Papers

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Physics

Tuesday 9 June

Fluid Dynamics

Rotation-to-translation conversion by geometric asymmetry in viscoelastic fluids

Takuya Kobayashi, Hiroki Kitano and Ryoichi Yamamoto

Microscale locomotion in Newtonian fluids is constrained by kinematic reversibility. Here we show that viscoelasticity provides a distinct route: an achiral fore-aft asymmetric body rotating in a viscoelastic fluid generates net translation through normal-stress-driven secondary flows. Direct numerical simulations combined with scaling analysis reveal the universal scaling law, $V\sim {\rm Wi}\cdot S$, where $\rm Wi$ is the Weissenberg number and $S$ is the skewness of the axial volume distribution. This result identifies a minimal geometric principle for rotation-induced propulsion in viscoelastic fluids, and suggests a route for active microrheology via propulsion-speed measurements.

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Quantum Physics

The Quantum Transition State

Pouya Khazaei

For nearly a century, the transition state—the structure organizing motion between reactants and products—has seemed to lack an exact quantum counterpart. Its geometry—a recrossing-free dividing surface whose one-way flux gives the exact rate—seems to require position and momentum direction at once, in tension with the uncertainty principle. The obstruction seems fundamental. It is not. The exact quantum equations generate a phase-space flow whose stable and unstable manifolds intersect in a unique bounded trajectory—the quantum transition state—that anchors such a surface. Quantum mechanics does not destroy the transition state; it becomes quantum.

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Adaptation and Self-Organizing Systems

Coupling-split clusters in a swarmalator model with uniform coupling disorder

K. P. O'Keeffe

We study the one-dimensional swarmalator model in which the phase coupling $K_i'$ is drawn from a uniform distribution. Our main result is a static coupling-split cluster, in which the population partitions across the threshold $K'=0$ that separates positively coupled ($K_i'>0$) from negatively coupled ($K_i'<0$) swarmalators, with smaller order parameter $s=\mu/\gamma$ set by the positive-coupling excess. The familiar async, phase-wave, and sync states persist, but each stability boundary feels a different part of the distribution: async the mean same-coordinate response, sync the most negatively coupled particle, and the phase wave the full density through a logarithmic characteristic equation. At a cusp where its Hopf and real-eigenvalue branches meet, the phase-wave dispersion has a double zero – the spectral signature of a Bogdanov–Takens point – and simulations nearby show a small-amplitude breathing limit cycle. For supports containing strongly negatively coupled particles the order parameters instead oscillate persistently.

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Applied Physics

Filamentary Transport and Thermoelectric Effects in Mushroom Phase Change Memory Cells

Md Samzid Bin Hafiz, Helena Silva and Ali Gokirmak

We performed a 2D finite-element electrothermal computational study of thermoelectric effects and filamentary electronic transport in Ge$_2$Sb$_2$Te$_5$ mushroom phase change memory cells during Reset and Set operations, accounting for spatial activation energy variations in amorphous Ge$_2$Sb$_2$Te$_5$ and phase-change dynamics. Reset operations with current going from the top electrode to the narrow 4 nm bottom electrode require $\sim$3x less energy and power, and $\sim$2x lower current to achieve the same Reset resistance, compared to the opposite polarity, due to thermoelectric effects. Filamentary conduction, electrical breakdown, thermal runaway, and local crystallization of amorphous Ge$_2$Sb$_2$Te$_5$ depend on current polarity and thermal boundary conditions, and determine the location, shape, and volume of the programming region, which may be significantly smaller than the semi-cylindrical mushroom region. The programming volume does not scale with contact dimensions larger than 10 nm. Larger contact areas introduce increased device-to-device and cycle-to-cycle variability due to filamentary conduction but are expected to lead to higher reliability and endurance.

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Monday 8 June

Computational Physics

Input-schema identifiability limits in physics-informed surrogates for mechanics-governed flow

Daniel Cieslak and Andrzej Czyzewski

Physics-informed and data-driven surrogates are increasingly used to approximate mechanics-governed flow fields, but the target quantities assigned to such models are not always identifiable from the input variables available at prediction time. We introduce an input-schema identifiability certificate for computational surrogates. Starting from a reduced physical model, the certificate decomposes a target field into components that are measurable from geometry, components that require boundary-condition information, and components identifiable only up to a symmetry quotient. This yields a pre-training audit: it predicts which oracle-channel interventions should reduce error, which should fail, and which ambiguity cannot be removed by changing the architecture, loss, optimizer, or sample size. We instantiate the framework for incompressible tubular flow using a Cosserat-rod reduction, where lumen velocity separates into a mesh-measurable tangent direction, a boundary-condition-dependent magnitude, and a signed-orientation ambiguity. Controlled experiments on patient-specific aortic CFD geometries, analytic Womersley flows, and an advection-diffusion transfer problem confirm the predicted pattern: supplying signed direction collapses angular error to the oracle regime, whereas supplying magnitude without orientation leaves the predicted sign ambiguity and yields 16-33 percent per-node sign flips. The results provide a mechanics-based diagnostic for deciding whether a surrogate modelling task is physically identifiable before training, and expose failure modes that aggregate error metrics can hide.

