Atomic Physics · Cross-listed from quant-ph
Phase-Stable Hologram Updates for Large-Scale Neutral-Atom Array Reconfiguration
Erdong Huang, Jiayi Huang, Hongshun Yao et al. (+2 more)
Dynamic
holographic optical tweezers provide a programmable route to array assembly and reconfiguration essential for scalable neutral-atom
quantum computation. However, phase mismatch between successive holograms can cause destructive interference during finite spatial light modulator (SLM) refresh. In this work, we analyze finite SLM refresh to establish a phase-stability criterion for prescribed trap amplitudes and develop a weighted-projective Gerchberg-Saxton (WPGS) method that efficiently approximates the corresponding phase-only complex-field optimization. Enforcing this phase constraint also reduces the number of iterations required for each update, achieving convergence within five iterations and enabling hologram generation within a few milliseconds. Numerical simulations of 2D and 3D reconfiguration involving more than $10^3$ traps, together with nonuniform-intensity interlayer transport, show that WPGS preserves endpoint intensity quality while suppressing inter-frame phase mismatch, transient intensity degradation, trap splitting, and motional heating. Similar suppression of phase mismatch and transient-intensity degradation is maintained under Gaussian SLM-plane illumination. These results establish the phase-stability criterion as a practical design principle for dynamic
holographic control and scalable neutral-atom array reconfiguration.
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All authors Erdong Huang, Jiayi Huang, Hongshun Yao, Xin Wang, Jin-Guo Liu
Biological Physics
On phenomenology of physical effects in axons
Jüri Engelbrecht, Kert Tamm and Tanel Peets
This paper deals with the mathematical modelling of signal propagation in nerve fibres. Due to the complexity of the processes where electrical, mechanical, and thermal effects are coupled, a phenomenological approach helps to build mathematical models. The ideas of phenomenology are briefly presented, and their application is described. These applications cover the modelling of ion currents (the Hodgkin-Huxley model), temperature effects, and inductance. This means that the ion currents through the biomembrane, the influence of endo- and exothermic reactions on temperature, and the influence of energy in a non-electrical form are taken into account using phenomenological variables, i.e., observables. Such an approach brings the mathematical models closer to reality. Using the concept of phenomenological inductance helps us better understand the propagation of an action potential in myelinated axons. In principle, contemporary mathematical models describing the process in axons are hybrid in nature, combining physical laws with phenomenology, i.e., with observables.
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Instrumentation and Detectors
Computational Microwave Imaging Relying on Orbital Angular Momentum Transmitarrays for Improved Diversity
Miguel Angel Balmaseda-Marquez, Guillermo Álvarez-Narciandi, María García-Fernández et al. (+3 more)
This work proposes the use of orbital angular momentum (OAM) waves to improve the performance of a computational imaging (CI) system. Specifically, in contrast to a solely frequency-diverse operation, leveraging multiple OAM waves leads to a significant increase in the diversity of the measurement modes of a CI system. This significantly reduces the frequency bandwidth required to achieve high-quality image reconstructions. A proof-of-concept prototype working at Ka-band frequencies is used to validate the proposed approach. The prototype consists of two metalized three-dimensional (3D) printed cavities, with fully-dielectric transmitarrays inside that generate OAM waves. Imaging results from various targets reveal that the CI system achieves superior imaging quality when multiple OAM waves are considered, compared to when it solely relies on frequency-diversity. This is specially noticeable in the case of complex distributed targets, which can only be reconstructed with the prototype when multiple OAM waves are used. Furthermore, it is shown that accurate image reconstructions can be obtained employing only one eighth of the operational bandwidth of the frequency-diverse system.
