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Physics Physics

physics Watch Physics

Monday 12 January

Instrumentation and Detectors · Cross-listed from cond-mat.mtrl-sci

Noise2Void for Denoising Atomic Resolution Scanning Transmission Electron Microscopy Images

William Thornley, Sam Sullivan-Allsop, Rongsheng Cai et al. (+3 more)

The Noise2Void technique is demonstrated for successful denoising of atomic-resolution scanning transmission electron microscopy (STEM) images. The technique is applied to denoising atomic resolution images and videos of gold adatoms on a graphene surface within a graphene liquid cell, with the denoised experimental data qualitatively demonstrating improved visibility of both the Au adatoms and the graphene lattice. The denoising performance is quantified by comparison to similar simulated data and the approach is found to significantly outperform both total variation and simple Gaussian blurring. Compared to other denoising methods, the Noise2Void technique has the combined advantages that it requires no manual intervention during training or denoising, no prior knowledge of the sample and is compatible with real time data acquisition rates of at least 45 frames per second.
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All authors William Thornley, Sam Sullivan-Allsop, Rongsheng Cai, Nick Clark, Roman Gorbachev, Sarah J. Haigh

Instrumentation and Detectors

Sub-Pixel Electron Beam Alignment for Machine Learning Characterization of Hybrid Pixel Detectors

Emiliya Poghosyan, Xiangyu Xie, Joakim Reuteler et al. (+6 more)

Due to their radiation hardness, kilohertz frame rates, and high dynamic range, hybrid pixel detectors have recently expanded their application range to electron diffraction and recently also electron imaging. However, these detectors typically have pixel sizes about ten times larger than those of direct electron detectors commonly used for imaging and more prominent electron multiple scattering effects. To overcome these limitations, machine learning approaches can be utilized to reconstruct the electron entrance point and achieve super-resolution. As this process is inherently stochastic, and machine learning relies on suitable training data, high-quality, representative training data are essential for developing models that achieve the best possible resolution. In this work, we present two novel experimental methods for generating such training data. The first method employs precise microscope alignment to scan the detector plane using a finely focused electron beam of 2 {\mu}m diameter, enabling controlled sub-pixel mapping. The second method utilizes specially designed aperture masks with sub-pixel-sized holes to accurately localize electron entry points. We developed and validated two experimental strategies for collecting training data at acceleration voltages of 60, 80, 120, and 200 keV, which enable sub-pixel labeling for hybrid pixel detectors. Notably, our methodology is broadly applicable to a wide range of hybrid pixel detectors.
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All authors Emiliya Poghosyan, Xiangyu Xie, Joakim Reuteler, Kirsty A. Paton, Luis Barba Flores, Benjamin Béjar Haro, Erik Fröjdh, Anna Bergamaschi, Elisabeth Müller

Computational Physics · Cross-listed from cond-mat.mtrl-sci

A density functional theory study of amino acids on Mg and Mg-based alloys

John Bolin, Amanda Goold, Olof Hildeberg et al. (+2 more)

Magnesium (Mg) has mechanical properties similar to bone tissue, and Mg ions take part in the metabolism. This makes Mg of interest for biocompatible degradable body implants, provided that its high corrosion rate can be inhibited. Slightly alloying Mg and adding surface coatings can slow down the corrosion processes without significantly changing the mechanical properties. Use of coating molecules that are native to the body increase the likelihood of making the surface biocompatible, for example by use of amino acids. We here present a density functional theory (DFT) study of the adsorption on Mg(0001) of the amino acids glycine, L-proline, and L-hydroxyproline (Hyp), the main amino acid content of collagen. We investigate how binding of the functional groups of Hyp are affected when Mg(0001) is slightly alloyed with zinc, lithium or aluminium, and we also model the immersion of the systems in a water environment to see how this affects the binding.
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All authors John Bolin, Amanda Goold, Olof Hildeberg, Alva Limbäck, Elsebeth Schröder

Atmospheric and Oceanic Physics

Inversion of Sea Ice Spectral Albedo to Estimate Under-Ice Transmittance

Christophe Perron, Bastian Raulier, Philippe Massicotte et al. (+2 more)

Sunlight radiation under snow-covered sea ice obtained from remote sensing could help assess under-ice primary production at pan-Arctic scale. Yet, the current remote sensing methods to estimate sunlight transmittance under sea ice is limited by its reliance on imprecise snow depth products and its inability to sense microstructure-driven variations in snow and ice light scattering properties. Based on Monte-Carlo simulations of radiative transfer, we developed an inversion method relying solely on spectral albedo to estimate transmittance under snow-covered sea ice. The method analyses albedo spectral information to derive the vertically resolved scattering properties of snow and sea ice above the freeboard. Assuming fixed columnar ice physical and optical properties, transmittance is then estimated. At ground level, our spectral albedo inversion method is more precise than the current approaches. We argue this is because it implicitly accounts for the variability in snow scattering properties. This method could significantly improve the satellite estimation of photosynthetically available radiation under sea ice, especially because it does not need snow depth.
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All authors Christophe Perron, Bastian Raulier, Philippe Massicotte, Martin Vancoppenolle, Marcel Babin

