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Physics Physics

physics Watch Physics

Monday 8 June

Chemical Physics

Persistent singlet electronic character in the multiexcitonic triplet-pair state of strongly coupled pentacene singlet fission dimers

Atandrita Bhattacharyya, Namana Venkatareddy, Sanjoy Patra et al. (+5 more)

Singlet fission converts an optically excited singlet state into a spin-entangled triplet pair state (TT$_1$)$^1$ that can, in principle, yield two free triplets for photovoltaics and/or a polarized high spin state for quantum technologies. Synthetically tunable templates suggest that the above photophysics is governed by a subtle but poorly understood interplay of molecular motifs, geometry and structural fluctuations. Here, we investigate the (TT$_1$)$^1$ state in a library of conformationally flexible pentacenic dimers, where a (TT$_1$)$^1$-specific near-IR spectral feature is readily available. Using a suite of polarization-controlled impulsive optical spectroscopies, we find that (TT$_1$)$^1$ formation is specific to planar conformations and is accompanied by large nuclear reorganization in the (TT$_1$)$^1$ photoproduct. Introducing polarization anisotropy to track the electronic character of the (TT$_1$)$^1$ species, supported by screened configuration interaction based electronic structure theory, we find that significant singlet-triplet electronic mixing is persistent throughout its evolution. This behavior is universal across diverse bridging motifs and indicates that, once the triplet pair is strongly bound, neither substantial nuclear reorganization nor structural fluctuations on longer timescales are sufficient to suppress persistent singlet-triplet electronic mixing, such that triplet-pair decorrelation is outcompeted by its decay. Our observations establish polarization-selective pump-probe and anisotropy as a direct optical probe of triplet pair decorrelation, complementary to spin-selective measurements at longer timescales.
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All authors Atandrita Bhattacharyya, Namana Venkatareddy, Sanjoy Patra, Kanad Majumder, Vithoba Hugar, Satish Patil, Manish Jain, Vivek Tiwari

Applied Physics

Hyperbolic dynamic friction models for viscoelastic sliding and rolling contact

Luigi Romano

This paper considers the sliding and rolling contact between viscoelastic bodies. Combining linear viscoelastic rheologies for bristle-like elements with nonlinear dynamic friction models, and under the assumption of small deformations and displacement gradients, it derives a class of viscoelasto-kinematic equations, formulated as a system of semilinear partial differential equations (PDEs) governing the evolution of the frictional force, bristle deformations, and internal state variables at the interface between the contacting bodies. The resulting system is analysed mathematically, demonstrating that linear viscoelasticity preserves the hyperbolic character of the PDE systems typically encountered in rolling contact. The proposed theory is illustrated through representative examples of both sliding and rolling contact, highlighting that these two processes, whilst often treated as distinct, may in fact exhibit closely related underlying dynamics. Overall, the framework provides a general theoretical setting applicable to a broad class of viscoelastic frictional systems.
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Optics

Helicity-Resolved Spatiotemporal Mapping of Chiral Plexcitons in Helicoids

Jeong Hyun Han, Sankaran Ramesh, Jaeyeon Jo et al. (+10 more)

Plasmon-exciton hybrids, or plexcitons, offer deeply subwavelength light-matter interactions with versatile pathways for energy redistribution. Incorporating chirality into such systems is particularly compelling, enabling spin-sensitive optical functionality that can operate on ultrafast timescales and within ultracompact volumes. Despite recent progress in chiral plexcitonic systems, how structural chirality and plasmon-exciton coupling determine chiroptical spectra and ultrafast energy flow remains elusive. Here we realize chiral plexcitons by functionalizing intrinsically chiral gold helicoid nanoparticles with molecular J-aggregates. Within a non-Hermitian framework, we trace the microscopic origin of the helicoid chiroptical response and its coupling to the excitonic transition, revealing how the helicity of light selectively addresses distinct hybrid responses. At the spatiotemporal extreme, we find that the gap-localized response not only enhances polarization-sensitive contrast but also strengthens the local hybrid interaction, leading to accelerated ultrafast relaxation. Together, these space-, time-, and polarization-resolved measurements provide a physically grounded and experimentally benchmarked picture of chiral plexcitonic coupling, identifying chirality as a practical control parameter for selectively steering nanoscale energy pathways and dynamics.
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All authors Jeong Hyun Han, Sankaran Ramesh, Jaeyeon Jo, Pavel Chabera, Ryeong Myeong Kim, Sung Hoon Cho, In Han Ha, Amitav Sahu, Yoonsang Tak, Jiawei Lv, Miyoung Kim, Ki Tae Nam, Tönu Pullerits

Geophysics

Artificial Intelligence for Subsurface Imaging Understanding: A Decade Review of Challenges, Methods, Benchmarks, and Outlook

Yimin Dou, Xinming Wu, Hui Gao et al. (+9 more)

Subsurface imaging interpretation bridges observed geophysical data and quantitative geological models, supporting hydrocarbon exploration, CO2 storage assessment, and geohazard monitoring. Over the past decade, machine learning and deep learning have substantially reshaped interpretation workflows. This review synthesizes the 2015-2025 literature across four tasks: structural interpretation, geobody identification, seismic facies analysis, and property estimation, tracing the field's evolution from classical machine learning through deep learning to emerging domain foundation models, and how these tasks couple within a single interpretation system. The task remains fundamentally different from other AI applications, facing ambiguous signals, interpretive non-uniqueness, sparse semantics, unfixed target locations, and scarce reliable annotations. We synthesize three defining challenges: interpretation under complex geological conditions, cross-survey semantic generalization under low information density, and the absence of reliable benchmarks. Addressing them will hinge on integrating human expertise, physical constraints, and geological priors into training and inference, and on treating uncertainty quantification as an intrinsic model output. We outline a forward-looking agenda: unified, jointly modelled interpretation systems with cross-task consistency; priors evolving from physics toward language and multimodal supervision; end-to-end uncertainty propagation; human-AI collaboration and agent-orchestrated workflows; and a more rigorous evaluation science supported by an AI-ready data ecosystem. The review is accompanied by an open benchmark resource (CIG-Bench), covering fault segmentation, relative geologic time estimation, geobody segmentation, and property modeling, with synthetic datasets, pretrained baselines, and quantitative evaluation: https://douyimin.github.io/CIG-bench
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All authors Yimin Dou, Xinming Wu, Hui Gao, Mingliang Liu, Tao Zhao, Zhi Zhong, Haibin Di, Min Jun Park, Robert G. Clapp, Zhixiang Guo, Long Han, Sergey Fomel

