Instrumentation and Detectors
Performance evaluation of an offshore wave measurement buoy in monochromatic waves
Xuepeng Fu, Frederick Driscoll, Rebecca Fao et al. (+4 more)
The accurate measurement of waves underpins marine energy resource characterization, device design, and project development. Datawell wave buoys are widely deployed around the world and have long served as a trusted standard for wave measurements. We quantify the measurement performance, including wave elevation and energy flux estimation, of a Datawell DWR-MkIII buoy using prescribed monochromatic heave motions on a large-amplitude six-degree-of-freedom motion platform at the National Laboratory of the Rockies, assuming the buoy behaves as an ideal wave follower. Commanded motions were validated with an optical motion tracking system while buoy elevation and raw acceleration were recorded. Wave elevations were propagated to wave energy flux estimation using four methods, including one frequency-domain method and three time-domain methods. The
Bayesian optimization was applied for design of experiments, and records from three test sites were also applied and evaluated in the present study. Results show two error regions within the nominal period range of 1.6s to 30s}. For wave periods between 5s and 25s, the buoy provides accurate wave height measurements. For short periods less than 5s, the 1.28Hz sampling frequency induces sub-Nyquist artifacts that bias elevation and can drive maximum energy flux estimation errors above 100%. For long periods exceeding 25s, the buoy reported elevation is underpredicted with error depending on period but relatively independent of wave height, with maximum wave height and wave energy flux errors reaching 64% and 87%, respectively. The analysis of field data also indicates that the currently recommended method for estimating wave energy flux may underestimate the wave energy flux.
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All authors Xuepeng Fu, Frederick Driscoll, Rebecca Fao, Calum Kenny, Kevin Patrick Griffin, Mark Murphy, Scott Lambert
Chemical Physics
Resonant Coupling Between Electromagnetic Waves and Protein Conformational Dynamics Revealed by Molecular Dynamics Simulations
Jiafei Chen, Yuanyuan Feng, Jingzhi Feng et al. (+3 more)
The biological effects of electromagnetic fields on
proteins remain controversial beyond well-established thermal mechanisms, particularly with respect to frequency-dependent responses. Here, we propose that electromagnetic waves can modulate
protein conformation through resonant coupling with intrinsic
protein dynamics. Molecular dynamics simulations were employed to characterize spontaneous conformational fluctuations in the absence of external fields, and a tiered screening strategy combined with fast
Fourier transform analysis was used to identify dominant intrinsic frequencies associated with periodically fluctuating non-covalent atom or residue pairs. Oscillating external electric fields were subsequently applied at resonant and off-resonant frequencies to evaluate conformational responses across diverse
protein systems. The results demonstrate that resonant excitation induces significantly enhanced backbone conformational deviations compared to off-resonant conditions, with the effect becoming more pronounced in structurally flexible and multichain
proteins. These findings provide atomistic evidence for frequency-specific resonance between electromagnetic fields and
protein conformational dynamics, offering mechanistic insight into frequency-dependent electromagnetic effects and a computational framework for electromagnetic wave-based modulation of
protein function.
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All authors Jiafei Chen, Yuanyuan Feng, Jingzhi Feng, Xinyun Zhang, Jinzhen Zhu, Qingmeng Xu
Atomic Physics · Cross-listed from cond-mat.quant-gas
A matter-wave Fabry-Pérot cavity in the ultrastrong driving regime
Jeremy L. Tanlimco, Eber Nolasco-Martinez, Xiao Chai et al. (+4 more)
When the length of an optical cavity is modulated, theory predicts exponential concentration of energy around particular space-time trajectories. Viewed stroboscopically,
photons in such a driven cavity propagate as if in a curved spacetime, with black hole and white hole event horizons corresponding to unstable and stable fixed points of the
evolution. Such phenomena have resisted direct experimental realization due to the difficulty of relativistically accelerating massive cavity mirrors. We report results of an experiment which overcomes this limitation by exchanging the roles of light and matter. A matter wave endowed with quasi-relativistic dispersion is confined between two barriers made of light, one of which is periodically translated at speeds comparable to the matter wave group velocity. In this strongly-modulated cavity we observe the emergence of the predicted bright and dark fixed point trajectories, and demonstrate that changing the modulation waveform can vary the number of fixed points and exchange their stability character. We observe signatures of nontrivial dynamics beyond those predicted for
photons, and attribute them to residual
curvature in the dispersion relation. In addition to experimentally realizing and characterizing cavity dynamics in the ultra-strong driving regime, these results point the way to implementations of related dynamics in electro-optic materials, with potential applications in pulse generation and signal compression.
