Peer Revenue Impact factor: volatile

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Physics

Watch Physics

Friday 3 July

Cosmology and Nongalactic Astrophysics

The giant radio source 0917+75: Origin and properties

G. Giovannini, N. Biava, M. Girardi et al. (+11 more)

Context. Several author have studied the giant radio source GRS0917 + 75 , but its origin remains unclear. Aims. This source is unusual, because of its large size and its location outside a rich cluster of galaxies. We aim to understand and discuss the properties and nature of this source and its connection to the environment. Methods. We conducted optical observations to obtain new spectroscopic data. We also acquired a LOFAR image at 144 MHz to derive information at low radio frequency. Moreover, we performed a new analysis of archival VLA data in the L and C bands for a multifrequency study of the source properties in the radio band. Results. From the observational data, we classify GRS0917 + 75 as a giant radio galaxy with a size of 1.5 Mpc and an estimated age of about 100 Myr. The optical parent galaxy is a bright low-excitation radio galaxy, the brightest member of a very poor group belonging to a large supercluster. GRS0917 + 75 is a peculiar low-power Fanaroff-Riley Class I giant radio galaxy with a bright central emission but no jet-like features. Conclusions. The existence of giant radio galaxies such as GRS0917 + 75 could explain the origin of relativistic particles and magnetic fields in low-density environments.
Read the full abstract

All authors G. Giovannini, N. Biava, M. Girardi, W. Boschin, R. Barrena, A. Bonafede, L. Feretti, C. Ferrari, F. Govoni, M. Iacobelli, M. Murgia, E. Orru', R. Pizzo, V. Vacca

Strongly Correlated Electrons

Pressure-Driven Structural Transitions without a Displacive Charge-Density Wave in La$_2$SmNi$_2$O$_7$

J. Huang, Sitaram Ramakrishnan, P. Rodière et al. (+7 more)

We investigated the structural properties of bilayer nickelate La$_2$SmNi$_2$O$_7$ as a function of pressure and temperature. At ambient conditions, we show that the material crystallizes as a monoclinic superstructure distinct from the one previously reported and close to the pseudo-orthorhombic structure of pristine La$_3$Ni$_2$O$_7$. No signatures of satellite reflections associated with charge density wave (CDW) ordering are detected at low temperature. Upon compression, a sequence of pressure-induced structural transitions from monoclinic to orthorhombic 15 GPa and then tetragonal 21 GPa symmetry is observed. Within the superconducting dome, the quality of the X-ray diffraction data enables structural refinements enabling theoretical models to understand the emergence of superconductivity.
Read the full abstract

All authors J. Huang, Sitaram Ramakrishnan, P. Rodière, P. Toulemonde, Z. Rahmany, V. Balédent, B. Vignolle, Sourav Marik, P. Fertey, P. Foury-Leylekian

Quantum Physics

Nested-Loop Trajectory-Informed Variational Quantum Solver for Interior-Point OPF

Farshad Amani and Amin Kargarian

Optimal power flow (OPF) solved by an interior-point method (IPM) requires repeatedly solving Newton linear systems. When variational quantum linear solvers (VQLS) are used, each IPM iteration involves an additional nested inner variational optimization loop, which can significantly slow the overall quantum-assisted IPM convergence. To address this challenge, this paper proposes a dual-level trainable quantum IPM framework for OPF that leverages early solver-generated trajectories rather than relying on single-point prediction. The key observation is that early IPM iterates provide informative primal-dual, slack, and barrier-variable evolution about the path to optimality, while early VQLS parameter updates provide useful information about the later variational search. At the quantum-solver level, a trainable parameter model uses a short prefix of the VQLS parameter trajectory to project the remaining variational search toward a lower-cost region. At the OPF-solver level, a second trainable model uses early primal-dual IPM iterates to project a later central path state, which is restored to an admissible point before IPM refinement continues. Simulation studies show that the proposed approach reduces the number of variational updates by up to $95\%$ while maintaining OPF objective values close to the classical IPM reference. A 2-bus demonstration on real quantum hardware is also included to validate the implementation of the proposed workflow.
Read the full abstract

Soft Condensed Matter

Stability and equilibria of a compressible elastic membrane in Stokes flow

Sho Kawakami, Han Zhou, Po-Chun Kuo et al. (+2 more)

We formulate a continuum model for a compressible lipid-bilayer membrane immersed in Stokes flow, replacing exact local area inextensibility by conservation of an areal phospholipid density. The membrane free energy combines Helfrich bending, spontaneous curvature, and a finite area-compression penalty, so that membrane tension becomes a constitutive response to lipid-density variation rather than a Lagrange multiplier enforcing local area conservation. The resulting interfacial stress includes normal elastic forces and tangential Marangoni stresses generated by lipid redistribution; these stresses arise from membrane compressibility and can produce an effective negative tension when the local lipid density exceeds its preferred value. We further derive the linear stability of circular membranes in two dimensions and spherical membranes in three dimensions under full Stokes hydrodynamic coupling. In both cases, bending stabilizes the base shape, while excess lipid density destabilizes it by favoring increased membrane area. The first instability occurs in the lowest nontrivial shape mode, m = 2 in two dimensions and j = 2 in three dimensions. Energy expansions near onset show that the two-dimensional instability is a pitchfork bifurcation, whereas the three-dimensional instability is generically transcritical because prolate and oblate perturbations are geometrically distinct. These results provide a controlled compressible extension of classical vesicle mechanics and directly connect lipid-density variation, membrane tension, hydrodynamic coupling, and shape instability.
Read the full abstract