Physics and Society

Characterizing and modeling the patterns of vehicle movement on road networks

Dongwon Kang, Jung-Hoon Jung, Jongwoo Lee et al. (+2 more)

Understanding vehicle movement on road networks is closely related to various practical and theoretical issues. While recent works have focused on which cost vehicles minimize while moving, how they move to minimize that cost remains less explored. In this work, we analyze large-scale data of individual vehicle trajectories in real-world road networks to identify cost-minimizing movement patterns of vehicles and the influence of road network structure on such movement. We observed that vehicle movements exhibit three phases: the beginning, middle, and end of trips. At the beginning and end, vehicles detour more, lose directional memory quickly, and travel at lower speeds than during the middle. In contrast, during the middle, they tend to detour less, maintain directional memory, and travel faster than at the beginning and end. Finally, at the beginning and end, vehicles exhibit similar detour and velocity patterns, except the direction of movement. To understand these patterns, we propose a double-layered network model mimicking the hierarchical structure of real-world road networks. We found that when vehicles move across our model network while minimizing travel time, they tend to concentrate on high-level roads, and the three observed movement phases are reproduced. Consequently, when a vehicle moves between a given origin-destination pair, it must enter and exit these high-level roads. This causes it to deviate from the trajectory that minimizes travel distance between the same origin-destination pair – particularly at the beginning and end of the trip. Our results reveal common patterns underlying individual vehicle movements that appear highly diverse at first glance, demonstrating that these patterns emerge because vehicles leverage the characteristics of hierarchical road networks to minimize travel time.
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All authors Dongwon Kang, Jung-Hoon Jung, Jongwoo Lee, Seunghoon Cheon, Young-Ho Eom

Computational Physics

GPU Acceleration of Collinear and Noncollinear DFT Using a Numerical Atomic Orbital-Based DFT Code

Hiroyuki Kawai, Takuya Sekikawa, Taisuke Ozaki et al. (+1 more)

We implement GPU acceleration of collinear and noncollinear density functional theory (DFT) calculations in the numerical atomic orbitals (NAOs) code OpenMX by offloading matrix multiplications and eigenvalue solves (plus selected auxiliary steps) to cuBLAS/cuSOLVER and OpenACC. Benchmarks on the Pegasus supercomputer (per node: a 48-core Intel Xeon Platinum 8468 CPU and one NVIDIA H100 GPU) compare GPU-accelerated and CPU-only runs under identical settings. For a 512-atom collinear case on two nodes (two GPUs total), the GPU-accelerated calculation achieves a 2.02 times speedup over a CPU-only run on two nodes (96 CPU cores total); for a 384-atom noncollinear case on two nodes (two GPUs total), the speedup is 2.60 times over two CPU-only nodes (96 cores). These results demonstrate practical GPU-accelerated DFT in an NAO-based code for both collinear and noncollinear calculations.
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All authors Hiroyuki Kawai, Takuya Sekikawa, Taisuke Ozaki, Yoshiaki Ōno

Fluid Dynamics

A Hybrid Generative Reduced-Order Model for the Minimal Flow Unit

Niccolò Tonioni, Lionel Agostini, Marcial Sanchis-Agudo et al. (+4 more)

A data-driven reduced-order modelling framework is proposed for wall-bounded turbulent flows to forecast the intermittent near-wall dynamics over extended time horizons from sparse sensor measurements. The approach combines a $\beta$-VAE-GAN, which compresses high-dimensional flow fields into a low-dimensional latent space, with a sensor-conditioned Transformer that forecasts the evolution of the latent variables. The temporal module employs Easy Attention, a static time-mixing operator that replaces the learnable query-key mechanism of standard self-attention at reduced computational cost, combined with an adapted AdaLN-Zero modulation mechanism for sensor-based conditioning. Evaluated on the Minimal Flow Unit ($Re_\tau = 200$) at $y^+ = 14$, the compression stage recovers $87\%$ of the turbulent kinetic energy within a four-dimensional latent space, exceeding the standard $\beta$-VAE baseline by more than $10\%$. The latent dimensions autonomously encode the characteristic timescales of the flow, with specific coordinates capturing the low-frequency signature of the near-wall regeneration cycle ($T^+ \approx 1724$), establishing the physical interpretability of the learnt representation. The sensor-conditioned Transformer maintains accurate forecasts over $17{,}288\,t^+$ from an initialisation window of only $128\,t^+$, whilst end-to-end inference reconstructs $82\%$ of the turbulent kinetic energy. The principal limitation is the attenuation of rare, extreme-amplitude events, a consequence of the encoder prioritising the most statistically recurrent flow states within the low-dimensional bottleneck. Nevertheless, the framework accurately reproduces the alternating active and quiescent phases of the regeneration cycle, demonstrating its suitability as a surrogate model for the intermittent dynamics of wall-bounded turbulence.
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All authors Niccolò Tonioni, Lionel Agostini, Marcial Sanchis-Agudo, Mohammad Umair, Franck Kerhervé, Laurent Cordier, Ricardo Vinuesa

Biological Physics · Cross-listed from q-bio.PE

Natural Selection in the Wake of Catastrophe

Jesse Young Lin, Omer Granek, Joshua Sodicoff et al. (+3 more)