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All authors Jeremy L. Tanlimco, Eber Nolasco-Martinez, Xiao Chai, S. Nicole Halawani, Eric Zhu, Ivar Martin, David M. Weld
Optics · Cross-listed from quant-ph
A levitated nano-accelerometer sensitized by quantum quench
M. Kamba, S. Otabe, K. Funo et al. (+2 more)
We realize a nanoscale accelerometer exploiting the nonequilibrium dynamics of a nanoparticle near the
quantum ground state. We explore the dynamics after quenching the trapping potential and find that rapid quenching provides an instance at which the sensitivity is enhanced due to the minimized uncertainty in the position. With rapid quenching, the observed sensitivity is in good agreement with a numerical simulation based on the
quantum Langevin equation and approaches to the limit given by the
quantum Fisher information. Our results open up a pathway to
quantum inertial sensing sensitized by exploiting quench dynamics.
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All authors M. Kamba, S. Otabe, K. Funo, T. Sagawa, K. Aikawa
Chemical Physics
Accurate Thermophysical Properties of Water using Machine-Learned Potentials
Tobias Hilpert and Georg Kresse
Simulating water from first principles remains a significant computational challenge due to the slow dynamics of the underlying system. Although machine-learned interatomic potentials (MLPs) can accelerate these simulations, they often fail to achieve the required level of accuracy for reliable uncertainty quantification. In this study, we use MACE - an equivariant graph neural network architecture that has been trained using an extensive RPBE-D3 database - to predict density isobars,
diffusion constants, radial distribution functions, and melting points. Although equivariant MACE models are computationally more expensive than simpler architectures, such as kernel-based potentials (KbPs), their significantly lower total energy errors allow for reliable thermodynamic reweighting with minimal bias. Our results are consistent with those of previous studies using KbPs; however, equivariant models can be validated against the ground-truth density functional theory (DFT) ensemble, providing a critical advantage. These findings establish equivariant MLPs as robust and reliable tools for investigating the thermophysical properties of water with DFT-level accuracy.
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Space Physics · Cross-listed from astro-ph.SR
The First Quantitative Study of Tail Regrowth of CME-Driven Disconnection in Comet C/2023 P1 Nishimura
Shaheda Begum Shaik, Guillermo Stenborg, Phillip Hess et al. (+3 more)
Comet C/2023 P1 (Nishimura) was observed by the
Solar Orbiter Heliospheric Imager (SoloHI), onboard the
Solar Orbiter spacecraft, from 2023 September 1 to 14. During this period, the ion tail of the comet exhibited continual fluctuations and four tail disconnection events (TDEs), each coinciding with the passage of a coronal mass ejection (CME). In this work, we report on the ion tail dynamics of the best observed TDE, which occurred on September 11. The SoloHI white-light images reveal an abrupt bending, subsequent kinks, and severing of a downstream portion of the pre-existing ion tail. The onset of disconnection occurred $\sim$6.5 hours after the projected passage of the CME leading edge in the images, consistent with a CME flank encounter. After the disconnection, the ion tail reformed within $\sim$24 hours, with a regrowth rate of $\sim$86$\pm7~\mathrm{km\,s^{-1}}$, indicating the rate at which newly ionized material forms along the
magnetic field draped around the comet's coma. After the TDE, the detached tail drifted anti-sunward at an estimated speed of $\sim$295$\pm20~\mathrm{km\,s^{-1}}$, comparable to the local CME flank's speed, suggesting that the severed
plasma was most likely carried away from the comet by the CME. This study provides the first direct, quantitative characterization of comet-CME interactions and the subsequent regrowth phase of a cometary TDE. These measurements were achievable by SoloHI's unique inner-heliospheric coverage, thanks to a combination of high photometric sensitivity, short exposure times, and a wide field of view that preserves the fine-scale tail dynamics.
