Optics
Photo-birefringent effects in crystalline AlGaAs mirror coatings
Chun Yu Ma, Jialiang Yu, Thomas Legero et al. (+5 more)
High-reflective crystalline $GaAs/Al_{0.92}Ga_{0.08}As$ coatings show reduced Brownian noise compared to conventional dielectric coatings. However, several ultra stable
laser systems observed additional noise sources that hinder the realization of the expected improvements in frequency stability. These additional noise sources are related to the birefringence of the coatings and its modification by intracavity light. The origin of the birefringence is not yet well understood and its modification via illumination remains unexplained. Here we present an extensive study on the steady-state and transient modification of the birefringence by intracavity light and by uniform illumination at various wavelengths using an optical cavity at room temperature. We find a unified description that suggests a primary two-
photon process for
photon energies below the bandgap of GaAs, or a single-
photon process at higher energies. Adding external illumination allows to reduce noise induced by
laser power fluctuations by balancing the photo-thermal-optic response of the mirrors and the photo-birefringent effect at a more favorable low intracavity power.
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All authors Chun Yu Ma, Jialiang Yu, Thomas Legero, Sofia Herbers, Daniele Nicolodi, Mona Kempkes, Fritz Riehle, Uwe Sterr
Chemical Physics
Intermolecular Interactions of Large Systems: Boron Nitrides, Acenes, and Coronenes
Vladimir Fishman, Jan M. L. Martin and A. Daniel Boese
In a recent contribution [Fishman, V.; Lesiuk, M.; Martin, J.M.L.; Boese, A.D., J. Chem. Theory Comput. 2025, 21, 2311-2324], we introduced another angle at benchmarking non-covalent interactions by not just benchmarking interaction energies of different species, but by considering the
evolution of interaction energies with increasing system size. Here, we extend the
benchmark set to more species, such as electrostatically bound borazine dimers as well as the minima structures of parallel displaced acene and coronene dimers. While the minimum structures of the parallel displaced acene dimers yield similar results to previously published sandwich-structured acenes, the borazine dimers behave vastly different, yielding yet a more complete picture on non-covalent interactions and their scalability. In contrast, the polycyclic aromatic hydrocarbon structures – coronenes sandwich-stacked and coronenes parallel displaced – give results consistent with those obtained for both types of the polyacene series, resulting in an updated estimate for the coronene dimer energy.
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Fluid Dynamics · Cross-listed from astro-ph.GA
Nonlinear Saturation of the Acoustic Resonant Drag Instability
Ben Y. Israeli, Jonathan Squire, Eric Moseley et al. (+1 more)
Resonant drag instabilities (RDIs) are a novel type of
dust/fluid instability relevant to a diverse range of astrophysical environments. They are driven by a resonant interaction between streaming
dust and waves in a background medium, which results in
dust density fluctuations and amplification of the waves. This broad class of instabilities includes recently-proposed modes incorporating acoustic and magnetohydrodynamic waves, as well as the well-studied disk streaming instability. As the study of RDIs is at an early stage, their
evolution beyond the linear regime is not well understood. In order to make inroads into the nonlinear theory of RDIs, we performed simulations of the simplest case, the acoustic RDI, in which sound waves in a gas are amplified by interaction with supersonically streaming
dust. This particular instability is of interest both due its potential relevance in various poorly ionized environments, and due to its resemblance to the fast magnetosonic RDI. We find that the nonlinear growth and saturation of the instability are characterized by a balance between time scales of instability growth and turbulent eddy turnover. The simulations demonstrate a saturated state possessing an anisotropic outer forcing range in which this balance is maintained, and suggest the presence of an isotropic turbulent inertial range below this scale. By presenting a model for the nonlinear growth and saturated state of the acoustic RDI, this work provides a framework for further study of the nonlinear behavior of this and other RDIs.
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All authors Ben Y. Israeli, Jonathan Squire, Eric Moseley, Amitava Bhattacharjee
Chemical Physics
Beyond Learning on Molecules by Weakly Supervising on Molecules
Gordan Prastalo and Kevin Maik Jablonka
Molecular representations are inherently task-dependent, yet most pre-trained molecular encoders are not. Task conditioning promises representations that reorganize based on task descriptions, but existing approaches rely on expensive labeled data. We show that weak supervision on programmatically derived molecular motifs is sufficient. Our Adaptive Chemical
Embedding Model (ACE-Mol) learns from hundreds of motifs paired with natural
language descriptors that are cheap to compute, trivial to scale. Conventional encoders slowly search the
embedding space for task-relevant structure, whereas ACE-Mol immediately aligns its representations with the task. ACE-Mol achieves state-of-the-art performance across molecular property prediction
benchmarks with interpretable, chemically meaningful representations.
