Optics
Visible dual-comb spectroscopy across more than 100 THz with lithium niobate nanophotonic waveguides
Carter Mashburn, Kristina F. Chang, Michael J. Wahl et al. (+11 more)
Broadband and high-resolution spectroscopy in the visible and ultraviolet is central to advances in multiple fields, including fundamental
quantum physics, biology, atmospheric science and astronomy. Traditionally, these measurements are performed with grating or
Fourier-transform spectrometers using incoherent light sources. Leveraging coherent light enables powerful frequency-comb-based techniques, but is limited by the technical complexity of efficiently generating broad spectral bandwidths from relatively narrowband and spectrally distant
laser sources. Current visible dual-comb spectrometers require implicit compromises between optical bandwidth, experimental simplicity, and acquisition speed. In this work, we introduce a simple and efficient dual-comb spectrometer that converts robust Er:fiber frequency combs from the near-infrared to the ultraviolet and visible with thin-film lithium niobate (TFLN) nanophotonic waveguides. Using real-time signal processing, we retrieve coherently averaged dual-comb spectra over nearly 120 THz of simultaneous bandwidth in the visible with 100 MHz spectral resolution. With these capabilities, we measure the broadband absorption spectrum of molecular iodine (I2), demonstrating the broadest visible spectral coverage of a dual-comb spectrometer to date. Additional measurements of NO2, atomic rubidium, and atomic sodium further illustrate the achievable combination of spectroscopic bandwidth, resolution, and intrinsic frequency accuracy. Our results demonstrate the powerful integration of low-power frequency combs, nonlinear nanophotonics, and digital signal processing to enable a compact, efficient and versatile approach to high-resolution mapping of complex absorption spectra across 500 THz in the UV-visible and near-infrared spectral regions for multiple applications beyond the research lab
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All authors Carter Mashburn, Kristina F. Chang, Michael J. Wahl, Mathieu Walsh, Daniel I. Herman, Matthew Heyrich, Tsung-Han Wu, Nazanin Hoghooghi, Ryoto Sekine, Luis Ledezma, Emily Jerris, Alireza Marandi, Jerome Genest, Scott A. Diddams
Optics · Cross-listed from cond-mat.mtrl-sci
Impact of crystallinity on the circular and linear dichroism signals in chiral perovskite
Reshna Shrestha and Wanyi Nie
Chiral perovskites owing to their broken mirror
symmetry exhibit selective absorption of circularly polarized light manifesting a strong circular dichroism (CD). CD spectroscopy has been a key technique to understand
chiral perovskites and how these
semiconductors achieve chirality at the molecular level. However, there is a debate on whether the observed CD is intrinsic to the
chiral crystal structures or is modulated by extrinsic phenomena particularly linear dichroism (LD) and linear birefringence (LB) effects. This work investigates the chiroptical properties of $(R-/S-\text{MBA})_2\mathrm{CuCl}_4$ (MBA = Methylbenzyl ammonium) series by thoroughly studying the contribution from LD and LB to the observed CD signals. The comparison of highly oriented and randomly oriented films exhibits notable LD and LB contributions to the observed CD, which are caused by orientation-dependent electric-field interactions and local anisotropy. Both randomly and highly oriented films exhibit distinct CD responses, with LD–LB effects largely dominating the CD in highly oriented films. This has been revealed by the obvious shift in the observed CD signals to the below-absorption-edge ($\sim 430$ nm) regime and broadening of features. Our findings demonstrate that careful consideration of
crystal orientation and structural effects is necessary for appropriate interpretation of CD spectra in
chiral perovskite thin films.
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Chemical Physics
Efficient, Equivariant Predictions of Distributed Charge Models
Eric D. Boittier and Markus Meuwly
A machine learning (ML) based equivariant neural network for constructing distributed charge models (DCMs) of arbitrary resolution, DCM-net, is presented. DCMs efficiently and accurately model the anisotropy of the molecular electrostatic potential (ESP) and go beyond the point charge representation used in conventional molecular mechanics (MM) energy functions. This is particularly relevant for capturing the conformational dependence of the ESP (internal polarization) and chemically relevant features such as lone pairs or {\sigma}-holes. Across conformational space, the learned charge positions from DCM-net are stable and continuous. Across the QM9 chemical space, two-charge-per-atom models achieve accuracies comparable to fitted atomic dipoles for previously unseen molecules (0.75 (kcal/mol)/e). Three- and four-charge-per-atom models reach accuracies competitive with atomistic multipole expansions up to quadrupole level (0.55 (kcal/mol)/e). Pronounced improvements of the ESP are found around O and F atoms, both of which are known to feature strongly anisotropic fields, and for aromatic systems. Across the QM9 reference data set, molecular dipole moments improve by 0.1 D compared with fitted monopoles. Transfer learning on dipeptides yields a 0.2 (kcal/mol)/e ESP improvement for unseen samples and a two-fold MAE reduction for molecular dipole moments versus fitted monopoles. Overall, DCM-net offers a fast and physically meaningful approach to generating distributed charge models for running pure ML or mixed ML/MM based molecular simulations. level (0.55 (kcal/mol)/e).
