuit QPU validation strategy. A three-stage NISQ mitigation pipeline, including readout error correction, bias subtraction, and slope regression, reduces single-pixel hardware MSE by ~8x. Validated on five quantum processors via Amazon Braket, the method achieves inter-platform Pearson r ~ 0.93-0.94 across all local-simulator pairs. Compared to a prior Quantum Fourier Transform (QFT) based amplitude-encoding baseline requiring 12-qubit global state preparation and a three-model ensemble (85.55% on PatchCamelyon), the proposed method uses shot-based measurements, executes on real quantum hardware, and achieves 79.80% accuracy with a single ResNet-50. A Lite configuration delivers a 17x preprocessing speedup at a 2.59% accuracy cost. To the best of our knowledge, this is the first quantum hardware implementation study with noise mitigation for histopathologic image classification.
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