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All authors Shaheda Begum Shaik, Guillermo Stenborg, Phillip Hess, Angelos Vourlidas, Karl Battams, Robin Colaninno
Atomic Physics
Trapping and cooling mechanisms in blue-detuned magneto-optical traps of molecules
Qinshu Lyu and M. R. Tarbutt
In red-detuned magneto-optical traps (MOTs) of molecules, sub-Doppler heating competes with Doppler cooling, resulting in high temperature and low density. A solution is offered by the blue-detuned MOT where sub-Doppler cooling dominates and the cloud is compressed. Several blue-detuned molecular MOTs have been implemented. A recent implementation relies on a pair of orthogonally polarized components whose frequency separation is smaller than the transition linewidth. We identify the trapping force in these MOTs. At a certain
magnetic field, there is a state that is dark to the
laser propagating in one direction, but not to the counter-propagating one. This Zeeman-induced dark state (ZIDS) sets up an imbalance in the
photon scattering rate, leading to a restoring force. We also study the role of the moving
lattices generated by the closely-spaced frequency components of the light. We show that there is a velocity-dependent force that drives the molecules towards the speeds of these moving
lattices, and that over a relevant range of
magnetic fields this combines with the ZIDS force to transport molecules towards the centre of the MOT. Here, gray molasses cooling, assisted by non-adiabatic transitions driven by the time-varying polarization of the light field, cools the molecules towards zero velocity. We study these mechanisms for model systems with simple level structures, then extend them to molecules with ground state hyperfine structure.
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Fluid Dynamics
Reviving the Suspension Balance Model
Mu Wang, Tingtao Zhou and John F. Brady
The Suspension Balance Model (SBM) [J. Fluid Mech. 275, 157 (1994)] for two-phase flows uses the momentum balance of the particle phase as a closure for the particle flux, showing that particle migration is driven by the divergence of the particle-phase stress. The underlying basis of this model was challenged by Nott~et~al.\ [Phys. Fluids 23, 043304 (2011)] where the authors argued that the hydrodynamic contributions to the suspension stress should not appear in the particle-phase momentum balance, being replaced by a different particle-phase stress. The particle-phase stress proposed by Nott~et~al., while mathematically correct, involves the partitioning of the (non-pairwise-additive) hydrodynamic forces, and care is needed to understand how the force on a chosen particle is affected by a second particle. We show by a simple two-particle calculation what is the proper partitioning, and show that it is consistent thermodynamically and gives the correct
equilibrium osmotic pressure of Brownian colloids. Using Stokesian Dyanmics suspension rheology, we quantitatively demonstrate that the hydrodynamic contribution to the suspension stress is virtually identical to particle-phase stress; the only difference is that the isolated single-particle hydrodynamic stress contribution – the Einstein viscosity correction – must be removed from the suspension stress when used to predict particle flux. Our results validate a key assumption of the SBM and therefore revive its physical foundation.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Accelerated Inorganic Electrides Discovery by Generative Models and Hierarchical Screening
Shuo Tao and Qiang Zhu
Electrides are exotic compounds in which excess electrons occupy interstitial regions of the
crystal lattice and serve as anions, exhibiting exceptional properties such as low work function, high electron mobility, and strong catalytic activity. Although they show promise for diverse applications, identifying new electrides remains challenging due to the difficulty of achieving energetically favorable electron localization in
crystal cavities. Here, we present an accelerated materials discovery framework that combines physical principles,
diffusion-based materials generation with hierarchical thermodynamic and electronic structure screening. Using this workflow, we systematically explored 1,510 binary and 6,654 ternary chemical compositions containing excess valence electrons from electropositive alkaline, alkaline-earth, and early transition metals, and then filtered them with a high throughput validation on both thermodynamical stability and electronic structure analysis. As a result, we have identified 264 new electron rich compounds within 0.05 eV/atom above the convex hull at the density functional theory (DFT) level, including 13 thermodynamically stable electrides. Our approach demonstrates a generalizable strategy for targeted materials discovery in a vast chemical space.
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Chemical Physics
Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs
Thomas Warford, Fabian L. Thiemann and Gábor Csányi
The training of foundational machine learning interatomic potentials (fMLIPs) relies on diverse databases with energies and forces calculated using ab initio methods. We show that fMLIPs trained on large
datasets such as MPtrj, Alexandria, and OMat24 encode inconsistencies from the Materials Project's selective use of the Hubbard U correction, which is applied to certain transition metals only if O or F atoms are present in the simulation
cell. This inconsistent use of +U creates two incompatible potential-energy surfaces (PES): a lower-energy GGA surface and a higher-energy GGA+U one. When trained on both, MLIPs interpolate between them, leading to systematic underbinding, or even spurious repulsion, between U-corrected metals and oxygen- or fluorine-containing species. Models such as MACE-OMAT and -MPA exhibit repulsion between U-corrected metals and their
oxides, limiting their value for studying catalysis and oxidation. We link the severity of this pathology to the oxygen number density in U-corrected training configurations. This explains why OMAT-trained models are most affected and suggests the issue might worsen as expanding future
datasets increasingly include configurations with low oxygen content, such as those generated through combinatorial exploration of multi-element or defect-containing systems. Our simple per-U-corrected-atom shift aligns PBE+U and PBE energies for identical structures, yielding a smoother PES compared to existing correction schemes, which target phase diagram accuracy. As a result, models trained on
datasets with our shift applied exhibit smaller mean absolute errors for the adsorption energies of oxygen on U-corrected elemental slabs. Since
datasets omitting +U entirely (e.g. MatPES, MP-ALOE) avoid these pathologies, we recommend excluding +U in future fMLIP
datasets. For existing
datasets, our post-hoc correction provides a low-cost improvement.