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Applied Physics · Cross-listed from math.NA
Scalable Preconditioners for the Pseudo-4D DFN Lithium-ion Battery Model
Thomas Roy, Nicholas W. Brady, Giovanna Bucci et al. (+5 more)
The pseudo-4D Doyle-Fuller-Newman (DFN) model enables predictive simulation of lithium-ion batteries with three-dimensional electrode architectures and particle-scale
diffusion, extending the standard pseudo-2D (P2D) formulation to fully resolve
cell geometry. This leads to large, nonlinear systems with strong coupling across multiple physical scales, posing significant challenges for scalable numerical solution. We introduce block-structured preconditioning strategies that exploit the mathematical properties of the coupled system, employing multigrid techniques for electrode-level operators and localized solvers for particle-scale
diffusion. Comprehensive scalability studies are performed across a range of geometries, including homogeneous and heterogeneous cubic
cells, flattened jelly-roll configurations, and triply periodic minimal surface electrodes, to assess solver robustness and parallel scalability. The proposed methods consistently deliver efficient convergence and enable the solution of battery models with hundreds of millions of degrees of freedom on large-scale parallel hardware.
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All authors Thomas Roy, Nicholas W. Brady, Giovanna Bucci, Nicholas R. Cross, Victoria M. Ehlinger, Tiras Y. Lin, Hanyu Li, Marcus A. Worsley
Optics
Miniaturised transmissive multi-plane light converters via laser-written geometric phase holograms cascaded in glass
Unė G. Būtaitė, Martynas Beresna and David B. Phillips
Multi-plane light converters (MPLCs) are an emerging beam shaping technology capable of deterministically mapping a basis of input spatial light modes to a different basis of output modes. The ability to perform such multi-modal spatial reformatting operations has many future applications in both classical and
quantum photonics, spanning from optical communications to photonic computing and advanced imaging. In this work we fabricate miniaturised transmissive MPLCs fully-encapsulated within a fused silica glass chip using single-step 3D direct
laser writing. Our approach relies on the formation of femto-second
laser induced birefringent nanogratings with a spatially controllable slow-axis orientation. Multiple layers of these nanogratings are
laser-written throughout the volume of the glass to create a sequence of axially separated
geometric phase holograms which imprint controllable phase patterns onto circularly polarised read-out light propagating through them. We construct and test a range of proof-of-concept
laser-written MPLCs operating in the visible (lambda = 633nm). These miniature beam multiplexers are formed from up to 5 separate phase masks of width ~260um, cascaded along a total length of ~2.7mm, thus occupying a compact volume of ~0.15 mm3. We first demonstrate Hermite-Gaussian (HG) mode sorters capable of diverting the energy carried by up to 28 overlapping HG modes into spatially separated output channels. We next create a 7-mode orthogonal speckle sorter, highlighting the universal nature of the spatial transformations it is possible to encode. Finally, we show analogue optical
matrix multiplications achieved by passively scattering light through these 3D structured glass elements. Our work begins to merge the concepts of free-space optics with 3D integrated photonics in glass and plots a path towards the rapid prototyping of robust monolithic MPLC technology.
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Computational Physics
Event-Chain Monte Carlo: The global-balance breakthrough
E.A.J.F. Peters
The seminal 2009 paper by Bernard, Krauth, and Wilson marked a paradigm shift in Monte Carlo sampling. By abandoning the restrictive condition of detailed balance in favor of the more fundamental principle of global balance, they introduced the Event-Chain Monte Carlo (ECMC)
algorithm, which achieves rejection-free, deterministic sampling for hard spheres. This breakthrough demonstrated that persistent, directional dynamics could dramatically accelerate equilibration in dense particle systems. In this commentary, we review this foundational work and elucidate its underlying mechanism using the broader Event-Driven Monte Carlo (EDMC) framework developed in subsequent years. We show how the original hard-sphere concept naturally generalizes to continuous potentials and modern lifted Markov chain formalisms, transforming a surprising specific result into a powerful general class of sampling
algorithms.
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Computational Physics · Cross-listed from cs.LG
Systematic Performance Assessment of Deep Material Networks for Multiscale Material Modeling
Xiaolong He, Haoyan Wei, Wei Hu et al. (+2 more)
Deep Material Networks (DMNs) are structure-preserving, mechanistic machine learning models that embed micromechanical principles into their architectures, enabling strong extrapolation capabilities and significant potential to accelerate multiscale modeling of complex microstructures. A key advantage of these models is that they can be trained exclusively on linear elastic data and then generalized to nonlinear inelastic regimes during online prediction. Despite their growing adoption, systematic evaluations of their performance across the full offline-online pipeline remain limited. This work presents a comprehensive comparative assessment of DMNs with respect to prediction accuracy, computational efficiency, and training robustness. We investigate the effects of offline training choices, including initialization, batch size, training data size, and activation regularization on online generalization performance and uncertainty. The results demonstrate that both prediction error and variance decrease with increasing training data size, while initialization and batch size can significantly influence model performance. Moreover, activation regularization is shown to play a critical role in controlling network complexity and therefore generalization performance. Compared with the original DMN, the rotation-free Interaction-based Material Network (IMN) formulation achieves a 3.4x - 4.7x speed-up in offline training, while maintaining comparable online prediction accuracy and computational efficiency. These findings clarify key trade-offs between model expressivity and efficiency in structure-preserving material networks and provide practical guidance for their deployment in multiscale material modeling.