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Fluid Dynamics
Turbulence teaches equivariance to neural networks
Ryley McConkey, Julia Balla, Jeremiah Bailey et al. (+5 more)
We show that the rotational nature of
turbulence affects how neural networks learn mappings between quantities governed by the Navier-Stokes equations. We train super-resolution models at different wall-normal locations in a turbulent channel flow, where anisotropy varies naturally, and test their generalization to new coordinate frames, new anisotropy regimes, and a higher Reynolds number. Our findings inform both the design of equivariant machine learning models for
turbulence and our understanding of how
turbulence shapes what those models learn. First, mappings that better respect the rotational
symmetries of the Navier-Stokes equations generalize better to new flows. Coordinate-frame generalization is therefore a key part of the broader generalization problem, since turbulent flows contain a wide range of local orientations. Second,
turbulence itself partially teaches equivariance to learned mappings, an effect we call implicit data augmentation. The effect strengthens with
dataset size and with isotropy, since a more isotropic
dataset samples more orientations under which the Navier-Stokes equations are covariant. Implicit augmentation is also scale-dependent, with smaller scales exhibiting lower equivariance error. This scale-dependency is consistent with Kolmogorov's hypothesis of local isotropy. Third, enforcing equivariance as an architectural inductive bias is the limit of these effects: an exactly equivariant network outperforms unconstrained CNNs on all generalization tests, with roughly an order of magnitude fewer parameters. We expect these effects to apply broadly to learned mappings between tensorial flow quantities, making them relevant to most machine learning applications in
turbulence.
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All authors Ryley McConkey, Julia Balla, Jeremiah Bailey, Ali Backour, Elyssa Hofgard, Tommi Jaakkola, Abigail Bodner, Tess Smidt
Fluid Dynamics
Sparse-Supervised Hybrid Parameterized Physics-Informed Neural Networks for Incompressible Flows Across Reynolds Numbers
A. Jangir, R. Clements, R. Goyal et al. (+1 more)
Physics-informed neural networks (PINNs) provide a mesh-free framework for solving partial differential equations by
embedding governing physics into neural-network training. Recent studies have shown that parameterized PINNs can learn Navier-Stokes solutions across Reynolds numbers by treating Reynolds number as an additional network input. However, physics-only PINNs often lose accuracy in convection-dominated high-Reynolds-number flows because of optimization stiffness and multiscale flow structures. This study presents a sparse-supervised hybrid parameterized PINNs framework for incompressible Navier-Stokes flows with regime-aware learning and localized Reynolds-number supervision. The approach is demonstrated for two-dimensional lid-driven cavity flow and further validated for backward-facing step flow. At low Reynolds numbers, physics-only PINNs accurately predict velocity and pressure fields using only governing equations and boundary conditions. At higher Reynolds numbers, sparse CFD supervision combined with transfer learning is introduced to improve predictive accuracy. Although the training range spans (500 < Re < 1000), CFD supervision is applied only within (750 < Re < 850) using just (3%-20%) of computational points. Results show that approximately (5%) supervised data are sufficient for accurate flow prediction. Comparisons with CFD simulations demonstrate strong agreement in velocity, pressure, vorticity, and reattachment characteristics across interpolation and limited extrapolation regimes. The proposed framework provides a practical and data-efficient hybrid strategy for incompressible flows across varying Reynolds numbers.
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All authors A. Jangir, R. Clements, R. Goyal, G. Tabor
Optics
HoloHema: Digital Holographic Hematology Analyzer
Andreas Erik Gejl Madsen
This industrial Ph.D. project, carried out in collaboration between Radiometer Medical ApS and SDU Centre for Photonics Engineering at the University of Southern Denmark, explored the use of digital
holographic microscopy (DHM) for the purposes of differential white blood
cell counts (dWBCs) in point-of-care (PoC) devices for acute care settings. Two DHM prototypes were developed; an initial lens-based system serving as the foundation for
algorithm development, and experimental validation of the approach, achieving 89.6% classification accuracy on a 3-part differential, and a subsequent lensless system for simplified design and increased field-of-view (FoV). Both prototypes employed convolutional neural networks (CNNs) for
cell classification. With further optimizations, the lensless system achieved classification accuracies of 92.65% and 89.44% on the 3-part and 5-part differential, respectively. With the lensless system, the derivation of the monocyte distribution width (MDW), a biomarker for sepsis, was also demonstrated. Additionally, pixel super-resolution and multi-wavelength DHM approaches were investigated to enhance the obtained
cell information. Finally, a proof-of-principle physics-informed neural network (PINN) for
holographic reconstruction was implemented, demonstrating the potential for machine learning (ML) reconstruction techniques. In summary, this work represents an initial exploration of DHM for dWBC in PoC devices, laying the groundwork for future research.
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Plasma Physics · Cross-listed from cond-mat.quant-gas
Density Modulations of Zero Sound
Leonardo Pisani
We study the density modulation of an interacting
Fermi gas caused by the uniform motion of an
impurity at zero temperature. For strong enough interaction among
Fermi atoms, the modulation propagates thanks to the excitation of the collective zero sound mode if the
impurity speed is above the zero sound threshold. We are able to assess, via a semi-analytic evaluation, the extent of the zero sound contribution to the density oscillation over and above the incoherent background of particle-hole excitations. Given the strong dependence of the results on the features of the gas interaction potential, we also analyze how they vary depending on its strength, range and shape.
