Space Physics · Cross-listed from cs.LG
Self-Supervised Representation Learning: From Spectral Foundation Models to Auroral Emission Spectra
Matthieu Le Lain, Gaël Cessateur and Sébastien Lefèvre
Auroral spectrographs such as the Auroral Spectrograph In Skibotn (ASIS) record hundreds of thousands of emission spectra, but only a few hundred can be labelled by an expert. To exploit the rest, we pretrain a 1D
Vision Transformer with a masked autoencoder on 223,000 unlabelled spectra. Without labels, its representation recovers the emission-line intensity ratios that physicists use to diagnose the precipitating particles (R^2 0.91 vs. 0.77 for an untrained control) and, under one linear probe, classifies as well as 13 features designed by experts. Fine-tuned, the model outperforms the previous supervised auroral classifier on its own
benchmark (macro-AP 88.5 vs. 77.8), reaches 0.870 mAP, and exceeds the same architecture trained from scratch by +0.159 with 10% of the labels; attribution shows that it uses both N2+ bands. Could an existing pretrained model replace it? Two astronomical spectral foundation models and a time-series model transfer according to their spectral window: SpectraFM, trained in the infrared, falls below the untrained control, whereas SpecFormer, trained in the optical, approaches in-domain pretraining without reaching it.
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Fluid Dynamics
Zero-Mean Oscillations Drive Recirculations in Potential Flows at Vanishing Reynolds Number
Alexandre S. Avaro, Leon V. Gebhard, Gabriel Amselem et al. (+1 more)
Recirculating flows are central to transport phenomena, but their generation in potential flows requires additional physical mechanisms. We show experimentally that recirculation at vanishing Reynolds number can be produced at prescribed locations by coupling oscillations of a Hele-Shaw
cell ceiling with an off-center passive obstacle. The net drift is well described by a potential flow model with no adjustable parameters. The resulting flows are localized around the obstacles and can be combined to generate programmable flow patterns at the microscale.
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All authors Alexandre S. Avaro, Leon V. Gebhard, Gabriel Amselem, Charles N. Baroud
Atomic Physics · Cross-listed from quant-ph
Optical characterization of excited-state spin Hamiltonians and clock transitions at telecommunication wavelength in Tm3+:YAlO3
Akshay Babu Karyath, Luozhen Li, Julien Bertrand et al. (+6 more)
The zero-
phonon line (ZPL) between the two excited-state
manifolds 3F4 and 3H4 of the trivalent thulium ion in yttrium aluminum perovskite (Tm3+:YAlO3) lies within the telecom E-band at around 1451 nm and exhibits an optical coherence time of a few {\mu}s at cryogenic temperatures. To improve this time in view of potential applications, e.g. single-
photon sources and optical
quantum memory, we characterized the
magnetic properties of the lowest Stark levels of these two
manifolds. Varying the external
magnetic field amplitude and orientation, we measured the enhanced nuclear Zeeman splitting by probing the field-dependent frequency detuning of side holes created by means of spectral hole burning, and we characterized the inhomogeneous line shift caused by the quadratic Zeeman interaction. Together, this allowed us to establish the
spin Hamiltonians for the lowest Stark level of the 3F4 and 3H4
manifolds, and to predict optical clock transitions for particular orientations and magnitudes of the
magnetic field
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All authors Akshay Babu Karyath, Luozhen Li, Julien Bertrand, Alexandre Kings, Tianyang Shen, Gabriel Aeppli, Simon Gerber, Guy Matmon, Wolfgang Tittel
Optics
Probing temperature dependent non-radiative decay channels in CrSBr with nonlinear optics
Minjiang Dan, Till Weickhardt, Paul Herrmann et al. (+9 more)
Among the recently discovered layered
magnetic materials, the antiferromagnet CrSBr has attracted considerable interest thanks to its stability, quasi-one-dimensional
crystal structure, and direct near-infrared band gap. These properties make it a suitable platform for combined electrical,
magnetic, and optical measurements, and thus for studying
exciton dynamics and transport across different
magnetic phases. In this work, we investigate the optical response of CrSBr across its paramagnetic and antiferromagnetic phases by combining nonlinear optical spectroscopy, including second-harmonic generation, third-harmonic generation, and two-
photon photoluminescence, with linear photoluminescence measurements. The observation of a clear difference in the temperature dependence of third-harmonic generation and photoluminescence intensities allows us to identify the onset of non-radiative recombination channels in the antiferromagnetic phase.