Living organisms, from bacteria to humans, are more likely to survive if their traits enhance fitness. In populations well adapted to their environmental niches, natural selection proceeds via rarely beneficial mutations. But when a catastrophe wipes out niche diversity, sudden adaptation often follows. Here, we present a data-validated theory of natural selection in the wake of catastrophe and unveil a simple law that emerges during recovery: the mean fitness relaxes inversely with time, with a prefactor proportional to the number of traits coupled to the post-catastrophe environment. We put our approach to test using experimental fitness landscapes measured following antibiotic administration to E. coli. The resulting mean trait adaptation is not described by gradient ascent on a fitness landscape, instead it follows an algorithm known as Levenberg-Marquardt optimization. Near fitness peaks, evolutionary trajectories are biased against greediness - from an optimization perspective, post-catastrophic selection is optimistic.
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All authors Jesse Young Lin, Omer Granek, Joshua Sodicoff, Seppe Kuehn, David Pincus, Vincenzo Vitelli

Chemical Physics

Static Electric Fields as a Model for Hydrogen-Bond-Induced Dissociation of HF and HCl

Megan Grace and Avdhoot Datar

The influence of static electric fields on the electronic structure and dissociation behavior of the polar diatomics \ce{HF} and \ce{HCl} is investigated using quantum chemical calculations. Ground- and excited-state potential energy surfaces (PESs) are computed as a function of bond distance and external electric field strength to examine field-induced modifications of chemical bonding. The calculations reveal pronounced bond softening and progressive destabilization of both molecules with increasing field intensity. Notably, the ground-state PES of \ce{HCl} becomes entirely dissociative at field strengths of approximately 450 MV/cm, whereas \ce{HF} requires a substantially stronger field of nearly 700 MV/cm to induce dissociation. This difference reflects the greater polarizability and weaker bond localization in \ce{HCl} relative to \ce{HF}, providing a molecular-scale perspective on the contrasting macroscale acid strengths of the two species. Field-dependent dipole moments further demonstrate the stronger electronic response of \ce{HCl} to external perturbations, highlighting how molecular polarizability drives electric-field-induced bond activation. Ultimately, these results map out a detailed picture of field-controlled dissociation in hydrogen halides, supporting the view that local electric fields generated by surrounding hydrogen-bonding networks play a key role in modulating bond activation and condensed-phase acidity.
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Computational Physics · Cross-listed from cs.MS

JAX-AMG: A GPU-Accelerated Differentiable Sparse Linear Solver Library for JAX

Yi Liu, Xiantao Fan and Jian-Xun Wang

Sparse linear systems from PDE discretizations are central to scientific computing, yet no existing JAX-ecosystem solver simultaneously provides GPU-accelerated algebraic multigrid (AMG), automatic differentiation (AD), and distributed multi-GPU execution. JAX-AMG fills this gap by wrapping the NVIDIA AmgX solver suite as a native JAX primitive, exposing AMG and Krylov methods with configurable preconditioners through a unified interface compatible with JIT compilation, reverse-mode AD via adjoint methods, batched solves, and MPI-based distributed execution. Solver caching amortizes setup costs across repeated solves, making JAX-AMG practical for PDE-constrained optimization and inverse problems. The result is a robust, scalable sparse linear algebra layer that integrates seamlessly into differentiable simulation and scientific machine learning pipelines.
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Physics Education

Discovering Misconceptions and Misunderstandings From Administrations of Research-Designed Multiple Choice Instruments

Martin Segado, Aaron Adair, John Stewart et al. (+1 more)

Misconceptions are "alternate hypotheses" that are incorrect according to established theories of how the world works. Often held with confidence by students, they are relatively context-insensitive, can seem like common-sense views, and are noted for being resistant to remediation using traditional instruction. To find misconceptions in Newtonian mechanics, we analyze ~34,000 administrations of the pioneering Force Concept Inventory using a flexible multidimensional item-response model for multiple-choice data. In contrast to most earlier work, we allow answer choices within each question to have different directions in the multidimensional space of student knowledge, essential for concept inventories in which distractors often codify distinct misconceptions. We uncover 22 robust, partly-overlapping dimensions whose distractors share a coherent theme identifiable with a misconception or misunderstanding. Motivated by the realization that many mirror previously-accepted theories of mechanics, we broadly sort these by historical era: Ancient (learned by infants but codified by Greeks), Medieval (reactions and extensions of Aristotelian ideas), and Post-Newtonian (including known modern misconceptions as well as two which appear novel). We also present a simple approach for computing "misconception scores" for students and classes. Examining these scores before and after instruction reveals surprisingly varied patterns of remediation in our sample: some misconceptions persist largely unchanged by instruction, while others are better remediated in below- or above-average students. In general, we find that many misconceptions are poorly remediated for students of average or lower ability. We hope our work will serve as a guide for developing, evaluating, and improving interventions for these while providing physics instructors with a valuable tool for class-level formative assessment.
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All authors Martin Segado, Aaron Adair, John Stewart, David Pritchard

2606.08986 · 8 Jun · An earlier version of this paper appeared in Chapter 3 of the first author's PhD thesis, "Intuitive but Wrong: Uncovering Student Misconceptions About Force and Motion With Bayesian Item-Response Methods" (Segado, 2025; https://hdl.handle.net/1721.1/165584)

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Physics and Society

Three-dimensional Fundamental Diagrams of Five-neighbor Particle Cellular Automata

