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Physics Physics

physics Watch Physics

Tuesday 23 June

Fluid Dynamics

Oscillatory liquid-metal flow in a channel under rapidly decaying applied magnetic field

Oleg Zikanov, Haroon Ahmad and Sergey Smolentsev

The channel flow of a liquid metal driven by a rapidly varying applied magnetic field is analyzed. The flow configuration, physical properties, and parameters correspond to a duct within a liquid-metal blanket of a nuclear fusion reactor under off-normal plasma conditions, such as plasma disruptions. The problem is solved numerically using a one-dimensional flow approximation. The longitudinal magnetic field, decaying at a typical rate on the order of 100 T/s, induces eddy currents that interact with a steady wall-normal magnetic field, generating the Lorentz force that drives the flow. Standing Alfvén waves are identified as the key mechanism controlling the liquid metal's response. These waves manifest as large-amplitude, gradually decaying oscillations of velocity, the induced magnetic field, and eddy currents. A parametric study predicts a severe response developing within the first few milliseconds of the event, with maximum flow velocities reaching several meters per second and Lorentz forces exceeding $10^9 \text{ N/m}^3$. Power-law approximations for the dependencies of the response characteristics on the flow parameters are developed. Finally, the effects of fluid compressibility and pressure waves are analyzed and found not to lead to a major modification of the flow evolution.
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Physics and Society

Wasserstein recurrence networks for multiscale time series pattern analysis

Béatrice Désy, Nicholas R. Golledge, Hana Ishii et al. (+1 more)

Time series data are often generated by systems which operate on multiple temporal scales, of which Earth's climate system is a paramount example. Variations in global climate are recorded in paleo-environmental archives as temporal patterns across a wide range of time scales, from seasonal or decadal to multi-millennial. In this context, recurrence analysis, where repeating patterns are identified in time series, is limited by the underlying properties of the distance function used and of the time series data themselves, especially in terms of temporal resolution and scale dependence. In this paper, we present a novel recurrence analysis framework designed for multiscale time series data with abrupt changes and irregular temporal resolution as found in paleoclimate records. We introduce a simple mathematical transform to use the $1-$Wasserstein distance for recurring pattern detection in time series. The scale invariance of $1-$Wasserstein distance distributions between patterns in Brownian motion is demonstrated numerically, which provides a principled threshold choice for recurrences. At any time scale, recurrences are defined as local minima of the distance, granted that they are below a threshold given by the probability of encountering patterns at least as similar in one-dimensional Brownian motion. Recurrences can be further combined according to a non-overlapping condition to yield a distinct set of multiscale recurring events. We provide examples of climatic applications from ice-rafted debris and ice core records, where detected recurrences have durations spanning over two orders of magnitude. Our method extends recurrence analysis to more complex time series data and provides new avenues for statistical identification and analyses of recurring events at multiple temporal scales.
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All authors Béatrice Désy, Nicholas R. Golledge, Hana Ishii, Markus Luczak-Roesch

Computational Physics · Cross-listed from math.NA

No 3D Matrices: A Unified Tensor-Product View of Matrix-Free Cartesian PDE Solvers

Yong Yi Bay and Kathleen A. Yearick

Every Cartesian three-dimensional PDE solver hides a structural secret that production CFD codes have used for half a century and that graduate-level textbooks rarely state plainly. The derivative matrices, the compact Padé line solves, the Galerkin mass inversions, the alternating-direction-implicit substeps, and even the fast Poisson and Helmholtz diagonalization transforms all factor along the coordinate axes and collapse into repeated one-dimensional banded kernels executed along the grid lines. The three-dimensional operator exists only on paper; it is never assembled, factored, or stored. This paper is the manual for that collapse. We derive the Kronecker-product algebra that makes it exact, carry it cleanly through central differences, compact schemes, tensor-product Galerkin, B-spline and isogeometric methods, collocation, ADI time stepping, and direct Poisson and Helmholtz solves, and bring into the open the three production tricks that turn the reduction into hardware-conscious floating-point throughput on real machines: the multi-right-hand-side reshape that exposes a sweep as one batched line kernel (a dense BLAS-3 GEMM when the line factor is dense or element-local, a banded or stencil kernel when it is not), the sum factorization that rescues high-order Galerkin from the $O(p^{2d})$ quadrature trap, and the pencil decomposition that keeps every direction contiguous across an MPI cluster. For fixed stencil width or fixed polynomial degree, the compute cost stays $O(N)$ in the total number of unknowns $N = N_x N_y N_z$; the operator storage drops to $O(N_x + N_y + N_z)$ up to bandwidth constants; direct separable Poisson and Helmholtz solvers add the expected transform cost; the line kernels are embarrassingly parallel. These facts are familiar to practitioners but rarely assembled in one place; this paper collects them and shows how to use them.
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Accelerator Physics