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All authors Minjiang Dan, Till Weickhardt, Paul Herrmann, Fabian Glatz, Marie-Christin Heißenbüttel, Thorsten Deilmann, Michael Rohlfing, Ksenia Mosina, Zdeněk Sofer, Benjamin Pingault, Julian Klein, Giancarlo Soavi
Computational Physics · Cross-listed from cs.MS
KnottedGraph: Scalable knotted-graph topology for scientific and mathematical discovery
Hakan Akgün, Xianquan Yan, Kehan Liu et al. (+2 more)
Scientific data span heterogeneous structures, including coordinates, networks, surfaces, volumes and fields, yet their
topology can be quantified within a common framework through graph connectivity, cycle structure, genus and spatial
embedding. Graph- and homology-based summaries do not determine spatial
embedding, while standard
knot and link
polynomials require extensions to accommodate branching graphs. Here, we introduce KnottedGraph, a computational framework that converts such scientific representations to knotted graphs that retain graph connectivity and spatial
embedding together. It constructs projected diagrams and PD codes, enabling various topological analyses, including Yamada-
polynomial evaluation for topological classification. For scalable exact evaluation, it combines partial resolutions that leave the same unresolved connections and optimizes their processing order; the resulting
algorithm is verified against published topological invariants of knotted graphs with up to 500 crossings. This scalability enables us to introduce an LLM-assisted mathematical-discovery methodology, in which computational topological data generated across knotted-graph families are used to identify candidate closed-form formulas. With this approach, we identify analytical Yamada-
polynomials for generic graph motif families exhibiting Abelian and non-Abelian word sequences. Together, these scalable capabilities make knotted-graph
topology computationally accessible across scientific domains, enabling large-scale classification and introducing a route from topological data to LLM-assisted AI4Math discovery.
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All authors Hakan Akgün, Xianquan Yan, Kehan Liu, Zhaoyun Chen, Ching Hua Lee
Optics
High-gain vortex transfer via activated forbidden transitions in molecular magnets
Fan Meng, Hao Zhu, Xin-Yao Huang et al. (+1 more)
Vortex light has garnered considerable interest in recent years owing to its distinctive properties and broad application potential. In this paper, we employ a conventional three-level ladder-type configuration in molecular magnets to realize high-gain
vortex light transfer by enabling otherwise forbidden transitions. We demonstrate that the intensity and phase of the generated
vortex signal field are governed by the detuning of the probe field and the strength of the control field, and that the topological charges of the incident and generated fields obey a well-defined algebraic relation during the
vortex transfer process. Furthermore, we show that, in molecular magnets, the Autler-Townes splitting (ATS) effect yields a larger
vortex signal field gain than electromagnetically induced transparency (EIT) over a broad parameter range in nonlinear three-wave mixing. This result suggests that the widely accepted view of EIT as the dominant enhancer of nonlinear optical effects may not hold universally. In addition, we revisit previous studies and re-examine the characterization of
vortex beam transfer efficiency, emphasizing the need for a more careful interpretation in multi-beam interaction systems. Leveraging the long
spin coherence and microwave transitions of molecular magnets, our results may enable
quantum information transfer, storage, computing, and radar imaging in solid-state platforms.
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All authors Fan Meng, Hao Zhu, Xin-Yao Huang, Guo-Feng Zhang
Optics
Distributed laser energy deposition enables fog-resilient optical communications
Malte C. Schroeder, Victor Moreno, Christophe Coreixas et al. (+2 more)
Fog and clouds remain major obstacles to free-space optical (FSO) communications because micron-sized droplets can impose tens of decibels of attenuation. Ultrashort
laser filamentation can transiently clear fog in laboratory-scale experiments, but its extension to atmospheric-scale propagation has remained uncertain. Here we demonstrate
laser-assisted fog clearing under atmospheric-scale propagation conditions over a 140-m optical path using a high-energy, high-average-power picosecond
laser and weak focusing to sustain extended multifilamentation. A steady-state transparent channel approximately 2 cm in diameter restores up to 12 dB of transmission for an independent optical beam. Increasing the clearing-
laser pulse energy primarily extends the multifilament region while maintaining a nearly constant longitudinally averaged deposited energy per unit length of ~2 mJ/m over the measured bundle length. This low average deposition, together with self-sustained filament propagation through dense fog, provides a favorable scaling route toward weather-resilient optical links.
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All authors Malte C. Schroeder, Victor Moreno, Christophe Coreixas, Jonas Latt, Jean-Pierre Wolf
Physics Education
Multiple harmonics from a hand-struck tube: a simple and precise measurement of the speed of sound
Francisco Echegorri, Martin Monteiro, Cecilia Stari et al. (+1 more)
The speed of sound in air can be determined from the resonance frequencies of the harmonic waves inside a thin cylindrical tube. A pressure wave is generated in the tube by striking several times one of its ends with an open hand and keeping the hand in place. In this way pulses of short duration are produced which propagate along the tube and decompose into a superposition of harmonic waves whose frequencies are determined by the harmonics present in a tube open at one end and closed at the other. The tube thus acts as a filter that reinforces those harmonic waves exhibiting an antinode of acoustic pressure at the closed end and a node at the open end. Their frequencies are determined by these boundary conditions, while waves with different frequencies cancel out by destructive interference. Recording the sound with a microphone connected to a computer and analyzing its spectrum with freely available software, the resonance frequencies are obtained and the speed of sound is determined with an accuracy better than $0.5\%$. The whole experiment requires nothing but a tube, a microphone and free software, so it is inexpensive, takes a few minutes to set up and is well suited to introductory physics laboratories.