Kazuya Okamoto and Daisuke Takahashi

We analyze five-neighbor particle cellular automata whose conventional two-dimensional fundamental diagrams are multivalued, but whose mean flow is uniquely determined by introducing a second density. We first consider binary rules for which the second density is conserved, and then examine rules for which the second density is not conserved but converges asymptotically. These examples give three-dimensional fundamental diagrams in which the mean flow is determined by the particle density and the second density. We then investigate whether this single-valued structure is preserved under real-valued max-plus extensions. There are some rules where two different max-plus extensions are introduced, and numerical simulations show that both extensions preserve the same single-valued three-dimensional fundamental diagram. These observations imply that, in constructing real-valued max-plus extensions, it is important to choose the flux function and the second density consistently.
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Instrumentation and Detectors

Study on the shielding efficiency of water, HDPE, and boron-loaded HDPE for neutron background of plastic scintillator neutrino detector

D.X. Lu, Y.H. Liu, X.S. Zhang et al. (+3 more)

Surface-level reactor antineutrino experiments usually have substantial cosmic ray induced neutron backgrounds, particularly with shallow overburden. The Array of Lattice for Anti-neutrino Reactor Monitoring (ALARM) is a plastic scintillator based experiment designed for reactor power monitoring. It will be deployed about 44 m from the core of a reactor at the Taishan Nuclear Power Plant. Placed at a depth of 9.6 meters below the surface, cosmic ray induced fast neutrons constitute a significant background, making an effective neutron shielding system essential for the experiment. For the shielding design of ALARM, we tested the shielding performance of three materials water, HDPE, and 40% boron-doped HDPE (BHDPE) against both fast and thermal neutrons. A thermal neutron detector composed of an EJ426 scintillator setup was first used to measure the shielding efficiency of these materials at various thicknesses using neutrons from an Am-Be source. A 30-cm thickness of BHDPE achieved a shielding efficiency exceeding 95% for both fast and thermal neutrons. Monte Carlo simulations of the EJ426 setup yielded results consistent with the experimental data. Simulation results for the shielding performance of the full ALARM shielding assembly are also presented.
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All authors D.X. Lu, Y.H. Liu, X.S. Zhang, F.P. An, G. Luo, W. Wang

Chemical Physics · Cross-listed from stat.CO

A Diffusion Monte Carlo algorithm employing depth first traversal and a stack instead of a swarm

Bastiaan J. Braams

Diffusion Monte Carlo (DMC) and Monte Carlo for particle transport with importance sampling both involve simulations of weighted walkers that undergo birth and death processes (splitting and Russian Roulette). The established implementations of these methods are quite different: Particle simulation Monte Carlo employs a stack to handle the splitting history whereas in traditional DMC one follows a swarm of walkers. The particle simulation Monte Carlo approach involves a depth first traversal of the visited configurations whereas the traditional DMC approach may be seen as a breadth first traversal. In the present work the implementation of a depth first, stack based approach to DMC is described and a complete code is presented. The depth first approach, called DMCD here, can be more memory efficient than the breadth first approach, both for total memory and for use of a memory hierarchy and of co-processors. The implementation appears very natural for population control and for descendant weighting and it unifies algorithmic treatment of the eigenvalue problem (DMC) with the linear equation problem (particle transport). A concern with DMCD that is not present in the breadth first approach, and that is successfully addressed here, is the need to maintain a pool of starters for use when a new walker is required and the stack is empty. The DMCD approach appears to have the potential to become the preferred implementation for many DMC applications.
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Instrumentation and Detectors

A Polarization-Decomposed Method for Simulating Inhomogeneous Birefringence in Laser-Interferometric Gravitational-Wave Detectors

Haoyu Wang, Yuta Michimura, Keiko Kokeyama et al. (+9 more)

Birefringence in test mass substrates is an emerging limitation for current and future laser-interferometric gravitational-wave detectors, particularly as detectors move toward higher circulating power, cryogenic operation, and crystalline optical materials. Spatially varying birefringence alters both the polarization state and spatial mode content of the intracavity field, reducing interference contrast and coupling into length and alignment control signals. Accurate modeling of these effects is complicated by the fact that most frequency-domain simulation tools employ scalar modal propagation and lack native support for polarization and two-dimensional substrate maps. In this work, we present a practical and general method for simulating inhomogeneous birefringence without modifying existing simulation frameworks. The approach represents the two polarization components as independent scalar fields and introduces their coupling through an equivalent triple-Mach-Zehnder construction that reproduces the Jones matrix of a birefringent medium. We demonstrate the method using realistic birefringence maps of the KAGRA sapphire input test masses. The technique is compatible with any frequency-domain interferometer model and enables efficient birefringence studies for next-generation gravitational-wave detectors.
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All authors Haoyu Wang, Yuta Michimura, Keiko Kokeyama, Daniel Brown, Yoichi Aso, Marc Eisenmann, Matteo Leonardi, Yutaro Enomoto, Takafumi Ushiba, Hiroaki Yamamoto, Masaki Ando, Kentaro Somiya

Plasma Physics

Design of a multifunctional Doppler backscattering diagnostic for the Pegasus-III Experiment

E. Wikarta, U. Kumar, V.H. Hall-Chen et al. (+6 more)

The Doppler backscattering (DBS) diagnostic measures flows and electron density fluctuations. Recent work indicates that DBS can also infer the magnetic pitch angle (Yeoh et al., NF 2026). We present the preliminary design of a DBS for the Pegasus-III Experiment. This DBS will serve two objectives. First, it will advance diagnostic science, by supporting understanding of the DBS instrumentation functions, using DBS to constrain the magnetic equilibrium, and data-driven inference of plasma properties from DBS signals. Secondly, it will support Pegasus-IIIs research directions, such as solenoid-free plasma initiation and O-X-B mode conversion for heating and current drive, since density fluctuations affect mode conversion efficiency and pitch angle measurements can be used to locate the mode conversion window. This ex-vacuum DBS system uses a single channel, tuneable Ka-band source, a corrugated horn antenna, and a homodyne I/Q receiver with baseband digitization. For greater flexibility, which is especially important for pitch angle measurements, the quasioptical elements include a rotatable spinner for O- and X-mode selection and a mirror with 2D steering. Using the Scotty beam-tracing code, for a range of poloidal launch angles $8^\circ$ to $18^\circ$ and a corresponding toroidal launch angle between $0^\circ$ to $3^\circ$ for maximal backscattered DBS power, we find that the DBS system is capable of measuring ion-scale density fluctuations $1\leq k_{\perp,c} \leq8 \text{ cm}^{-1}$ at a range of normalized radial coordinates from the outer core ($\rho \sim 0.65$) to just beyond the last-closed flux surface (LCFS), where $\rho=0$ corresponds to the magnetic axis and $\rho=1$ the LCFS. The system is also designed with additional toroidal steering capability, $-4^\circ$ to $8^\circ$, to resolve the toroidal response needed for magnetic pitch angle measurements.
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All authors E. Wikarta, U. Kumar, V.H. Hall-Chen, S.J. Diem, X. Li, K.T.E. Chua, T.S.P. See, A.C. Sontag, Z. Wilderspin