Fluid Dynamics

Landau damping of disturbances in nearly inviscid inflectional shear flows

Evgeny V. Polyachenko, Ilia G. Shukhman and Michael Karp

We investigate the structure of damped two-dimensional perturbations in unstable plane-parallel shear flows with an inflection point. In inviscid flows within the stable wavenumber region $k$, no regular eigenmodes exist – the frequency spectrum $\omega$ consists of a continuous set of singular van Kampen modes with real frequencies. Nevertheless, initial perturbations of the total vorticity integrated across the flow decay exponentially, resembling the behavior of an eigenmode with complex eigenfrequency ${\rm Im}\,\omega<0$ (Landau damping). However, the vorticity itself does not decay but becomes increasingly corrugated across the flow. We demonstrate that accounting for arbitrarily small viscosity transforms this exponentially decaying perturbation into a true eigenmode in which the vorticity preserves its spatial form. We numerically trace the transformation of the vorticity structure of this mode and its disappearance as viscosity approaches zero. We discuss similarities and differences between the behavior of damped perturbations in the transition from inviscid to nearly inviscid flows in hydrodynamics and their behavior in plasma and homogeneous stellar systems during the analogous transition from collisionless to very weakly collisional systems.
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Plasma Physics

Role of Shafranov shift, zonal structures on the behavior of TAEs, AAEs and microinstabilities in the presence of energetic particles

B. Rofman, G. Di Giannatale, A. Mishchenko et al. (+8 more)

In future nuclear fusion reactors, even a small fraction of fusion-born energetic particles (EP) about 100 times hotter than the thermal bulk species, contributes substantially to the kinetic pressure and therefore affect the MHD equilibrium, mainly via the Shafranov shift. In this work, we perform first-principles numerical simulations using the gyrokinetic, electromagnetic, global code ORB5 to study the effect of a self-consistent finite $\beta$ equilibrium on the arising Alfvén Eigenmodes (destabilized by EPs), Ion Temperature Gradient (ITG), and Kinetic Ballooning Modes (KBM) microturbulence (destabilized by thermal species). Linearly, we explore the complex interplay between EP fraction, bulk gradients and a self-consistent Shafranov shift on the plasma stability. We choose single toroidal mode numbers to represent the system's instabilities and study the characteristic nonlinear evolutions of TAEs, KBMs and ITGs separately and including the axisymmetric field response to each mode separately. This study focuses on the impact of Shafranov shift equilibrium consistency, as well as the self-generated zonal ${E \times B}$ flows, the saturation levels and resulting heat and particle fluxes. In the ITG cases including the $n=0$ perturbations reduces turbulent fluxes, as expected, however, for the TAE cases including the $n=0$ perturbations is shown to enhance the fluxes. We show for the first time that Axisymmetric Alfvén Eigenmodes (AAEs) play a role in this mechanism.
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All authors B. Rofman, G. Di Giannatale, A. Mishchenko, E. Lanti, A. Bottino, T. Hayward-Schneider, J.N. Sama, A. Biancalani, B.F. McMillan, S. Brunner, L. Villard

Space Physics · Cross-listed from astro-ph.HE

A Unified Charge-Dependent Modulation Model for AMS-02 Proton and Antiproton Fluxes during Solar Minimum

Hui-Ming Zhang, Su-Jie Lin, Jie Feng et al. (+4 more)

We develop a unified charge-dependent solar modulation model by solving the three-dimensional Parker transport equation, incorporating a realistic wavy heliospheric current sheet to treat drift effects self-consistently. Using a local interstellar spectrum from GALPROP constrained by Voyager data, we fit the model to time-resolved proton and antiproton fluxes measured by the Alpha Magnetic Spectrometer-02 (AMS-02) from May 2011 to June 2022, excluding the heliospheric magnetic field (HMF) polarity-reversal epoch (mid-2012 to early 2015). To enable rapid parameter scans, we employ neural-network-based surrogate models to compute propagation and modulation matrices efficiently. The results demonstrate that the model simultaneously describes the observed proton and antiproton fluxes with physically reasonable parameters, providing a unified account of charge-dependent modulation.
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All authors Hui-Ming Zhang, Su-Jie Lin, Jie Feng, Jie-Teng Jiang, Yu-Dong Cui, Yi-Han Liu, Li-Li Yang