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Physics and Society

The Sustainability Paradox of Biodegradable Packaging: A Life Cycle Perspective on Chitosan-Based Food Packaging

Maria Cosic, Milena Cosic, Amy Kim et al. (+9 more)

Chitosan-based nanomaterials are being increasingly explored as sustainable alternatives to petroleum-derived food packaging, yet their environmental performance across the full life cycle remains insufficiently understood. This review critically evaluates these systems from a life cycle perspective and examines how material origin, processing pathways, functional performance, and end-of-life behavior collectively influence sustainability outcomes. Beginning with chitin extraction from crustacean waste, key processing steps, including demineralization, deproteinization, and nanoparticle synthesis, are assessed in terms of chemical intensity, energy demand, and associated emissions. Manufacturing routes, including solvent-based and green synthesis approaches, are compared with those of conventional plastics to identify relative environmental burdens. The use phase is analyzed with respect to antimicrobial functionality, shelf life extension, and potential reductions in food waste. End-of-life pathways, including biodegradation and composting, are evaluated alongside uncertainties related to degradation behavior and nanoparticle fate. By synthesizing these stagewise interactions, this review highlights critical trade-offs that are often overlooked in sustainability narratives and examines whether chitosan-based nanomaterials provide net environmental benefits in food packaging applications.

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All authors Maria Cosic, Milena Cosic, Amy Kim, Siddhartha Pahari, Grace Wu, Rayna Zhou, Trisha Daftari, Shaily Gupta, Reyna Michelle Suneel, Srinivasan Latha, Rashi Sharma, Utkarsh Chadha

Physics and Society

A Benchmark Generator of Realistic Logistic Transport Networks with Nonlinear Edge Costs and Transshipment Capacities

Varvara Kovaleva and Alexander Ponomarenko

Routing many commodities through a logistic network at minimum joint cost of vehicle movements and transshipment at intermediate storages becomes substantially harder – and substantially more realistic – once two features are present together: edge costs that are nonlinear in the transported volume, because freight travels in fixed-capacity vehicles, and hard upper bounds on the volume reloaded at each storage. No public benchmark captures this combination, and the locations of commercial distribution centres are proprietary. We close this gap with a parameterised generator of synthetic logistic networks, calibrated on real road graphs of nine European countries, five U.S.\ states and the European part of Russia extracted from OpenStreetMap. The generator reproduces the structural signatures of upper-level distribution networks – near-planar topology, low vertex degree, short edges and large diameters – by combining Zipf-distributed city sizes, a spatial-network budget model trading edge-building cost against routing convenience, centrality-driven storage capacities, and a doubly constrained gravity demand model. We release 35 fully synthetic instances ($10$–$150$ storages) and 15 real-geometry instances in an open, self-describing, fully reproducible file format. An empirical characterisation computed directly from the released files shows that synthetic and real-geometry instances are structurally consistent (two-sample Kolmogorov–Smirnov distances of $0.10$ on degree and edge-length distributions, with small and quantified mean deviations), and a baseline-solver study demonstrates that the corpus discriminates instance difficulty across orders of magnitude. The benchmark is a faithful, reusable testbed for the development and comparison of exact and heuristic algorithms.

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Physics and Society

Mobility-Informed Coupling of ABM, PDE, and ODE Models for Pandemic Simulation in Germany

Kristina Kehrer and Tim O. F. Conrad

Simulating epidemic spread across an entire country requires balancing fine-grained realism with computational feasibility. We address this trade-off with a multiscale, hybrid modeling framework for simulating the spread of COVID-19 across Germany. The spatial domain is split into regions, each represented either by a high-resolution agent-based model (ABM) incorporating mobility data from mobile phones or by a faster, less detailed model based on partial (PDEs) or ordinary differential equations (ODEs). Data-driven jump processes model mobility between regions, enabling individuals to be transferred between model domains. Building on earlier studies on pairwise coupling strategies, we develop a unified framework that combines all three model classes within a single simulation environment. To demonstrate the framework's utility, we systematically compare ABM, PDE, and ODE representations of Berlin embedded in a nationwide simulation of Germany, investigate regional travel restrictions, and evaluate the Zero-COVID and No-COVID strategies. The results indicate that model resolution can be reduced in sufficiently homogeneous regions without substantially altering epidemic dynamics. Further, they reveal that mobility restrictions can lead to non-intuitive outcomes, including cases in which regional border closures increase infection numbers both locally and nationally. These effects are observed even between non-adjacent regions, illustrating how emergent, system-wide dynamics arise from local mobility restrictions. We quantify computational performance in terms of runtime savings and validate the framework against real-world infection data. The results show that the hybrid framework substantially reduces computational cost without sacrificing predictive accuracy, offering a practical tool for evaluating regional mobility restrictions and public health interventions at national scale.