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All authors Miguel Angel Balmaseda-Marquez, Guillermo Álvarez-Narciandi, María García-Fernández, Carlos Molero Jiménez, William Whittow, Okan Yurduseven
Computational Physics
Physics-informed automated surface reconstructing via low-energy electron diffraction based on Bayesian optimization
Xiankang Tang, Ruiwen Xie, Jan P. Hofmann et al. (+1 more)
Low-energy electron diffraction (LEED) is a cornerstone technique for determining surface atomic structures[heldStructureDeterminationLowenergy2025], yet the quantitative analysis of electron diffraction intensity as a function of incident electron energy – that is, LEED-I(V) analysis – remains a complex inverse problem. In this work, we tackle quantitative LEED-I(V) analysis based on physics-informed
Bayesian optimization (BO). By
embedding multiple scattering LEED forward models directly into a trust-region BO loop, our approach simultaneously optimizes both structural and experimental parameters, adaptively adjusting trust regions for efficient exploration of complex non-convex parameter spaces without manual intervention. The robustness and scalability of the approach are demonstrated using the Ag(100)-(1$\time$1) and Fe\textsubscript{2}O\textsubscript{3}(1$\overline{1}$02)-(1$\time$1) surfaces as examples. Our work establishes a general framework for solving inverse problems in various characterization techniques, unlocking a physics-informed efficient, reproducible, and autonomous paradigm.
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All authors Xiankang Tang, Ruiwen Xie, Jan P. Hofmann, Hongbin Zhang
Fluid Dynamics
Cavitation-bubble Interaction with an Initially Perturbed Free Surface
Jingyu Gu, Zirui Liu, A-Man Zhang et al. (+1 more)
The interaction of a spark-generated cavitation bubble with an initially perturbed free surface is investigated experimentally, numerically, and analytically. By exploiting contact-line pinning, we accurately prescribe an initial meniscus with a thin, hydrophilic-coated rod inserted into the
liquid. A pronounced surface cavity, driven by the oscillating bubble, forms and penetrates downward to a scale comparable to the bubble itself. The coupled cavity-bubble system exhibits two distinct regimes – coalescence and non-coalescence – separated by a critical condition governed by the non-dimensional stand-off parameter $\gamma$ and the initial meniscus height $h_m$. In the non-coalescence regime, the cavity evolves through inception, expansion, and rebound/jetting. The maximum cavity length $h_c$ follows a power-law scaling $h_c\propto\gamma^{\alpha}$ with $\alpha=-2.7$ (experiments) and $\alpha=-2.6$ (simulations) for $1.5\lesssim\gamma\lesssim3$, where inertia dominates. Deviations emerge for $\gamma\lesssim1.5$ (strong nonlinearity) and $\gamma\gtrsim3$ (surface tension and viscosity become noticeable). An analytical model based on the Rayleigh-Plesset equation combined with nonlinear Rayleigh-Taylor instability theory captures the trend and confirms that $h_m$ plays only a secondary role relative to $\gamma$. In the coalescence regime, atmospheric air vents into the bubble through the merged cavity, weakening the collapse intensity and reducing the associated pressure peak. We also examine air/
liquid compressibility and boundary layer effects, whose significance grows as $\gamma$ decreases. These findings are relevant to surface-jetting technologies, cavitation-erosion mitigation, and
underwater-noise suppression.
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All authors Jingyu Gu, Zirui Liu, A-Man Zhang, Shuai Li
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Light-modulated exchange bias in multiferroic heterostructures
Huan Tan, Zheng Ma, Cynthia Bou Karroum et al. (+7 more)
Magnetic straintronics, the strain-mediated control of
magnetic anisotropy, has emerged as a key direction for next-generation energy-efficient technologies. In multiferroic
heterostructures, magnetoelectric coupling is typically achieved by applying an electric field on a ferroelectric phase, inducing strain through the converse piezoelectric effect, which is then transferred to the adjacent
ferromagnetic phase. As an alternative, strain can be remotely modulated through the photostrictive effect induced by light. While light-driven control of
magnetic anisotropy has been explored, optical modulation of more complex phenomena such as exchange bias remains largely unaddressed. Here, we demonstrate significant light-induced modulation of exchange bias and magnetization switching at room temperature in a Pb(Mg1/3Nb2/3)O3-Pb(Zr,Ti)O3 (PMN-PZT)/Fe80Ga20(FeGa)/Ir20Mn80(IrMn) multiferroic
heterostructure, driven by visible-light-photostriction. The magnetization state correlates with the light intensity, enabling multi-level states with light power densities as low as 0.1 W cm-2. These findings suggest a promising route toward low-power, multistate, and wireless opto-
magnetic memory applications.