Chemical Physics · Cross-listed from quant-ph

Quantum Mechanical Studies of Photodissociation Dynamics on Quantum Computers

Zikun Zhuang, Chengdong Yang, Yuchen Wang et al. (+2 more)

Theoretical quantum dynamics calculations scale deeply with system size, rendering classical calculations intractable for complex systems. While quantum computing offers a natural solution, its application to nuclear quantum dynamics remains scarce. Here, we present a quantum algorithm to study photodissociation dynamics on quantum computers, benchmarked on the NOCl molecule. The wavefunction is propagated via a split-operator method, utilizing the Quantum Fourier Transform and unitary transformation matrix to switch representations. To impose outgoing boundary conditions on a truncated grid, we use a non-unitary absorbing potential propagator, implemented through a dilation scheme. The photodissociation cross section is calculated from the auto-correlation function, which is extracted using the Hadamard test. Our quantum computing results agree well with benchmarks under ideal conditions, and we further demonstrate that the algorithm is robust to noise and statistical sampling errors, indicating the promising application of noisy devices to quantum dynamics studies.
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All authors Zikun Zhuang, Chengdong Yang, Yuchen Wang, Dong H. Zhang, Bin Zhao

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

Chemical tuning of magnetic ordering and cryogenic magnetocaloric response in zircon-type Gd1-xErxVO4

Ming Zeng, Muqing Su, Liang Ming et al. (+4 more)

Chemical substitution offers an effective route to tune magnetic ordering and magnetocaloric performance in rare-earth oxides for cryogenic refrigeration. Here we investigate the structural evo lution, magnetic properties, and magnetocaloric effect of polycrystalline zircon-type Gd1-xErxVO4 (x=0, 0.1, 0.25, 0.5, and 0.75). Powder X-ray diffraction confirms that all samples crystallize in the tetragonal zircon structure without detectable impurity phases. Substitution of Gd3+ by the smaller Er3+ ion produces a systematic lattice contraction and modifies the magnetic behavior of the rare-earth sublattice. In particular, the magnetic ordering temperature is suppressed from 3.65(2) K in GdVO4 to 2.76(2) K in Gd0.9Er0.1VO4 , accompanied by a weakening of the spin-flop-like field-induced anomaly observed in the parent compound. A low Er concentration correspondingly improves the low-temperature magnetocaloric performance, with Gd0.9Er0.1VO4 exhibiting a max imum magnetic entropy change of 45.1 J kg-1 K-1 for mu_0 Delta H=7T. These results demonstrate that weak Er substitution effectively tunes the competition among exchange interactions, dipolar coupling, and magnetic anisotropy, optimizing the balance between magnetic ordering and available spin entropy in zircon-type rare-earth vanadates, which is crucial for developing efficient cryogenic refrigeration materials.
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All authors Ming Zeng, Muqing Su, Liang Ming, Xiaolong Yang, Wang Chen, Lingwei Li, Hai-Feng Li

Optics · Cross-listed from cs.ET

Silicon Photonics Testing: Design for Testability, Fault Detection, and Manufacturing Variation Analysis in Photonic Integrated Circuits

Pratishtha Agnihotri, Priyank Kalla and Steve Blair

This paper proposes a design-for-test (DFT) methodology and architecture for testing and validation of silicon photonic integrated circuits. We describe the design of silicon photonic circuits and components that comprise the proposed DFT architecture. The designs are extensively simulated and validated as test-access and fault-detection circuitry. We demonstrate how the DFT approach can be deployed on photonic integrated circuits and how they can be tested for correct operation, in terms of signal power and phase. The application is demonstrated on two distinct types of designs – an optical neural network comprising optical devices in a feed-forward topology, and on an optical logic circuit with feedback loops.
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Sunday 7 June

Physics and Society · Cross-listed from econ.GN

Simulating a Post-Automation Economy

Leonard Wossnig

We develop an agent-based, stock-ow-consistent model of an economy undergoing automation, built to ask which scal instrument reaches the durable surplus that articial intelligence creates. The model separates two channels: a competitive return on reproducible robotic capital, and a mobile, foreign-held intellectual-property rent earned by AI. Production is an endogenous nested-CES technology; wealth concentration is microfounded through heterogeneous, persistent returns to wealth; and taxation and capital mobility are modelled as behavioural responses. The central result is that the durable surplus is the foreign-held AI rent, a cross-border licence fee that corporate, robot, and compute or token taxes largely miss and that only a source-based levy (a digital-services-style tax or a withholding) reaches. The appropriate policy depends decisively on whether a country owns the automation or imports it: for a host that owns the rent the problem is domestic inequality, reached by progressive and wealth taxes; for a rent-importing host the problem is base erosion and a gradual transfer of capital ownership abroad, which a residence-based wealth tax cannot reach. We report conditional orderings, stress-tested with global (Sobol) sensitivity and a formal stability analysis, rather than point forecasts.
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Physics and Society · Cross-listed from eess.SY