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General Physics

Geometric Phase-Space Structure in Cosmological Solutions of Einstein's Field Equations

Hassan Ugail

Einstein field equations allow cosmological dynamics to depart from the Friedmann-Lemaitre-Robertson-Walker (FLRW) idealisation in several physically different ways. Matter may become spatially inhomogeneous, the local expansion scalar may vary across a hypersurface, the expansion may acquire anisotropic components through shear, and the free gravitational field may be encoded in nonzero Weyl curvature. The key question is not only how far a model is from FLRW, but which geometric mechanism is responsible. A single departure from FLRW number cannot distinguish these mechanisms. This paper introduces a compact geometric diagnostic framework that keeps them separate while using standard quantities in general relativity. The framework is observer-explicit and domain-explicit, intended as a practical tool for comparing analytic and numerical solution families rather than as a new invariant classification of spacetime. Buchert's kinematical backreaction is retained as a derived explanatory quantity rather than a separate axis, since it is already fixed by the expansion-variance and shear contributions. A single curvature normalisation is used for all Weyl diagnostics. The method is tested on six benchmarks, namely FLRW, Bianchi-I, Kasner, Lemaitre-Tolman-Bondi dust, scalar-perturbed FLRW, and tensor-perturbed FLRW. These benchmarks occupy distinct regions of the diagnostic space, and the magnetic Weyl contribution appears only in the tensor case. The classification remains stable under changes of perturbation amplitude, spatial resolution, averaging domain, constraint reliability, and a leading-order observer tilt. The curvature expressions for the exact benchmarks are verified symbolically against metric-derived Weyl invariants, and the supporting computer code, numerical results, tables, and figures are publicly available.

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Soft Condensed Matter

Inverse Laplace Transform for Dynamic Light Scattering: Impact of Regularization

Pierre Pajuelo, Stéphane G. Roux, Adrien Meynard et al. (+3 more)

Dynamic Light Scattering (DLS) analyzes particle dynamics from the autocorrelation functions of scattered light intensity, yet extracting accurate relaxation time distributions from noisy data is challenging. We develop an inverse problem approach to recover this distribution by inverting the Laplace transform with physics-based regularization, called the CONTIN method. We improve it to use it on noisy data, across a wide range of time scales, with a selection of the regularization strength through a data-driven L-curve criterion. Our approach enhances robustness under high noise and reveals multi-scale dynamics in complex systems. Validation is performed on simulated data, compared to the Cramér-Rao bound and to parametric methods, and on experimental data from Carbopol microgels. It demonstrates superior accuracy over parametric methods, especially for broad time distributions. The algorithm's logarithmic discretization and variance-reduced correlation estimation enhance performance, offering a powerful tool for non-parametric DLS analysis and deeper insights into soft matter dynamics.

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All authors Pierre Pajuelo, Stéphane G. Roux, Adrien Meynard, François Liénard, Éric Freyssingeas, Pierre Borgnat

General Relativity and Quantum Cosmology

An approximate application of quantum gravity to the rotation problem

R. Michael Jones

Arbitrary initial conditions allow solutions of Einstein's field equations for General Relativity to have arbitrarily large relative rotation of matter and inertial frames. The “Rotation Problem” is to explain why the measured relative rotation rate is so small. Nearly any reasonable theory of quantum gravity can solve the rotation problem by phase interference. Even as early as about a quarter of a second after the initial singularity, quantum cosmology would limit the cosmologies that contribute significantly to a path integral calculation to have relative rms rotation rates less than about $10^{-51}$ rad/year. Those calculations are based on using 50 e-foldings during inflation. For 55 or 60 e-foldings, the cosmologies contributing significantly to the path integral would have even smaller relative rotation rates. In addition, although inflation dominates the calculation, even if there had been no inflation, the cosmologies contributing significantly to the path integral would have relative rotation rates less than about $10^{-32}$ rad/year at about a quarter of a second after the initial singularity. These calculations are insensitive to the details of the theory of quantum gravity because the main factor depends only on the size of the visible universe, the Planck time, the free-space speed of light, the Hubble parameter, and the number of e-foldings during inflation. These calculations use the Einstein-Hilbert action in quantum gravity, including large-scale relative rotation of inertial frames and the matter distribution, in which each “path” is a cosmology with a different rms relative rotation rate. The calculation shows that the action is an extremum at zero rms relative rotation rate.

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Physics and Society

Sovereign Stress Avalanches and Network Amplification in Latin America

Diego Vallarino

This paper studies sovereign stress avalanches and network amplification in Latin American credit markets using monthly J.P. Morgan EMBI Global Diversified spreads for eleven sovereigns over 2007-2026. Country stress events are defined as positive log-spread innovations exceeding country-specific volatility thresholds, and regional avalanches count the number of stressed countries in each month. The empirical design combines finite-sample power-law diagnostics, threshold robustness checks, a country-level reshuffling placebo, and rolling correlation, partial-correlation, and minimum-spanning-tree networks. Avalanche sizes are heavy-tailed, with an estimated exponent of 1.77, while spread changes and inter-event times lie in a heavy-tail boundary regime. The placebo shows synchronization far above independent stress timing, with p-values below 0.001. Large avalanches coincide with denser and more spectrally amplifying raw-correlation networks, but not after partial-correlation filtering, indicating common-factor co-movement rather than conditional regional propagation. Network metrics describe contemporaneous stress regimes rather than early-warning signals. The results provide a finite-size criticality framework for monitoring sovereign fragility in emerging markets.