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All authors Francisco Echegorri, Martin Monteiro, Cecilia Stari, Arturo C Marti
Physics and Society · Cross-listed from math-ph
Unifying non-Markovian Dynamics and Agent Heterogeneity in Scalable Stochastic Networks
Aurelien Pelissier, Miroslav Phan, Didier Le Bail et al. (+2 more)
Stochastic processes underpin dynamics across biology, physics, epidemiology, and finance, yet accurately simulating them remains a major challenge. Classical approaches such as the Gillespie
algorithm are exact for Markovian, time-independent systems, where propensities depend only on the current state and
agents of a given type are statistically identical. While efficient, this framework misses a defining feature of many real systems: heterogeneity and
memory at the level of individual
agents.
Cells may divide or differentiate on distinct intrinsic timescales, individuals may preferentially interact with specific partners, and inter-event-time distributions can deviate strongly from the exponential. We introduce MOSAIC (Modeling of
Stochastic Agents with Individual Complexity), a general and scalable framework that embeds
agent-specific properties directly into the dynamics. MOSAIC unifies heterogeneous rates, dynamic interaction preferences, and both Markovian and non-Markovian waiting-time distributions within a single
stochastic formalism, while retaining Gillespie-like computational cost.
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All authors Aurelien Pelissier, Miroslav Phan, Didier Le Bail, Niko Beerenwinkel, Maria Rodriguez Martinez
Optics
From OpenSPIM to FlowSPIM: Enhancing the versatility of a light sheet microscope through an iterative design process
Endre Joachim Lerheim Mossige, Sergey Ponomartsev, Natalia Smirnova et al. (+12 more)
Light sheet microscopy is ideal for the 3D imaging of large biological specimens, yet systematic documentation on adapting microscope designs to diverse experimental requirements remains limited. We present the iterative process used to design and build a modular light-sheet microscope and adapt it to different sample types and experimental conditions. Starting from a stripped-down OpenSPIM platform, we gradually make our microscope more versatile by implementing new hardware, such as additional
laser channels and optical filters, and new protocols for sample handling. In its final iteration, FlowSPIM is a new flow-based incubation technique for 3D tissue culture.
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All authors Endre Joachim Lerheim Mossige, Sergey Ponomartsev, Natalia Smirnova, Wietske van der Ent, Siri Andresen, Xian Hu, Felix Margadant, Rainer Heintzmann, Roland Kádár, Kesavan Sekar, Helene Knævelsrud, Camila Vicencio Esguerra, Stefan Krauss, Dag Kristian Dysthe, Alexander Refsum Jensenius
Computational Physics
Trajectory Paradox is a Boundary Layer!
Kush Kumar, Sovan Lal Das and Shakti S. Gupta
This study derives the asymptotic conditions under which the massless stretched string formulation is valid for the equivalent dynamics of the inertial string carrying a moving point mass. Consequently, the governing equation under such conditions can be written as a singularly perturbed partial differential equation when the inertia of the string is included. When the inertia of the string is ignored, the governing equation reduces to a non-homogeneous hypergeometric ordinary differential equation governing the transverse motion of the moving mass. This hypergeometric equation has a discontinuous solution at the terminating boundary known as the trajectory paradox. However, numerically solving the actual equation yields a continuous solution with a rapid variation near the boundary. The complete solution resembles the inner solution as well, whereas the hypergeometric ordinary differential equation leads to an outer solution, revealing that the trajectory paradox is a boundary layer.
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Geophysics
Data-driven analysis of muon flux variations in muography time series at the Sos Enattos Mine
M. Tramontini, J.-C. Ianigro and J. Marteau
Muon flux variations in muography time series can provide information on density-related changes in geological targets. A common approach is to define regions of interest before constructing flux time series, but this choice may dilute coherent signals or mask localized variations. We present a data-driven analysis of two
muon data acquisitions at the Sos Enattos Mine in Sardinia, Italy. The method aggregates lines of sight into overlapping spatial blocks, computes standardized flux deviations, and applies principal component analysis through singular value decomposition. The leading components reveal spatially coherent structures and distinct temporal evolutions. In each run, the first mode explains more than 20% of the variance, while the first eight modes explain approximately 80%. Regions selected from the spatial modes show anticorrelated and delayed flux variations. These results show that the proposed approach can identify candidate regions of coherent
muon-flux variability without imposing them a priori.
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Optics
Feedback cooling of a levitated nanoparticle in a radially polarized vector beam trap
Felipe Almeida, M. Rademacher, J. M. H. Gosling et al. (+1 more)
We demonstrate optical levitation and feedback cooling of a silica nanoparticle (radius = 78~nm) trapped within a radial vector beam (RVB) in high
vacuum. Radial vector beams, when focused by a high-NA lens, produce a smaller focal spot than conventional Gaussian beams, yielding tighter optical potentials and potentially reduced
photon-recoil heating rates. A
vortex wave plate was used to generate the RVB using an aspheric lens (NA = 0.77) to form the trap. Cold-damping feedback was used to cool the center of mass motion in the radial and axial trap directions to temperatures in the mK range. At higher temperatures and pressures, additional trap frequencies arising from the nonlinear RVB optical potential are observed. This demonstration is a step toward exploiting structured light for levitated
quantum optomechanics where these traps can be used to suppress bulk and motional heating.