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All authors Xiaolong He, Haoyan Wei, Wei Hu, Henan Mao, C.T. Wu
Instrumentation and Detectors · Cross-listed from eess.IV
Information Theory: An X-ray Microscopy Perspective
Charles Wood
X-ray microscopy (XRM) is commonly used to obtain three-dimensional information on internal microstructure, but the imaging pipeline introduces noise, redundancy and information loss at multiple stages. This paper treats the XRM workflow as an information-processing system acting on a finite information budget. Using
entropy, mutual information and Kullback-Leibler divergence, we quantify how acquisition, denoising,
alignment, sparse-angle sampling, dose variation and reconstruction reshape the statistical structure of projection data and reconstructed volumes. Case studies based on the Walnut 1
dataset illustrate how these processes redistribute information and impose bottlenecks. We summarise the workflow using a unified information budget and show that mutual information provides a reconstruction-agnostic indicator of fidelity, supporting quantitative comparison and optimisation of XRM protocols, particularly under low-dose or time-constrained conditions
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Data Analysis and Statistics · Cross-listed from stat.CO
PoissonRatioUQ: An R package for band ratio uncertainty quantification
Matthew LeDuc and Tomoko Matsuo
We introduce an R package for
Bayesian modeling and uncertainty quantification for problems involving count ratios. The modeling relies on the assumption that the quantity of interest is the ratio of Poisson means rather than the ratio of counts. We provide multiple different options for
retrieval of this quantity for problems with and without spatial information included. Some added capability for uncertainty quantification for problems of the form $Z=(mT+z_0)^{p}$, where $Z$ is the intensity ratio and $T$ the quantity of interest, is included.
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Medical Physics
High-fidelity 3D multi-slab diffusion MRI using Slab-shifting for Harmonized 3D Acquisition and Reconstruction with Profile Encoding Networks (SHARPEN)
Ziyu Li, Karla L. Miller and Wenchuan Wu
Three-dimensional (3D) multi-slab imaging is a promising approach for high-resolution in vivo
diffusion MRI (dMRI) due to its compatibility with short TR (1-2 s), providing optimal signal-to-noise ratio (SNR) efficiency. A major challenge, however, is slab boundary artifacts arising from non-ideal slab-selective RF excitation. Non-rectangular slab profiles reduce signal intensity at slab boundaries, while profile overlap across adjacent slabs introduces inter-slab crosstalk, where repeated excitation shortens the local TR and limits T1 recovery. To mitigate slab boundary artifacts without increasing scan time, we build on slab profile encoding and propose Slab-shifting for Harmonized 3D Acquisition and Reconstruction with Profile Encoding Networks (SHARPEN). For different
diffusion directions, SHARPEN applies inter-volume field-of-view shifts along the slice direction to provide complementary slab profile encoding without prolonging acquisition. Slab profiles are estimated using a lightweight self-supervised neural network that exploits consistency across shifted acquisitions and known physical properties of slab profiles and
diffusion images, and corrected images are reconstructed accordingly. SHARPEN was validated using simulated and prospectively acquired high-resolution in vivo data and demonstrates accurate slab profile estimation and robust boundary artifact correction, even in the presence of inter-volume motion. SHARPEN does not require high-quality reference training data and supports subject-specific training. Its efficient GPU-based implementation delivers faster and more accurate correction than NPEN, yielding slice-wise quantitative profiles that closely match those from reference 2D acquisitions. SHARPEN enables high-quality dMRI at 0.7 mm isotropic resolution on a 3T clinical scanner, highlighting its potential to advance submillimeter dMRI for neuroscience research.
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Atomic Physics
Two-photon-excited fluorescence spectroscopy of Rb atoms in a magneto-optical trap
Alan McLean, Christian Drago, Daniel Podos et al. (+5 more)
We report the results of two-
photon-excited fluorescence (TPEF) measurements of the $5\mathrm{S}_{1/2} \rightarrow 5\mathrm{D}_{5/2}$ transition of $^{85}$Rb and $^{87}$Rb cooled in a magneto-optical trap (MOT). We observe TPEF at excitation powers as low as 1 $\mu$W or fluxes as low as $2.98_{-0.94}^{+1.37} \times 10^{18}\ \text{photons}\,\text{cm}^{-2}\,\text{s}^{-1}$ ($^{85}$Rb) and $3.31_{-1.33}^{+2.21} \times 10^{18}\ \text{photons}\,\text{cm}^{-2}\,\text{s}^{-1}$ ($^{87}$Rb). Our results demonstrate that optically cooled Rb is a promising platform for observing sensitive two-
photon spectral signatures at low
photon fluxes.