Platooning Connected, Autonomous, and Human-Driven Vehicles: A Deep Reinforcement Learning-based Approach

Zhen Qina, Dong-Fan Xie, Heng Ma et al. (+2 more)

Conventionally, existing vehicle platooning approaches are designed for connected vehicles, typically including connected autonomous vehicles and connected human-driven vehicles. Non-connected vehicles, such as non-connected autonomous or human-driven vehicles, are not incorporated. As a result, these platooning approaches may not properly reflect real-world mixed traffic conditions at the current stage. To address this limitation, this study proposes a hybrid platooning pattern that conditionally permits non-connected vehicles to join platoons, thereby enhancing platooning diversity and flexibility. However, it was found that the unregulated integration of non-connected vehicles can trigger rapid platoon expansion, significantly amplifying the risk of disturbance propagation in traffic flow. This, in turn, exacerbates the inherent conflict between traffic throughput and stability. To mitigate these challenges, this paper further develops a hybrid platooning control strategy based on deep reinforcement learning (DRL). This strategy integrates vehicle dynamics, platoon topology, and traffic flow states through a multi-level state representation network, enabling a dynamic trade-off between traffic capacity and stability. Numerical simulations demonstrate that the proposed strategy effectively suppresses velocity disturbance propagation by dynamically optimizing platoon structures, thereby significantly enhancing the stability and safety of mixed traffic while reducing fuel consumption and emissions.
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All authors Zhen Qina, Dong-Fan Xie, Heng Ma, Xiaomei Zhao, Zhengbing He

Data Analysis and Statistics · Cross-listed from eess.SP

Uniform Asymptotics of the Pseudo Wigner-Ville Distribution for Nonlinear Chirps

Vincenzo Pierro, Maurizio Feo, Maurizio Ricciardi et al. (+4 more)

The analysis of non stationary signals in complex physical systems often relies on Time Frequency distributions. Among these, the Pseudo Wigner Ville Distribution (PWVD) stands out for its superior resolution but is mathematically challenging due to its inherent quadratic nonlinearity. This nonlinearity generates complex interference artifacts and cross terms in the phase space, potentially obscuring the physical features of the signal, particularly for nonlinear chirps. In this work, we establish a mathematically grounded framework for the PWVD for general windowed nonlinear chirps. By leveraging the theory of oscillatory integrals with coalescing stationary points, we derive a uniform asymptotic expansion that bridges the gap between heuristic signal processing and semiclassical geometric approaches (Berry's chord construction). The resulting closed form representation, expressed in terms of symmetric incomplete Airy functions, provides a unified description of the nonlinear transform's behavior, regularizing the transition across the instantaneous frequency caustics. While the framework is general, we show its power on two illustrative examples: the high precision nonlinear chirps of coalescing binaries in gravitational-wave astronomy and radar nonlinear chirps for pulse compression applications. The analytical results successfully predict the structure of interference patterns and quantify the systematic bias in peak based frequency estimation. Therefore, this study establishes a systematic bridge between nonlinear mathematical analysis and precision experimental physics, validating the PWVD as a robust tool for detailed source characterization in high signal to noise regimes.
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All authors Vincenzo Pierro, Maurizio Feo, Maurizio Ricciardi, Rocco P. Croce, Theo Demma, Innocenzo M. Pinto, Paolo Addesso

Instrumentation and Detectors

Absolute intensity measurement of pulsed muon beams using in-beam activation

R. Mizuno, M. Niikura, T. Matsuzaki et al. (+6 more)

The absolute number of negative muons contained in a beam is essential for many experiments at accelerator facilities, but determining this number in pulsed beams has been difficult, particularly at high intensities. A method utilizing the yield of the $\beta$ delayed $\gamma$ rays from the residual nuclei following the muon nuclear capture reaction has recently been demonstrated for determining the muon number in the pulsed muon beam. In particular, the in-beam activation method employs isotopes with short lifetimes, enabling the beam intensity to be monitored during beam irradiation. However, the applicability of this method was limited because the required production branching ratio (BR) data were available only for a limited number of isotopes, restricting its use to specific experimental conditions. To broaden the applicability of this method, we searched for new candidate isotopes that are suitable for beam intensity measurement using the in-beam activation method and assessed their feasibility to a wide range of experiments. Based on the strength of the production BR, the muon capture probability, the practical detection efficiency of the detector, and the rarity of the target material in the surrounding structures, the reactions $^\mathrm{nat}$Ag ($\mu^-, \nu_\mu x$) $^{107m}$Pd and $^\mathrm{nat}$Pd ($\mu^-, \nu_\mu x$) $^{107m, 105m}$Rh are identified as a useful references for determining the number of muons in pulsed beams using the in-beam activation method.
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All authors R. Mizuno, M. Niikura, T. Matsuzaki, A.D. Hillier, K. Ishida, S. Kawase, T. Kawata, K. Kitafuji, D. Tomono

Geophysics

RO-LiDAR GeoQuickView: A Web Platform for Exploring Public LiDAR-Derived Elevation Data in Romania

Alexandru Hegyi

Public elevation data can support landscape research, environmental interpretation, planning, education, and public engagement, but their practical reuse is often limited by fragmented delivery and specialist processing requirements. This paper presents RO-LiDAR GeoQuickView, an independent, voluntary, and non-commercial Web-GIS initiative for exploring and reusing publicly accessible elevation data in Romania. The platform integrates LiDAR-derived digital terrain models (DTMs) and complementary elevation models of different resolutions, publishes standardized hillshade visualizations for immediate browser access, supports participatory landscape documentation, and provides a spatial index for direct raster retrieval. Its most detailed currently integrated component is the 0.5 m LAKI III Zone A DTM coverage for Caras-Severin, Gorj, Mehedinti, and Dolj counties. LAKI III Zone B, comprising Suceava, Neamt, Bacau, and Vrancea counties, is the next scheduled high-resolution extension. It will be integrated after the public products become available and pass through the same harmonization and quality-control workflow. The platform also incorporates LAKI II and additional public altimetric sources through a source-aware processing workflow that accommodates different acquisition units, including one-kilometre cells and larger raster blocks. The paper documents the data architecture, harmonization steps, quality-control procedures, access modes, application range, and limitations. The platform is conceived as an accessibility layer over public data infrastructures: it supports rapid discovery and preliminary interpretation, but it does not replace official products, specialist modelling, expert review, or field verification.
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Atomic Physics