All authors Sho Kawakami, Han Zhou, Po-Chun Kuo, Yoichiro Mori, Yuan-Nan Young

Strongly Correlated Electrons

What Makes Three-Dimensional Quantum Spin Liquids Possible?

Yasir Iqbal and Ronny Thomale

Quantum spin liquids are often introduced through a low-dimensional intuition: weak coordination and strong zero-point motion frustrate conventional magnetic order. This view has shaped much of the field, but it misses another route to quantum disorder. In many frustrated magnets, the more natural starting point is not a fluctuating version of an ordered state, but a locally constrained manifold of low-energy configurations. Within such a manifold, quantum dynamics can generate an emergent gauge theory, with fractionalized excitations and collective modes absent in ordinary magnets. Here we ask why such phases can be stable in three spatial dimensions. We argue that three-dimensional quantum spin liquids need not be regarded as fragile exceptions to the tendency of 3D magnets to order. They can arise when local constraints suppress premature order selection, coherent tunneling processes connect the constrained manifold, and the topology of gauge defects permits a deconfined regime. For compact gauge theories, three dimensions can even reverse the usual dimensional intuition: regimes that are unstable or fine-tuned in lower dimensions may become stable over finite regions of parameter space. We organize the discussion around constraint-driven Coulomb phases, weak harmonic order-by-disorder selection, and the role of spin-orbit or multipolar interactions in generating the required microscopic dynamics. We close with experimental diagnostics, candidate materials, and open theoretical questions for three-dimensional quantum spin liquids
Read the full abstract

Cosmology and Nongalactic Astrophysics

Strong-lensing effects in high-redshift massive black-hole binary population inference

Yan-Heng Jin and Wen-Biao Han

High-redshift massive black-hole binary (MBHB) mergers provide a probe of black-hole seed formation and early galaxy assembly, but the population detected by LISA can be modified by galaxy-scale strong lensing. We quantify this effect for MBHB mergers at $10\leq z\leq20$ and assess its impact on the inference of intrinsic formation-channel fractions. We use four channels, corresponding to light- and heavy-seed scenarios with delayed and non-delayed mergers, and compute 4-year LISA-detectable event numbers with and without strong lensing. To bracket the uncertain lens population, we compare a conservative velocity-dispersion-function (VDF) prescription with an optimistic halo-mass-function (HMF)-based prescription. We consider a reference mixture with equal seed-channel weights and additional mixtures in which the intrinsic seed population is weighted toward selected formation channels, and jointly infer the formation fractions and the strength of the lensing contribution. Strong lensing does not affect all channels equally: it can change the detected channel mixture as well as the total number of detections. This effect is weak for the conservative VDF prescription, but becomes significant in the high-lensing-rate HMF case, where neglecting strong lensing can bias the recovered formation fractions. The inference precision depends on the underlying intrinsic channel composition, while the lensing contribution is more accurately recovered in the high-lensing-rate case. These results indicate that galaxy-scale strong-lensing effects and event-count information should be included when using high-redshift MBHB detections to infer intrinsic formation-channel fractions.
Read the full abstract

High Energy Physics (Theory)

Falling through the horizon of a quantum black hole

Victor Franken, Thomas G. Mertens and Bruno de S. L. Torres

We study quantum-gravitational effects on the response of an infalling detector as it crosses the horizon of a near-extremal black hole in the framework of quantum JT gravity. These effects are incorporated via the gravitational dressing needed to define both the infalling trajectory and the local observables probed by the detector in a diffeomorphism-invariant way. In the black hole exterior, a preferred choice of dressing to the Schwarzian mode of JT gravity can be motivated in connection to geometric modular flow. We show how to extend this dressing to the black hole interior, defining local observables that are gravitationally dressed to both boundaries in the thermofield double state. The gravitational dressing connects the near-horizon region to the IR sector of the Schwarzian theory, leading to measurable effects as the horizon is approached. We find that the infalling detector is able to locally determine the location of the horizon and its temperature, violating the equivalence principle, but without encountering a firewall.
Read the full abstract

High Energy Physics (Phenomenology)

First look at the evaluation of two-loop Feynman integrals for radiative return processes