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Data Analysis and Statistics · Cross-listed from cond-mat.mtrl-sci
Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design
Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi et al. (+2 more)
The design of sustainable materials requires access to materials performance and sustainability data from literature corpus in an organized, structured and automated manner. Natural
language processing approaches, particularly large
language models (LLMs), have been explored for materials data extraction from the literature, yet often suffer from limited accuracy or narrow scope. In this work, an LLM-based pipeline is developed to accurately extract
alloy-related information from both textual descriptions and tabular data across the literature on high-
entropy (or multicomponent)
alloys (HEA). Specifically two databases with 37,711 and 148,069 entries respectively are retrieved; one from the literature text, consisting of
alloy composition, processing conditions, characterization methods, and reported properties, and other from the literature tables, consisting of property names, values, and units. The pipeline enhances materials-domain sensitivity through
prompt engineering and
retrieval-augmented generation and achieves F1-scores of 0.83 for textual extraction and 0.88 for tabular extraction, surpassing or matching existing approaches. Application of the pipeline to over 10,000 articles yields the largest publicly available multicomponent
alloy database and reveals compositional and processing-property trends. The database is further employed for sustainability-aware materials selection in three application domains, i.e., lightweighting, soft
magnetic, and corrosion-resistant, identifying multicomponent
alloy candidates with more sustainable production while maintaining or exceeding
benchmark performance. The pipeline developed can be easily generalized to other class of materials, and assist in development of comprehensive, accurate and usable databases for sustainable materials design.
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All authors Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi, B. Pabitramohan Prusty, Rohit Batra
Space Physics · Cross-listed from astro-ph.HE
$\gamma$-Ray Lines – Signatures of Nucleosynthesis, Cosmic Rays, Positron Annihilation, and Fundamental Physics
Thomas Siegert, Francesca Calore, Pierre Jean et al. (+6 more)
The
nuclear $\gamma$-ray lines in the MeV range of the electromagnetic spectrum hold a vast variety of astrophysical, particle-physical, and fundamental physical information that is otherwise extreme difficult to access. MeV $\gamma$-ray line observations provide the most direct evidence for ongoing nucleosynthesis in
galaxies by measuring freshly produced radioactive isotopes from massive
stars,
supernovae, classical novae, or binary
neutron star mergers. Their flux ratios can determine the low-energy
cosmic-ray spectrum in different objects and of the Milky Way as a whole. Different phases of the interstellar medium are traced by hot nucleosynthesis ejecta, cooling positrons, or
cosmic-ray interactions with molecular clouds. Positron annihilation itself can be considered as an astrophysical messenger as their production and destruction in typical space environments is inevitable. Finally, as-of-yet unknown signatures from beyond standard model physics might have their elusive imprints in $\gamma$-ray lines. This Chapter gives an overview of historical $\gamma$-ray line measurements, newest results, and open questions that may only be solved by a new generation of MeV
telescopes.
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All authors Thomas Siegert, Francesca Calore, Pierre Jean, Mark Leising, Nicolas de Séréville, Gerald H. Share, Vincent Tatischeff, Wei Wang, Meng-Ru Wu
Instrumentation and Detectors
Doberman: a modular and distributed slow control system for small- to medium-scale experiments
Jaron Grigat, Darryl Masson and Marc Schumann
We present Doberman (Detector OBsERving and Monitoring ApplicatioN), a lightweight, modular, and open-source slow control system designed for small-to medium-scale physics experiments. Doberman addresses the gap between heavyweight industrial SCADA frameworks and ad hoc laboratory solutions by providing a flexible software architecture that supports heterogeneous instrumentation, distributed deployment, automated control, and robust alarm handling. The web-based graphical user interface Doberview provides live and continuously updated visualization, configuration, and control of the entire experiment, supporting both routine operation and rapid response to exceptional conditions. Doberman has been deployed and validated in multiple experimental setups, ranging from a remotely operated underground gamma-ray spectrometer to a large, highly instrumented
liquid xenon test facility with several hundred monitored quantities. Doberman and Doberview are released under permissive open-source licenses, and the software, documentation, and example device integrations are publicly available.
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Fluid Dynamics
Navier-Stokes-driven analysis of mean and fluctuating wall shear stress in turbulent channel flow
Le Yin, Yongyun Hwang and John Christos Vassilicos
We propose a Navier-Stokes-driven analysis of the mean and fluctuating wall shear stress (WSS) applied to turbulent channel flow data from direct numerical simulations at friction Reynolds numbers up to $Re_\tau\approx 2000$. Starting from the streamwise momentum equation, we derive exact integral equations that relate the square plane-average and the square fluctuating WSS to wall-normal integrals of terms combining shear with acceleration, shear with pressure-radient, and shear with viscous
diffusion. The square plane-average WSS can be well approximated by the product of plane-average shear and plane-average acceleration integrated over the buffer layer with corrections from the mean pressure gradient which diminish as the reciprocal of the Reynolds number. The square fluctuating WSS is similarly well approximated by the shear-acceleration and shear-pressure-gradient covariances integrated over the buffer layer, but the latter increases in magnitude with Reynolds number and is therefore not negligible. The acceleration fluctuations around the plane-average acceleration consist of a local Eulerian fluctuating acceleration, an advective acceleration and a term which gives rise a
turbulence production contribution to the shear-acceleration covariance. By Taylor's frozen
turbulence hypothesis the Eulerian acceleration and the streamwise mean advection part of the advective acceleration cancel each other. The shear-acceleration covariance is characterised by a near-wall peak which results from
turbulence production and, more specifically, sweeps.