A nuclear clock synchronized to $^{229}$Th

Beichen Huang, Gaowei Yan, Qi Xiao et al. (+36 more)

Atomic clocks have made time and frequency the most precisely measured quantities in physics, progressing from microwave standards that realize the SI second to optical clocks with unprecedented precision. A nuclear clock transfers the frequency reference from an electronic to a nuclear transition, and the uniquely low-lying, laser-accessible isomeric transition in $^{229}$Th currently offers the most practical route to compact, robust timekeeping and sensitive tests of fundamental physics. Realizing such a clock requires turning spectroscopy of the $^{229}$Th nuclear resonance into a stable discriminator for steering a traceable oscillator. Here we demonstrate a $^{229}$Th nuclear clock by stabilizing a continuous-wave, narrow-linewidth 148.4-nm vacuum-ultraviolet (VUV) laser to a resolved, weakly temperature-sensitive nuclear transition in $^{229}$Th$:$CaF$_2$ crystals. A $10$-$\mu\mathrm{W}$ VUV source generated by four-wave mixing in cadmium vapour and phototube-based frequency-modulation absorption readout provide a fast, high-signal-to-noise nuclear discriminator. The clock reaches a fractional frequency instability of $5\times10^{-13}/\sqrt{\tau/\mathrm{s}}$ for averaging time $\tau$. Clock-transition frequencies measured in two independently fabricated crystals agree at the $10^{-13}$ level and are consistent with previous VUV-comb measurements on other $^{229}$Th:CaF$_2$ crystals. These results establish laser-addressed nuclei as operational clock references and provide a reproducible solid-state platform for compact nuclear clocks, nuclear quantum sensors and precision tests of fundamental physics.
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All authors Beichen Huang, Gaowei Yan, Qi Xiao, Wenhao Bu, Chengchun Zhao, Zhen Zhang, Chao Yan, Zhi-Ang Chen, Peixiong Zhang, Gleb Penyazkov, Zhenhai Zhan, Lingfeng Yan, Yuefei Wang, Lin Li, Shanming Li, Dapeng Jiang, Xiaobo Qian, Xuegang Liu, Qiange He, Taoxiang Sun, Haochen Tian, Bingkun Lu, Ningyuan Ma, Juxian Li, Yanzhang Wu, Qiaorui Gong, Yuxiang Li, Haoyu Shi, Xiangliang Li, Longsheng Ma, Shining Zhu, Yuxiang Mo, Jun Lin, Li You, Yige Lin, Xibo Zhang, Yin Hang, Liangbi Su, Shiqian Ding

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

A Framework to Model Stellar Irradiated Disks with Frequency-dependent Absorption and Scattering Opacities in Athena++

Stanley A. Baronett, Yan-Fei Jiang, Zhaohuan Zhu et al. (+2 more)

The frequency dependence of opacity is crucial for determining the thermal structure of protoplanetary disks, which in turn influences disk dynamics and planet formation. Yet many disk models adopt simplified thermodynamics, and common radiation-hydrodynamic approaches often use gray opacities, ignore scattering, and yield inaccurate results in regions with intermediate optical depth. We present a comprehensive framework that models stellar irradiation with frequency-dependent absorption and scattering across all optical depths using the Athena++ finite-volume code, extended with multigroup radiation transport and newly implemented radial rays to more accurately represent the stellar flux. To calibrate this framework, we focus exclusively on hydrostatic disk models, allowing us to isolate radiative effects and evaluate the method without additional dynamical complexity. Because dust opacity increases strongly with frequency, ultraviolet stellar irradiation heats the tenuous disk atmosphere while the optically thick midplane remains cooler. This vertical temperature gradient is captured more accurately when more frequency bands are used or when scattering is included. Our hydrostatic models achieve equilibrium temperatures that differ from Monte Carlo radiative-transfer benchmarks on average by 2–5% with 64 frequency bands and 7–11% with 3 bands. Reducing the number of bands lowers computational cost by at least an order of magnitude while increasing the maximum possible temperature deviation only from 8% to 19%. This calibration demonstrates the accuracy and efficiency of the framework and provides a solid foundation for future self-consistent studies of irradiated protoplanetary disks, including fully dynamical simulations and applications involving chemical processes and time-dependent stellar luminosity.
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All authors Stanley A. Baronett, Yan-Fei Jiang, Zhaohuan Zhu, Shangjia Zhang, Philip J. Armitage

Chemical Physics

Collisional energy transfer in ethanimine + He system

Vivek Vijay, Francesca Tonolo, Ernesto Quintas-Sánchez et al. (+5 more)