Slow Extraction Beam Commissioning for the Mu2e Experiment at Fermilab

V. Nagaslaev, G.Annala, J.Berlioz et al. (+7 more)

Following the successful completion of the Muon g-2 experiment run at Fermilab, the Muon Campus facility has been reconfigured from delivering 3 GeV muon beams to the g-2 storage ring to providing slow-extracted 8 GeV proton beam spills for the Mu2e experiment. The first full-scale commissioning run with slow extraction was conducted during the 2025 run, followed by the second run in early 2026. We present the results and current status of this commissioning campaign.
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All authors V. Nagaslaev, G.Annala, J.Berlioz, G.Deinlein, B.Drendel, J.Morgan, A.Narayanan, J.StJohn, D.Still, D.VanderMeulen

Fluid Dynamics

Liquid Jet in Crossflow: Review of Breakup modes and Injector Geometry Effects

Anubhav Sinha

This review focuses on the liquid jet in crossflow (LJIC) configuration. LJIC is one of the most common strategies used for fuel injection in aerospace applications. It is popular due to its simplicity and efficient atomization characteristics. The aerodynamic force of the airflow is utilized to break liquid jet into small droplets. The objective of the present work is to give a basic overview of the physical processes involved in the breakup and penetration of LJIC. Breakup modes and underlying mechanisms are discussed in detail. Various modes are described and associated non-dimensional numbers are explained. Injector geometry which is often overlooked in literature is paid special attention. The mechanism of liquid jet instability getting triggered by velocity profile redistribution is explained using experimental and computational results. Surface waves on liquid jets are discussed. A theoretical model used to predict the wavelength of surface waves is described. DNS results are used to demonstrate the growth of surface instability on a liquid jet. Jet penetration and trajectory in the presence of crossflow are discussed. Various trajectory equations and the parameters used are discussed in detail. Progress in computational studies for LJIC is highlighted and challenges are discussed.
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Atmospheric and Oceanic Physics

Machine learning is revolutionizing weather forecasting – the next step is a change in how we work

Peter Dueben, Peter Bauer, Oliver Fuhrer et al. (+2 more)

Following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, this article shifts attention from forecast output to the working practices that make prediction systems possible. We argue that machine learning and recent digital technologies will reshape the forecasting value chain: how models are coded and developed, how observations and Earth-system data are exploited, how data and computing are managed, how systems are verified, and how information is created, evaluated and turned into services. We discuss six non-exhaustive areas in which agentic software engineering, open and compressed data, shared verification workflows, interactive computing and generative methods may make modelling, evaluation and service creation faster, more interactive and more widely accessible. These changes will require weather and climate centres to adapt their infrastructures, data stewardship, trust and quality-assurance frameworks, skills and service delivery while maintaining scientific understanding, operational reliability, human expertise and their public-service role.
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All authors Peter Dueben, Peter Bauer, Oliver Fuhrer, Nikolay Koldunov, Jørn Kristiansen

Fluid Dynamics

Solver Exactness, Learned Flexibility: Equivariant Boundary-Correction Operators for Stokes Flow

Denis Gueyffier

Computing the viscous Stokes flow around a shape requires solving a boundary-integral equation, and for a new shape the solve must begin from scratch. Learned operators promise to spread this cost across shapes, but it is not clear what such an operator retains of the solver it replaces, or what determines whether it transfers to shapes it was not trained on. We make both questions answerable by choosing a problem which is exactly solvable except for a single term: a second-kind boundary-integral problem is solved exactly using a kernel-independent fast summation, while the boundary correction, which has no closed form, is learned. The solver's guarantees carry over unchanged: exactness on the closed-form part, $O(N)$ scaling, $SO(3)$-equivariance to machine precision, and an $O(N)$ differentiable adjoint. We then make precise what the learning contributes. It does not contribute to accuracy, differentiability, or scaling $O(N)$, all of which are provided by the solver. Learning contributes a one-time cost in that the forward map is trained once and then evaluated on a new shape in a single pass rather than resolved. Measured against baselines, the learned map is $5$ to $16\times$ more data-efficient than a black-box DeepONet and maintains a $\sim\!2.5\times$ lower in-distribution error than a geometry-aware operator, although that operator is stronger in the low-data limit and the learned map is less reliable out of distribution. We trace this fragility to the global parameterization and reduce it with a local equivariant kernel. The exactly-solvable setting yields clarity about the mechanism: geometric generalization is governed by invariance and coverage, not by conditioning or by capacity.
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History and Philosophy of Physics · Cross-listed from gr-qc

Diffeomorphism-Invariant Quantities in Phase Space: More than Correlations

Álvaro Mozota Frauca

A popular view in the foundations of diffeomorphism-invariant theories is that their physical content is encoded in correlations or `observables': a set of phase space functions that have vanishing Poisson brackets with the constraints related to diffeomorphisms. In this article I study the phase space structure of models with a temporal diffeomorphism invariance and prove a series of formal results that challenge this view in a few ways. First, I show how this view is not applicable to all the phase space trajectories of every diffeomorphism-invariant theory. Second, I show how correlations can be proved to be invariant only in a way that generalizes the standard definition of invariance and in a way that does not provide smooth phase space functions. Third, I prove that spatiotemporal relations are also invariant. Fourth, I prove that spatiotemporal structures are indispensable for defining the invariant content of diffeomorphism-invariant models. Finally, I comment that these results are expected to be generalizable for models invariant under $d$-dimensional diffeomorphisms, which represents a challenge for some views in the foundations of general relativity and quantum gravity.
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Optics · Cross-listed from cond-mat.quant-gas