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Computational Physics · Cross-listed from nucl-th
Exterior complex scaling enables physics-informed neural networks for quantum scattering
Jin Lei
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations, yet their application to
nuclear scattering has been hindered by the oscillatory, non-decaying nature of scattering wave functions. In this work, I demonstrate that exterior complex scaling (ECS) transforms scattering boundary conditions into exponentially decaying waves suitable for neural network solutions, enabling PINNs to solve
nuclear reaction problems for the first time. I develop a driven-equation formulation where the source term is confined to the real axis, avoiding the need to analytically continue
nuclear potentials into the complex plane. The method is validated on nucleon-
nucleus scattering (n+$^{40}$Ca at $E_{\text{lab}}=20$~MeV) with 21 partial waves, achieving phase shift accuracy of $\Delta\delta \lesssim 0.1^\circ$ for the strongly absorbed channels ($\ell \leq 4$) and $\Delta\delta \leq 0.60^\circ$ for all channels up to $\ell = 10$, when compared to conventional solvers. I further demonstrate the approach on heavy-ion scattering ($^6$Li+$^{208}$Pb at 40~MeV) with 41 partial waves and strong Coulomb effects, where an auto-adaptive anchor warm-down for weak-source channels yields a mean S-
matrix accuracy of $|\Delta|S_\ell|| \approx 3 \times 10^{-3}$ across the full angular momentum range, including the absorption-to-transparency transition region. This work establishes the foundation for extending PINNs to inverse problems where end-to-end differentiability enables direct fitting of optical potential parameters, coupled-channel reactions, and few-body scattering where traditional grid methods face exponential scaling.
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Plasma Physics
Nonlinear Dynamical Friction from the Doppler-Shifted Equilibrium Memory Kernel
N. R. Sree Harsha, Zhenyuan Yu, Chuang Ren et al. (+2 more)
We present a statistical mechanics framework for modeling
equilibrium friction coefficients using the Generalized Langevin Equation (GLE). We show that the kernel, obtained via the Fluctuation-Dissipation
Theorem (FDT) from the
stochastic force autocorrelation measured in a thermal
equilibrium state, is sufficient to model the dynamics of the system in a Non-
Equilibrium Steady State (NESS). This approach provides a computationally efficient path to modeling complex
equilibrium friction problems. We apply this framework to the canonical problem of test particle drag in a uniform
plasma. The GLE formalism is shown to naturally capture non-Markovian phenomena through the moments of the kernel, including an effective mass renormalization and oscillatory relaxation. We demonstrate that the standard Chandrasekhar stopping power formula arises naturally as the Markovian limit of this
equilibrium memory kernel. These theoretical predictions are quantitatively validated by direct Particle-in-
Cell simulations, which confirm the predicted oscillatory structure of the
memory kernel. This work thus establishes a practical method for predicting
equilibrium friction properties from first-principles
equilibrium simulations.
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All authors N. R. Sree Harsha, Zhenyuan Yu, Chuang Ren, Virginia Billings, Michael Huang
General Physics
The Hubble Tension in Light of the Symmetry of Scale Invariance
Frederic Courbin and Andre Maeder
When the expansion rate of the Universe at recombination is used to infer the present expansion rate $H_0$, the value derived in the $\Lambda$CDM model, $H_0=67.4$ km/s/mpc, is about in 6$\, \sigma$ tension with the value measured locally, $H_0=74$ km/s/mpc. In this work, we consider instead the expansion history in the context of the
symmetry of scale-invariant
vacuum (SIV model). We first perform two major cosmological tests: the Hubble diagram for type-Ia
supernovae and the fundamental relation between $H_0$, the age of the Universe, and the total density of matter, $\Omega_m$. This allows us to constrain $\Omega_m$ in SIV, with both tests giving the best agreement for $\Omega_m \simeq 0.20$. We then study the physical connections of the dynamical and thermal states of the Universe at recombination with the present Hubble constant, $H_0$, and the present temperature, $T$, in the $\Lambda$CDM and SIV contexts. We find that, in SIV, the properties at recombination may be conveyed to the present ones ($T=2.726$ and $H_0$ at $z=0$) without any tension, indicating $H_0=74$ km/s/mpc in spite of the anchoring on the CMB. This is due to the slightly different expansion and temperature histories of the two cosmological models. Importantly, this happens to occur for $\Omega_m \simeq 0.20$, as constrained in SIV with
supernovae and
cosmic age. This suggests that the Hubble tension currently found between $H_0$ values in the early and late Universe may simply be the result of $\Lambda CDM$ ignoring the small but still measurable effects of scale invariance.