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All authors Alan McLean, Christian Drago, Daniel Podos, Chengyi Luo, Caleb Brzezinski, Ting-Wei Hsu, John Sipe, Ralph Jimenez
Optics
High-resolution spectroscopy of Pr3+ions in YAl3(BO3)4:Pr3+. Crystal-field, hyperfine,and electron-deformation interactions
B. Z. Malkin, T. A. Igolkina, K. N. Boldyrev et al. (+3 more)
Optical transmission spectra of YAl3(BO3)4
crystals doped with the Pr3+ ions in concentrations 1 and 2.5 at. % were studied by high-resolution (up to 0.05 cm-1)
Fourier spectroscopy, including in
magnetic field parallel to the trigonal c axis of the
crystal. The g factors of several
crystal-field doublets of Pr3+ were determined. The
crystal-field calculations performed using the exchange-charge model and high-resolution spectroscopy data allowed us to obtain a physically reasonable set of
crystal-field parameters. The observed splitting of a number of doublets in zero
magnetic field is explained by the presence of random
lattice deformations. Simulation of the profiles of observed deformational doublets was carried out taking into account both hyperfine and electron-deformation interactions. The width of the distribution function of random strains was estimated. The main sources of random strains in YAl3(BO3)4:Pr3+
crystals are discussed.
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All authors B. Z. Malkin, T. A. Igolkina, K. N. Boldyrev, A. Baraldi, E. P. Chukalina, M. N. Popova
Atmospheric and Oceanic Physics
Machine Learning-Ready Data Sets for the Analysis and Nowcasting of Atmospheric Radiation at Aviation Altitudes
Viacheslav M Sadykov, Zachary M Watkins, Dustin Kempton et al. (+10 more)
Nowcasting and
forecasting of the radiation environment in the Earth's lower atmosphere are critical for the safety of aircraft and spacecraft crews and passengers. Currently, this problem is addressed by employing statistical and physics-based models that take into account particle transport and precipitation. However, given the increased number of radiation measurements available to the community, it is possible to start developing data-driven approaches. We prepared Machine Learning-ready (ML-ready)
datasets to nowcast the effective dose rates at aviation altitudes. The presented
datasets contain 92,476 individual measurements from 589 flights obtained by the Automated Radiation Measurements for Aerospace Safety (ARMAS) experiment from 2013 to 2023. The ARMAS measurements are augmented with the properties of the Geospace environment, such as
solar soft X-ray and
proton fluxes,
solar wind properties, secondary
cosmic ray
neutrons, space weather indexes, and global
solar activity indicators (such as daily sunspot number). ARMAS data are separated into three partitions, ensuring that (1) the data points from a single flight remain within the same partition, and (2) each partition samples the flight locations and Geospace environment conditions equally. Several versions of the
datasets allow predictions based on point-in-time measurements and use up to 24 hours of Geospace parameter history. The test of the use case demonstrates a possibility of nowcasting ARMAS measurements with accuracies slightly better than the considered physics-based models. The publicly available ML-ready
datasets could serve as the first step in data preparation for ML-driven nowcasting and
forecasting of the radiation environment.
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All authors Viacheslav M Sadykov, Zachary M Watkins, Dustin Kempton, William Jones, Sanjib K C, Griffin T Goodwin, Xiaochun He, W Kent Tobiska, Irina Kitiashvili, Christopher Mertens, Shubha Ranjan, D Glenn Deardorff, Ryan Spaulding
Chemical Physics
tmQM-RDF Dataset: a Knowledge Graph Representing Transition Metal Complexes
Luca Cibinel, Trond Linjordet, Johan Pensar et al. (+3 more)
Transition Metal Complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one in which machine learning and computational approaches can offer a substantial aid. For this reason, appropriate and accessible
datasets, collecting a wide range of information, are required in order to facilitate the effective analysis and investigation of such compounds. This paper contributes to the data modelling effort via the introduction of the transition metal
quantum mechanics RDF (tmQM-RDF)
dataset, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 50k TMCs. These descriptions are both qualitative and quantitative in nature, encompassing the compositional nature of TMCs in terms of their constituting ligands, as well as the entirety of their molecular graphs. An example of the power of the proposed representation is presented, showcasing how the information available in tmQM-RDF can be exploited for TMC manipulation tasks, achieving promising performance even with relatively simple probabilistic models.
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All authors Luca Cibinel, Trond Linjordet, Johan Pensar, David Balcells, Riccardo De Bin, Basil Ell
Chemical Physics
Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
Gyoung S. Na and Chanyoung Park
Various representation learning methods for molecular structures have been devised to accelerate data-driven chemistry. However, the representation capabilities of existing methods are essentially limited to atom-level information, which is not sufficient to describe real-world molecular physics. Although electron-level information can provide fundamental knowledge about chemical compounds beyond the atom-level information, obtaining the electron-level information in real-world molecules is computationally impractical and sometimes infeasible. We propose a method for learning electron-informed molecular representations without additional computation costs by transferring readily accessible electron-level information about small molecules to large molecules of our interest. The proposed method achieved state-of-the-art prediction accuracy on extensive
benchmark datasets containing experimentally observed molecular physics. The source code for HEDMoL is available at https://github.com/ngs00/HEDMoL.