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All authors Huan Tan, Zheng Ma, Cynthia Bou Karroum, Matthieu Liparo, Jean-Philippe Jay, David Spenato, David T. Dekadjevi, Luis Martinez Armesto, Alberto Quintana, Jordi Sort
Physics and Society
Disentangling Large-Scale Supply Networks: f-HiCoNE Framework for Flow-Hierarchical Clustering via Combinatorial Hodge Decomposition
Taiyo Nakatani and Takaaki Aoki
Modern society relies on complex supply chains to sustain the flow of goods and services that are essential to daily life. While traditional supply chain theory assumes a clear, hierarchical flow from upstream suppliers to downstream customers, observable real-world transaction networks rarely exhibit this acyclic structure. Instead, detailed inter-firm data reveal that interwoven networks are heavily entangled by cyclic flows. Consequently, without appropriate partitioning of these massive inter-firm networks, the latent flow-hierarchical structures that are central to supply chain concepts remain obscure. To address this analytical challenge, we introduce the flow-Hierarchical Community Network Extraction (f-HiCoNE) framework. By applying combinatorial Hodge decomposition, this approach disentangles the complex inter-firm network by isolating the acyclic gradient flow to quantify the flow-hierarchical parts and partition the graph. By applying f-HiCoNE to a nationwide transaction
dataset of approximately 650,000 firms, we successfully extracted functional supply-chain clusters. These clusters demonstrated strong flow-hierarchical organisation, wherein the upstream-downstream positioning of firms was accurately captured by local scalar potentials, revealing distinct geographically localised industrial ecosystems. This study provides a map that helps firms understand their surrounding environment and locate their position within an inter-firm network and opens a new research avenue focused on flow-hierarchy clustering in supply chain analysis.
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Atomic Physics
Continuous thermochemical sources of AlF molecules
Pulkit Kukreja, Priyansh Agarwal, Maximilian Doppelbauer et al. (+10 more)
The AlF molecule, currently subject to
laser cooling and trapping efforts, has the advantage that it can be efficiently produced in a thermochemical reaction between sublimated aluminum trifluoride and aluminum metal. Here we present a series of experiments with continuous molecular beam sources of AlF, utilising this reaction. We demonstrate a compact AlF molecular beam oven whose total far-field brightness is $5\times 10^{15}$ molecules per steradian per second at 923~K, just below the melting temperature of aluminum metal. The continuous output from the oven begins to exceed the peak brightness of a jet-cooled, ablation-based supersonic AlF source for the $v=0$, $J=7$ level, and we obtain an excellent signal-to-noise ratio with the oven in pulsed
laser ionisation spectroscopy experiments. By delivering flux from the oven into a cryogenic Ne buffer gas
cell, we lower the rotational temperature of the beam to around 30~K and reduce its most probable forward velocity from 600~ms$^{-1}$ to 200~ms$^{-1}$. In addition, we demonstrate that AlF can be made in a simple dispenser package, and observe that molecules thermalise to the laboratory temperature after colliding with
vacuum chamber walls of the experiment. The resulting transient AlF vapour may enable direct loading of a molecular magneto-optical trap.
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All authors Pulkit Kukreja, Priyansh Agarwal, Maximilian Doppelbauer, Jionghao Cai, Xiangyue Liu, Eduardo Padilla, Sebastian Kray, Henrik Haak, Russell Thomas, Stefan Truppe, Boris G. Sartakov, Gerard Meijer, Sid Wright
Physics and Society
Intercity mobility reveals the hyperbolic geometry of city systems
Zhaoya Gong, Bin Liu, Chenglong Wang et al. (+5 more)
The hierarchy and proximity are key dimensions of urban relational processes, but their interplay in shaping intercity interactions and the underlying structures of city systems remain unclear. We develop a novel
geometric model of city systems
embedding intercity mobility into a latent
hyperbolic geometry, which unravels the measures of hierarchy and proximity accounting for their interplay. It is successfully validated against 12 different nationwide intercity mobility
datasets. We find a bottom-up emergence of city hierarchies, along which the variations of city-hinterland relations are non-stationary in terms of their nesting and range properties. Such non-stationarity originates from trade-offs between city hierarchy and hinterland range in determining the formation of city-hinterland structures. Hierarchy- and proximity-dominated urban processes can be elucidated from examining dynamics of the trade-offs. The revealed urban relational processes of city systems are at the core of the emerging science of cities and crucial for spatial planning and regional policymaking.