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General Physics

Generalized Fock–Lorentz Transformations from Projective Conformal Coordinates: Covariant Structure, Sector Classification, and Oscillator Limits

Abdelmalek Boumali, N. Jafari and M. Botshekananfard

We develop a covariant projective formulation of generalized Fock–Lorentz (GFL) transformations based on the auxiliary Minkowski coordinates $X^{\mu}=x^{\mu}/[1+a_{\nu}x^{\nu}/R]$, where $R$ is a deformation length and $a^{\mu}$ is a constant deformation vector. Ordinary Lorentz transformations acting linearly on $X^{\mu}$ induce nonlinear transformations of the physical coordinates $x^{\mu}$ with a unique denominator $\mathcal{D}_{a}(x;\Lambda)$ fixed by the conformal factor. The construction gives a unified invariant interval, clarifies the singular hypersurface of the projective chart, and separates three inequivalent sectors according to the causal character of $a^{\mu}$: time-like, space-like, and null. We emphasize two points that are often obscured by analogy with the standard FL case: the coordinate velocity of light is generally defined by an implicit linear relation, and the familiar explicit FL expression is valid only in the purely time-like sector. The time-like apparent mass $m_{\rm app}(t)=m_{0}/(1+ct/R)$ and the associated one-dimensional Klein–Gordon and Dirac oscillator spectra are treated here only as limiting consistency checks of the generalized spacetime construction and are related explicitly to the companion momentum-space-dual formulation. The genuinely new dynamical result is obtained in the space-like sector, where the weak-gradient apparent mass generates a parity-breaking cubic anharmonicity; the first-order cubic shift vanishes by parity, while the combined second-order cubic and first-order quartic corrections yield a definite $R^{-2}$ shift of the oscillator operator. These results provide a transparent basis for future applications of projective relativistic kinematics without relying on a dark-universe interpretation.

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Mathematical Physics

Quasi-Product States and Factor Types for the One-Dimensional Hard-Core Model

Farrukh Mukhamedov and Yuri Suhov

We study the von Neumann algebra generated by a quasi-product state associated with the one-dimensional hard-core lattice gas. If $\kappa>0$ denotes the activity (fugacity), the transfer-matrix construction gives the stationary transition matrix \[ P=\begin{pmatrix}p&q\\ 1&0\end{pmatrix}, \qquad p+q=1, \qquad \kappa=\frac{q}{p^2}. \] The stationary Markov measure on the hard-core path space induces a faithful diagonal state on the corresponding path $AF$-algebra. We give a state-preserving identification of its GNS von Neumann algebra with the Feldman–Moore algebra of the measured tail equivalence relation. The relation is hyperfinite and ergodic, and its nonzero ratio set is $\kappa^{\mathbb Z}$. Consequently, the generated algebra is the hyperfinite factor of type $\mathrm{II}_1$ when $\kappa=1$, and of type $\mathrm{III}_{\lambda}$, where $\lambda=\min\{\kappa,\kappa^{-1}\}$, when $\kappa\ne1$. The point $\kappa=1$ is not a thermodynamic phase transition: the pressure and particle density are analytic for all $\kappa>0$. We also describe the precise support-corner relation with the GNS algebra of the diagonal Gibbs state on the quasi-local spin algebra, and we determine the centralizer and the flow of weights.

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Chemical Physics

My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents

César Ojeda, Darius A. Faroughy, Maryam Karimi et al. (+3 more)

Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes. We introduce My Chemical Harness, a route-native evolutionary framework for goal-directed molecular design in which the search population consists of executable synthetic pathways rather than isolated molecular graphs. Each route is built from purchasable building blocks and reaction templates, executed by deterministic chemistry tools, and scored through task-specific molecular oracles. Large language models (LLMs) are used only as strategy controllers that select high-level preferences over route length, move type, reaction families, motifs, and exploration pressure, while local code performs route construction, validation, deduplication, scoring, selection, and memory updates. This separation lets the LLM guide exploration without allowing it to introduce hallucinated products or unsupported reaction steps. On a soluble epoxide hydrolase proxy task, our LLM agent improves over single pass LLM and deterministic controllers, reaching state-of-the-art performance across the sEH score, synthetic accessibility score, and AiZynthFinder success rate metrics. These results suggest that constrained LLM agents can play a significant role in molecular discovery without requiring training, fine-tuning, or dedicated generative models.