Physics and Society

Mind the Temperature Gap: The Role of Pit Thermal Energy Storage in a Sector-Coupled Energy System with High-Temperature District Heating

Caspar Schauß, Amos Schledorn, Tom Kähler et al. (+3 more)

Pit thermal energy storage (PTES) provides large-scale thermal storage capacity in district heating systems, supporting flexibility on both daily and seasonal scales. Most existing large-scale energy system studies on PTES do not account for temperature differences between storage and the network. Neglecting these temperature differences can result in less efficient PTES integration, since they affect usable energy capacity and introduce additional costs for discharge requiring temperature boosting. To explore how temperature constraints shape the system-level value of PTES, we use PyPSA-DE, an open-source sector-coupled capacity expansion model of Germany and neighboring countries in a scenario with net zero carbon emissions for 2045. To isolate PTES effects, we examine counterfactual scenarios: systems without PTES, idealized systems with PTES but without temperature constraints, and feasible systems with boosting. We find that PTES reduces German annual system costs by 135-345 M EUR per year relative to systems relying solely on tank storage. Lowering maximum forward temperatures from 124 degrees C to 95 degrees C decreases district heating costs by 7.6 percent without PTES and 10 percent with PTES. Idealized scenarios without temperature constraints yield district heating cost savings of up to 15 percent, indicating that temperature-agnostic modeling overestimates PTES benefits. PTES provides economic value even under current high temperatures, though temperature misalignment limits its contribution during peak demand due to the need for boosting. The findings highlight the role of PTES in leveraging low-price electricity through electrified heating while emphasizing the importance of explicitly accounting for temperature constraints.
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All authors Caspar Schauß, Amos Schledorn, Tom Kähler, Kristina Schumacher, Mathias Ammon, Tom Brown

Fluid Dynamics

Multi-pathline flow visualization using PIV images

Yukun Sun, Elijah James, Frank Fang et al. (+4 more)

One of the oldest flow visualization techniques is through multiple pathlines generated by the movement of seeding particles spatially distributed in the flow. In the computerized era, particle images are used in quantitative measurements, such as particle image and particle tracking velocimetry (PIV and PTV). Here, we present several methods for post-processing raw particle images to generate enhanced flow visualization without a need for conducting additional experiments. Three post-processing methods will be shown: 1) controlling the exposure time, 2) color-coding temporal information, and 3) changing the frame of reference. We showcase how employing these three methods can highlight different flow features in three canonical flow cases: vortex ring, leading edge vortex, and turbulent boundary layer. In addition to the quantitative flow field, the multi-pathline visualization is expected to augment our ability to observe fluid flow from many different perspectives.
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All authors Yukun Sun, Elijah James, Frank Fang, Jasper Agrawal, Christopher Dougherty, Cong Wang, Chris Roh

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

Learning About Learning: A Path from Spin Glasses to Artificial Intelligence

Denis D. Caprioti, Matheus Haas, Constantino F. Vasconcelos et al. (+1 more)

The Hopfield model, originally inspired by spin glasses, occupies a central place at the intersection of statistical mechanics, neural networks, and artificial intelligence. Despite its conceptual simplicity and broad applicability, it is rarely integrated into the undergraduate physics curriculum. We present the Hopfield model as a pedagogically rich framework that naturally unifies core topics from the undergraduate physics curriculum and that provides a concise introduction based on concepts such as a model's energy function, dynamics, and pattern stability. We discuss practical aspects of its simulation and provide simulation codes. We also propose problems designed to mirror research practice, which can be included in undergraduate classes.
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All authors Denis D. Caprioti, Matheus Haas, Constantino F. Vasconcelos, Mauricio Girardi-Schappo

Instrumentation and Detectors

Performance Benchmarks for 2-View and 3-View Fiber-Projection Fine-Grained Particle Detectors

Haohui Che and Guang Yang

Fine-grained scintillator detectors are critical for precision measurements in nuclear and particle physics, where accurate reconstruction of interaction vertices and secondary particle directions enables separation of signal from background events. A well-known design choice is the fiber readout geometry: traditional 2-View systems use orthogonal X and Y fibers, while next-generation 3-View designs add a third Z-fiber layer that provides unambiguous 3D voxel identification. The 2-View approach suffers from combinatorial ”ghost hits”, which are false 3D candidates arising from fiber projection ambiguities, that degrade reconstruction performance in high-multiplicity events. This paper presents comprehensive simulation benchmarks quantifying the performance difference between 2-View and 3-View geometries across key metrics. We find that the 3-View geometry reduces ghost hits by 30 to 90% depending on event topology, provides robust vertex resolution across complex topologies, and maintains superior angular resolution for shower direction reconstruction. These benchmarks inform the design optimization of future detectors and provide quantitative guidance for reconstruction algorithm development across a broad range of experiments including neutrino physics, rare kaon/pion decays, and collider calorimetry.
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Physics and Society · Cross-listed from math.OC