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All authors Zhaoya Gong, Bin Liu, Chenglong Wang, Pengjun Zhao, Xiang Li, Kaixiang Zhang, Changcheng Kan, Xingjian Liu
Chemical Physics · Cross-listed from cond-mat.quant-gas
Neural-network quantum states for solving few-body problems: application to Efimov physics
Sora Yokoi, Shimpei Endo and Hiroki Saito
Neural-network
quantum states have recently emerged as a powerful method for solving
quantum many-body problems, with notable successes in
lattice systems. Here, we extend this approach to strongly interacting few-body problems in continuous space, and demonstrate its capability by computing the Efimov states and associated few-body bound states. Using a fully connected feedforward neural network with Jacobi coordinates as inputs, combined with a projection method, we compute the ground and first excited states for three- to six-body systems of identical
bosons at unitarity, as well as a mass-imbalanced fermionic system consisting of two identical
fermions and a third particle. The obtained energies of the ground and first excited states agree well with previously reported results. Furthermore, the proposed approach also reproduces key features of Efimov states, including the discrete scale invariance, the characteristic
geometric structure of the wave function, and the critical-mass behavior in mass-imbalanced fermionic systems. Our method can be readily applied to a broad class of strongly correlated few-body problems in continuous space.
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Atmospheric and Oceanic Physics
Future Amplification of Moist Weather Extremes in the Midlatitudes
Funing Li and Talia Tamarin-Brodsky
Moist heatwaves and convective storms frequently co-occur, posing compound risks. Although historically concentrated in the tropics, these moist weather extremes are projected to intensify substantially towards the midlatitudes, with regions downstream of major highland terrains, including northeastern Asia and eastern North America, emerging as hotspots of future change. Yet their physical drivers remain uncertain. Here we show that the intensification of concurrent moist heat and convection extremes in the midlatitudes is tightly constrained by changes in low-level atmospheric inversions. Specifically, we find that amplified warming over western highlands is transported downstream by prevailing westerlies, strengthening low-level thermal inversions and raising the attainable maxima of moist heat and convection. Targeted model experiments confirm the critical role of orographically elevated heating in driving these extremes. Our results reveal a mechanistic pathway for compound extremes and highlight low-level inversions as a key factor for emerging midlatitude risks of moist heat and severe weather under
climate change.
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Applied Physics
Approximation Models for Shared Mobility Rebalancing Under Structured Spatial Imbalance
Wenbo Fan, Zhouyun Chen and Weihua Gu
Shared mobility systems (e.g., shared cars and ride-hailing services) generate persistent spatial imbalances as vehicles concentrate at popular destinations, leaving trip origins depleted of supply. Operators incur substantial costs in repositioning empty vehicles, and quantifying the theoretical minimum rebalancing distance is practically important. Exact computation requires solving a transportation linear program that is challenging at the city scale. Closed-form approximation models are derived for the minimum rebalancing distance in rectangular service regions. Parallel derivations are presented for the Manhattan metric (grid road networks) and the Euclidean metric (unconstrained movement). A scalar spatial imbalance index condenses the full demand pattern into a single interpretable quantity. Both models share a unified structure: the per-vehicle rebalancing distance scales with the square root of service area, the imbalance index, and a shape factor that depends solely on the aspect ratio. Calibration and validation against 500 exact LP solutions per metric confirm the area-scaling exponent to within 2% of the theoretical prediction, across three demand distribution families. An empirical case study using January 2026 New York City for-hire vehicle trip data across 263 traffic analysis zones confirms that the formula generalizes to real-world, network-constrained demand. The results equip operators and system designers with solver-free, theoretically grounded tools for benchmarking rebalancing performance and optimizing service, rebalancing frequency, and demand-management interventions.