Mattia Pozzoli and William J. Torres Bobadilla

Precision studies of radiative return processes at low-energy electron–positron colliders require next-to-next-to-leading order QED predictions retaining full dependence on the electron mass. We present the calculation of planar two-loop four-point Feynman integrals relevant for initial-state radiation contributions to these processes. The calculation presents considerable analytical complexity, due to the presence of a nested square root and of integrals associated with elliptic geometries. We construct differential equations for the Feynman integrals which are polynomial in the dimensional regulator, and are suitable for numerical integration. We demonstrate stable numerical evaluations throughout the physical region relevant for low-energy experiments, despite the presence of large hierarchies of scales. Our results provide essential building blocks for NNLO predictions for radiative return processes.
Read the full abstract

Quantum Physics

Measurements Number Scaling in the Quantum Approximate Optimization Algorithm for MaxCut: A Statistical Analysis

Inbar Chefer, Uri Shaham and Adi Makmal

We provide a statistical analysis of the measurement (shot) requirements of the quantum approximate optimization algorithm (QAOA) for the MaxCut problem. We derive sufficient conditions on the number of shots per cost operator evaluation to: (a) estimate the expected cost to within a relative error $\delta$ and a confidence $1-\epsilon$, and (b) ensure SGD-based parameter optimization converges to a target relative suboptimality level with high probability. In addition, we provide an explicit bound on the number of SGD iterations required to reach the target accuracy. Our analysis reveals an unexpected scaling phenomenon: for specific graph classes, which we formally characterize, the total shot budget needed to achieve a fixed relative-performance metric decreases as the instance size grows. This result complements earlier cost function concentration arguments regarding parameter optimization redundancy, thereby highlighting the potential for high-performance, low-overhead QAOA implementations for large-scale MaxCut instances. To assist practitioners, we translate our analytical findings into practical rules of thumb for shot-budget allocation and validate these results with numerical simulations, offering new insights into the interplay between graph size, structural complexity, and resource requirements in QAOA.
Read the full abstract

Computational Physics · Cross-listed from cs.LG

CSympNet-ID: conformal-symplectic map learning for linearly damped Hamiltonian systems

Jiale Gong, Pengzhan Jin, Dongyang Kuang et al. (+2 more)

Learning dissipative dynamics from discrete observations is essential for reliable long-horizon prediction and physically meaningful parameter identification. For linearly damped Hamiltonian systems, the exact flow is generally not symplectic but conformally symplectic, contracting the canonical symplectic form by a scalar factor that reflects the net dissipation. We propose Conformal Symplectic Networks with damping identification (CSympNet-ID), a discrete-time map-learning framework that learns the one-step flow map directly from snapshot pairs while enforcing exact discrete conformal symplecticity by construction, without penalty terms or projection. The architecture composes an exact symplectic neural core with explicit diagonal scaling layers whose factors are parameterized exponentially by a scalar damping-rate parameter, thereby guaranteeing positivity and interpretability of the learned dissipation factor. We establish a scaling-conjugacy factorization for conformal symplectic maps and derive a pointwise-in-step density result for CSympNet-ID. We evaluate an irregular-step damped oscillator, a damped spring-mass chain, a damped nonlinear cubic oscillator, and additional high-dimensional extensions. CSympNet-ID gives the most favorable overall results among the compared models in the reported experiments, particularly in data-scarce regimes, target contraction-law recovery, and high-dimensional tests where unstructured baselines degrade rapidly.
Read the full abstract

All authors Jiale Gong, Pengzhan Jin, Dongyang Kuang, Lu Li, Yifa Tang

Quantum Physics

An End-to-End Multi-Stage Kill-Chain Attack on Quantum Neural Networks: Demonstration on Trapped-Ion Hardware

Cedric Brügmann, Daniel Herr, Daniel Ohl de Mello et al. (+7 more)

We demonstrate an end-to-end, multi-stage attack against a quantum neural network (QNN) model that is executed on a trapped-ion quantum computer. Our chain combines side-channel reconnaissance, crosstalk characterization, adversarial example generation, and a physical crosstalk attack that realizes the adversarial perturbation on the device. We cover the full attack chain on ion traps and report the corresponding superconducting-hardware experiments in the appendix. We discuss implications for QaaS providers and hardware mitigations.
Read the full abstract

All authors Cedric Brügmann, Daniel Herr, Daniel Ohl de Mello, Pascal Debus, Maximilian Wendlinger, Kilian Tscharke, Juris Ulmanis, Alexander Erhard, Arthur Schmidt, Fabian Petsch

Materials Science

Color Centers in Cubic Boron Nitride

William Stenlund, Joel Davidsson, Viktor Ivády et al. (+2 more)

Cubic boron nitride (c-BN) is a wide-bandgap semiconductor (WBGS) with potential applications in both power electronics and quantum technologies. Color centers in WBGS can be used as single photon emitters and quantum sensors. Several zero phonon lines have been measured in c-BN experiment but not yet identified. To systematically probe the combinatorially complex chemical space of defects, we generate a large-scale point defect data set for c-BN. We apply density functional theory calculation implemented in a high-throughput workflow Automatic Defect Analysis and Qualification (ADAQ) to broadly screen for point defect complexes containing s- or p-elements. More than 8000 defects have been calculated in different charge and spin states. The calculated properties are stored in defect database and are then filtered to find defects with properties similar to the NV-center in diamond. More accurate calculations using hybrid functionals are then performed on a selected set of promising defects to further assess their suitability for quantum technology. In particular, we reexamined the ONVB defect which likely explains the GC-2 line. The hybrid calculations also suggest other defect candidates with bright emission, such as two carbon defects and the NaB- defect.
Read the full abstract