Speaking of Opinions: Comparing Approaches to Modelling Opinion Manipulation

Luisa Estrada, Sasha Glendinning and Andrew Nugent

This review outlines the major approaches to modelling opinion formation and manipulation in mathematics and computer science. Key tools such as ordinary and partial differential equations, stochastic models, control theory, and interaction protocols are introduced and compared as methods for describing manipulation. The review is separated into those models using a continuous opinion space and those using discrete or binary opinions, with the advantages and disadvantages of each discussed. Finally, the authors provide an interdisciplinary perspective on the field of opinion dynamics and its social significance.
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Physics and Society

A note on thermodynamics of the production processes

Vladimir Pokrovskii

The process of creating goods and services, measured by their value, is considered a process of creating complexity. This allows us to consider the production system as an open thermodynamic system, and to develop a simple heuristic model of the production process. The model includes three production factors: the index of complexity of production equipment (physical capital $K$), human activity (labour $L$), and the substitutive capacity of equipment (substitutive work $P$). The latter is a contribution to the theory of production from the thermodynamic approach, which also involves introducing technological characteristics of production equipment, such as labour requirement ($\overline{\lambda}$) and energy requirement ($\overline{\varepsilon}$), which indicate the amounts of labour and energy required to operate production equipment. By applying thermodynamic principles, we can understand how labour can be replaced by capital and derive the production function with four different formulations. Two of them are known and used by researchers for interpretation the production phenomena; the thermodynamic approach provides some foundation for economic theory, allowing us to decompose unambiguously the growth rate of output over technological level and the growth rates of production factors. Introducing substitute work as a factor of production and technological characteristics of capital expands our ability to plan and analyse production processes.
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Chemical Physics

Dynamics of hyperpolarized nuclear spins at Zeeman-insensitive points

James Eills, Anushka Singh, Amir-Mahyar Teimoori et al. (+3 more)

Avoided crossing (LAC) transitions are central to spin-based precision measurements since they provide strong observability and field-perturbation insensitivity, but while observed commonly in isolated-atom or engineered solid-state platforms, such transitions are unexploited in molecules in liquids. Here we report a solution-state, nuclear-spin LAC transition in hyperpolarized [1-$^{13}$C]-fumarate. The singlet-triplet LAC, occurring near 400 nT produces pronounced magnetization oscillations at approximately 2 Hz with a coherence time of 25 s, three times longer than the longest single-spin $T_2^*$. In the regime of high spin concentration and high polarization the sample's internal dipolar field couples measurably to the transition, providing a direct probe of nonlinear spin dynamics. These results establish nuclear-spin LACs as a platform for quantum sensing in liquids.
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All authors James Eills, Anushka Singh, Amir-Mahyar Teimoori, Irene Marco-Rius, Morgan W. Mitchell, Michael C. D. Tayler

Space Physics

Coordinate Systems and Transforms in Space Physics: Terms, Definitions, Implementations, and Recommendations for Reproducibility

R.S. Weigel, A.Y. Shih, R. Ringuette et al. (+6 more)

In space physics, acronyms for coordinate systems (e.g., GEI, GSM) are commonly used; however, differences in their definitions and implementations can prevent reproducibility. In this work, we compare definitions in online resources, software packages, and frequently cited journal articles and show that implementation differences can lead to transformations between same-named coordinate systems and position values from different data providers to differ significantly. Based on these comparisons and results, and to enable reproducibility, we recommend that (a) a standard for acronyms and definitions for coordinate systems is developed, similar to equivalents in astronomy or earth sciences; (b) a standards body develops a citable database of reference data needed for these transforms. For software that computes coordinate transforms, we also recommend that their developers provide explicit comparisons of their implementations with the results of (b) and documentation on implementation choices. Additionally, we provide recommendations for scientists and metadata developers to ensure that sufficient information is provided to enable reproducibility. Finally, we document that spacecraft positions from data providers can differ both because of differences in how they implemented transforms and because of differences in the original source of the position data, and provide recommendations to improve the documentation of spacecraft positional datasets.
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All authors R.S. Weigel, A.Y. Shih, R. Ringuette, I. Christopher, S.M. Petrinec, S. Turner, R.M. Candey, G.K. Stephens, B. Cecconi