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All authors César Ojeda, Darius A. Faroughy, Maryam Karimi, Payam Zarrintaj, Mir Mehdi Seyedebrahimi, Martín Carballo-Pacheco

Statistical Mechanics

Numerical simulations of the spread from the mean of the SLE and Multiple SLE dynamics

Phillip Kim and Vlad Margarint

The Schramm-Loewner Evolution (SLE) describes a family of fractal curves that arise in the study of the scaling limits of many planar Statistical Physics models. These curves are modeled using the Loewner Differential Equation for the conformal maps $g_t(z)$ with a Brownian motion driver. Using Euler's Method, in the current work we performed numerical experiments to study at a fixed time the quantities $|g_t(z) - \overline{g_t(z)}|$ and $Re(g_t(z)) - Re(\overline{g_t(z)})$, where $Re$ denotes the real part and $\overline{g_t(z)}$ refers to the sample average. These random variables measure the 'spread' of the dynamics from the average behavior at fixed time. One of the scopes of this work is to give numerical predictions for future theoretical investigations on these quantities. When investigating these quantities in the SLE case our experiments predict that the distribution is bimodal when the dynamics started close to the origin, and it can become bell-shaped if the dynamics is started further from the origin. In the second part, we performed experiments for a Multiple SLE model whose driver is Dyson Brownian Motion. Due to singularity in the dynamics of the drivers and the many data points needed, this part is challenging from a computational perspective. In the multiple SLE case, our experiments predict that the distribution is bell-shaped in all cases. In addition, we check the changes in the distributions as we vary the parameter $\kappa$ in the SLE case and $\beta$ in the Multiple SLE case.

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Popular Physics

The Era of Extremely Large Optical Telescopes II: The GMT and TMT

Priya Hasan

The advent of Extremely Large Telescopes (ELTs) ground-based optical and infrared observatories with primary mirrors exceeding 20m marks a transformative era in observational astronomy. This article explores the dawn of this new age, focusing on two of the next generation of optical/ infrared facilities: the Giant Magellan Telescope (GMT) and the Thirty Meter Telescope (TMT)\footnote{An accompanying paper by the author is on the European Extremely Large Telescope (ELT).} We describe the key technological innovations enabling the construction of these ambitious observatories, notably, segmented mirror design, advanced adaptive optics (AO), and laser guide star systems. Collectively, these breakthroughs yield more than an order of magnitude increase in both light-gathering area and spatial resolution, delivering image sharpness that, for widefield observations, surpassing space-based telescopes. We shall also discuss the comparison of space and ground-based telescopes and highlight their individual strengths and why space telescopes are not the solution to the satellite interference to ground-based telescopes. Ground based telescopes will continue to always be leaders in astronomical observations.

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Strongly Correlated Electrons

Coherent manipulation of Kondo Majoranas in two-channel Kondo setups

Yashar Komijani and C. J. Bolech

We study coherent manipulation of Majorana zero modes emerging in overscreened two-channel Kondo systems. Using compactified lattice models, we show that these interacting Kondo Majoranas support non-local qubits and admit teleportation, fusion, and braiding operations. In particular, we identify a distinction between non-topological and genuinely topological Y-junction geometries, the latter realizing a non-Abelian geometric holonomy. Our results establish a proof-of-principle route toward coherent control of non-Abelian anyons beyond conventional free-fermion platforms.

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Nuclear Theory

Nonflow Subtraction Beyond Two-Particle Correlations

Zaining Wang, Jiangyong Jia, Jinhui Chen et al. (+3 more)

Establishing collective flow in small collision systems is a crucial and necessary condition for pinning down the minimum conditions for quark-gluon plasma (QGP) formation. In two-particle correlations, nonflow has been subtracted with good control, pushing the reach of flow measurements down to very small event particle multiplicity $N$. However, the multi-particle nature of collectivity has not been established in the same $N$ regime, because the residual nonflow surviving the subevent procedure in multi-particle cumulants has never been quantified. We develop a general nonflow subtraction framework for $m$-particle cumulants, built around the approximate $1/N^{m-1}$ scaling of nonflow in the independent-source picture. Correlators containing $v_1$ serve as nonflow-dominated estimators, since the $p_{\mathrm T}$-integrated dipolar flow is expected to be small. Using HIJING as a controlled nonflow-only environment, we test the subtraction for three target observables ($\langle v_2^2\rangle$, $\langle v_2^2\delta p_{\mathrm T}\rangle$, and $c_2\{4\}$) in O+O at $\sqrt{s_{\mathrm{NN}}} = 5.36$ TeV and 200 GeV and in $d$+Au at $\sqrt{s_{\mathrm{NN}}} = 200$ GeV. Most of the nonflow is removed, with residual fractions typically within 20-30% when converted to the two-particle level, though the best estimator differs across the three targets. We identify a multiplicity-reweighting correction, previously overlooked in two-particle correlations, that explains the long-standing undersubtraction of the naive $1/N$-scaling method; its impact grows as a power of the correlator order. The framework gives a systematic route to nonflow subtraction beyond two-particle correlations, broadening the class of multi-particle observables accessible to the small-system flow program.