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All authors Felipe Almeida, M. Rademacher, J. M. H. Gosling, P. F. Barker
Optics · Cross-listed from quant-ph
Computational aberration-retrieval with entangled photons
Baptiste Courme, YoonSeok Baek and Hugo Defienne
Adaptive optics (AO) is central to high-resolution microscopy, yet conventional implementations often require repeated correction-and-measurement cycles whose number increases with aberration complexity. This measurement burden can become prohibitive when the available temporal window for correction is short or when
photon budgets are constrained. Here, we demonstrate an AO strategy that exploits the spatial correlations of entangled
photon pairs to retrieve aberrations directly. By using the second-order correlation function as an aberration-sensitive metric, the required correction can be determined with only a few measurements, without relying on image-based feedback or iterative optimization. This correlation-based approach establishes an efficient framework for AO leveraging
quantum light, offering a new route toward
quantum-enhanced microscopy in regimes where conventional correction strategies become challenging.
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Optics · Cross-listed from quant-ph
Single-shot coherent process tomography and mid-infrared polarimetry with undetected photons
Marthe Zeja, Wen-Zhe Yan, Zhibo Hou et al. (+1 more)
Measurements with undetected
photons infer properties of a probe beam that is never detected, transferring mid-infrared information onto silicon-friendly wavelengths.
Tomography in this paradigm has so far relied on switched settings or scanned phases, referenced across a drift-sensitive campaign; no protocol reads all Jones parameters from one frame. We show that a folded nonlinear interferometer with a diagonally pumped crossed-
crystal source performs coherent process
tomography of the undetected beam in a single spectrometer frame: it reconstructs all seven parameters of the beam's round-trip Jones
matrix, the coherent part of the channel. Birefringent group-delay walk-off, normally a calibration nuisance, acts as a frequency multiplexer: each Jones-
matrix element is assigned its own carrier, a spectral fringe period paired with a detector port. Because the multiplexing exploits the down-conversion bandwidth rather than merely tolerating it, a single
Fourier transform per detector trace returns all four moduli and all three relative phases across that bandwidth, i.e., the full round-trip Jones spectrum up to a global phase, with no scan or setting change (after a one-time reference frame). Simultaneity makes every relative phase immune to common-mode drift, and a dark carrier, a self-interference term that unitarity forces to vanish, provides a built-in null test. For realistic parameters of a periodically poled KTP source the scheme yields wavelength-resolved mid-infrared polarimetry across 3.5-4.2 microns (~80 spectral points per Jones element) on a silicon camera, extending to depth-resolved Jones
matrices, i.e., polarization-sensitive optical coherence
tomography with undetected
photons. A Fisher-information analysis, the first we are aware of in undetected-
photon tomography, sets the precision budget and compares delay multiplexing to sequential protocols at equal
photon number.
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All authors Marthe Zeja, Wen-Zhe Yan, Zhibo Hou, Sven Ramelow
Physics and Society
Reachability Does Not Imply Searchability in Expanding Networks
Antonio Scala
Routing and search respond in opposite ways to rapid network expansion. Short graph distances make a known destination easy to reach, while the neighborhood accessible within a few hops can be vastly larger than any finite inspection budget. We show that this tension imposes an
algorithm-independent constraint on sparse search. If $m_q$ relevant nodes are placed without structural information among $N$ nodes and at most $M$ nodes can be inspected, any target-blind exploration protocol has success probability at most $Mm_q/N$, irrespective of correlations between successive inspections, of revisits, and of any structural preference the exploration rule may have. Geometry determines when this constraint becomes relevant: a target becomes accessible with order-one probability when the graph-distance ball volume satisfies $V(R_c)\sim N/m_q$. At this scale, any inspection mechanism achieving a fixed success probability $\delta>0$ must enrich the probability of inspecting relevant nodes by at least order $N/(Mm_q)$ relative to the neutral target density. For uniformly searched candidate sets, this entails a vanishing visible fraction of the accessible region. Thus rapidly expanding networks can make rare targets geometrically close while target-blind search remains ineffective. Numerical results on Krioukov
hyperbolic random graphs and two-dimensional
lattices show that this separation can arise within only a few hops in the rapidly expanding case, while the
lattice accessibility radius grows algebraically.