Instrumentation and Detectors

Performance of the Pair Spectrometer in Hall D at Jefferson Lab

Alexander Somov, Sergey Somov and Vladimir V. Berdnikov

This article describes the performance of the pair spectrometer installed in experimental Hall D at Jefferson Lab and its operation in multiple experiments with the GlueX detector. The primary purpose of the pair spectrometer is the precise determination of the flux of beam photons incident on the GlueX target, a critical input for physics analyses such as absolute cross-section measurements. The photon energy spectrum is determined by reconstructing electron-positron pairs produced in a thin converter inserted into the photon beam. The spectrometer is integrated into the GlueX trigger system, enabling continuous real-time monitoring of the photon flux and the recording of $e^+e^-$ pair candidates for offline analysis. In addition, the pair spectrometer provides a versatile test facility for evaluating calorimeter prototypes using leptons with well-defined energies produced via electromagnetic pair production.
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Computational Physics · Cross-listed from cond-mat.str-el

Machine learning nonequilibrium phase transitions in charge-density wave insulators

Yunhao Fan, Sheng Zhang and Gia-Wei Chern

Nonequilibrium electronic forces play a central role in voltage-driven phase transitions but are notoriously expensive to evaluate in dynamical simulations. Here we develop a machine learning framework for adiabatic lattice dynamics coupled to nonequilibrium electrons, and demonstrate it for a gating induced insulator to metal transition out of a charge density wave state in the Holstein model. Although exact electronic forces can be obtained from nonequilibrium Green's function (NEGF) calculations, their high computational cost renders long time dynamical simulations prohibitively expensive. By exploiting the locality of the electronic response, we train a neural network to directly predict instantaneous local electronic forces from the lattice configuration, thereby bypassing repeated NEGF calculations during time evolution. When combined with Brownian dynamics, the resulting machine learning force field quantitatively reproduces domain wall motion and nonequilibrium phase transition dynamics obtained from full NEGF simulations, while achieving orders of magnitude gains in computational efficiency. Our results establish direct force learning as an efficient and accurate approach for simulating nonequilibrium lattice dynamics in driven quantum materials.
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Instrumentation and Detectors

Large Language Models for Physics Instrument Design

Sara Zoccheddu, Shah Rukh Qasim, Patrick Owen et al. (+1 more)

We study the use of large language models (LLMs) for physics instrument design and compare their performance to reinforcement learning (RL). Using only prompting, LLMs are given task constraints and summaries of prior high-scoring designs and propose complete detector configurations, which we evaluate with the same simulators and reward functions used in RL-based optimization. Although RL yields stronger final designs, we find that modern LLMs consistently generate valid, resource-aware, and physically meaningful configurations that draw on broad pretrained knowledge of detector design principles and particle–matter interactions, despite having no task-specific training. Based on this result, as a first step toward hybrid design workflows, we explore pairing the LLMs with a dedicated trust region optimizer, serving as a precursor to future pipelines in which LLMs propose and structure design hypotheses while RL performs reward-driven optimization. Based on these experiments, we argue that LLMs are well suited as meta-planners: they can design and orchestrate RL-based optimization studies, define search strategies, and coordinate multiple interacting components within a unified workflow. In doing so, they point toward automated, closed-loop instrument design in which much of the human effort required to structure and supervise optimization can be reduced.
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All authors Sara Zoccheddu, Shah Rukh Qasim, Patrick Owen, Nicola Serra

Atomic Physics · Cross-listed from cond-mat.quant-gas

Excitation spectrum of a bright solitary wave in a Bose-Einstein condensate and its connection with the Higgs and the Goldstone modes

G. M. Kavoulakis

We consider the problem of Bose-Einstein condensed atoms, which are confined in a (quasi) one-dimensional toroidal potential. We focus on the case of an effective attractive interaction between the atoms. The formation of a localized blob (i.e., a “bright" solitary wave) for sufficiently strong interactions provides an example of spontaneous symmetry breaking. We evaluate analytically and numerically the excitation spectrum for both cases of a homogeneous and of a localized density distribution. We identify in the excitation spectrum the emergence of the analogous to the Goldstone and the Higgs modes, evaluating various relevant observables, gaining insight into these two fundamental modes of excitation.
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Optics