Quantum Geometry in the Continuum: Solitons in Shallow Lattices

Koorosh Sadri and Mikael C. Rechtsman

The quantum geometry of electronic, photonic, and atomic lattice systems quantifies the distance in Hilbert space between Bloch states at neighboring lattice momenta. This quantity has profound implications for flat-band systems especially, characterizing surprising behavior such as superfluidity and superconductivity when the group velocity is zero and no transport would be expected for non-interacting particles. However, when the band is not flat, the effects of quantum geometry are often intertwined with and partly masked by the band dispersion. Here, we show that in weakly interacting bosonic systems in the critical dimension (i.e., two dimensions for Kerr nonlinearity), the deviation from critical behavior due to the presence of the lattice is governed by the quantum geometry, which is directly proportional to the fourth-order dispersion. Furthermore, we identify the family of continuous lattice potentials that saturates the bound on the quantum metric for a given effective mass tensor.
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Optics

Generation of continuous-wave laser light at 148.4 nm using cavity-enhanced second harmonic generation in $BaMgF_4$

Keerthan Subramanian, Hiroki Tanaka, Simon J. Herr et al. (+22 more)

The development of a nuclear optical clock based on \textsuperscript{229}Th is expected to influence the next generation of time and frequency standards. At its heart is the need for a continuous wave (CW) at laser at 148.4~nm to drive the clock transition between the ground and excited isomeric state. In this work we experimentally investigate the potential of BaMgF\textsubscript{4} crystals to create a CW solid state laser at the vacuum ultraviolet (VUV) wavelength of 148.4~nm via cavity-enhanced second harmonic generation. For this purpose, a BaMgF\textsubscript{4} crystal was grown, optically polished and periodically poled. The crystal was inserted into a power-enhancement cavity, resonant at the fundamental wavelength of 296.8~nm and the generated laser light at 148.4~nm was characterized. Within this proof-of-concept investigation, a VUV output power of typically ($16\pm1$)~pW is obtained. This marks the first time that this type of crystal is used to generate VUV laser light. The experimental findings are compared to theoretical expectations and provide a clear path for future improvements, which could have implications not only for compact laser sources in the VUV but also solid-state as well as single-ion based \textsuperscript{229}Th nuclear clocks.
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All authors Keerthan Subramanian, Hiroki Tanaka, Simon J. Herr, Nutan Kumari Sah, Gaurav Jha, Florian Zacherl, Srinivasa Arasada Pradeep, Valerii Andriushkov, Ke Zhang, Darius Fenner, Yumiao Wang, Milena Hugenschmidt, Frank Kühnemann, Gaetano G. M. Bonetti, Shoichi Ui, Matthias Bickermann, Chenxi Ma, Xian Zheng, Michael Zopf, Bettina Lommel, Jan C. Müller, Stephan Hannig, Dmitry Budker, Ferdinand Schmidt-Kaler, Lars von der Wense

Instrumentation and Detectors

Machine Learning Approaches for Improved Scalability of Metallic Magnetic Calorimeters

Marc Oliver Herdrich, Toni Mattis, Günter Weber et al. (+3 more)

Metallic Magnetic Calorimeters (MMCs) are a promising new tool for high precision X-ray spectroscopy. However, the complexity of the detector response and the need for scalable processing pipelines pose significant challenges for their widespread adoption. In this work, we explore the application of Machine Learning (ML) methods to address these challenges and enhance the performance of MMCs. We demonstrate how ML can be used for pulse classification and artifact rejection, as well as for pulse shape analysis and feature extraction. By leveraging unsupervised learning techniques for label auto-discovery and supervised learning for classification and regression tasks, we show that ML can provide robust and scalable solutions for MMC signal processing. Our results indicate that ML-based approaches can achieve comparable performance to traditional methods while offering greater adaptability and efficiency, paving the way for the next generation of high-precision X-ray spectroscopy with MMCs.
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All authors Marc Oliver Herdrich, Toni Mattis, Günter Weber, Daniel Aaron Schnauß-Müller, Johanna Hanke Walch, Thomas Stöhlker