All authors William Stenlund, Joel Davidsson, Viktor Ivády, Rickard Armiento, Igor A. Abrikosov

Optics

Mid-IR single- and dual-electro-optic comb generation with an ultrafast modulator

Gia-Long Ngo, Luca Lucia, Stefano Pirotta et al. (+9 more)

Mid-infrared (mid-IR) frequency combs are powerful tools for molecular sensing, industrial monitoring, and precision spectroscopy, yet their development beyond 5 um remains limited. Electro-optic modulation offers a promising path toward compact, agile comb generation, but extending this approach into the mid-infrared has been hindered by the lack of practical, high-performance modulators. Here we present an approach that leads to efficient generation of mid infrared frequency combs around 9 um, by employing ultrafast, room temperature, free space electro optic intensity modulators. By driving a single modulator with short electrical pulse trains, we realize both single- and dual-comb operation from a continuous-wave quantum cascade laser, providing a compact and versatile platform for mid-IR spectroscopy. This scheme produces combs with tunable repetition rates down to the megahertz range with direct observation on an electrical spectrum analyzer without any interferometer. As a proof of concept, we perform single- and dual-comb spectroscopy of a germanium etalon and an ammonia cell, achieving resolution far beyond that of conventional Fourier-transform infrared (FTIR) spectrometers and highlighting the potential of this approach for precise measurements in the long-wavelength molecular-fingerprint region. These results establish high-performance mid-IR modulators as a promising route toward practical, energy-efficient frequency-comb systems for sensing and spectroscopy.
Read the full abstract

All authors Gia-Long Ngo, Luca Lucia, Stefano Pirotta, Mario Malerba, Lianhe H Li, Stefano Barbieri, Victor Merupo, Edmund H Linfield, Benoit Darquié, Jean-Michel Manceau, Adel Bousseksou, Raffaele Colombelli

High Energy Astrophysical Phenomena

Exploring the Magnetic Field Structure of the Milky Way with Pulsars in the SKA Era

Jun Xu, J. L. Han and Weicong Jing

The magnetic field structure of the Milky Way can offer critical insights into the origin of galactic magnetic fields. Measurements of magnetic structures of the Milky Way are still sparse in far regions of the Galactic disk and halo. Pulsars are the best probes for the three-dimensional structure of the Galactic magnetic field, primarily owing to their highly polarized short-duration radio pulses, negligible intrinsic Faraday rotation compared to the contribution from the medium in front, and their widespread distribution throughout the Galaxy across the thin disk, spiral arms, and extended halo. In this article, we give an overview of Galactic magnetic field investigation using pulsars. The sensitive SKA1 design baseline (AA4) will increase the number of known pulsars by a factor of around three, and the initial staged delivery array (AA*) will probably double the total number of the current pulsar population. Polarization observations of pulsars with the AA* telescopes will give rotation measures along several thousand lines of sight, enabling detailed exploration of the magnetic structure of both the Galactic disk and the Galactic halo.
Read the full abstract

2607.03314 · 3 Jul · Published in Advancing Astrophysics with the SKA II (AASKAII), 2026 (arXiv:2606.20366). Report-no: AASKAII/JunXu01. Advancing Astrophysics with the SKA II (AASKAII) outlines the transformative scientific advances that will be enabled by the SKA telescopes. An earlier version of this chapter was published in The Open Journal of Astrophysics with arXiv ID: arXiv:2512.16159

Abstract PDF $MAG ×7$GALAC ×6$PLSR ×6$HALO ×3 +3

Quantum Physics

High Success Probability, Fidelity, and Purity Nonlinear Optical Two-Qubit Gates on Chip

Minghao Shang, Hua-Ying Liu, Ying Wei et al. (+5 more)

Optical two-qubit gate with high success probability, fault-tolerant fidelity, and high-purity outputs is a fundamental yet unsolved challenge, essential for large-scale optical quantum computing toward quantum advantage. Here, we propose a feasible scheme for such gate using thin-film lithium niobate platform, enabling ḩi}(2) nonlinear photon-photon interaction with 100% efficiency. By decoupling photon interaction and qubit flip operations, fidelity ceiling is removed, and output state purity is recovered by spectral-phase pre-compensation based on a full-spectral photon interaction model, yielding a CNOT gate with 84% success probability, 93% purity, and unity fidelity.
Read the full abstract

All authors Minghao Shang, Hua-Ying Liu, Ying Wei, Xiaoyi Liu, Qianhao Ning, Lijian Zhang, Shi-Ning Zhu, Zhenda Xie

Cosmology and Nongalactic Astrophysics

Euclid: Measuring the intrinsic alignment of galaxies around cosmic voids in the \Euclid Flagship simulation\