Self-optimizing multichannel optical computing

Fatma Nur K{\i}l{\i}nç and Uğur Teğin

Optical computing offers ultrafast, energy-efficient alternatives to conventional digital processors, yet most implementations remain confined to single-channel processing, severely underutilizing light's information capacity. Here we demonstrate a self-optimizing multichannel optical computing architecture based on multi-plane light conversion that natively processes RGB images and structured numerical data throughout the optical domain. We introduce two complementary optimization strategies that enable autonomous performance adaptation without differentiable forward models. First, Bayesian optimization tunes channel mixing coefficients to minimize crosstalk and enhance feature separability at the input level. Second, a hardware-in-the-loop protocol based on self-organized criticality leverages avalanche dynamics to autonomously navigate the high-dimensional phase landscape, enabling the system to self-optimize through stochastic multi-scale perturbations. Across medical imaging, natural image classification, and regression tasks, multichannel processing with random phase masks improves accuracy by 26–58 percentage points over raw pixel baselines, with RGB systematically outperforming grayscale by 5–6 percentage points. Self-optimization strategies provide additional gains of 6–7 percentage points through autonomous adaptation at complementary system levels. Our work establishes self-optimizing multichannel optical computing as a practical platform for real-world machine learning applications.
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Optics · Cross-listed from quant-ph

Coupling a discrete state to a quasi-continuum: A model quantum mechanical system that interpolates between Rabi oscillations and decay-revival dynamics

Enes Kutay İşgörür, Osman Cevheroğlu and Arkadaş Özak{\i}n

We formulate a quantum mechanical system consisting of a single discrete state coupled to an infinite ladder of equally-spaced states, the coupling between the two being given by a Lorentzian profile. Various limits of this system correspond to well-known models from quantum optics, namely, the narrow resonance limit gives the Rabi system, the wide resonance limit gives the Bixon-Jortner system, the wide resonance, true continuum limit gives the Wigner-Weisskopf system, and the fixed resonance, true continuum limit gives a system that is typically studied by methods developed by Fano. We give a semi-analytical solution of the eigenvalue problem by reducing it to a transcendental equation, and demonstrate the aforementioned limiting behaviors. We then study the dynamics of the initial discrete state numerically, and show that it gives a wide range of behaviors in various limiting cases as predicted by our asymptotic theory including exponential decay, revivals, Rabi oscillations, and damped oscillations. The ability of this system to interpolate between such a rich set of behaviors and existing model systems, and the accessibility of a semi-analytical solution, make it a useful model system in quantum optics and related fields.
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Optics

Exciton coherence propagation measured with non-local four-wave mixing micro-spectroscopy

Mateusz Raczyński, Amadeusz Dydniański, Karolina Ewa Połczyńska et al. (+8 more)

Coherence transfer is a multi-disciplinary topic of interest, including chemistry, biology and physics. In quantum technologies, achieving non-local coherent coupling between solid-state qubits is of the utmost importance. Here, we demonstrate that excitons - i.e. electron-hole pairs bound by the Coulomb force within a quantum well - can act as a medium for mesoscopic optical coherence transfer in semiconductors. To this end, we use a femtosecond laser pulse to resonantly generate excitons within the light cone. These excitons can then either recombine radiatively or scatter out of the light cone, gaining an in-plane momentum in the process. In samples without disorder, such as the CdTe quantum wells used here, the resulting fast excitons can diffuse over mesoscopic distances before recombining radiatively. Using coherent nonlinear micro-spectroscopy, we carry out exciton time-of-flight measurements. Specifically, we monitor the spatio-temporal propagation of launched exciton wave packets, selectively observing their coherence or density on a scale of up to 10$\,\mu$m. Our proof-of-principle experiment demonstrates that free excitons inherit a phase modulation from the optical pulsed excitation and can generate coherent links within excitonic circuits, offerring a higher level of miniaturisation and compactness than photonic or polaritonic architectures.
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All authors Mateusz Raczyński, Amadeusz Dydniański, Karolina Ewa Połczyńska, Gabriela Szwed, Adam Szczerba, Jin-Woo Jung, Gilles Nogues, Wolfgang Langbein, Piotr Kossacki, Wojciech Pacuski, Jacek Kasprzak

Medical Physics

Standardized Images and Evaluation Metrics for Tomography

Anna Frixou, Theodoros Leontiou, Efstathios Stiliaris et al. (+1 more)

Advances in instrumentation and computation have enabled increasingly sophisticated tomographic reconstruction methods. However, existing evaluation practices – often based on simple phantoms and global image metrics – are limited in their ability to differentiate among modern high-fidelity reconstructions. A standardized, quantitative framework capable of revealing subtle yet meaningful differences is therefore required. We introduce such a framework, built upon two core components. The first is a set of four standardized reference images – Source, Detector, Ideal, and Realistic – each derived from physical modeling and representing a distinct stage in the imaging and reconstruction chain. The second is a suite of diagnostic and quantitative tools that remain sensitive in regimes where conventional metrics (e.g., SSIM, PSNR, NMSE, CC) tend to saturate. These include pixel-wise $\chi^2$ and difference maps, their quantitative characterization, spectral decomposition of intensity distributions, and Region-of-Interest (RoI)-based metrics. Application of this framework to MLEM and RISE-1 reconstructions using software phantoms demonstrates its ability to expose discrepancies that might elude detection by conventional global metrics. While developed in the context of SPECT, the methodology generalizes to other tomographic modalities, providing a reproducible, interpretable, and physically grounded basis for evaluating reconstruction fidelity in the high-performance regime.
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All authors Anna Frixou, Theodoros Leontiou, Efstathios Stiliaris, Costas N. Papanicolas