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Fluid Dynamics
Assessing engineering wake models against operational data: insights from the Lillgrund wind farm wake steering campaign
Diego Siguenza-Alvarado, Matthew Harrison, Mohammadreza Mohammadi et al. (+4 more)
Validating engineering wake models under real-world operational conditions is essential for improving wind farm performance predictions. This study uses a unique
dataset from the Lillgrund offshore wind farm, collected during the Horizon 2020 TotalControl campaign, integrating synchronous Supervisory Control and Data Acquisition (SCADA) and Light Detection and Ranging (
LiDAR) measurements under both baseline operation and active wake steering conditions. Four analytical wake-model combinations, implemented in the LongSim software developed by DNV, are evaluated using different formulations for velocity deficit, added
turbulence, wake superposition and wake deflection. The analysis focuses on time-averaged wake velocity deficit profiles and turbine- and farm-wide power output, normalised by reference velocity and power. Model accuracy is assessed using mean absolute error (MAE) metrics. The models generally reproduce wake deficit trends associated with varying wake overlap under baseline conditions, as well as wake deflection caused by intentional yaw misalignment during wake steering operation. Normalised velocity deficit MAE values range from 7% to 15%, with discrepancies mainly linked to inflow heterogeneity, near-wake complexity and model-specific parameterisations. Power prediction errors increase with farm depth. Model combinations incorporating cumulative wake superposition and refined
turbulence formulations show improved agreement with field measurements; however, all models struggle to capture localised flow features. Normalised turbine-level power output MAE ranges from 3% to 23%, while farm-wide power output errors range between -13% and +30%. Accurate farm-level predictions may conceal compensating errors at individual turbines. Future work should focus on improved inflow characterisation and blockage effects to enhance predictive reliability.
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All authors Diego Siguenza-Alvarado, Matthew Harrison, Mohammadreza Mohammadi, Pragya Vishwakarma, Ervin Bossanyi, Lars Landberg, Majid Bastankhah
Optics
Photoionization in KTN deflectors by light in the near-infrared imaging window
Samuel Stanek, Harishankar Jayakumar, Christopher Warkentin et al. (+2 more)
Electro-optical deflectors (EODs) offer unparalleled scanning speed for
laser-scanning microscopy and other applications, but suffer from limited deflection range. EODs based on potassium tantalate niobate (KTN)
crystals feature some of the highest number of resolvable spots. These deflectors rely on internal electric fields generated by trapped electrons to enable beam scanning. However, visible light induces rapid photoionization of trapped charges, thus KTN-based deflectors are typically continuously recharged with a bias voltage that effectively limits the range of the deflector. Recent work has proposed the use of KTN-based EODs for biological imaging with infrared excitation light, but quantitative data on near-infrared photoionization is lacking. Here, we present quantitative measurements of photoionization in KTN deflectors across the NIR-I and NIR-IIa biological imaging windows (700 - 1300 nm), a range that is particularly important for deep tissue imaging and nonlinear microscopy. Using a two-beam polarization interferometer, we measured trapped charge density as a function of
photon fluence. We observed that the photoionization rate decreases dramatically with increasing wavelength. The charge density decay curves exhibit multi-exponential behavior that cannot be explained by a single-trap model without recapture, indicating the presence of multiple trap species or substantial recapture. These measurements provide critical quantitative guidance for selecting operating wavelengths and charge-scan duty cycles for KTN-based EODs in near-infrared imaging applications.