P. Vielzeuf, M.-C. Cousinou, A. Pisani et al. (+149 more)

We present a methodology to measure the intrinsic alignment (IA) signal of galaxies in the vicinity of cosmic voids using the \Euclid-like Flagship cosmological simulation from the Euclid Consortium. The IA signal is quantified and compared with the predictions of the linear alignment (LA) model, providing one of the first detailed investigations of this effect in underdense large-scale environments. While the IA signal around cosmic voids has received little attention to date, it may constitute a non-negligible systematic in forthcoming cosmological analyses that exploit void-lensing measurements. Our analysis examines red and blue galaxy populations separately, enabling a comparison of their alignment behaviour in void environments with the corresponding trends measured in galaxy-galaxy correlations. We find that the redshift evolution of the IA amplitude in cosmic voids is broadly consistent with that measured in the general galaxy population for both colour-selected samples. Additionally, our modelling allows us to estimate the linear bias of voids, $b_{\rm V}(z)$ which characterises how cosmic voids trace the underlying dark-matter density field, for voids with radii in the range $10 < R_{\rm V}/(h^{-1} {\rm Mpc}) < 15$. The measured bias exhibits a positive trend with redshift, consistent with theoretical predictions for the clustering of underdense regions. These results highlight the importance of accurately modelling IA in void studies, both to mitigate systematic effects in void-lensing cosmology and to further improve our understanding of galaxy-environment interactions in low-density regions of the Universe.
Read the full abstract

All authors P. Vielzeuf, M.-C. Cousinou, A. Pisani, S. Escoffier, E. J. Gonzalez, W. d'Assignies, E. Jullo, M. Ghodsi Yengejeh, A. Kovács, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, H. Degaudenzi, S. de la Torre, G. De Lucia, H. Dole, F. Dubath, X. Dupac, M. Farina, R. Farinelli, S. Farrens, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, O. Mansutti, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, C. Neissner, S.-M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, A. Pezzotta, S. Pires, G. Polenta, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, A. Secroun, E. Sihvola, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, T. Castro, M. Sereno, M. Viel, D. Navarro-Gironés

Lattice Field Theory

Operator Learning in Lattice QCD: Spectral Reconstruction

Alessandro De Santis

In this work, we propose a novel supervised machine-learning-based strategy for extracting smeared spectral functions from Euclidean correlation functions. The strategy revisits the numerically ill-posed spectral reconstruction problem within the framework of Operator Learning through the use of DeepONet-like architectures. To illustrate the method, we construct an ensemble of neural networks trained on mock data generated from a specific class of functions. This ensemble is then employed to estimate the systematic uncertainty associated with the fact that a neural network provides only an approximation to the target operator. The procedure is fully validated on previously unseen noisy mock data. To demonstrate the potential of the method for phenomenological applications, we reconstruct the inclusive rate above the four-particle threshold and up to high energies in the $1+1$-dimensional O(3) non-linear $\sigma$ model, starting from correlation functions computed in Monte Carlo simulations. The final result is consistent with the known analytic spectral density and, compared with the state-of-the-art Hansen-Lupo-Tantalo algorithm, exhibits a significant reduction in the total uncertainty. While the extent to which this improvement persists in the absence of prior physical knowledge remains to be quantified, the proposed strategy can be naturally extended to more phenomenologically relevant observables and, more generally, to other operations commonly encountered in lattice QCD.
Read the full abstract

Fluid Dynamics

Mean-flow-based reduced-order models of turbulent channel flow

Ian Addison-Smith, Igor A. Maia, André V. G. Cavalieri et al. (+1 more)

Reduced-order models (ROMs) for turbulent flows based on Galerkin projection can achieve reasonable accuracy using equation-based modal bases derived from the linearized Navier-Stokes equations through the controllability and observability Gramians. The use of the modal bases obtained from linearized equations around a mean state has been seen to enhance the first- and second-order statistics in the ROM, but the use of the mean state was not necessarily extended to the equations of motion, as it implies the treatment of the divergence of the Reynolds stresses in the Galerkin projection. In this work, we present a mean-flow-based framework for ROMs in which the projection of the Reynolds stresses is solved through a modified modal basis and the knowledge of the mean flow. This framework achieves turbulence statistics comparable to those of a reference direct numerical simulation (DNS) in a minimal channel at $Re_{\tau} \approx 185$. Short-time forecasting with this framework is assessed, where balanced truncation modal bases outperform controllability modes in ROMs, yielding a reconstruction of the velocity field comparable to the Galerkin projection of proper orthogonal decomposition (POD) modes. This framework can extend analysis based on linearisations around the mean turbulent flow, which became widespread in recent years, to include explicitly non-linear interactions between modes, enabling accurate models at higher Reynolds number.
Read the full abstract

All authors Ian Addison-Smith, Igor A. Maia, André V. G. Cavalieri, Benjamin Herrmann