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Fluid Dynamics
Goal-Oriented Weighting of Reynolds-Stress Data for Learning Turbulence Models in Complex Flows
Zhuolin Zhao, Haochen Wang, Youngwoo Kim et al. (+2 more)
Data-driven
turbulence models for the Reynolds-averaged Navier-Stokes (RANS) equations offer a promising route to improving predictions of complex flows. Such models, specifically the
turbulence constitutive relations, can be learned efficiently from high-fidelity Reynolds-stress data without evaluating the RANS equations during training. However, conventional losses weight all tensor-component and spatial errors equally, although their influence on a quantity of interest (QoI) can differ significantly; reducing the aggregate stress error therefore does not necessarily lead to an improved QoI prediction. Training against flow-level observations accounts for this dependence but requires repeated, potentially expensive RANS solutions. In canonical shear flows, physical
reasoning can identify the shear component as the only one relevant for the mean-flow prediction. Motivated by this example, we propose a goal-oriented method to select and weight Reynolds-stress training data for complex flows. For a specified QoI, a single offline adjoint evaluation quantifies its sensitivity to local perturbations of each Reynolds-stress component. These sensitivities are converted into fixed weights in the supervised loss, enabling training without further RANS solutions. We validate the weighting in square-duct and periodic-hill flows, then apply it to a film-cooling jet in crossflow, where it improves velocity and cooling-effectiveness predictions over uniformly weighted training despite using a velocity-only QoI. Beyond
turbulence modelling, this work suggests a broader strategy for aligning supervised learning with downstream prediction goals while preserving training efficiency.
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All authors Zhuolin Zhao, Haochen Wang, Youngwoo Kim, Solkeun Jee, Heng Xiao
Plasma Physics
Bayesian inference of non-Maxwellian distribution functions from collective Thomson scattering spectra
Kentaro Sakai, ByungJun Kang, Akito Nakano et al. (+1 more)
We investigate
Bayesian inference of non-Maxwellian electron distribution functions from collective Thomson scattering (CTS) spectra. We represent distribution functions using multiple candidate models of different complexity and perform inference on synthetic spectra generated from known non-Maxwellian distribution functions. Our analysis demonstrates that a sufficiently flexible model can approximate the overall shape of the ground-truth distribution function. The most plausible model is identified based on model evidence, a statistical measure representing the probability to obtain the observed data given the model. When the candidates include the ground-truth model, the model evidence favors the ground-truth model. Without the ground-truth model as a candidate, the model evidence favors the candidate model with the fewest parameters that adequately approximates the data. The posterior probability density function of the most plausible model reveals the characteristic features and associated uncertainties of the underlying distribution function. The origin of the additional spectral peaks is attributed to a combination of modifications to the dispersion relation and reduced Landau damping. This enables the objective and data-driven identification of distribution functions directly from observed CTS spectra.
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All authors Kentaro Sakai, ByungJun Kang, Akito Nakano, Takeo Hoshi
Chemical Physics · Cross-listed from q-bio.MN
Large-deviations theory for growing chemical reaction networks
Praful Gagrani, Ignacio Madrid and Tetsuya J Kobayashi
Growing biochemical systems are intrinsically noisy, with one important source of stochasticity arising from the discrete reaction events of the underlying biochemical network. Because growing systems do not generally admit stationary abundance distributions, it is unclear how to separate this intrinsic chemical noise from other sources of variability in population and single-
cell data. Here we develop a large-deviation theory for exponentially growing
stochastic chemical reaction networks by decomposing abundance into volume and composition. In these coordinates, balanced growth corresponds to a stable composition together with exponentially increasing volume. We show that composition fluctuations satisfy a large-deviation principle with speed equal to the volume of the growing system, and the corresponding quasipotential is selected by a solution of the contact Hamilton–Jacobi equation. We also derive a fluctuation theory for accumulated observables and show that their covariances decay with accumulated-volume, rather than with physical time. Applications to a minimal autocatalytic network and a coarse-grained cellular growth model demonstrate how the theory predicts composition quasipotentials, growth-rate fluctuations, and correlations between observables. The framework therefore provides a
stochastic null model for single-
cell heterogeneity generated by the intrinsic stochasticity of the underlying chemical reaction network.
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Chemical Physics
Active-Space Quantum Simulation of N$_2$ Hydrogenation at a Ru Single-Atom Site on Ru(0001)
Geet Gupta
Selective activation of dinitrogen (N$_2$) under mild conditions is difficult: N$\equiv$N is one of the strongest bonds in chemistry, and most heterogeneous catalysts capable of breaking it require high temperature and pressure. Atomically dispersed transition-metal sites offer a computationally tractable route to studying the strongly correlated intermediates involved. We connect periodic DFT calculations with correlated active-space calculations on a finite, non-periodic surface fragment, demonstrating the workflow for the hydrogenation step RuH$_2$(N$_2$)* $\rightarrow$ RuH(NNH)* at an isolated Ru$_1$ site on Ru(0001). A first-shell fragment is extracted from the periodically relaxed structure, an active space is selected with Active Atomic Valence Space (AVAS) around the Ru-N/N-H bond reorganization, and natural-orbital truncation reduces its cost while retaining the relevant correlated degrees of freedom. The reduced
Hamiltonian is mapped to
qubits with the Jordan-Wigner transformation and solved with the adaptive derivative-assembled pseudo-Trotter variational
quantum eigensolver (ADAPT-VQE). Dynamic correlation beyond the active space is examined with strongly contracted NEVPT2 and DSRG-MRPT2. AVAS gives a 22-
qubit active space, which natural-orbital truncation compresses to 16
qubits, reproducing the untruncated CASCI state energies to within 0.21 kcal/mol (reaction-energy error 0.2 kcal/mol). Statevector ADAPT-VQE in this reduced space converges for both reaction states under a fixed pool-gradient stopping criterion. NEVPT2 yields anomalously large, state-imbalanced corrections for the finite Ru fragment, while DSRG-MRPT2 retains an endothermic reaction energy at its default flow parameter, although its magnitude depends strongly on that parameter.