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All authors Zaining Wang, Jiangyong Jia, Jinhui Chen, Shengli Huang, Chunjian Zhang, Zhengxi Yan

Data Analysis and Statistics

Confidence, Statistical Evidence and Relative Belief with Applications to a Problem in Particle Physics

Michael Evans and Siqi Zheng

Probability theory provides a clear definition of what is meant by evidence in favor, against or none either way, of an event occurring for an unobserved response, via the principle of evidence. This is immediately applicable when carrying out a proper Bayesian analysis. Even without a prior, this imposes restrictions on reported inferences as these need to reflect the likelihood ordering. Relative belief inferences satisfy this requirement and, when the errors in these inferences are controlled, they also satisfy repeated sampling, or frequentist, requirements such as achieving given confidence levels. Relative belief inferences are considered here for the construction of intervals for uncertainty quantification in the context of a Poisson model for a signal with background noise. These intervals are contrasted with the well-known Feldman-Cousins intervals for this problem.

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Biological Physics · Cross-listed from eess.IV

POPSICLE: Benchmark Datasets for Segmentation and Localization in CryoET

Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens et al. (+6 more)

Cryo-electron tomography (cryoET) has emerged as a powerful tool in structural and cellular biology by enabling direct visualization of macromolecular structures within intact cells, thereby linking molecular architecture to cellular organization in a native context. Realizing the full potential of cryoET, however, increasingly depends on advances in computational analysis, particularly machine learning (ML), to interpret its complex and information-rich data. Despite rapid progress, ML development for cryoET remains bottlenecked by the lack of standardized, well-annotated benchmarks. Existing evaluations are typically small, task-specific, and are assembled in isolation, limiting robust comparisons across methods. Here, we present POPSICLE, a benchmark suite for cryoET segmentation and macromolecular localization built from the CryoET Data Portal - an open, ML-ready repository of tomographic data, metadata, and annotations. POPSICLE spans eukaryotic and prokaryotic systems, both purified and fully in situ samples, and dense voxel-wise segmentation as well as sparse localization tasks. Built on a living data resource, it can expand as new datasets and annotations become available. Baseline experiments reveal substantial variation in model rankings across tasks, underscoring the need for benchmarks tailored to the unique characteristics of cryoET rather than evaluation practices adapted from adjacent biomedical imaging domains. POPSICLE thus provides an open and extensible foundation for reproducible ML evaluation in cryoET.

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All authors Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens, Zhuowen Zhao, Ariana Peck, Gus L.W. Hart, Grant J. Jensen, Bridget Carragher, Dari Kimanius

Optics

Multi-channel Optical Vision Model

Ali Momeni, Guillaume Noetinger, Tim Tuuva et al. (+1 more)

Spatial multiplexing is one of the natural strengths of optics, yet in optical neural networks, it is often used mainly as parallel throughput. Here, we show that spatial multiplexing in an optical neural network can be used not only to process multiple inputs in parallel, but also to define a trainable representational coordinate of the model. In three implemented scenarios, parallel-input processing, class-code readout and channel-mixed feature interaction, spatial channels act as independent learners, structured code dimensions, and interacting feature groups. The programmable free-space optical processor is trained through an online physical-forward/surrogate-backward scheme, where measured optical outputs define the forward pass while a differentiable surrogate estimates gradients and is continually fine-tuned during training from newly acquired optical data. We demonstrate these channel roles in image classification and regression tasks using multi-layer architectures with more than one million trainable optical phase parameters. We further implement a hybrid optical-electronic vision-language model, in which the optical neural network provides visual tokens to a digital transformer decoder for controlled image-captioning tasks. These results establish spatially multiplexed optical channels as a programmable feature and readout space for hybrid optical vision models.

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All authors Ali Momeni, Guillaume Noetinger, Tim Tuuva, Romain Fleury

Pattern Formation and Solitons

Generalization of a localized-state formation mechanism in finite lattices with interaction nonlinearity

Huajie Song and Haitao Xu

We study how time-periodic, spatially localized states are born from the linear spectrum of a finite lattice as the nonlinearity is switched on. In earlier work we treated this question for a diatomic chain with on-site nonlinearity and developed a framework that continues a near-edge linear mode in amplitude and controls the resulting perturbation series uniformly in the chain length. The present paper shows that the same framework applies to the more difficult case of Fermi–Pasta–Ulam–Tsingou (FPUT) interaction nonlinearity. The key is a structural relation between the FPUT and on-site nonlinearities, which allows the estimates obtained in the on-site setting to be transferred to the FPUT setting. As before, the analysis yields a quantitative radius of convergence, $\eps=\Theta(1/\sqrt{n})$ for a chain of length $2n$, below which the near-edge mode stays extended and above which the orbit localizes and its frequency leaves the band. The diatomic chain is used only as a test case; both the formation mechanism and the method are model-independent and are expected to extend to other short-range nonlinearities and to higher dimensions.