General Relativity and Quantum Cosmology

Reconstructing $f(R)$ gravity from Viaggiu Holographic Dark Energy under Hubble, Event Horizon and Granda Oliveros cutoffs

Arushi Jhunjhunwala and Sayani Maity

This work explores the reconstruction of $f(R)$ gravity within the framework of newly proposed Viaggiu holographic dark energy (VHDE), which incorporates entropy corrections due to the dynamical nature of cosmological horizons. By implementing a correspondence between VHDE and curvature-induced energy density, we generate explicit forms of $f(R)$ for different infrared cutoffs, namely the Hubble horizon, future event horizon, and Granda Oliveros cutoff. The resulting models are analyzed graphically through the cosmological parameters such as the equation of state and deceleration parameter, revealing a successful description of the transition from decelerated to accelerated expansion. The viability of the reconstructed models is further examined through stability conditions and local gravity constraints, that yields the nature of the models as free from ghost and tachyonic instabilities and demonstrates VHDE-inspired $f(R)$ gravity model as a consistent and flexible geometric framework for explaining the late-time cosmic acceleration of the Universe.
Read the full abstract

Quantum Physics

Dissipative preparation and stabilization of d-mode multinomial cat states

S. Zhao and A. Metelmann

Engineering dissipation with tailored steady states has become a powerful approach for preparing and stabilizing quantum states. In this framework, engineered dissipative processes continuously steer a system towards desired target states while suppressing unwanted noise. However, extending this idea to multimode systems is challenging and remains largely unexplored, although this class of states is a powerful resource for quantum sensing and quantum information processing applications. Here, we propose a general method to design the required dissipative processes for the generation of multimode cat states in bosonic systems. We show that the engineered dissipation prepares such states from the vacuum with high fidelity and robustly stabilizes them against decoherence. As a result, their lifetime is extended by several orders of magnitude compared to natural decay times, which in turn enhances their applications in quantum techonologies. We specifically focus on the preparation and stabilization of two-mode binomial cat states and discuss a pathway for the implementation in superconducting circuit. However, our scheme can also scale up to arbitrary d-mode multinomial cat states associated to $\mathfrak{su}(d\ge2)$ algebras, and thus, our scalable framework provides a feasible route towards stabilizing compact nonclassical states.
Read the full abstract

Fluid Dynamics · Cross-listed from math.NA

Accelerating droplet-laden Stokes flow simulations with hierarchical surrogate modeling

Davide Pradovera, Thomas Frachon and Sara Zahedi

We present a surrogate modeling strategy for Stokes flows with liquid droplets suspended in a carrier fluid. Our approach is based on a multi-fidelity framework. At the lowest fidelity, droplets are treated as passive tracers, neglecting their influence on the ambient flow field. Building on this approximation, we derive a PDE that represents the current modeling error. This error equation is then solved approximately to correct the flow field and the procedure is iterated. Two fidelities are employed in an alternating fashion: Stokes flow in the absence of droplets and flow around a single droplet in free space. By systematically combining these models, the method captures droplet-flow, droplet-boundary, and droplet-droplet interactions. In this work, the framework is developed and validated for circular, non-deforming droplets in two spatial dimensions. The geometric self-similarity of the droplets allows us to construct an efficient offline-online strategy based on the reuse of precomputed single-droplet solutions. Extensions to deformable droplets are also discussed. Numerical experiments demonstrate the accuracy and efficiency of the proposed surrogate in a variety of tests, including scenarios with up to $10^4$ droplets. Notably, we show that the proposed surrogate achieves substantially reduced computational cost compared to fully resolved multi-fluid simulations with state-of-the-art software.
Read the full abstract

Statistical Mechanics

Variance of the $SIS$ Epidemic on Networks: A Diffusion Approximation

Lucija Nora Farkaš, Sebastian Morel Balbi, Hrvoje Štefančić et al. (+2 more)

Functional laws of large numbers (FLLNs) describe the mean-field trajectory of epidemics on networks, but say nothing about the fluctuations around it. These fluctuations are governed by moments of the degree distribution not relevant at the level of the mean. A rigorous functional central limit theorem (FCLT) exists for the susceptible–infected ($SI$) process on configuration-model graphs, but no analogue exists for $SIS$, where recovery reintroduces vertices into the susceptible pool with partially known neighborhoods, breaking the clean neighborhood distribution the $SI$ derivation relies on. We develop a tractable variance approximation for Markovian $SIS$ on configuration-model graphs, combining Gleeson's approximate master equation (AME) framework with a van Kampen system-size expansion in the spirit of the $SI$ FCLT. We derive a closed drift and diffusion matrix for a reduced susceptible/$SI$-edge/$SS$-edge count vector and obtain the time-dependent covariance via the associated Langevin/Lyapunov equation. Validation against Gillespie simulation across Poisson, regular, and power-law networks shows close agreement, with deviations near the epidemic threshold and in strongly heterogeneous networks.
Read the full abstract

All authors Lucija Nora Farkaš, Sebastian Morel Balbi, Hrvoje Štefančić, István Zoltán Kiss, Vinko Zlatić