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Medical Physics
Improving Multi-Delay-ASL through specialized reconstruction
Ingmar Sorgenfrei, Qinyang Shou, Ingrid Barth et al. (+3 more)
Purpose: Although image reconstruction has received relatively little
attention in ASL research to date, it has the potential to address several challenges in ASL. As well as speeding up measurements by increasing undersampling and reducing measurement artifacts, it can improve the signal-to-noise ratio (SNR) of a given data set and the reproducibility of examinations. Methods: This work focuses on extending a dedicated ASL reconstruction approach (ASL-TGV) (Spann et al. (2020)) to multi-delay data and applying it to a high-resolution pCASL test-retest
dataset. To show not only improvement in the Perfusion Weighted Images (PWIs), Cerebral Blood Flow and Arterial Transit Time was estimated and the test-retest reliability was estimated using the within subject Coefficient of Variance (wsCV), the Intraclass Correlation Coefficient (ICC) and Root-Mean-Squared-Error (RMSE). Results: The Perfusion-Weighted-Images reconstructed from highly undersampled single-shot data using ASL-TGV are clearly improved compared to a fully-sampled reference from the same data even after denoising, especially for long PLDs and the outermost slices. SNR calculated in grey and white matter ROIs shows an improvement of 62% and 35% respectively. The CBF maps produced from the ASL-TGV images have an improved test-retest reliability. Conclusion: ASL-TGV, which is now implemented in the Berkeley Advanced Reconstruction Toolbox (BART), can be used with any type of ASL labeling or data acquisition and any existing image post-processing pipeline and can improve SNR for the PWIs and reproducibility of the CBF maps.
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All authors Ingmar Sorgenfrei, Qinyang Shou, Ingrid Barth, Martin Uecker, Danny JJ Wang, Rudolf Stollberger
Optics
Laser induced broad band white emission from transparent Cr4+:YAG ceramics: Origin of broadband emission
M. Chaika and W. Strek
Laser-induced white (light) emission was observed from transparent Cr:YAG ceramics irradiated with a focused continuous wave beam of light from an infrared
laser diode. The
laser-induced white emission is detected only on the surface of the sample and is not observed in volume. It is found that the intensity of the emission increases exponentially with the
laser power density above the threshold. The impact of broadband emission on the power of the transmitted
laser beam through the sample was measured. The disappearance of broadband emission due to displacement of the
laser beam or an increase in ambient pressure leads to a decrease in the power of transmitted
laser beam. Origins of the
laser-induced white light emission along with its characteristic features are discussed in terms of multiphoton absorption, intervalence charge transfer and ionic space charge models.
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Physics and Society
Warned alike, AI agents avoid the less-crowded road while people take it
Takahiro Ezaki, Naoto Imura and Katsuhiro Nishinari
AI
agents built on a few shared models increasingly act for many people. A shared forecast about others can align their choices and change how scarce capacity is allocated. We tested this feedback in a two-road congestion game. Adding one sentence warning that others might follow a routing tip made populations of 50 GPT
agents crowd one road while avoiding the nearly empty alternative. Average travel time rose from 64 to 95 min, although any crowded-road
agent could have saved 69 min by switching alone. The warning discouraged the very move it predicted. The pattern persisted for 100 rounds. Two other model families shifted the same way without locking onto one road. Twelve all-human groups (240 participants) stayed near balance under numerical reports or the tip and warning. In 24 mixed groups with a further 240 participants, imbalance grew with the share of
agents in the registered analysis, while people increasingly took the road the
agents avoided. Collective costs stayed below the allagent reference, but with 15
agents and 5 humans,
agent seats averaged 80 min, compared with 44 min for human seats. Shared forecasts can thus sustain collective inefficiency among similar
agents. A better group average can also hide an unequal burden. Evaluations of AI
agents that share resources should test populations, treat messages as interventions and report who bears the costs.