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Applied Physics
A scalability benchmark study of model order reduction techniques for very large, strongly coupled vibroacoustic problems
Sander Metting van Rijn, Linus Taenzer, Paolo Tiso et al. (+1 more)
Model Order Reduction (MOR) can significantly reduce the computational cost of vibroacoustic simulations. While most MOR research focuses on single-domain systems (e.g., structural dynamics or computational fluid mechanics), this work compares MOR techniques for large multi-domain problems to identify methods that remain efficient and accurate at very large scales. In particular, harmonic response simulations of vibroacoustic fluid-structure coupled systems used to compute transfer functions from an input force to either structural acceleration or pressure in the heavy fluid domain are of high interest. To achieve this, the most common MOR techniques based on modal methods and Krylov subspace methods are compared for multi-material systems. To assess the feasibility and accuracy of these techniques for different system sizes, a scalable
benchmark model of a water-filled Plexiglass cylinder is developed, with mesh sizes from 10,000 to 1,000,000 Degrees of Freedom (DOF). The quality of the models is assured by validation against experimental data. The geometry, model data, and experimental results are made available so that they can be used as a
benchmark for further studies. For systems larger than 100,000 DOF, the investigated modal methods become impractical due to
memory limitations, even on powerful workstations. Among the tested techniques, a Krylov subspace two-level orthogonal Arnoldi reduction, combined with symmetrization and conditioning of the system
matrices, provides the most accurate and efficient approximation of the target transfer functions - particularly for large-scale models up to 1,000,000 DOF. This approach achieves a speedup of up to 600 times compared to the full model.
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All authors Sander Metting van Rijn, Linus Taenzer, Paolo Tiso, Bart Van Damme
Physics and Society
Geometric Organization and Inference of Shortest Path Nodes in Soft Random Geometric Graphs
Zhihao Qiu, Sámuel G. Balogh, Xinhan Liu et al. (+2 more)
The shortest path problem is related to many dynamic processes on networks, ranging from routing in communication networks to signaling in molecular interaction networks. When the network is fully known, the shortest path problem can be solved precisely and in
polynomial time. If, however, the network of interest is only partially observable, the shortest path problem is no longer straightforward. Inspired by the shortest path problem in partially observable networks, we investigate the
geometric properties of shortest paths in Euclidean Soft Random
Geometric Graphs (SRGGs). We find that shortest paths are aligned along geodesic curves connecting shortest path endpoints. The strength of the shortest path
alignment, as quantified by the average distance to geodesic from shortest path nodes and the average path stretch, is higher for larger SRGGs with short-range connections. In addition, we find that the strength of the shortest path
alignment is non-monotonic with respect to the average degree of the SRGG. Based on these observations, we establish the conditions under which the
alignment of shortest paths may be sufficiently strong to allow the identification of shortest path nodes based on their proximity to geodesic curves. We show that in partially observable networks with uncertain node positions, our
geometric approach can outperform network-based shortest-path
algorithms. In practical settings, our findings may have applications to
navigation, wireless routing, and flow characterization in infrastructure networks.
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All authors Zhihao Qiu, Sámuel G. Balogh, Xinhan Liu, Piet Van Mieghem, Maksim Kitsak
Optics
PhoQuPy: A Python framework for Automation of Quantum Optics experiments
Srivatsa Murali and Anshuman Kumar
We present the automation of a confocal photoluminescence (PL) scanning system for the identification and characterization of single-
photon emitters (SPEs) in
quantum materials. The setup excites the sample with a
laser and acquires a spectrum at each spatial coordinate in a raster scan pattern. A double-acquisition method is used to remove
cosmic ray artifacts by comparing subsequent measurements at the same spatial coordinate. Once identified, the emitter is further characterized via a HBT setup, thereby measuring lifetime as well as second-order autocorrelation g(2) measurements to confirm singlephoton emission. The system integrates Python-based hardware control for motorized stages, spectrometer acquisition, and post-processing, with a migration to a galvo-mirror scanning approach for using it along with a cryostat for low temperature measurements. Our results demonstrate spatially resolved PL maps and temperature-dependent spectra, highlighting the capability of the setup to efficiently
benchmark SPE performance. We further went on to perform automation of other experiments such as a Non-Linear Interferometry setup for
Quantum Imaging with Undetected Light and a
Fourier Transform Imaging Spectroscopy using a common path birefringence Interferometer to obtain hyperspectral images of our samples.
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Classical Physics · Cross-listed from nlin.SI
Painleve solitons of AKNS system and irrational algebraic solitons of NLS equations
Man Jia, Xia-Zhi Hao, Ruo-Xia Yao et al. (+2 more)
A novel
symmetry decomposition approach is introduced to derive the so-called “Painlevé
solitons” of the Ablowitz-Kaup-Newell-Segur (AKNS) system. These Painlevé
solitons propagate against a background governed by a Painlevé transcendent, establishing a fundamental generalization of the well-known
elliptic solitons concept. We demonstrate that while
elliptic solitons arise from the combination of translation invariance and square eigenfunction
symmetry, a different
symmetry combination-scaling invariance, Galilean invariance, and square eigenfunction
symmetry-generates “Painlevé IV
solitons” for the AKNS system. This discovery represents a significant theoretical advance in integrable systems theory. By selecting special solutions of the Painlevé IV equation, we obtain explicit forms of several previously unknown classes of solutions for the AKNS system and the nonlinear Schrödinger (NLS) equation: irrational algebraic
solitons, rational algebraic
solitons, and
parabolic cylindrical function
solitons. These results dramatically expand the known solution landscape of one of the most important integrable models in mathematical physics, with broad implications for nonlinear wave phenomena across multiple physical disciplines including optics, Bose-Einstein condensates, and fluid dynamics.