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

A harmonised dataset for Earth system foundation models

Carlos Rodriguez-Pardo and Massimo Tavoni

Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human systems that both drive and respond to environmental change. The lack of a unified global training resource that combines climate, land, ocean, cryosphere, infrastructure, hazards, and socioeconomic data on a common grid hinders progress toward truly multimodal Earth system foundation models. We present WorldTensor, a harmonised global dataset that aligns hundreds of environmental and socioeconomic variables to a standardised 0.25$^\circ$ spatial grid and annual temporal framework. WorldTensor integrates reanalysis products, remote sensing, emissions inventories, land use reconstructions, hydrological observations, infrastructure and hazard datasets, and socioeconomic indicators within a single representation designed for machine learning workflows. To build the dataset, we regridded inputs across heterogeneous native resolutions and projections, rasterised point and vector datasets into spatially meaningful gridded fields, and reconciled temporal coverages ranging from daily observations to sparse multiyear socioeconomic snapshots. All outputs are distributed as NetCDF files with standardised coordinates, variable metadata, and a common CF metadata convention. WorldTensor provides a reproducible resource for training and evaluating foundation models that learn coupled dynamics across environmental and human systems at planetary scale.
Read the full abstract

Physics and Society

Scaling laws in complex component systems as consequences of heterogeneous sampling

Luca Allegri, Johannes Nauta and Manlio De Domenico

Complex component systems are collections of discrete units such as species, words, genes, whose observed realizations are naturally summarized by component counts. Many empirical laws have been observed in those systems, such as Taylor's law, Zipf's law, and Heaps' law, and domain-specific mechanisms are often employed to explain their emergence but, despite their ubiquity, a unifying framework remains elusive. In this work, we propose a null model showing that, under heterogeneous latent rates and finite sampling, several commonly observed scaling relations can arise without invoking domain-specific mechanisms. Taylor's law, for instance, reflects a crossover between sampling noise and genuine system heterogeneity and it is largely insensitive to the detailed latent distribution, while Zipf's and Heaps' laws arise from the convergence of order statistics and distinct component counts under heavy-tailed but otherwise generic priors. Our work thus suggests that these ubiquitous patterns are better interpreted as a transient sign of statistical convergence instead of fundamental principles that require tailored generative explanations.
Read the full abstract

Quantum Physics

Quantum Probability and Quantum Information Theory

Hans Maassen

This is an introductory course on quantum probability and quantum information, based on matrix algebras.

Mesoscale and Nanoscale Physics

Many-body quantum chaos in excitonic spectra from first principles

Daniel Hernangómez-Pérez and Rafael A. Molina

We demonstrate that realistic excitonic many-body Hamiltonians obtained from first-principles GW-Bethe-Salpeter equation calculations can exhibit quantum chaos governed by random-matrix universality. Considering a prototypical van der Waals heterostructure (WS$_2$-graphene), with and without lattice disorder, we analyze their energy-resolved spectral correlations and identify a disorder-driven crossover from regular to complete chaotic dynamics. We show that while pristine samples exhibit incomplete chaos (non-ergodicity) due to an approximate valley symmetry that restricts excitonic mixing, the presence of disorder-induced electronic flat bands act as a catalyst for valley mixing to drive the system into a fully developed chaotic (ergodic) regime with reduced symmetry. Crucially, fluctuations in many-body oscillator strengths are shown to follow universal Porter-Thomas statistics, directly linking the underlying quantum chaos and experimentally accessible optical observables. Finally, by examining long-range spectral correlations, we estimate the Thouless time associated to excitonic mixing across the entire many-body bandwidth. Our results establish excitons as a highly tunable platform for probing many-body ergodicity and its spectroscopic signatures in realistic interacting 2D materials.
Read the full abstract

Astrophysics of Galaxies

Physically motivated AGN emissivity profiles and their effects on quasar microlensing signatures. 1. Multi-epoch accretion disc size inference

Scott Hagen and Carina Fian

Quasar microlensing is uniquely sensitive to the size-scale of the accretion flow, offering one of the few direct probes of the accretion structure on micro-arcsecond scales. However, microlensing-based measurements in the optical and UV often find sizes systematically larger than expected from standard Shakura-Sunyaev disc theory, commonly referred to as the disc-size problem similar to that seen in continuum reverberation campaigns. But this assumes that all the emission comes from a single compact disc, neglecting the diffuse emission from the BLR which originates on much larger spatial scales. In this paper we directly quantify the effect of large-scale diffuse emission on the observed microlensing signatures. We adapt the physically motivated agnsed model to construct energetically self-consistent emissivity profiles in any given bandpass. Since this also predicts the full SED, we combine these SEDs with cloudy to give a diffuse BLR component. We convolve these models with representative microlensing magnification maps, and generate mock microlensing light curves to directly assess the inferred source size under different physical conditions. While the detailed shape of the disc emissivity profile has only a higher-order effect on the microlensing profile, the inclusion of the BLR makes a significant impact since this naturally smooths out the caustic network over larger scales. This introduces a significant bias when interpreted purely as a compact disc. However, the strength of this bias depends predominantly on the fractional contribution of the diffuse emission to the SED in the bandpass being considered, as this sets the effective half-light radius, giving an important wavelength dependence. We conclude that part of the excess in microlensing-inferred accretion disc sizes could arise from interpreting a composite (disc+BLR) picture as a single compact disc.
Read the full abstract