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Chemical Physics
LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning
Xinwu Ye, Yicheng Mao, Yuxuan Liao et al. (+16 more)
Current chemical large
language models (LLMs) predominantly rely on explicit Chain-of-Thought (CoT) to solve complex
reasoning problems. However, forcing nonverbal tacit chemical logic into discrete natural
language imposes a fundamental “modality mismatch,” creating an artificial bottleneck for
reasoning. We introduce LatentChem, a
reasoning interface that decouples chemical logic from linguistic generation, enabling the model to process information via continuous thought vectors and dynamic perception. Our investigation reveals a pivotal emergent behavior: spontaneous internalization, defined here as self-selected under outcome-only optimization. When optimized for task success, the model abandons verbose textual derivations in favor of implicit latent computation, suggesting that it identifies the continuous
manifold as a more native substrate for chemical logic. This paradigm shift also proves to be a superior computational strategy: LatentChem achieves a 59.88% non-tie win rate against the strong CoT baseline on the rigorous ChemCoTBench, while delivering a broad 10.84$\times$ average reduction in
reasoning step overhead (5.96$\times$ wall-clock speedup) across all evaluated
benchmarks. Our results provide empirical evidence that chemical
reasoning is more naturally and effectively realized as continuous latent dynamics rather than discretized linguistic trajectories.
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All authors Xinwu Ye, Yicheng Mao, Yuxuan Liao, Jia Zhang, Yimeng Liu, Li Hao, Fang Wu, Zhiwei Li, Zehong Wang, Zhiyuan Liu, Zhenfei Yin, Li Yuan, Philip Torr, Huan Sun, xiangxiang Zeng, Mengdi Wang, Le Cong, Shenghua Gao, Xiangru Tang
Atomic Physics
Proton-Size Resolution of the Hyperfine Puzzle in Hydrogen
Gerald A. Miller
Baym and Farrar (arXiv:2601.02300v1) have recently pointed out a puzzle in understanding the role of the hyperfine interaction in the ground state of a hydrogen atom. If one uses a variational wave function in which the Bohr radius, $a_0$ is replaced by a variational radius parameter, $R$, first-order perturbation theory can give a contribution to the energy proportional to $-1/R^3$. This raises the question of why the hyperfine interaction does not lead to collapse of hydrogen. I show that including the effects of the non-zero size of the
proton leads to a resolution of the puzzle such that the variational procedure yields a value of $R$ that is indistinguishable from $a_0$.
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Fluid Dynamics
Non-reciprocal Binary-fluid Turbulence
Biswajit Maji, Nadia Bihari Padhan, Axel Voigt et al. (+1 more)
Although effective non-reciprocal interactions have been investigated in a variety of fields, their consequences have not been explored in hydrodynamical
turbulence. We initiate such an exploration by introducing non-reciprocal binary-fluid tubulence and uncover its properties by developing a two-dimensional (2D) Non-Reciprocal Cahn-Hilliard-Navier-Stokes (NRCHNS) model. We show that, as we increase the strength of the non-reciprocal terms, this model displays a hitherto unanticipated type of
turbulence, with an inverse cascade of energy and an energy spectrum $E(k)\sim k^{-5/3}$, reminiscent of the well-known inverse cascade in forced, 2D fluid
turbulence, but distinct from it, in so far as it develops a non-reciprocal flux $\mathbf J$. We demonstrate how NRCHNS
turbulence suppresses $J(t) = |\mathbf J|$, as the Reynolds number increases. We compare and contrast 2D NRCHNS
turbulence with its fluid-
turbulence counterpart by examining spectra, fluxes, spectral balances, flow
topologies, and signatures of multifractality.
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All authors Biswajit Maji, Nadia Bihari Padhan, Axel Voigt, Rahul Pandit
Space Physics · Cross-listed from astro-ph.SR
Towards a Fully Automated Pipeline for Short-Term Forecasting of In Situ Coronal Mass Ejection Magnetic Field Structure
Hannah T. Rüdisser, Emma E. Davies, Ute V. Amerstorfer et al. (+6 more)
We present an automated pipeline for operational short-term
forecasting of coronal mass ejection (CME)
magnetic field structure at L1, coupling arrival time prediction, in situ detection, and iterative flux rope reconstruction, following near-real-time remote-sensing CME identification. The system is triggered by new entries in the CCMC DONKI database and first applies the drag-based ELEvo model to determine whether an Earth impact is expected and estimate arrival time. This estimate defines a temporal window constraining the search for CME signatures in real-time L1 in situ
solar wind data, where the
magnetic obstacle (MO) is automatically detected using the deep learning model ARCANE. Upon MO onset, iterative reconstructions with the semi-empirical flux rope model 3DCORE are performed, using a Monte Carlo fitting scheme, producing continuously updated forecasts of the remaining
magnetic field profile. We evaluate the pipeline using 3870 archived DONKI entries and archived NOAA real-time in situ data from 2013-2025, assessing forecast performance at different stages of MO observation. For 61 events with an associated ground-truth counterpart in the ICMECAT catalog, forecasts based on initial MO data already achieve performance comparable to full-event reconstructions. Typical errors are ~5 hours in timing of
magnetic field extrema and ~10 nT in field strength metrics, with limited systematic improvement as more of the event is observed. Results show substantial event variability and systematic underestimation of extrema, indicating deviations from ideal flux rope assumptions. These findings demonstrate the potential of fully autonomous real-time
forecasting while highlighting limitations imposed by event complexity and model representational capacity.