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Materials Science

Robust AI-Driven Discovery of Electronic Metal Phosphide Semiconductors

Benhao Zhu, Muhammad Faizan, Zewei Li et al. (+4 more)

Metal phosphides have diverse bonding motifs and coordination environments, making them promising for optoelectronic and thermoelectric applications, but their chemical space remains underexplored. Here we report an AI-driven high-throughput discovery workflow that combines generative materials design, machine-learning interatomic potentials, and targeted density functional theory (DFT) calculations. ICSD-derived Wyckoff-site substitution and MatterGen-based conditional structure generation are used to expand the candidate space beyond existing phosphide databases. A domain-finetuned DPA3 machine-learning potential then enables efficient prescreening of thermodynamic and dynamical stability before DFT validation. This workflow identifies 3,574 previously unreported stable phosphide structures, including 196 semiconductors with HSE06 band gaps of 0-3.0 eV. By screening these new semiconductors together with experimentally known phosphide semiconductors, we identify 30 promising optoelectronic candidates and 26 promising thermoelectric candidates, including seven newly discovered optoelectronic materials and eight newly discovered thermoelectric materials. These results provide a candidate pool for experimental synthesis and show that combining generative AI with machine-learning interatomic potentials can accelerate the discovery of functional semiconductor materials.

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All authors Benhao Zhu, Muhammad Faizan, Zewei Li, Wenshuo Li, Feifei Ren, Jiahao Xie, Lijun Zhang

Applied Physics

Limits of Trap-assisted Photomultiplication Gain

Ardalan Armin

Photodiodes based on trap-assisted current injection can exhibit internal photomultiplication with apparent quantum efficiencies far exceeding unity, raising the question of whether such gain fundamentally enhances detector sensitivity. We employ a minimal analytical framework based on a single gain-active trapped state coupling photogenerated carriers to contact injection. The gain is intrinsically self-limiting: the injection process that amplifies the current simultaneously accelerates relaxation of the gain-enabling state, producing an inherently nonlinear, operating-point-dependent response. The form of this nonlinearity is not universal – once the trap level is generalized to an energetic distribution and recombination is allowed to be bimolecular, the same mechanism yields superlinear, linear, or strongly sublinear responses. A single chord gain is therefore not a meaningful device descriptor, and chord-gain comparisons across the literature conflate devices in different regimes. Treating trap occupancy and injection as coupled stochastic processes, we show that internal gain introduces a strictly non-negative fluctuation penalty from the dissipative dynamics that sustain the gain state. A local, small-signal detectivity exhibits a finite optimum yet cannot exceed the intrinsic thermodynamic limit of the underlying unity-gain photodiode. Gain is thus equivalent to driven stochastic amplification: it can suppress downstream readout noise, but cannot reduce the fundamental noise floor set by the primary photodetection process.

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Astrophysics of Galaxies

Milky Way's warped disc traced by AGB stars

Tanya Kushwahaa, Mikako Matsuura, Jason A. S. Hunt et al. (+3 more)

While the presence of the Galactic warp has long been established from observations of \HI\, gas, the Gaia measurements of over 1 billion stars with parallaxes have enabled much more detailed studies using stellar populations. Here, we demonstrate that asymptotic giant branch (AGB) stars, an evolved phase of low- and intermediate-mass stars, can serve as an effective tracer of the Galactic warp. We use two distinct AGB populations: C-rich AGB stars, representing stars of about 1~Gyr in age with main-sequence masses of 2–2.5~\Msun, and intermediate-mass (3–5~\Msun) O-rich AGB stars, corresponding to ages of 100–300~Myr. The downward warp traced by O-rich AGB stars is consistent with that found from Cepheids, which is expected given their similar ages. The more numerous C-rich AGB stars clearly reveal the Galactic warp over a wide range of azimuthal angles. Their warp appears to reach larger amplitudes than that of Cepheids across azimuthal angles. Our results show that C-rich AGB stars, together with intermediate-mass O-rich AGB stars, provide new constraints on the Galactic warp at intermediate stellar ages, offering a new insight into the stellar age and warp amplitude relation.

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All authors Tanya Kushwahaa, Mikako Matsuura, Jason A. S. Hunt, Daisuke Kawata, Roger Wesson, Timothy A. Davis

High Energy Physics (Experiment)

Performance of the Eos detector with water

Eos Collaboration: S. Arora, M. Askins, A. J. Bacon et al. (+62 more)

In this manuscript we present the first results from Eos, a four tonne optical detector located at the University of California, Berkeley. The primary goal of Eos is to demonstrate the performance capabilities of scintillation-based, 'hybrid' detector technology for future neutrino detectors. The data presented were collected while both the inner target vessel and the outer buffer vessel were filled with water. The water target acts as a well-understood medium that produces only Cherenkov light, which can be used to calibrate and develop the detector model and reconstruction algorithms prior to the deployment of scintillating material. Using deployed optical and radioactive calibration sources, a series of detailed detector calibrations are performed. These enable a suite of tests for various reconstruction algorithms. Simulations that use calibrated models are compared with the data across a variety of different types of calibration sources, source positions, and rotations.