Geophysics · Cross-listed from cs.LG

Geo-Strat-RL: Learning Geological Event Reasoning from Verifiable Tasks

Lukas Mosser

To evaluate whether vision-language models can reason about geological histories, it is necessary to construct observations for which the underlying process history is known. Furthermore, reasoning over geological histories is not just a question of recognizing visual patterns, but also of understanding temporal and structural relationships that may be only indirectly visible or highly ambiguous. When ground-truth event histories are not uniquely identifiable or are unavailable, it remains an open challenge to teach models capable of visual reasoning to produce valid geological reconstructions that are consistent with both observed evidence and geological principles. We therefore investigate whether defining a verifiable geological reasoning task can improve geological event reconstruction across observation domains through reinforcement learning with verifiable rewards (RLVR). To this end, we present Geo-Strat-RL, a synthetic environment that generates stratigraphic observations and compact visible-evidence event histories. The environment combines a geological generator with an executable verifier that scores chronology, event identity, deposition, and structural relationships. We show that RLVR improves geological reconstruction in vision-language models (VLMs), increasing geological content scores on held out stratigraphic diagrams. We further evaluate the same held-out geological histories in a synthetic seismic observation domain by converting the generated scenes into acoustic-impedance-derived amplitude sections. In this controlled paired-renderer setting, we present evidence that geological reasoning learned from stratigraphic diagram-domain RLVR training transfers to synthetic seismic representations without seismic-specific training examples, supporting the hypothesis that RLVR can teach reusable geological reasoning concepts across related observation formats.
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Chemical Physics

ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potential

Linying Zhang and Julija Zavadlav

Implicit solvent machine learning potentials (MLPs) offer a powerful route to bridging the gap between accuracy and efficiency in molecular simulations. However, existing models have largely focused on aqueous environments, overlooking the diverse and important roles of non-aqueous solvents in areas such as organic synthesis and battery technology. Here, we present ConSolv, a solvent-conditional MLP architecture that explicitly incorporates solvent effects on solute interactions through an attention-based solvent-embedding block. By combining experimental solvation free energy data with ab initio data, we train a single implicit solvent MLP that is transferable across 66 common organic solvents. ConSolv outperforms classical explicit solvent methods and selected ab initio implicit solvent approaches across multiple solvation free energy benchmarks, and demonstrates generalization to unseen solvents. Beyond solvation free energies, the model shows close agreement with experimental nuclear magnetic resonance (NMR) data for $\gamma$-fluorohydrin molecules in chloroform. ConSolv's architecture is readily extensible to broader chemical spaces and alternative training strategies, while its attention-based design supports explainable artificial intelligence (AI) analysis that can help elucidate complex, solvent-dependent molecular interactions.
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Data Analysis and Statistics · Cross-listed from astro-ph.IM

Empirical-Bayes Unfolding of $\gamma$-ray Spectra

A. H. Mjøs, E. Lima, A. Kvellestad et al. (+2 more)

Unfolding observed $\gamma$-ray spectra is an ill-conditioned Poisson inverse problem. Detector response effects and finite energy resolution make distinct non-negative emitted $\gamma$-ray spectra nearly indistinguishable after forward mapping, so direct inversion can strongly amplify statistical fluctuations. Here, we present an empirical-Bayes hierarchical unfolding method that preserves the Poisson counting structure, enforces non-negativity, and incorporates background through a joint ON/OFF likelihood. The prior on the emitted spectrum is centered on an automatically selected Richardson-Lucy reference spectrum, with an adaptive width that remains broad in weakly constrained regions. Posterior inference is performed with the No-U-Turn Sampler, and simultaneous uncertainty bands are reported for the resolution-limited unfolded spectrum. Our Bayesian method provides a robust and extensible framework for uncertainty quantification in unfolding, and a direct comparison with a recent frequentist regularized maximum-likelihood method gives highly consistent unfolded spectra in representative high- and low-statistics cases.
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All authors A. H. Mjøs, E. Lima, A. Kvellestad, A. C. Larsen, M. Hjorth-Jensen

Chemical Physics · Cross-listed from cond-mat.soft

Curvature-induced smectic-C order of tangentially anchored hard spherocylinders on a sphere with a rigidly locked director field

Jonathan Washburn, Hartmut Löwen and Elshad Allahyarov

We study the strict locked-orientation limit of hard spherocylinders on a sphere, in which the rod axes are rigidly locked to a prescribed tangential director field and cannot reorient. Because the bulk hard-rod phase diagram contains no smectic-C phase, any coherent tilt isolates a geometric curvature mechanism rather than a finite-stiffness equilibrium effect. A ratio-symmetric recognition cost fixes the layer spacing at the bulk close-contact value and yields a hierarchy of geometric statements: the lower edge of the smectic-area window at $45^\circ$ follows from reciprocal symmetry; the upper edge at $58.3^\circ$ is a falsifiable channel-saturation hypothesis; the smectic-A to smectic-C boundary is a closed-form prediction; and the rod tilt angle is set by the rod-to-radius ratio, modulated by a chirality envelope peaking near $24^\circ$. Locked-orientation Monte Carlo across fifteen geometries confirms these predictions with no fitted elastic constants: the smectic area peaks at $55^\circ$, and a coherent smectic-C window is detected.
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Physics and Society · Cross-listed from astro-ph.IM

Collaborating with Artists in the Search for Life

Jack Madden, Cybele Collins, Mia Rollins et al. (+1 more)