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Optics
$Q$ Factors Exceeding $10^{4}$ in Wavelength-to-Subwavelength-Scale Free-Space Resonators
Darrell E. Omo-Lamai, Varun Dolia, Yanyu Xiong et al. (+7 more)
Free-space-addressable optical resonators that combine long
photon lifetimes (high $Q$ factors) with strong spatial localization of optical fields (small mode volumes, $V_m$) enhance light-matter interactions with facile far-field excitation. The Purcell factor governing spontaneous emission enhancement scales as $Q\,V_m^{-1}$. Periodically asymmetric resonators, in which perturbations convert bound modes into radiating modes, offer a route to free-space resonances, with the radiative $Q$ factor tuned by the
geometric and optical strength of the asymmetry-inducing perturbations. However, free-space resonators that simultaneously achieve high $Q$ and small $V_m$ have remained rare. This limitation arises in part because existing designs do not tailor
geometric and optical asymmetries concurrently, thus limiting access to high-$Q$ regimes. Here, we show that jointly tuning
geometric and optical asymmetries unlocks a biaxial radiative landscape with iso-$Q$ contours that connect disparate perturbations with equivalent $Q$ factors. We demonstrate this framework with very-large-scale-integrated single-crystalline Si nanoantenna pixels (VINPix) with out-of-plane perturbations of 35-150 nm amorphous Si, SiN$_x$, and SiO$_2$. We experimentally establish biaxial $Q$ factor control in air and achieve $Q$ factors up to $76,000$ at wavelength-scale mode volumes ($V_m \sim 1.7\,\lambda_0^3\,n_{\mathrm{eff}}^{-3}$) in simultaneously imaged arrays of $>80$ resonators in water. Furthermore, we computationally demonstrate 50-nm-wide slotted VINPix that reach $Q$ factors of $10^6$ at subwavelength mode volumes ($V_m \sim 0.2\,\lambda_0^3\,n_{\mathrm{eff}}^{-3}$) with 20 nm SiO$_2$ perturbations, yielding Purcell factors as high as $5 \times 10^5$ in an all-dielectric free-space resonator.
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All authors Darrell E. Omo-Lamai, Varun Dolia, Yanyu Xiong, Chih-Yi Chen, Parivash Moradifar, Priyanuj Bordoloi, Sajjad AbdollahRamezani, Sahil Dagli, Halleh Balch, Jennifer A. Dionne
Accelerator Physics
Direct stroke measurement of Piezos for cavity frequency tuner of the ILC prototype cryomodule using a Laser Displacement Sensor
Rishabh Bajpai, Mathieu Omet and Ashish Kumar
Piezoelectric actuators are critical for achieving high accelerating gradients and preventing RF trips in narrow-bandwidth superconducting
radio-frequency (SRF) cavities by compensating for detuning caused by Lorentz force detuning. Depending on the maximum acceleration gradient an appropriate piezo stroke requirement has to be fulfilled. Since the stroke of piezo actuators decreases at cryogenic temperatures, evaluating their performance under such conditions is essential. Common characterization methods either use the SRF cavity itself as a sensor or rely on capacitance measurements during cool-down. Both these approaches do not measure the stroke directly and involve a trade-off between measurement precision and experimental simplicity, as well as cost and time. We developed a new method for the direct and precise measurement of piezo stroke at cryogenic temperature inside a cryocooler-cooled cryostat using a
laser displacement sensor. The setup was used to characterize and evaluate two piezo actuators for cavity frequency tuners of the ILC prototype cryomodule, which is currently being built at KEK. In this article we are reporting on the development, setup, test, and application of this novel method, allowing the direct stroke measurement of piezos in
vacuum and at cryogenic temperatures.
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Instrumentation and Detectors
Characterization of GS20 and CLYC Detectors for Neutron Resonance Transmission Analysis in High Radiation Environments
Shayaan Subzwari, Benjamin McDonald and Areg Danagoulian
Advanced reactor concepts based on the thorium fuel cycle offer several advantages over conventional uranium-fueled systems, but they also stress-test the existing NDA toolbox for international safeguards. In particular, the presence of 232U and its ~MeV gamma-emitting daughters in thorium-based spent fuel creates a harsh radiological environment that complicates gamma-based active interrogation safeguard techniques. NRTA has emerged as a promising safeguards technique due to its isotopic specificity in the epithermal range and its robustness against non-resonant shielding. However, deploying NRTA in thorium safeguards requires
neutron detectors that maintain timing performance and quantitative accuracy in intense gamma fields. This paper reports a comparative characterization of two candidate detectors for portable NRTA: GS20 and CLYC. GS20 has already been demonstrated as an effective epithermal detector in portable NRTA systems but offers limited
neutron–gamma discrimination. CLYC, by contrast, provides strong pulse-shape discrimination (PSD) but has a much longer scintillation decay time and includes 133Cs, whose resonances partially overlap with key actinide resonances in the epithermal region. Using a DT-driven NRTA setup with a 2 m flight path, we compare GS20 and CLYC in measurements of a 1.50 mm tungsten target under both “clean” conditions and in an artificially constructed high gamma-radiation environment produced by an auxiliary source as a way of emulating a highly radioactive 233U target. The results indicate that CLYC, despite its long decay time, provides significantly more precise NRTA measurements in high radiation environments than GS20. For thorium-based safeguards scenarios where 233U must be identified and quantified in the presence of intense gamma backgrounds, CLYC-like detectors with strong PSD appear to be the more reliable choice.