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All authors Hannah T. Rüdisser, Emma E. Davies, Ute V. Amerstorfer, Christian Möstl, Eva Weiler, Andreas J. Weiss, Justin Le Louëdec, Martin A. Reiss, Gautier Nguyen
Classical Physics · Cross-listed from cs.LG
From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers
Ziming Liu, Sophia Sanborn, Surya Ganguli et al. (+1 more)
Can general-purpose AI architectures go beyond prediction to discover the physical laws governing the universe? True intelligence relies on "world models" – causal abstractions that allow an
agent to not only predict future states but understand the underlying governing dynamics. While previous "AI Physicist" approaches have successfully recovered such laws, they typically rely on strong, domain-specific priors that effectively "bake in" the physics. Conversely, Vafa et al. recently showed that generic
Transformers fail to acquire these world models, achieving high predictive accuracy without capturing the underlying physical laws. We bridge this gap by systematically introducing three minimal inductive biases. We show that ensuring spatial smoothness (by formulating prediction as continuous regression) and stability (by training with noisy contexts to mitigate error accumulation) enables generic
Transformers to surpass prior failures and learn a coherent Keplerian world model, successfully fitting ellipses to planetary trajectories. However, true physical insight requires a third bias: temporal locality. By restricting the
attention window to the immediate past – imposing the simple assumption that future states depend only on the local state rather than a complex history – we force the model to abandon curve-fitting and discover Newtonian force representations. Our results demonstrate that simple architectural choices determine whether an AI becomes a curve-fitter or a physicist, marking a critical step toward automated scientific discovery.
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All authors Ziming Liu, Sophia Sanborn, Surya Ganguli, Andreas Tolias
Optics
Dissipative Kerr Soliton Self-Balancing from Kerr-Induced Synchronization
Pradyoth Shandilya, Kartik Srinivasan, Curtis R. Menyuk et al. (+1 more)
Integrated frequency comb sources are a key enabling technology for frequency metrology applications. Their on-chip integration promises to bring metrology capacity outside of the lab, particularly since they can operate at low continuous-wave pump
laser power in the dissipative Kerr
soliton (DKS) regime. Yet, such small foot-print and low power comes at a cost: higher noise and overall lower comb power. In particular, this translates to highly challenging detection and locking of the carrier-envelope offset, necessary for complete stabilization of the comb. Recently, Kerr-induced synchronization (KIS) of a DKS to a reference
laser has been demonstrated as a tool for passive all-optical stabilization of DKS microcombs, with fundamental modification to the DKS and microcomb properties. Here, we demonstrate that the combination of additional power from the reference
laser (now part of the DKS) and the KIS phase locking that pins the repetition rate together fundamentally alter the DKS, forcing an energy redistribution to maintain its center of mass. We demonstrate this self-balancing effect theoretically, which in a pure quadratic dispersion resonator leads to reference-dependent
recoil. With higher-order dispersion through which the DKS yields phase-matched dispersive waves (DWs), we demonstrate that self-balancing increases the DW radiation, experimentally showing a 22 dB increase of comb teeth at 780 nm in an octave-spanning microcomb for efficient deployable carrier-envelope offset detection.
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All authors Pradyoth Shandilya, Kartik Srinivasan, Curtis R. Menyuk, Grégory Moille
Optics
Nonlinear quadrupole topological insulators
Rujiang Li, Wencai Wang, Yongtao Jia et al. (+3 more)
Higher-order topological
insulators (HOTIs) represent a family of topological phases that go beyond the conventional bulkboundary correspondence. d-dimensional n-th order HOTIs maintain (d - n)-dimensional gapless boundary states (in particular, zero-dimensional corner states in the case of d = n = 2). HOTIs of the Wannier type cam be extended into the nonlinear regime. Another prominent class of HOTIs, in the form of multipole
insulators, was investigated only in the linear regime, due to the challenge of simultaneously achieving both negative hopping and strong nonlinearity. Here we propose the concept of nonlinear quadrupole topological
insulators (NLQTIs) and report their experimental realization in an electric
circuit lattice. Quench-initiated dynamics gives rise to nonlinear topological corner states and topologically trivial corner
solitons, in weakly and strongly nonlinear regimes, respectively. Furthermore, we reveal the formation of two distinct types of bulk
solitons, one existing in the middle finite gap under the action of weak nonlinearity, and another one found in the semi-infinite gap under strong nonlinearity. This work realizes another member of the nonlinear HOTI family, suggesting directions for exploring novel
solitons across a broad range of topological
insulators.
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All authors Rujiang Li, Wencai Wang, Yongtao Jia, Ying Liu, Pengfei Li, Boris A. Malomed
Plasma Physics
Collision of two radial rarefaction waves in unmagnetized ambient plasma: effects of the ambient plasma density
Margaux François, Mark E. Dieckmann, Lorenzo Romagnani et al. (+2 more)
The expansion of two circular rarefaction waves in
vacuum or in a thin ambient
plasma is examined with particle-in-
cell simulations that resolve two spatial dimensions. In the simulation with no ambient
plasma, the rarefaction waves interpenetrate near the
symmetry line between both rarefaction wave centers. The exponential density decrease of rarefaction waves with distance implies that the sum of their density does not lead to a density maximum near the
symmetry line. The absence of a density maximum, which would yield a repelling electric potential for the inflowing rarefaction wave ions near the
symmetry line, and the high interpenetration speed of the ion beams lead to ion-ion instabilities rather than shocks in the overlap layer. The simulations with ambient
plasma show that the rarefaction waves pile up the ions of the ambient
plasma near the
symmetry line. A localized piston of hot ambient ions forms. If its density is large enough, its thermoelectric field allows reverse shocks to grow in the rarefaction waves. These reverse shocks move slowly in the simulation frame and enclose a slab of downstream
plasma. A decrease in the speed of the rarefaction wave ions upstream of the shocks with time leads to their collapse.