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All authors Man Jia, Xia-Zhi Hao, Ruo-Xia Yao, Fa-Ren Wang, S. Y. Lou
History and Philosophy of Physics
From Florence to Fermions: a historical reconstruction of the origins of Fermi's statistics one hundred years later
Roberto Casalbuoni and Daniele Dominici
Aim of this paper is to retrace the path that led the young Enrico
Fermi to write his paper on the statistics of an ideal monatomic gas. This discovery originated in his interest, which he had shown since his formative years, in the absolute
entropy constant and in the problems he highlighted in Sommerfeld's quantization in the case of identical particle systems. The fundamental step taken by
Fermi in writing his work on statistics was to apply the Exclusion Principle, formulated for electrons in an atom and which could therefore have been a pure effect due to dynamics, to a system of non-interacting particles.
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Optics
Passive Incoherent Ultrafast Mid-Infrared Upconversion Imaging and Its Calibration
Jin-Peng Li, Zhi-You Li, Zhao-Qi-Zhi Han et al. (+7 more)
Ultrafast mid-infrared (MIR) imaging is a key enabling capability for monitoring transient thermal and
plasma phenomena in scientific diagnostics and industrial safety. However, conventional cryogenic MIR cameras face a fundamental trade-off between frame rate, noise, and pixel format. Here we report a passive, incoherent MIR imaging platform that leverages sum-frequency upconversion in chirped periodically poled lithium niobate (CPLN) to translate broadband 3–5um scenes to the near-infrared, enabling ultrafast acquisition on a silicon-based intensified CCD (iCCD). In fast-kinetics mode we achieve a physical frame rate of 100kHz with microsecond-scale gate control, and we directly capture the full
evolution of an air-breakdown electric arc, resolving its rapid ignition, expansion, and decay dynamics. Beyond demonstrating ultrafast passive imaging, we introduce a drift-aware calibration workflow based on Allan deviation analysis to quantitatively select the gate width and averaging strategy under realistic slow-drift and multiplicative noise. This combined capability – ultrafast passive MIR imaging plus operationally meaningful calibration – provides a practical route toward real-time thermal surveillance and early-warning systems for hazardous fast transients.
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All authors Jin-Peng Li, Zhi-You Li, Zhao-Qi-Zhi Han, Xiao-Hua Wang, He Zhang, Yin-Hai Li, Bo-Wen Liu, Wen-Tao Luo, Zhi-Yuan Zhou, Bao-Sen Shi
Physics and Society
The impact of heterogeneity on the co-evolution of cooperation and epidemic spreading in complex networks
Mehran Noori, Nahid Azimi-Tafreshi and Mohammad Salahshour
The dynamics of herd immunity depend crucially on the interaction between collective social behavior and disease transmission, but the role of heterogeneity in this context frequently remains unclear. Here, we dissect this co-evolutionary feedback by coupling a public goods game with an epidemic model on complex networks, including multiplex and real-world networks. Our results reveals a dichotomy in how heterogeneity shapes outcomes. We demonstrate that structural heterogeneity in social networks acts as a powerful catalyst for cooperation and disease suppression. This emergent effect is driven by highly connected hubs who, facing amplified personal risk, adopt protective strategies out of self-interest. In contrast, heterogeneity in individual infection costs proves detrimental, undermining cooperation and amplifying the epidemic. This creates a “weakest link” problem, where individuals with low perceived risk act as persistent free-riders and disease reservoirs, degrading the collective response. Our findings establish that heterogeneity is a double-edged sword: its impact is determined by whether it creates an asymmetry of influence (leverage points) or an asymmetry of motivation (weakest links), recommending disease intervention policies that facilitate cooperative transition in hubs (strengthening the leverage point) and homogenize incentives to weakest links.
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Chemical Physics
Unified MPI Parallelization of Wave Function Methods: iCIPT2 as a Showcase
Qingpeng Wang, Ning Zhang and Wenjian Liu
The integration of
quantum chemical methods with high-performance computing is indispensable for handling large systems with modest accuracy or even small systems but with high accuracy. Continuing with the unified implementation of non-relativistic and relativistic wave functions methods within the MetaWave platform (J. Phys. Chem. A. 2025, 129, 5170), we present here a unified MPI parallelization of the methods by abstracting ever computational step of a method as a dynamically-scheduled loop via ghost process, followed by a global reduction of local results from each node. The algorithmic abstraction enables the use of a single MPI template in various steps of different methods. Taking iCIPT2 [J. Chem. Theory Comput. 2021, 17, 949] as a showcase, the parallel efficiencies achieve 94% and 89% on 16 nodes (1024 cores) for the perturbation and whole calculations, respectively. Further combined with an improved
algorithm for the
matrix-vector product in the
matrix diagonalization and an orbital-configuration-based semi-
stochastic estimator for the perturbation correction, this renders large active space calculations possible, so as to obtain
benchmarks for the automerization of cyclobutadiene, ground state energy of benzene and potential energy profile of ozone. It is also shown that the error of iCIPT2 follows a power law with respect to the number of configuration state functions.