Plasma Physics

A radiation two-phase flow model for simulating plasma-liquid interactions

Ke-Jian Qian, Zhu-Jun Li, Tao Tao et al. (+3 more)

In laser-produced plasma (LPP) extreme ultraviolet (EUV) sources, deformation of a tin droplet into an optimal target shape is governed by its interaction with a pre-pulse laser-generated plasma. This interaction is mediated by a transient ablation pressure, whose complex spatio-temporal evolution remains experimentally inaccessible. Existing modeling approaches are limited: Empirical pressure-impulse models neglect dynamic plasma feedback, while advanced radiation-hydrodynamic codes often fail to resolve late-time droplet hydrodynamics. To bridge this gap, we propose a radiation two-phase flow model based on a diffuse interface methodology. The model integrates radiation hydrodynamics for the plasma with the Euler equations for a weakly compressible liquid, extending a five-equation diffuse interface formulation to incorporate radiation transport, thermal conduction, and ionization. This formulation enforces pressure and velocity equilibrium across the diffuse interface region, with closure models constructed to ensure correct jump conditions at interfaces and asymptotically recover the pure-phase equations in bulk regions. Then, we apply the model to simulate a benchmark pre-pulse scenario, where a 50 micron tin droplet is irradiated by a 10 ns laser pulse. The simulations capture the rapid plasma expansion and subsequent inertial flattening of the droplet into a thin, curved sheet over microsecond timescales. Notably, the model reproduces experimentally observed features (such as an axial jet) rarely replicated in prior simulations. Quantitative agreement with experimental data for sheet dimensions and velocity validates the approach. The proposed model self-consistently couples laser-plasma physics with compressible droplet dynamics, providing a powerful tool for fundamental studies of plasma-liquid interactions in LPP-EUV source optimization.
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All authors Ke-Jian Qian, Zhu-Jun Li, Tao Tao, De-Hua Zhang, Rui Yan, Hang Ding

Medical Physics

Overcoming the limitations of NMR Field Probes: A Novel Integrated Sensor Utilizing Pre-Polarization for (Ultra) Low Field MRI

Pavel Povolni, Dominique Goerner, Praveen Iyyappan Valsala et al. (+9 more)

Access to magnetic resonance imaging (MRI) remains severely limited in low- and middle-income countries, especially in sub-Saharan Africa, despite rising rates of non-communicable diseases. Low-field MRI presents an affordable, locally developable diagnostic solution, but its performance is constrained by magnetic field instability. We present a novel NMR field probe designed to overcome these challenges using a rapid non-adiabatic switch-off of a pre-polarization field resulting in precessing spin magnetization. Achieved by first use of high-voltage silicon carbide transistors operating in controlled avalanche breakdown, it measures the Larmor frequency without prior field knowledge, unlike conventional probes. This capability is crucial during magnet development with often unknown fields, allowing early detection of magnet issues, and offering an urgently needed tool for magnet design and image-quality improvement. Validated from 1 mT to 45 mT (up to 1,000 times stronger than similar systems) its low-cost, modular design supports replication, upgrades, and enhanced field control, helping expand global MRI access.
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All authors Pavel Povolni, Dominique Goerner, Praveen Iyyappan Valsala, Lukas Gebert, Georgiy Solomakha, Nicolas Kempf, Felix Glang, Judith Samlow, Ruben Schnitzler, Ingmar Kallfass, Kai Buckenmaier, Klaus Scheffler

Chemical Physics · Cross-listed from astro-ph.EP

Functionalization of Benzene Ices by Atomic Oxygen

Elettra L. Piacentino, Alexandra McKinnon, Nora Hänni et al. (+6 more)