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Data Analysis and Statistics · Cross-listed from astro-ph.CO
Replacing Gaussian Processes with Neural Networks in Pulsar Timing Array Inference of the Gravitational-Wave Background
Shreyas Tiruvaskar and Chris Gordon
Bayesian inference of nanohertz gravitational-wave background models in
pulsar timing array analyses often relies on Gaussian-process interpolators to avoid repeated, computationally expensive strain-spectrum calculations. However, Gaussian-process training becomes a bottleneck for large training sets. We test whether probabilistic neural networks can replace Gaussian processes in this role for both a self-interacting dark matter model and a phenomenological environmental model. We find that neural networks recover consistent posteriors while significantly reducing both training and Markov chain Monte Carlo runtime, with the largest gains for the more computationally demanding model.
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Chemical Physics
Assessing the impact of nodal surface optimization in fixed-node diffusion Monte Carlo on non-covalent interactions
Kousuke Nakano, Benjamin X. Shi, Dario Alfè et al. (+1 more)
Diffusion quantum Monte Carlo (DMC) and coupled cluster theory [CCSD(T)] are widely-employed
benchmark methods for noncovalent interactions (NCIs). However, recent studies have reported notable discrepancies across several hydrogen-bonded and dispersion-dominated systems, raising questions on the accuracy of the approximations underlying each approach. In DMC, the dominant error is expected to stem from the fixed-node approximation, where the nodal surface is typically taken from a single Slater determinant derived from a density functional theory or Hartree-Fock calculation. In this work, we assess the impact of nodal surface optimization on DMC predictions for 12 compounds spanning diverse NCIs, using a recently proposed antisymmetrized geminal power ansatz with natural orbitals. We find improved agreement with CCSD(T) for hydrogen-bonded systems, while having negligible effect for dispersion-dominated systems. These results provide a practical and computationally efficient route to resolving discrepancies in hydrogen-bonded interactions, while offering insight into the remaining differences in dispersion-dominated systems.
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All authors Kousuke Nakano, Benjamin X. Shi, Dario Alfè, Andrea Zen
Atomic Physics
Elucidating Au-C Bonding via Laser Spectroscopy of Gold Monocarbide
Rory M. Weldon, Danielle M. Darling, Nicole M. Albright et al. (+4 more)
Gold monocarbide (AuC) has been produced and characterized using
laser spectroscopy, representing the first reported observation of AuC. We recorded the optical spectrum of gas-phase AuC between 400 nm and 700 nm, assigning excitations from the $\mathrm{X}\,^2\Pi_{1/2}( (2\sigma)^2 (2\pi)^1 )$ ground state to states arising from the $(2\sigma)^2 (3\sigma^\ast)^1 $ and $(2\sigma)^1 (2\pi)^2 $ configurations. Dispersed-fluorescence spectra are used to study the vibrational and
spin-orbit structure of the ground state, branching ratios and radiative lifetimes of the excited states, and the Au–C bond dissociation energy. A molecular orbital diagram is used to rationalize the nature of AuC's low-lying electronic states. The data serve as valuable
benchmarks of relativistic theory and are relevant to
quantum science and precision measurements with cold molecules.
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All authors Rory M. Weldon, Danielle M. Darling, Nicole M. Albright, Kendall L. Rice, Phaedra L. Salerno, K. Cooper Stuntz, Benjamin L. Augenbraun