Art and science collaborations that go beyond outreach and advertisement in service of science have the potential to unlock new ways of seeing and understanding the Universe that science alone cannot reach. In this white paper for the NASA Decadal Astrobiology Research and Exploration Strategy (DARES) request for information, we outline examples and benefits of artscience and research-creation methods for astrobiology. The search for life and its origin is inherently interdisciplinary and requires novel approaches that could benefit from the training artists receive in design thinking, contextualization, speculation, and community building. We take a look at this process in action through the work of Robert Irwin during the 1970 NASA Habitability Symposium, Carl Sagan's approach to mixing art and science, and the Transition Design framework of creativity-led problem solving. Each example underscores a specific advantage of deeper art-science collaborations: Irwin's creative approach to problem-solving broke scientists from conventional thought patterns, Sagan's contextualization helped align scientific work with ethical and societal considerations, and design-led research is shown to improve planning and efficiency, even for problems as complex as searching for life. Specific implementation recommendations include specifically allowing funding for artist consultations in research grants, reviving NASA's artist-in-residence program, and supporting artscience training initiatives within the astrobiology community.
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All authors Jack Madden, Cybele Collins, Mia Rollins, Ashika Capirala

2606.24943 · 23 Jun · NASA-DARES 2025 white paper, 5 pages, 3 figures. Originally submitted in 2025 under topic 8 (Prepare for the Discovery of Life Beyond Earth and Subsequent Post-Discovery Activities), which now falls under focus area 9 (Astrobiology in Society)

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Atomic Physics · Cross-listed from quant-ph

Rapid Cavity-Based Mid-Circuit Measurement and Feedforward in a Neutral Atom Array

Tsai-Chen Lee, Jacquelyn Ho, Yue-Hui Lu et al. (+4 more)

Measuring part of a quantum system in the midst of its evolution and acting on the result in real time is essential for numerous quantum information protocols. Neutral-atom arrays are a leading platform for quantum information processing, but their mid-circuit measurement-and-feedforward cycle times have remained slow, typically exceeding 1 ms. Here we demonstrate fast mid-circuit measurement and real-time feedforward in an array of atomic qubits coupled to a high-finesse optical cavity. Local light shifts tune individual data qubits out of resonance with the cavity, shielding their coherence, while a near-resonant probe drives a selected qubit whose emission is collected with Purcell enhancement. Mid-circuit measurements of four qubits with sub percent infidelity reduce the coherence of a fifth unmeasured data qubit by less than 2%. We implement real-time feedforward to correct measurement-induced phase shifts and to realize an adaptive circuit for optimal quantum state discrimination and conditional state preparation. Our approach reduces the measurement-and-feedforward cycle time to below 100 $\mu$s and establishes optical cavities as a route to fast control of neutral-atom quantum systems.
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All authors Tsai-Chen Lee, Jacquelyn Ho, Yue-Hui Lu, Tai Xiang, Nathaniel B. Vilas, Zhenjie Yan, Dan M. Stamper-Kurn

Chemical Physics

Polyatomic Thermal Radiative Dissociation in Microcavities

Enes Suyabatmaz, Tuan Nguyen and Raphael F. Ribeiro

Blackbody infrared radiative dissociation (BIRD) activates molecules through successive absorption of ambient thermal photons until the internal energy reaches a dissociation threshold. Because these radiative transition rates depend on the electromagnetic density of states (DOS), structured infrared environments provide a route to control thermal unimolecular dissociation. Here we develop a state-resolved master-equation framework for polyatomic BIRD in a planar Au/MgO multilayer cavity, where the reactive cluster $\mathrm{(H_2O)_2Cl^-}$ is studied. The cavity modifies the kinetics through the DOS sampled by anharmonic fundamental, overtone, and combination transitions. We show that MgO surface phonon polaritons produce strong near-field enhancements in the central vacuum reaction region of a microcavity. We find that short cavities with thick polar crystal layers yield the largest BIRD enhancements due to enhanced evanescent surface phonon polariton contributions. We further include collisions with a methane bath gas and show that cavity DOS engineering shifts the crossover between BIRD and collisional activation. These results establish Reststrahlen-band DOS engineering as a practical strategy for controlling polyatomic BIRD in infrared microcavities.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci

Mechanism of Band Gap Formation in Beam Networks

Kwangmin Lee, Charles Emmett Maher, Katherine A. Newhall et al. (+1 more)

Band gaps are commonly attributed to Bragg scattering or local resonance, yet it remains unclear whether these mechanisms govern band gap formation in beam networks. In this work, we explain band gap formation in beam networks in terms of a new mechanism, geometry-induced coupling between deformation modes. Specifically, band gap onset arises from axial-bending coupling at lattice nodes and scales with the axial cutoff frequency of a one-dimensional periodic beam, whereas band gap termination is primarily governed by high-frequency rotational branches associated with beam geometry. This mechanism holds for both periodic and disordered beam networks. In periodic lattices, it manifests through beam orientations at lattice nodes, whereas in disordered networks it manifests through short-beam statistics arising from variations in beam length. Together, these results establish a unified mechanism for band gap formation across both periodic and disordered beam networks, providing new insight into the physical origin of band gaps in beam-network materials.
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All authors Kwangmin Lee, Charles Emmett Maher, Katherine A. Newhall, Ryan C. Hurley

Physics and Society · Cross-listed from cond-mat.stat-mech

Exact log-odds representation and mean-field criticality of a growing social group model