The ethanimine molecule, CH3CHNH, is one of the prebiotic molecules detected by astronomers in chemically-rich molecular clouds in the Galactic Center. The observations indicate a non-equilibrium distribution of rotational state populations in both the E- and Z-isomers of ethanimine, resulting from the competition between radiative processes and collisions with background gases such as He and H2. Accurate interpretation of these observations requires the use of radiative transfer models with collisional state-to-state transition processes included. Here, in order to compute cross sections for state-to-state transitions in both ethanimine isomers, accurate potential energy surfaces for their interaction with a He atom were constructed and three complementary methods for inelastic scattering were utilized: full-quantum coupled-channel and coupled-states methods, and the mixed quantum/classical theory. Strong propensities of transitions toward $\Delta j = 0$ and either $\Delta k_a = 0$ (with $\Delta k_c = \pm 1$) or $\Delta k_c = 0$ (with $\Delta k_a = \pm 1$) are reported and the origin of this effect is identified. Small but non-negligible differences between energy transfer in the two isomers, on the order of 10%, were found. The utility of the mixed quantum/classical approach to collisional energy transfer at higher collision energies is discussed.
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All authors Vivek Vijay, Francesca Tonolo, Ernesto Quintas-Sánchez, Adrian Batista Planas, Carolin Joy, Richard Dawes, François Lique, Dmitri Babikov

Plasma Physics

High-dimensional inverse design of inertial fusion implosions via differentiable simulation

A. J. Crilly, P. Travis, J. P. Brodrick et al. (+2 more)

Inertial confinement fusion implosion design requires simultaneous optimisation of strongly coupled target and driver parameters across high-dimensional design spaces. Existing automated design approaches typically rely on non-differentiable radiation-hydrodynamics codes treated as black boxes, making optimisation increasingly expensive as dimensionality grows. In this work, we present a differentiable simulation approach for high-dimensional inverse design of inertial confinement fusion implosions. Automatic differentiation through a differentiable implosion physics model, driven by an external pressure pulse, provides gradients of implosion objectives with respect to design parameters, enabling gradient-based optimisation. The framework is applied to 25 kJ OMEGA-scale direct-drive implosions, optimising 500-parameter laser pulses across sampled target geometries. The optimised pulse recovers a near-isoentropic rise to peak power without that structure being imposed. Neural-network pulse parameterisations are additionally explored as a means of accelerating design-space exploration. These results establish differentiable implosion modelling as a promising tool for ICF design, while motivating further work on adjoint robustness and higher-fidelity differentiable simulators.
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All authors A. J. Crilly, P. Travis, J. P. Brodrick, J. B. Coughlin, A. S. Joglekar

Chemical Physics

When Three-Dimensional Conformer Ensembles Improve Molecular Property Prediction Beyond Two-Dimensional Fingerprints: A Systematic Study

Bryan Cheng, Austin Jin and Jasper Zhang

When do three-dimensional conformer ensembles improve molecular property prediction beyond two-dimensional fingerprints? We provide the first systematic, mechanistically grounded answer. Through ~1,000 experiments spanning 13 model configurations, 14 regression targets, and 2 classification targets across MoleculeNet, QM9, and MARCEL benchmarks, we discover selective complementarity: conformer ensemble statistics extracted via Distribution Kernel Operators (DKOs) yield statistically significant RMSE reductions on solvation-dependent properties (ESOL -11.0%, p < 10^{-9}; FreeSolv -13.5%, p < 3x10^{-5}; 10-seed paired validation) while providing no benefit for electronic or steric tasks. Three lines of evidence confirm this selectivity has a physical rather than statistical basis: improvement is larger under scaffold splits than random splits (+11.9% vs. +8.5% on ESOL), concentrates on large, flexible molecules (+18.9% for heaviest quartile), and grows monotonically with training data. We establish a four-tier performance hierarchy: end-to-end 3D GNNs (SchNet, PaiNN; 21-42% over fingerprints) >= engineered physicochemical descriptors (PMI/SASA/USR) > Morgan fingerprints + XGBoost > all neural conformer ensemble methods, confirmed by two architecturally diverse GNNs and revealing that the pre-computed feature bottleneck limits ensemble approaches. Feature attribution and mutual information analysis expose the mechanistic asymmetry: conformer mean features carry 2-8x more information per feature than fingerprint bits, yet covariance features contribute <2% of model signal, explaining why five simple scalar invariants outperform all complex covariance architectures (p < 0.001). These findings yield an empirical property taxonomy and a practical decision framework for when conformer generation is worth the investment.
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Medical Physics

Investigating the Uncertainty of Cellular Microenvironment Parameter Estimations via Diffusion MRI Cytometry

Wen Li, Yan Dai, Arely Perez Rodriguez et al. (+3 more)

This study aims to identify cell microenvironment parameters that can be robustly estimated from IMPULSED diffusion MRI signals and to develop a reliable mapping-based estimation framework. Diffusion MRI signals were simulated using the established IMPULSED model with one pulsed gradient spin echo sequence and two oscillating gradient spin echo sequences at different frequencies. Five cellular parameters were considered: cell diameter ($d$), intracellular diffusion coefficient ($D_{in}$), intracellular volume fraction ($V_{in}$), extracellular diffusion coefficient ($D_{ex}$), and the frequency-dependent slope of $D_{ex}$ ($\beta_{ex}$). Parameter uncertainty was quantified using Jacobian-based sensitivity analysis at an SNR of 30, representing clinically achievable conditions on a 1.5T MRI scanner. To enable direct parameter mapping, signals were logarithmically transformed, reduced in dimension using principal component analysis, and then used to estimate parameters with linear regression, fourth-order polynomial regression, and a fully connected four-layer neural network. Model validation was performed in vitro using MC38 cell lines. Uncertainty analysis identified $d$, $V_{in}$, and $D_{ex}$ as robustly derivable parameters, each with relative uncertainty below 1.0. Among the tested models, the four-layer neural network performed best, with mean absolute errors of 1.7 $\mu$m for $d$, 5.06% for $V_{in}$, and 0.28 $\mu$m$^2$/ms for $D_{ex}$. In vitro validation showed a 6.7% error in cell diameter estimation. These results demonstrate that IMPULSED dMRI can support robust estimation of key cell microenvironment parameters and provide a practical framework for noninvasive assessment of tumor microenvironment changes during radiation therapy response monitoring.
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All authors Wen Li, Yan Dai, Arely Perez Rodriguez, Todd Aguilera, Jie Deng, Xun Jia