Small aromatic molecules, including functionalized derivatives of benzene, are known to be present throughout the different stages of star and planet formation. In particular, oxygen-bearing monosubstituted aromatics, likely including phenol, have been identified in the coma of comet 67P. This suggests that, earlier in the star and planet formation evolution, icy grains may act as both reservoirs and sites of functionalization for these small aromatics. We investigate the ice-phase reactivity of singlet oxygen atoms (O($^1$D)) with benzene, using ozone as a precursor that is readily photodissociated by relatively low-energy. Our experiments show that O($^1$D) efficiently reacts with benzene, forming phenol, benzene oxide, and oxepine as the main products. Phenol formation is temperature-independent, consistent with a barrierless insertion mechanism. In contrast, the formation of benzene oxide/oxepine shows a slight temperature dependence, suggesting that additional reaction pathways involving either ground-state or excited-state oxygen atoms may contribute. In H$_2$O and \COO ice matrices we find that dilution does not suppress formation of phenol. We extrapolate an experimental upper limit for the benzene-to-phenol conversion fraction of 27-44$\%$ during the lifetime of an interstellar cloud, assuming O($^1$D) production rates based on CO$_2$ ice abundances and a cosmic-ray induced UV field. We compare these estimates with a new analysis of data from the comet 67P, where the C$_6$H$_6$O/C$_6$H$_6$ ratio is 20$\pm$6$\%$. This value lies within our estimated range, suggesting that O($^1$D)-mediated chemistry is a viable pathway for producing oxygenated aromatics in cold astrophysical ices, potentially enriching icy planetesimals with phenol and other biorelevant compounds.
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All authors Elettra L. Piacentino, Alexandra McKinnon, Nora Hänni, Amit Daniely, Estefania Rossich Molina, Tamar Stein, Jennifer Bergner, Mahesh Rajappan, Karin I. Öberg

Optics

High-Speed Non-Volatile Barium Titanate Field Programmable Photonic Gate Array

Cristina Catalá-Lahoz, Jose Roberto Rausell-Campo, Daniel Pérez-López et al. (+10 more)

Programmable integrated photonics aims to replicate the versatility of field-programmable gate arrays in the optical domain. However, scaling these systems has been prevented by the high power consumption and thermal crosstalk of conventional volatile phase shifters. Here, we demonstrate the first non-volatile field-programmable photonic gate array, implemented on a hybrid silicon-barium titanate platform. Unlike traditional thermo-optic devices that require constant power to maintain a state, our device utilizes ferroelectric domain switching to provide non-volatile memory, allowing optical circuits to be programmed and retained without any holding power or electrical bias. The hexagonal waveguide mesh integrates 58 programmable unit cells and 116 actuators, achieving nanosecond-scale switching speeds of 80 nanoseconds while reducing static power consumption to negligible levels (560 nanowatts per {\pi} phase shift). To validate this platform, we configured the mesh to perform diverse signal processing functions, including tunable filtering, 4x4 linear unitary transformations, and optical routing. This work establishes non-volatile ferroelectric silicon photonics as a scalable, heat-free platform essential for the next generation of energy-efficient photonic computing.
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All authors Cristina Catalá-Lahoz, Jose Roberto Rausell-Campo, Daniel Pérez-López, Lucas Güniat, Clarissa Convertino, Felix Eltes, Jean Fompeyrine, Charis Mesaritakis, Adonis Bogris, Quentin Wilmart, Jonathan Faugier-Tovar, Benoit Charbonnier, José Capmany

Biological Physics

Noise enhances odor source localization

Francesco Marcolli, Martin James and Agnese Seminara

We address the problem of inferring the location of a target that releases odor in the presence of turbulence. Input for the inference is provided by many sensors scattered within the odor plume. Drawing inspiration from distributed chemosensation in biology, we ask whether the accuracy of the inference is affected by proprioceptive noise, i.e., noise on the perceived location of the sensors. Surprisingly, in the presence of a net fluid flow, proprioceptive noise improves Bayesian inference, rather than degrading it. An optimal noise exists that efficiently leverages additional information hidden within the geometry of the odor plume. Empirical tuning of noise functions well across a range of distances and may be implemented in practice. Other sources of noise also improve accuracy, owing to their ability to break the spatiotemporal correlations of the turbulent plume. These counterintuitive benefits of noise may be leveraged to improve sensory processing in biology and robotics.
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Optics

Observation of anomalous exciton polariton bands in PEPI perovskite based microcavity at room temperature

Chunzi Xing, Xiaokun Zhai, Chenxi Yang et al. (+9 more)

Recently anomalous energy bands with negative mass attract intensive attention where non Hermiticity plays an important role. In this work we observe anomalous exciton polariton bands in PEPI perovskite based microcavity at room temperature. We simulate the anomalous band structure using a non-Hermitian coupled oscillator model which agree with experiments very well. Our results offer to study non-Hermitian polariton wave dynamics at room temperature.
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All authors Chunzi Xing, Xiaokun Zhai, Chenxi Yang, Peilin Wang, Jiaxiang Mu, Xinmiao Yang, Yao Li, Xianxiong He, Yong Zhang, Haitao Dai, Liefeng Feng, Tingge Gao