Xingfu Ke and Fanyuan Meng

We present an exact analytical reformulation of a growing social group model – a Hamiltonian-free nonequilibrium process in which a group grows by noisy, consensus-driven admission. Cast as a gradient flow on logarithmic time, the fixed-point structure collapses to a single self-consistent equation: $\arctanh(\phi^*) = m \cdot \arctanh(\alpha\phi^*)$, where $\phi$ is the polarization, $\alpha=1-2\eta$ the evaluation reliability, and $m$ the number of evaluators. The equation has a direct log-odds interpretation: each verdict contributes log-likelihood ratio $2\arctanh(\alpha\phi)$; unanimity accumulates $m$ independent evidence pieces. The dynamics thus constitutes an exact mean-field theory of self-consistent inference, ordering when the collective gain $m\alpha$ overcomes the dilution of growth. We develop a systematic three-layer framework: core theory (Landau-like effective potential, comparison with the mean-field Ising model, and features without equilibrium counterpart), mathematical foundations (criticality from correlated verdicts, Pólya-urn martingale convergence, and an RG-like flow with group size as scale), and complementary perspectives on irreversibility and information geometry. A frozen-$N$ Freidlin–Wentzell quasipotential yields Kramers-type escape estimates for metastable states, while Monte Carlo simulations collapse onto a parameter-free deterministic master curve on logarithmic time. Systematic comparison with the mean-field Ising model reveals shared critical exponents but a nested arctanh structure unique to growth. These results provide a detailed analytical characterization of a minimal model of growth-driven collective behavior and map which elements of the equilibrium critical toolbox – suitably reinterpreted – survive without a Hamiltonian.
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Instrumentation and Detectors

LF-MightyPix: A second HV-MAPS prototype for the {LHCb} Mighty-Tracker

Toko Hirono, Sebastian Bachmann, Lucas Dittmann et al. (+5 more)

For the future high-luminosity operation of the LHCb experiment, the downstream tracker will be upgraded to the Mighty-Tracker. A key part of this upgrade is the introduction of silicon pixel detectors, MightyPix, in the central region of the tracker. We have developed MightyPix prototype chips using High-Voltage Monolithic Active Pixel Sensors fabricated in a commercially available CMOS process on high-resistivity wafers. The second prototype chip, LF-MightyPix, is fabricated in the LFoundry 150 nm CMOS process. LF-MightyPix has a chip size of 3.5 mm $\times$ 4.0 mm and a pixel size of 0.1 mm $\times$ 0.1 mm. For each pixel hit, both the time of arrival and the time over threshold are recorded to ensure correct bunch-crossing identification at 40 MHz. The results presented in this paper confirm compatibility with the MightyPix requirements.
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All authors Toko Hirono, Sebastian Bachmann, Lucas Dittmann, Richard Leys, Nicolas Striebig, Celina Welschoff, Hui Zhang, Ivan Peric

Classical Physics · Cross-listed from math.AP

A new perspective in linear Cauchy Elasticity: variational minimum principles for statics, dynamics, and heterogeneous materials

Amit Acharya

A variational minimum principle for linear elastodynamics of a possibly heterogeneous material without a stored energy function is developed. It involves a change of variables to dual fields, and results in a degenerate elliptic Euler-Lagrange system, even when the primal elastodynamics is hyperbolic. Uniqueness assertions for the dual dynamic and static problems and implications of the degenerate ellipticity are sketched. Some implications pertaining to heterogeneous materials and ones with indefinite elastic moduli are discussed.
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Atomic Physics

Two-Electron Effects Extend High-Harmonic Generation into the keV Regime

Isobel McSweeney, Andres Marchisio, Javier Rivera-Dean et al. (+4 more)

Two-electron processes can generate high harmonics beyond the conventional single-active-electron cutoff. Motivated by recent experimental evidence of an extended secondary plateau in the helium high-harmonic spectrum [S. Wang et al, Optica, (2023); S. Wang et al, In Print in Nature Photon., (2026)], we present a two-electron generalisation of the strong-field approximation. We analyse the resulting expressions using the saddle-point method and determine the extended cutoff. We find good agreement with classical predictions of cutoff scalings of $4.7$ and $5.5$ times the ponderomotive energy, which significantly exceed the established single-electron scaling of 3.17. We calculate high-harmonic spectra generated via a two-electron process in helium atoms driven by an intense few-cycle infrared laser pulse. Our results demonstrate that the harmonic spectrum extends far beyond the water window, reaching photon energies up to $\approx 1.2\,\mathrm{keV}$ in the soft x-ray region. The large spectral bandwidth can support the generation of sub-attosecond soft x-ray pulses, which are of particular interest for probing ultrafast dynamics across matter, including applications in core-level spectroscopy and biological imaging.
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All authors Isobel McSweeney, Andres Marchisio, Javier Rivera-Dean, Philipp Stammer, Paraskevas Tzallas, Marcelo F. Ciappina, Maciej Lewenstein

Geophysics

Offset-continuation-trajectory stacking based on common-reflection-point kinematics for five-dimensional prestack dataset regularization and enhancement