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All authors Margaux François, Mark E. Dieckmann, Lorenzo Romagnani, Xavier Ribeyre, Emmanuel d'Humières
Optics · Cross-listed from quant-ph
Phase-sensitive characterization of a quantum frequency converter by spectral interferometry
Mateusz J Olszewski, Kasper Hecht Alexander, Michael T M Woodley et al. (+3 more)
We introduce an experimental technique for complete phase-sensitive characterization of arbitrary
unitary spectral-temporal transformations of optical modes. Our method recovers the complex spectral transfer function, or Green's function, of a frequency converter by analyzing spectral interference in the response to a tunable bichromatic probe. We perform a proof-of-concept experiment on a frequency conversion module based on Bragg-scattering four-wave mixing in photonic
crystal fiber. Our results validate our technique by recovering useful information in the phase of the Green's function, revealing the relative positions of regions of active frequency conversion and passive dispersive propagation within the module. Our work introduces a new approach to characterizing the performance of a variety of active devices with diverse applications in emerging
quantum technologies.
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All authors Mateusz J Olszewski, Kasper Hecht Alexander, Michael T M Woodley, Leah R Murphy, Peter J Mosley, Alex O C Davis
Fluid Dynamics
Diffusiophoresis of a non-polar fluid droplet laden with soluble ionic surfactants
Subrata Majhi and Somnath Bhattacharyya
We investigate the diffusiophoresis of a non-polarizable droplet laden with soluble ionic surfactant, for which the surface charge arises from adsorption of surfactant at the fluid-fluid interface. Unlike previous studies that assume either a fixed surface charge or instantaneous
equilibrium between the interface and the adjacent electrolyte, we formulate the interfacial transport based on the mass-balance framework incorporating Langmuir adsorption-desorption kinetics and finite surface diffusivity. The coupled electrokinetic problem is solved using a perturbation approach. Analytical expressions for the droplet mobility and interfacial velocity are derived for insoluble surfactants. We demonstrate that assuming uniform, immobile surface charge leads to unphysical predictions, including negative chemiphoresis and singular mobility, whereas allowing the surface charge to evolve through interfacial surfactant redistribution yields continuous and physically consistent droplet diffusiophoresis. Interfacial kinetic exchange is found to play a central role. Increasing the desorption rate enhances surfactant redistribution and Marangoni stress, weakens the negative mobility, reverses the direction of motion through competition between electrophoretic and chemiphoretic contributions, and subsequently leads to a strong enhancement of positive mobility before eventual saturation in the transport-limited regime. The dependence of mobility on viscosity ratio and electrolyte composition of different salts further reveals how mixed electrolytes provides a robust means of tuning droplet motion. This study highlights the critical role of finite-rate surfactant dynamics and interfacial transport in determining the diffusiophoresis of fluid particles, with implications for manipulating droplets in microfluidic and varying-salinity environments.
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Atomic Physics
Quasiperiodic nondipole ionization dynamics in the x-ray stabilization regime
Aleksandr V. Boitsov, Karen Z. Hatsagortsyan and Christoph H. Keitel
Recent advances in strong x-ray
laser techniques enable the study of nonlinear multiphoton ionization in extreme high-frequency fields. Although the stabilization regime in such fields is theoretically established, its modified properties in the nondipole regime for long
laser pulses remains unknown. Here, we numerically investigate the strong-field ionization of an atom in a long XUV
laser pulse in the nondipole regime. Our study of the time-dependent
quantum dynamics reveals a quasiperiodic modulation of the ionization yield as a function of pulse duration. We demonstrate that the Coulomb-field-induced slow oscillation of the ionized electron wave packet during the interaction is responsible for the observed modulation of the ionization yield. Furthermore, we scrutinize the unusual
photon momentum sharing between the photoelectron and the ion in this extreme regime. These effects are observable in upcoming x-ray free-electron
laser facilities.