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Chemical Physics
Observing Phase Space Evolution During Unimolecular Decay
Abhirami Vijayakumar, Raidel Martin Barrios, Meenu Upadhyay et al. (+4 more)
Direct time-resolved observation of large amplitude dynamics during ground state statistical unimolecular decay remains an experimental challenge. Here, an experimental Time-Resolved Coulomb Explosion Imaging (TR-CEI) study (J. Chem. Phys. 151, 174301, 2019) of the canonical unimolecular reaction NO$_2$ $\rightarrow$ NO($^2\Pi, v=0, J)$ + O($^3P_j$) at threshold dissociation energies is modeled and analyzed. Quasi-classical trajectory (QCT) simulations on an accurate and validated ground state potential energy surface (PES), with energy and angular momentum constraints from known product state distributions, are used to characterize the dissociation dynamics. TR-CEI projects suddenly, via ultrafast Strong Field Ionization (SFI), the evolving neutral ground state wavepacket onto the repulsive doubly charged cationic state which then impulsively fragments into the NO$^{+}$ + O$^{+}$ ion pair. Within the Coulomb recoil approximation, which applies at longer NO–O (Jacobi) separations, the ion pair CEI-energy is inversely proportional to the scalar distance between the neutral ground state NO and O moieties at the moment of SFI, thus providing a direct measure of the evolving average NO–O distance during unimolecular decay. Results from QCT simulations agree favourably with the TR-CEI measurements. Phase space
evolution, recurrence time distributions and Lyapunov exponent analysis are presented which support the conclusion that TR-CEI directly observes phase space
evolution during unimolecular decay. For the present system, the time scale for the phase space distribution to achieve statisticality is estimated to be 2 to 3 ps.
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All authors Abhirami Vijayakumar, Raidel Martin Barrios, Meenu Upadhyay, JingChun Wang, Andre Staudte, Albert Stolow, Markus Meuwly
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Energetically Driven Structure Matching for Autonomous Total X-ray Scattering Experiments
Emil J. P. Frost, Martin A. Karlsen, Jonas H. Jensen et al. (+6 more)
The emergence of autonomous laboratories motivates rapid conversion of experimental data into reliable atomistic models on time-scales compatible with closed-loop optimization. Here we develop an energetically driven structure matching framework for analysis during ongoing total X-ray scattering experiments. Using data from gold nanoparticles, we match against idealized spherical, octahedral, decahedral, and icosahedral geometries, their machine-learned interatomic potential (MLIP)-relaxed structures, and molecular dynamics (MD) ensembles. Idealized models are fast to generate but can misassign morphology and systematically underestimate size by neglecting surface relaxation, strain, and thermal disorder. MLIP relaxation markedly improves both, while MD ensemble averaging agrees best with experiment. We therefore introduce a hierarchical workflow combining rapid idealized screening with targeted MLIP and MD refinement of top candidates, delivering improved structural feedback without interrupting autonomous operation. This framework provides a route towards autonomous campaigns in which the target structure itself can be updated in response to the evolving energy landscape of structures compatible with the experimental data.
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All authors Emil J. P. Frost, Martin A. Karlsen, Jonas H. Jensen, Rodrigo Moreno, Jørn Lambertsen, Jonathan Quinson, Andy S. Anker, Alexander Bagger, Tejs Vegge
Computational Physics
$U(1)$ Gauge-Equivariant Representation Learning of Bloch States
Chengyan Zhang, Xian Xu, Bowen Hou et al. (+1 more)
Representation learning, or featurization, of mean-field Bloch states provides an important route for incorporating electronic information into machine-learning models of first-principles condensed-phase systems. A challenge in representing Bloch states is the gauge redundancy of each state. In this work, we propose to address this issue by explicitly incorporating gauge equivariance in the machine-learning framework. We develop a $U(1)$-equivariant autoencoder that compresses the plane-wave-basis Bloch states obtained from density functional theory into a low-dimensional latent representation. We achieve an average overlap of 0.970 between original wavefunctions and the reconstructions from a 64-dimensional latent space using a
dataset of two-dimensional
insulators. We further show that $U(1)$ equivariance endows the latent space with physically meaningful structures, including smoothness and topological information. Finally, we present a proof-of-principle application in which the latent representations are used to predict the off-diagonal elements of the $GW$ self energy
matrix, and demonstrate the importance of enforcing gauge equivariance in the prediction.
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All authors Chengyan Zhang, Xian Xu, Bowen Hou, Diana Y. Qiu
Atomic Physics · Cross-listed from astro-ph.SR
Near-infrared spectra of chemically peculiar stars and applications to kilonova spectra
Nanae Domoto, Masaomi Tanaka, Wako Aoki et al. (+11 more)
Kilonova spectra provide information on rapid
neutron-capture nucleosynthesis in
neutron star mergers. However, it remains challenging to identify individual elements in kilonova spectra, primarily due to incomplete atomic data for heavy elements at near-infrared wavelengths. Recent work demonstrated that spectra of chemically peculiar
stars offer excellent laboratory for studying kilonova spectra. In this paper, to expand our understanding of strong transitions of heavy elements at near-infrared wavelengths, we present high-resolution near-infrared spectra of 14 chemically peculiar
stars covering a range of effective temperatures, surface gravities, and abundance patterns, obtained with Subaru/IRD. The Sr II, Ce III, and Gd III lines, previously suggested to be among the strongest transitions, are detected in the spectra of most of our targets, confirming their intrinsic importance. In addition, Eu II and Nd III lines are detected in several
stars. By comparing observed equivalent widths and abundance ratios, we show that under a highly heavy-element-enriched abundance pattern, Eu II and Nd III lines may contribute to kilonova spectral features comparably to Sr II. Importantly, we find no unidentified lines systematically stronger than these transitions despite the larger stellar sample. These results further support the primary importance of the Sr II and Ce III transitions and provide new insights into the potential contribution of Eu II and Nd III.