Tiago A. Coimbra, Rodrigo Bloot, Caian Benedicto et al. (+2 more)

Prestack seismic data regularization and enhancement are critical steps for reliable imaging and inversion, particularly in five-dimensional (5D) dataset geometries affected by irregular sampling, noise contamination, and incomplete spatial coverage. These limitations often degrade event continuity and compromise the physical consistency of conventional interpolation methods. This study introduces a physics-informed framework for 5D prestack dataset reconstruction based on a multi-parameter common-reflection-point (CRP) traveltime stacking operator. The proposed offset-continuation-trajectory (OCT) operator derives coherent stacking trajectories from wavefront propagation, isochronous surface geometry, specular reflection, and diffraction kinematics. All kinematic parameters are estimated directly from the data through a global coevolutionary optimization strategy. The method reconstructs missing traces and enhances spatial continuity by stacking seismic events along physically consistent traveltime surfaces, preserving both reflection and diffraction kinematics. Applications to synthetic and field datasets demonstrate improved signal-to-noise ratio, enhanced structural continuity, and reliable recovery of unrecorded amplitudes without introducing artificial events. The results indicate that incorporating physically constrained traveltime models into the regularization process provides a robust, geologically consistent alternative to purely mathematical interpolation techniques, thereby improving data fidelity for subsequent imaging and quantitative interpretation.
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All authors Tiago A. Coimbra, Rodrigo Bloot, Caian Benedicto, Nicholas T. Okita, Jorge H. Faccipieri Junior

Geophysics · Cross-listed from astro-ph.EP

Coupled atmospHere Interior modeL Intercomparison (CHILI). I. Evolutionary Modelling – Primordial Magma Oceans of Earth and Venus

Harrison Nicholls, Joshua Krissansen-Totton, Tim Lichtenberg et al. (+22 more)

Earth and Venus represent two evolutionary outcomes arising from initially molten 'magma ocean' periods, followed by lifetimes of chemical and geophysical divergence. Their physics is common to all rocky planets and is accessible to simulations that adopt coupled interior-atmosphere modelling approaches. Our understanding of planet histories and interpretation of current states is dependent on this modelling, yet existing codes vary in their approximations. Here, we present the first results from the Coupled atmospHere Interior modeL Intercomparison (CHILI) project; benchmarking planetary evolution codes in the context of Earth and Venus to identify key model sensitivities. Our 'nominal' Earth models predict magma ocean solidification timescales within 4 Myr of thermal evolution, and are consistent with empirical constraints on Earth's early history. Venus scenarios exhibit more diverse behaviours where prolonged magma ocean stages can be conditionally sustained for 50 Myr. Cooling timescales correlate with initial hydrogen and carbon budgets, but model-specific treatments of volatile partitioning and vertical energy transport introduce substantial inter-model variance. Different parametrisations of mantle geodynamics, convection, melting curves, rheological properties, and radiative transfer give rise to divergent evolutionary behaviours. Discrepancies in atmospheres generated by magma ocean outgassing underscore these differences, although C-H-O compositions with surface pressures exceeding 100 bar are favoured. This intercomparison identifies critical sensitivities in volatile partitioning, escape processes, mantle viscosity, and melting. Validating these treatments is essential for enabling deep insight into the early histories of the Solar System's terrestrial planets, and for drawing meaningful interpretations from ongoing observational exoplanet campaigns.
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All authors Harrison Nicholls, Joshua Krissansen-Totton, Tim Lichtenberg, Laura Schaefer, Keiko Hamano, Maxime Maurice, Henri Samuel, Alexandra Papesh, Carlos Ortiz-Quintana, Junellie Perez, Yamila Miguel, Denis Sergeev, Philipp Baumeister, Spanan Dash, Leoni Janssen, Jonathan Keathley, Alexandre de Larminat, Emmanuel Marcq, Lena Noack, Hugo Pelissard, Bo Peng, Emma Postolec, Ramses Ramirez, Mariana Sastre, Andrea Zorzi

Applied Physics

Material identification using laboratory X-ray beam tracking: quantitativeness and signal-to-noise ratio requirements

Sumera Rehman, Ashkan Ajeer, Connor Darling et al. (+3 more)

Simultaneous structural and elemental characterisation of a specimen in a non-destructive manner is an instrumental approach with applications in a variety of fields including energy materials, cultural heritage and life sciences. This is routinely performed at synchrotron facilities, e.g. by combining X-ray imaging and X-ray fluorescence. In this work we describe an approach based on a monochromatic implementation of X-ray beam tracking (XBT), a multimodal imaging technique compatible with standard laboratory sources. Monochromatic XBT gives simultaneous access to quantitative absorption and phase properties of the sample, which are related to the atomic number and the electron density respectively: their combination allows for material discrimination. Here we focus on investigating the effect of the signal-to noise ratio on the quantitativeness of the results, hence on the elemental identification. We present an XBT experiment performed using a standard X-ray laboratory source to identify the composition of three different test samples made out of Ag, Fe and Cu. These specific materials were selected as relevant to archaeological studies e.g. when specimen buried for centuries are in contact with the surrounding soil containing traces of these metals. We review the results, current limitations and provide guidance for future developments for structural and elemental characterisation in a laboratory setting.
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All authors Sumera Rehman, Ashkan Ajeer, Connor Darling, Marco Endrizzi, Alessandro Olivo, Silvia Cipiccia