Materials Science

Anomalous Structural Response of Quasi-One-Dimensional Antiferromagnetic Metal KMn6Bi5 under high pressure

Hanming Ma, Qingxin Dong, Tong Shi et al. (+11 more)

We report high-pressure single-crystal X-ray diffraction measurements on the quasi-one-dimensional (Q1D) antiferromagnetic metal KMn6Bi5 up to 12.5 GPa, revealing the detailed pressure evolution of its atomic coordination environment. We find that the lattice exhibits pronounced anisotropic compressibility-the relative changes in the a and b lattice parameters reach a/a0=0.91 and b/b0 = 0.94 at 12.5 GPa-and a distinct structural anomaly emerges near 11 GPa without any symmetry-breaking. Detailed structural analysis further uncovers an anomalous hardening of the Mn nanotubes between 5 and 11 GPa, followed by a configuration optimization of the Mn/Bi nanotubes around 11 GPa. These features correlate closely with the reported pressure-temperature phase diagram of KMn6Bi5 and compare favorably with the chemical pressure effects induced by substituting K with Na, Rb, or Cs. Our findings provide key microscopic insights into how coordination environment modulation governs the stability of electronic orders in low-dimensional systems.
Read the full abstract

All authors Hanming Ma, Qingxin Dong, Tong Shi, Xiaoli Ma, Zhongjin Wu, Shaoheng Ruan, Pengtao Yang, Zhaoming Tian, Jianping Sun, Yoshiya Uwatoko, Genfu Chen, Xiaohui Yu, Bosen Wang, Jinguang Cheng

Medical Physics

Contextual Cellular Growth (ConCeG) of neural cells for realistic grey matter tissue generation for diffusion MRI simulations

Charlie Aird-Rossiter, Kadir Şimşek, Maëliss Jallais et al. (+3 more)

Accurate interpretation of diffusion magnetic resonance imaging (dMRI) signals in grey matter (GM) remains challenging due to the complex, heterogeneous, and densely packed cellular environment. Numerical phantoms provide a controlled framework for investigating the relationship between microstructure and diffusion signals, yet existing approaches often lack the morphological realism and multi-cellular organisation required to faithfully represent GM tissue. In this work, we introduce Contextual Cellular Growth (ConCeG), a generative framework for creating individual cells or constructing dense, three-dimensional, multi-cellular GM substrates informed by real neuronal and glial morphologies. The method combines topological neuron synthesis with a spatially constrained growth network, allowing for the controlled generation of heterogeneous cellular environments with realistic intra- and extracellular compartments. Synthetic cells are generated using morphological and topological characteristics derived from biological reconstructions. We validate the framework through comparisons of structural features with real cellular data, demonstrating strong agreement in branch order, length, angle, and tortuosity distributions. Power spectrum analysis further shows that both intracellular compartments reproduce the spatial correlations observed in biological tissue. Together, these results show ConCeG provides a biologically grounded framework for generating grey matter substrates suitable for large scale diffusion MRI simulation.
Read the full abstract

All authors Charlie Aird-Rossiter, Kadir Şimşek, Maëliss Jallais, Derek K. Jones, Lida Kanari, Marco Palombo

Optics

Overcoming the low signal-to-noise problem for hybrid mode-selective photonic lantern-based wavefront correction using machine learning

Nathan K Long, Barnaby Norris, Daniel S Dahl et al. (+10 more)

Hybrid mode-selective photonic lanterns transform an input complex point-spread function into several single-mode outputs, where a selected core feeds the fundamental mode to a photonic science instrument, while the remaining cores are used for wavefront sensing in a closed-loop adaptive optics system. A neural network maps the intensities of the wavefront sensing cores to an estimated wavefront correction, which is applied to an upstream deformable mirror. However, there exists a trade between maximizing the amount of light reserved for the photonic instrument and the reduced signal-to-noise ratios for the wavefront sensing cores. We explore wavefront correction for the Seidr instrument, a part of the Asgard Suite for the Very Large Telescope Interferometer. We evaluate different neural network architectures, comparing wavefront estimation performance for different wavefront error types, as a first step toward addressing the signal-to-noise trade-off. Results show transformer neural networks as a promising solution for temporal photonic lantern-based wavefront estimation.
Read the full abstract

All authors Nathan K Long, Barnaby Norris, Daniel S Dahl, Christopher H Betters, Julia J Bryant, Nick Cvetojevic, Sergio Leon-Saval, Frantz Martinache, Marc-Antoine Martinod, Akira Rodziewicz-Ryan, Adam K Taras, Jin Wei, Peter G Tuthill