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Chemical Physics
Mimyria: Machine learned vibrational spectroscopy for aqueous systems made simple
Philipp Schienbein
Vibrational spectroscopy provides a powerful connection between molecular dynamics (MD) simulations and experiment, but its routine use in condensed-phase systems remains limited. We introduce mimyria, a modular and automated framework that orchestrates electronic-structure reference calculations, trains atom-resolved machine-learning response models, and generates IR and Raman spectra from MD trajectories within a unified workflow. We introduce the polarizability gradient tensor (PGT) as a novel atom-resolved machine-learning target property for Raman spectroscopy, complementing the established atomic polar tensor (APT) for IR spectroscopy. As a necessary prerequisite, we demonstrate how both PGTs and APTs can accurately be computed from electronic-structure theory, validate them across formally equivalent derivative formulations, and thereby
benchmark their numerical consistency. We then employ machine learning as an efficient surrogate to represent the validated APT and PGT response functions on aqueous
benchmark systems. We validate the trained models directly at the level of the spectrum against explicit ab initio reference calculations and find that IR and Raman spectra converge with surprisingly small training sets. Moreover, spectral agreement improves more rapidly than the root-mean-square error (RMSE). While RMSE is straightforward to compute, statistically converged reference spectra are generally impractical to obtain, motivating the need to relate model-level errors to observable-level accuracy. By connecting these complementary error measures, we provide practical guidelines and early-stopping criteria for achieving sufficient spectral fidelity. By integrating response-tensor learning, automated training, and spectral-domain validation into a unified workflow, mimyria enables data-efficient and quantitatively reliable vibrational spectroscopy.
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Chemical Physics
Molecular origin of 31P-NMR chemical shifts of phosphate groups with bivalent counter ions
Leo Christanell, Karl-Jakob König, Julian Holzinger et al. (+2 more)
The electrostatic interactions of phosphate groups and counter ions critically affect the structure, function and reactivity of DNA or RNA. We present a joint experimental-theoretical investigation of dimethyl phosphate (DMP-) in aqueous solution, an established model system of the sugar-phosphate backbone. Utilizing 31P-NMR spectroscopy as probe of phosphate-ion association, variations of Mg2+ and Ca2+ content exhibit a systematic shielding of the 31P chemical shift ({\delta}iso(31P)) with moderate temperature dependence. Enhanced sampling molecular dynamics (MD) and ab initio (GIAO-DF-LMP2) level of theory are used to reveal the microscopic mechanism. Simulations are performed for a configurational ensemble of DMP-ion geometries and their first solvation shells, demonstrating (i) the spatial convergence of changes of the
nuclear shielding constant {\sigma}iso(31P), (ii) the intramolecular
geometric origin of short-timescale {\sigma}iso(31P) fluctuations and (iii) an average shift of {\sigma}iso(31P) of about 3-5 ppm upon contact ion pair formation with Mg2+ or Ca2+ ions. A quantitative analysis of {\delta}iso(31P) for varying ion content and temperature allows us to extract the temperature-dependent fraction of the contact ion pair species, indicating that solvent separated or free ion pairs are the energetically preferred species. The results impose boundary conditions for improvements of phosphate ion force fields and establish the interactions underlying the changes of {\delta}iso(31P).
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All authors Leo Christanell, Karl-Jakob König, Julian Holzinger, Anne K. Schütz, Benjamin P. Fingerhut
Data Analysis and Statistics · Cross-listed from cs.LG
Disentanglement by means of action-induced representations
Gorka Muñoz-Gil, Hendrik Poulsen Nautrup, Arunava Majumder et al. (+4 more)
Learning interpretable representations with variational autoencoders (VAEs) is a major goal of representation learning. The main challenge lies in obtaining disentangled representations, where each latent dimension corresponds to a distinct generative factor. This difficulty is fundamentally tied to the inability to perform nonlinear independent component analysis. Here, we introduce the framework of action-induced representations (AIRs) which models representations of physical systems given experiments (or actions) that can be performed on them. We show that, in this framework, we can provably disentangle degrees of freedom w.r.t. their action dependence. We further introduce a variational AIR architecture (VAIR) that can extract AIRs and therefore achieve provable disentanglement where standard VAEs fail. Beyond state representation, VAIR also captures the action dependence of the underlying generative factors, directly linking experiments to the degrees of freedom they influence.
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All authors Gorka Muñoz-Gil, Hendrik Poulsen Nautrup, Arunava Majumder, Paulin de Schoulepnikoff, Florian Fürrutter, Marius Krumm, Hans J. Briegel
Optics
Thermoelastic surface acoustic waves in low-loss silicon nitride integrated circuits
Zheng Zheng, Ahmet Tar{\i}k Iş{\i}k, Akshay Keloth et al. (+3 more)
Acousto-optic modulation in photonic integrated
circuits harness the applications that include signal processing,
quantum photonics and microwave photonics. However, silicon nitride ($\rm{Si_3N_4}$), as a main-stream low-loss scalable photonic platform, suffers from the lack of piezoelectric effect and therefore the hybrid co-integration with other materials is always required for acousto-optic modulation. Here, we employed thermoelastic surface acoustic waves (SAW) in a 8 dB/m propagation loss $\rm{Si_3N_4}$ integrated
circuits without adding extra materials. A phase modulation efficiency enhancement of 13.6 dB is realized with a multi-pass configuration. Furthermore, a single-sideband intermodal scattering with a suppression ratio of 8 dB is measured and an intensity modulation is observed by incorporating the phase modulation into a ring resonator spectral. This thermoelastic SAW technique, as an initial step of acousto-optic modulation in low-loss $\rm{Si_3N_4}$ platform, is promising for integrated microwave photonics and programmable photonics applications.
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All authors Zheng Zheng, Ahmet Tar{\i}k Iş{\i}k, Akshay Keloth, Kaixuan Ye, Peter van der Slot, David Marpaung