Chemical Physics

Quantum nuclear and band-dispersion effects recover near-UV absorption in short-hydrogen-bonded organic crystals

Jonas Hänseroth, Max Großmann, Malte Grunert et al. (+4 more)

Near-UV optical absorption is increasingly reported in hydrogen-bonded organic and biomolecular materials lacking aromatic or extended pi-conjugated chromophores, yet its microscopic origin remains unresolved and electronic-structure calculations often overestimate experimental absorption onsets. Here, we combine machine-learned interatomic potentials for large-scale classical and quantum nuclear sampling with periodic excited-state calculations to address this discrepancy in L-pyroglutamine ammonium, an experimentally established glutamine-derived crystal containing a well-resolved short hydrogen bond and exhibiting non-aromatic near-UV optical response. Using controlled in silico ion substitutions that vary the surrounding hydrogen-bond environment while preserving this scaffold, we compute optical spectra from configurations sampled along classical and quantum nuclear trajectories using hybrid-functional time-dependent density functional theory. We show that nuclear quantum effects stabilise proton-sharing configurations that are strongly suppressed classically, redshifting the lowest bright excitations by 0.5-0.8 eV and raising the fraction of configurations with bright excitations below 6 eV from approximately 3% to approximately 30%. Explicit Brillouin-zone sampling provides a further, mechanistically distinct redshift of 0.5-1.1 eV, reflecting modest but significant indirect electronic character. Only when both effects are incorporated does the calculated onset recover the experimental 3.8-4.5 eV range. These results establish quantum proton fluctuations and reciprocal-space convergence as cooperative but physically distinct ingredients required for predictive optical spectroscopy of strongly hydrogen-bonded molecular materials.
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All authors Jonas Hänseroth, Max Großmann, Malte Grunert, Ali Hassanali, Erich Runge, Christian Dreßler, Muhammad Nawaz Qaisrani

Instrumentation and Detectors

A Reproducible Benchmark and Evidence-Retrieval Software Framework for Silicon Detector R&D Literature

Tianqi Gao, Ruobing Jiang, Dawei Fu et al. (+2 more)

Silicon pixel detector R&D depends on a large and rapidly growing technical literature, including beam-test and irradiation studies, performance measurements, simulation, and design reports. Locating the supporting evidence passage for a measurement, operating condition, or design decision is therefore a computing and data-science challenge for detector-development workflows. General-purpose language models are insufficient unless grounded in traceable primary sources, particularly in a domain with specialised terminology, configuration-dependent measurements, and rapidly evolving experimental results. We address this with a reproducible, general-purpose framework for evidence-grounded retrieval over technical literature, using silicon pixel detector R&D as a demanding validation domain. The framework combines sparse lexical retrieval, dense semantic retrieval, and hybrid reciprocal-rank fusion, with an optional graph-guided exploration layer and grounded, abstention-aware response generation. The accompanying benchmark provides manually curated chunk-level evidence annotations, source-level diagnostics, semantic relevance checks, and negative-query abstention tests over two detector query sets. We evaluate six retrieval configurations across 378 source documents and 8,442 indexed chunks. Hybrid sparse-dense retrieval gives the strongest strict evidence recovery, achieving Hit@5 of 0.917 on the core benchmark and 0.951 on the curated extension benchmark, while graph-based methods are more effective for literature exploration and source discovery. Graph expansion is therefore best employed as a discovery layer over the hybrid retrieval backbone. The framework provides reusable software for traceable, 1 evidence-grounded knowledge access in silicon detector R&D and high-energy physics instrumentation.
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All authors Tianqi Gao, Ruobing Jiang, Dawei Fu, Qiang Li, Matthew Kenzie

Computational Physics · Cross-listed from cond-mat.dis-nn

On the localization transition from MAA to AA models

Hangdong Qiu and Yunhua Wang

Despite their potential similarity between the mosaic Aubry-André (MAA) and AA models, the MAA model allows mobility edges (MEs), whereas the AA model does not. Here we develop a new double quasiperiodic MAA (DMAA) model consisting of one primitive MAA with nonzero even-site potentials and the other modified one with both nonzero odd-site potentials and a tunable amplitude factor, to reveal how localization transitions evolve from MAA to AA models. Interplays and competitions among the extended, critical and localized states arising from superpositions of double quasi-periodic MAA potentials enable new twice and multiple localization-delocalization transitions besides the original single localization transition. Our numerical calculations on inverse participation ratio, normalized participation ratio, fractal dimension and real-space wavefunction distribution confirm such localization features. The continuum model simulations on the experimental polariton modes also yield consistent results and hence validate their experimental feasibility. The constructed DMAA model provides a new framework for studying the localization transition processes between two analogous quasiperiodic models and broadens the understanding of Anderson localization.
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