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Medical Physics
Human-organ-scale x-ray fluorescence ghost imaging for radioisotope-free diagnostics
E. Levinson, R. H. Shukrun, N. Vigano et al. (+1 more)
A wide range of diagnostic information in medicine is currently obtained using radioactive tracers. While central to
nuclear medicine, these methods are inherently constrained: radiation dose limits repeat examinations, short tracer half-lives and complex logistics restrict access and raise costs, and their relatively poor spatial resolution often necessitates complementary CT or MRI. Here we present a first proof-of-concept demonstration of a non-radioactive alternative based on x-ray fluorescence (XRF) computational ghost imaging (CGI) at the human-organ scale. Using a thyroid phantom filled with iodine solution as a model system, we show that structured illuminations combined with fluorescence detection reconstruct the iodine distribution with high fidelity. This approach eliminates the need for radioactive tracers while preserving image quality, and in principle can reach spatial resolution comparable to CT. Beyond this demonstration, XRF-CGI establishes a generalizable framework for non-radioactive tracer imaging, opening a route toward safer, repeatable, and more accessible diagnostics.
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All authors E. Levinson, R. H. Shukrun, N. Vigano, S. Shwartz
Optics
Topological robustness of optical skyrmions through a real-world free-space link
Cade Peters, Vagharshak Hakobyan, Alice Drozdov et al. (+3 more)
Structured light offers a promising solution for the increasing data demands of modern optical networks, opening up new degrees of freedom that can be leveraged for greater channel capacity and more bits per
photon. However, its implementation is hindered by real-world distortions, for example, atmospheric
turbulence in free-space, with severe and rapidly evolving phase perturbations that alter the amplitude, phase and vectorial polarization structure of the beam. Here, we demonstrate that optical
topologies in the form of skyrmions are highly resilient to the effects of real-world atmospheric
turbulence. We create and transmit these particle-like
topologies of light through a 270~m free-space optical link, revealing their robustness across a wide variety of conditions and
turbulence strengths. While we observe severe distortion in the states' underlying degrees of freedom, we show that the topological numbers are preserved in all cases. We account for fast changes to the medium, where the channel produces statistically averaged outcomes, by probing the state's decoherence, showing that while the degree of polarisation consequently decays, the
topology remains intact. Using
topology, we show information can be transmitted through the channel with almost perfect fidelity (>98%) in most cases, only decreasing to 86% in the most severe conditions tested. Our work is the first to demonstrate the potential for optical
topologies as reliable and robust information carriers in a real-world environment and points to the potential for other complex channels too, offering attractive features for classical and
quantum communication alike.
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All authors Cade Peters, Vagharshak Hakobyan, Alice Drozdov, Etienne Brasselet, Mitchell Cox, Andrew Forbes
Applied Physics
Organic Hydrogen Sensors for Potential Use in Safety-Critical Environments
Annika Morgenstern, Lucas Viriato, Frank Ortmann et al. (+8 more)
Accurate monitoring of the hydrogen concentration is critical for optimizing fuel
cell performance, minimizing purge losses, and reducing long-term degradation. Conventional hydrogen sensors often rely on catalytic materials and face limitations such as the need of oxygen purging when operated in fuel
cell environments. Here, we report the discovery of a novel hydrogen-sensing mechanism based on organic molecules, without the use of catalytic metals. The sensor is based on a typical vertical stack geometry, containing $\mathrm{Alq_3}$ as active organic material. Upon exposure to hydrogen, the device shows an increase in resistivity, yielding a reliable sensor signal that varies linearly with hydrogen concentration, temperature, and humidity, and exhibits a relative response of up to 3.5 % at 100 %vol hydrogen. By exposing the sensor to an external
magnetic field, the rise and fall times of the sensor response were found to be tunable. This novel organic sensor demonstrates sensitivity across a wide range of hydrogen concentrations under fuel
cell-relevant conditions and beyond. This new class of hydrogen sensors with high miniaturization potential and cost efficiency paves the way for real-time hydrogen monitoring and advanced control strategies in fuel
cells, the chemical industry, or energy storage applications.