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All authors Nanae Domoto, Masaomi Tanaka, Wako Aoki, Salma Rahmouni, Tomoyuki Kudo, Hiroki Harakawa, Teruyuki Hirano, Takayuki Kotani, Takashi Kurokawa, Masayuki Kuzuhara, Jun Nishikawa, Takuma Serizawa, Motohide Tamura, Akitoshi Ueda
Applied Physics
FSSDataBase: a reconstructable dataset of simulated frequency-selective surface structures and scattering responses
Xinke Kuang, Shiyun Ma, Yuanyuan Wang et al. (+1 more)
Data-driven design of frequency-selective surfaces (FSSs) requires reusable
datasets that link structural geometry to electromagnetic response under documented simulation conditions. Here we present FSSDataBase, an openly available collection of 5,000 procedurally generated single- and multilayer FSS unit
cells simulated using Ansys HFSS. The
dataset covers 10-20 GHz with transverse-electric and transverse-
magnetic relfection and transmission responses, including magnitude and phase, for recorded incidence angles of $0^{\circ}$ and $30^{\circ}$. Each record links binary structural masks and JSON-based reconstruction metadata to raw response samples and derived magnitude labels. Structural descriptors, response-distance measures and task-oriented cost functions characterize
geometric and electromagnetic variation and support screening for polarization stability, angular-selective bandwidth and matched-amplitude, 180{\deg} phase-separated structure pairs. Accompanying code supports model reconstruction, data processing and configuration-driven generation of additional samples. Beyond conditional forward modelling and response-based structure
retrieval, FSSDataBase has the potential to serve as a reference
dataset for deep-learning-based FSS inverse design, supporting fair comparisons across network architectures under shared data splits,input conditions and evaluation protocols.
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All authors Xinke Kuang, Shiyun Ma, Yuanyuan Wang, Jiang Wu
Applied Physics · Cross-listed from cond-mat.mtrl-sci
A hydrogen-informed Rice-Beltz model for crack-tip dislocation emission under mixed-mode loading
Kai Zhao
The fundamental competition between crack-tip cleavage and dislocation emission dictates the ductile-to-brittle transition in crystalline solids. In hydrogen-charged environments, this delicate balance is disrupted, yet existing models often struggle to self-consistently capture the atomic-scale thermomechanical modulation of the emission barrier. Here, we propose the hydrogen-informed Rice-Beltz framework to quantify the critical stress intensity factors (SIFs) required for initial dislocation nucleation under mixed-mode (I+II) loading. By integrating the elastic interaction of the dilatational field of interstitial hydrogen and the applied stress fields against the incipient dislocation core, the model recovers the non-monotonic relation between the most probable SIF and the hydrogen concentration. Unlike the classical pure mode-I (or mode-II) scenario, the minimization of the local strain energy density is employed to generate closed-form nominal driving forces, which are subsequently embedded into a transition-state-theory framework. This atomistically-informed approach yields the most probable SIF for the first dislocation emission event as a function of the loading rate, slip angle, and hydrogen concentration, providing a rigorous, parameter-free boundary condition for the onset of hydrogen-modulated crack-tip plasticity.
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Instrumentation and Detectors
Parnassus for the CLD Detector: A Generative Machine-Learning Surrogate for Detector Simulation and Reconstruction at the FCC-ee
Umar Sohail Qureshi, Benjamin Nachman and Caterina Vernieri
Detector simulation and event reconstruction will be computationally expensive for future $e^+e^-$
collider programs and are currently critical bottlenecks for accurate feasibility studies. To address this challenge, we build a Parnassus model for the CLD detector concept. Parnassus is a framework for automatically tuning a surrogate model, in our case, a conditional flow matching neural network, to emulate a full detector simulation and reconstruction. We train on $e^+e^-\to Z\to q\bar q$ events at $\sqrt{s}=91.2$ GeV processed through a Geant4 simulation of the CLD detector concept and the Pandora particle-flow reconstruction and reproduce single-particle kinematics, particle-IDs, and impact-parameter distributions. We also examine jet- and event-level features, inclusively and split by flavor, and find excellent fidelity, significantly better than the parameterized program Delphes. Furthermore, we train a
transformer-based flavor tagger on the reconstructed particle-flow constituents and show that the surrogate preserves the $b/c/s/q$ discrimination of the full CLD reconstruction. The Parnassus CLD model achieves a generation cost of about 1.2 ms (40 ms) per event on a single GPU (CPU), over three (two) orders of magnitude faster than full simulation and reconstruction. Our model is publicly available for feasibility and design studies.
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