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All authors Eos Collaboration: S. Arora, M. Askins, A. J. Bacon, Z. Bagdasarian, A. Baldoni, L. Bartoszek, M. Bergevin, Y. Bezawada, E. Blucher, J. Boissevain, R. Bonventre, E. J. Callaghan, D. F. Cowen, K. DeHolton, M. Diwan, M. Dubnowski, P. Englezos, S. Gadamsetty, C. Grant, B. Harris, M. R. Hebert, S. Jeon, T. Kaptanoglu, A. Katt, J. R. Klein, T. Kroupova, L. Lebanowski, A. Mastbaum, C. Mauger, G. Mayers, M. Miller, J. Nachtman, S. Naugle, J. Newby, M. Newcomer, A. Nikolica, G. D. Orebi Gann, A. Phipps, L. Pickard, R. C. Pitelka, L. Ren, A. Rincon, R. Rosero, N. Rowe, H. J. Ryoo, J. Ryshkewitch, J. Saba, S. Schoppmann, J. Shen, M. Smiley, H. Song, H. Steiger, B. Tam, E. Tiras, W. H. To, M. R. Vagins, R. Van Berg, J. Wallig, G. Wendel, M. Wetstein, M. Wurm, G. Yang, M. Yeh, E. D. Zimmerman, A. Zummo

Instrumentation and Detectors

Electric Field Optimization of High-Voltage Vacuum Feedthroughs

Lin Si, Evan Angelico and Giorgio Gratta

We report on the optimization of high voltage vacuum feedthroughs meant to minimize the electric field on the vacuum side of the device. We find that commercial feedthroughs generally have center conductor diameters which are too small, resulting in unnecessarily large fields. We study the problem with analytical calculations and finite element analysis, and present a simple, optimized retrofit for the commercial feedthroughs studied here, without compromising their outgassing properties. This work is important for applications whereby the “vacuum side” of the feedthrough is, in fact, filled with a dielectric which may not have the voltage rigidity of vacuum.

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Quantum Physics

Variational Approach for Uniform Quantum Permutation Generators

Farzam Nosrati, Nicolás Borrajo, Antonio Fernández Anta et al. (+1 more)

Uniform permutation generation is a fundamental task in both classical and quantum computation, with applications ranging from cryptography to quantum optimization and quantum error correction. Existing exact quantum constructions typically require all-to-all qubit connectivity and quadratic circuit depth. We develop a variational quantum circuit framework for uniform permutation generation under connectivity constraints, in which the circuit architecture is determined by the underlying interaction graph and the variational parameters are optimized to enforce the target permutation statistics. In particular, we present explicit controlled-SWAP-based unitary constructions that achieve exact uniformity with quadratic circuit size and linear depth $O(n)$ on linear nearest-neighbor topologies. Our approach, therefore, removes the need for all-to-all connectivity while improving the depth of previous exact constructions by a factor. We further prove that a quantum Beneš-like architecture is intrinsically non-uniform. Despite its logarithmic depth and ability to realize any permutation it cannot generate a uniform distribution over permutations for any choice of variational parameters. These results clarify the role of circuit topology in exact permutation generation and identify variational quantum circuits as a natural framework for hardware-constrained uniform sampling. More broadly, this work suggests that exact uniform permutation generation is a strictly stronger requirement than mere permutation realizability, and lays the groundwork for a formal complexity separation between the two.

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All authors Farzam Nosrati, Nicolás Borrajo, Antonio Fernández Anta, Vincenzo Mancuso

Disordered Systems and Neural Networks · Cross-listed from q-bio.NC

Multifractal Signatures of Ageing and Dementia Development: A Multifractal Space-Filling Curve Analysis

Marta Lotka, Jacek Grela, Zbigniew Drogosz et al. (+2 more)

Multifractality is an effective formalism for quantifying the nonlinear, scale-free properties of complex data. In this study, we propose a novel and efficient methodology, termed Multifractal Space-filling Curve Analysis (MFSCA), for quantifying the correlation structure of multidimensional data. Within this framework, the original multidimensional data - while preserving both local and long-range organisational properties - are projected onto a one-dimensional representation using a fractal space-filling curve. The resulting one-dimensional signal is then analysed using multifractal algorithms. We demonstrate the utility of the method using both artificially generated multifractal structures and real data. In particular, we apply MFSCA to analyse magnetic resonance imaging (MRI) data from Alzheimer patients at different stages of dementia. Based on the results, we estimate the multifractal profiles of the brain for healthy subjects of different ages as well as for dementia patients. The analysis reveals that the spatial organization of brain structures, as measured by the degree of multifractality, progressively weakens with age and the development of dementia. A transition from multifractality to monofractality is observed both in control groups, when comparing the Young Control and Elderly Control groups, and among dementia subjects of similar age but at different stages of the disease, namely early dementia and mild cognitive impairment. Thus, from the perspective of multiscaling properties, the heterogeneous characteristics of spatial brain organization deteriorate under worsening conditions, leading to a homogeneous and weakly correlated structure. These findings not only effectively capture key aspects of brain organisation, but also demonstrate that the multifractality of MRI data can serve as a marker of structural brain changes.

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All authors Marta Lotka, Jacek Grela, Zbigniew Drogosz, Jeremi K. Ochab, Paweł Oświęcimka