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All authors Annika Morgenstern, Lucas Viriato, Frank Ortmann, Christopher Bickmann, Lukas Hertling, Dominik Weber, Dietrich R.T. Zahn, Karla Hiller, Thomas v. Unwerth, Daniel Schondelmaier, Georgeta Salvan
Applied Physics · Cross-listed from cond-mat.mtrl-sci
ZnCdO:Eu Epitaxially Grown Alloys for Self-Powered Ultrafast Broadband Photodetection
Igor Perlikowski, Eunika Zielony, Aleksandra Wierzbicka et al. (+3 more)
Photodetectors (PDs) are essential in imaging, communication, and sensing technologies. However, their reliance on external power makes them energy-consuming. This creates a strong need for self-powered PDs as a sustainable alternative. ZnO is a promising
semiconductor material due to its pyroelectric properties, stemming from non-centrosymmetric wurtzite
crystal structure, enabling the pyro-phototronic effect that enhances response speed. Properties of ZnO can be tailored via alloying and doping. Thus, this work explores thin layers of ZnCdO:Eu random
alloys grown by molecular beam epitaxy (MBE) on silicon substrates, with varying Cd content. The study shows that doping with Eu notably affects growth kinetics, promoting strong [0001] orientation preference. Moreover, photoluminescence measurements confirm the successful incorporation of Eu3+ ions into the structure. Electrical measurements show that the introduction of Cd eliminates the problem of Schottky barrier formation on the ZnO/Si interface. The n-ZnCdO:Eu/p-Si junctions exhibit rectifying behavior and generate photocurrent across 380-1150 nm wavelength range without external electrical bias. Utilizing the pyro-phototronic effect, these devices achieved ultrafast response times: rise time below 10 us and decay time below 5 us for 405 nm and 650 nm illumination - placing them among the fastest self-powered
oxide-based detectors that do not rely on additional performance-enhancing layers.
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All authors Igor Perlikowski, Eunika Zielony, Aleksandra Wierzbicka, Anastasiia Lysak, Rafal Jakiela, Ewa Przezdziecka
Chemical Physics
UGA-SSMRPT2 – A Multireference Perturbation Theory Predicting Accurate Electronic Excitation Energies in Diverse Molecular Systems
Shamik Chanda, Pratyush Bhattacharjya, Avijit Sen et al. (+1 more)
UGA-SSMRPT2, the
spin-free perturbative analogue of Mukerjee's State-Specific Multireference Coupled Cluster Theory (MkMRCC) is known to be successful for size-extensive and intruder-free construction of dissociation curves. This work demonstrates that UGA-SSMRPT2 is also an accurate and computationally inexpensive framework for computing excitation energies. The method achieves near-chemical accuracy for the vast majority of $\pi \to \pi^*$, $n \to \pi^*$, charge-transfer, valence-Rydberg and Rydberg excited states commonly used for benchmarking electronic structure theories for excited states. Our results demonstrate that UGA-SSMRPT2 excitation energies lie within 0.20 eV of EOM-CCSD and/or well-established theoretical best estimates often surpassing the popular MRPT2 approaches like NEVPT2, CASPT2, and MCQDPT while typically requiring smaller active spaces. Its state-specific formulation circumvents the well-known intruder-state problem and eliminates the need for empirical parameters such as IPEA shifts in CASPT2. This work proposes UGA-SSMRPT2 as a robust, and scalable approach for modeling challenging electronic excited states.
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All authors Shamik Chanda, Pratyush Bhattacharjya, Avijit Sen, Sangita Sen
Optics
Mechanisms of localization in a finite harmonically confined optical superlattice
A. Katsaris, I. A. Englezos, C. Weitenberg et al. (+2 more)
We investigate the impact of harmonic confinement in a finite optical superlattice and reveal the different mechanisms that can lead to the emergence of localized states. The optical superlattice, with odd or even number of unit
cells, can exhibit either a trivial or a non-trivial underlying
topology, characterized by the corresponding Zak phase. We focus on a distinct localization mechanism in the intermediate harmonic trapping frequency regime. Specifically, the four lowest-lying eigenstates in this regime form an effective four-level system in the topologically non-trivial configuration. Larger trapping frequency values drive the system into a harmonic trap dominated regime, featuring classical pairing and localization of all states of the lower band, as in a usual optical
lattice. For the lower trapping frequency regime, the fate of topological edge states is discussed. Our results are based on exact diagonalization and on a tight-binding approximation that maps the continuous to a discrete system. We address several aspects relevant to the experimental implementation of optical superlattices and provide a brief illustration of the dynamics, highlighting direct ways to observe and distinguish between the different localization mechanisms.
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All authors A. Katsaris, I. A. Englezos, C. Weitenberg, F. K. Diakonos, P. Schmelcher
Physics Education
Lessons Learned from Integrating Generative AI into an Introductory Undergraduate Astronomy Course at Harvard
Christopher W. Stubbs, Dongpeng Huang, Jungyoon Koh et al. (+2 more)
We describe our efforts to fully integrate generative artificial intelligence (GAI) into an introductory undergraduate astronomy course. Ordered by student perception of utility, GAI was used in instructional Python notebooks, in a subset of assignments, for student presentation preparations, and as a participant (in conjunction with a RAG-encoded textbook) in a course Slack channel. Assignments were divided into GAI-encouraged and GAI-discouraged. We incentivized student mastery of the material through midterm and final exams in which electronics were not allowed. Student evaluations of the course showed no reduction compared to the non-GAI version from the previous year.
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All authors Christopher W. Stubbs, Dongpeng Huang, Jungyoon Koh, Madeleine Woods, Andrés A. Plazas Malagón