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All authors Samuel Stanek, Harishankar Jayakumar, Christopher Warkentin, James Leger, Aaron Kerlin
Plasma Physics · Cross-listed from cs.LG
Conditional Denoising Model as a Physical Surrogate Model
José Afonso, Pedro Viegas, Rodrigo Ventura et al. (+1 more)
Surrogate modeling for complex physical systems typically faces a trade-off between data-fitting accuracy and physical consistency. Physics-consistent approaches typically treat physical laws as soft constraints within the loss function, a strategy that frequently fails to guarantee strict adherence to the governing equations, or rely on post-processing corrections that do not intrinsically learn the underlying solution geometry. To address these limitations, we introduce the {Conditional Denoising Model (CDM)}, a generative model designed to learn the geometry of the physical
manifold itself. By training the network to restore clean states from noisy ones, the model learns a vector field that points continuously towards the valid solution subspace. We introduce a time-independent formulation that transforms inference into a deterministic fixed-point iteration, effectively projecting noisy approximations onto the
equilibrium manifold. Validated on a low-temperature
plasma physics and chemistry
benchmark, the CDM achieves higher parameter and data efficiency than physics-consistent baselines. Crucially, we demonstrate that the denoising objective acts as a powerful implicit regularizer: despite never seeing the governing equations during training, the model adheres to physical constraints more strictly than baselines trained with explicit physics losses.
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All authors José Afonso, Pedro Viegas, Rodrigo Ventura, Vasco Guerra
Plasma Physics
A joint diffusion approach to multi-modal inference in inertial confinement fusion
Michael S. Jones, Justin Kunimune, Daniel Casey et al. (+3 more)
A combination of physics-based simulation and experiments has been critical to achieving ignition in inertial confinement fusion (ICF). Simulation and experiment both produce a mixture of scalar and images outputs, however only a subset of simulated data are available experimentally. We introduce a generative framework, called JointDiff, which enables predictions of conditional simulation input and output distributions from partial, multi-modal observations. The model leverages joint
diffusion to unify forward surrogate modeling, inverse inference, and output imputation into one architecture. We train our model on a large ensemble of three-dimensional Multi-Rocket Piston simulations and demonstrate high accuracy, statistical robustness, and transferability to experiments performed at the National Ignition Facility (NIF). This work establishes JointDiff as a flexible generative surrogate for multi-modal scientific tasks, with implications for understanding diagnostic constraints, aligning simulation to experiment, and accelerating ICF design.
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All authors Michael S. Jones, Justin Kunimune, Daniel Casey, Bogdan Kustowski, Eugene Kur, Kelli Humbird
Chemical Physics
StochasticGW-GPU: rapid quasi-particle energies for molecules beyond 10000 atoms
Phillip S. Thomas, Minh Nguyen, Dimitri Bazile et al. (+6 more)
$\mathtt{StochasticGW}$ is a code for computing accurate Quasi-Particle (QP) energies of molecules and material systems in the GW approximation. $\mathtt{StochasticGW}$ utilizes the
stochastic Resolution of the Identity (sROI) technique to enable a massively-parallel implementation with computational costs that scale semi-linearly with system size, allowing the method to access systems with tens of thousands of electrons. We introduce a new implementation, $\mathtt{StochasticGW-GPU}$, for which the main bottleneck steps have been ported to GPUs and which gives substantial performance improvements over previous versions of the code. We showcase the new code by computing band gaps of hydrogenated silicon clusters ($\textrm{S}\textrm{i}_{\textrm{x}}\textrm{H}_{\textrm{y}}$) containing up to 10001 atoms and 35144 electrons, and we obtain individual QP energies with a statistical precision of better than $\pm0.03$ eV with times-to-solution on the order of minutes.
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All authors Phillip S. Thomas, Minh Nguyen, Dimitri Bazile, Tucker Allen, Barry Y. Li, Wenfei Li, Mauro Del Ben, Jack Deslippe, Daniel Neuhauser
Atmospheric and Oceanic Physics
A Tolerance-Based Framework for Spatio-Temporal Forecast Validation Using the gamma-Index
Cyril Voyant
Classical field forecast evaluation relies mainly on local scores such as RMSE or MAE. These metrics severely over-penalize small spatial or temporal displacements of coherent structures, a limitation known as the double-penalty issue and common to many
forecasting domains. The present paper introduces a tolerance-based framework built on the three-dimensional gamma index, initially designed for medical dose verification, as a unified acceptance criterion for gridded forecasts. The method embeds explicit margins in space (DTA), time (TTA), and intensity (IDT), and evaluates whether predictions agree with observations within predefined physical bounds rather than through pixel-wise differences only. A synthetic illustration is first used to show why conventional metrics can misrepresent usable forecasts. The approach is then applied to satellite-derived SSI fields to demonstrate operational behaviour on a real
dataset. Results confirm that the gamma criterion preserves structural consistency under minor positional noise while isolating physically significant discrepancies. The formulation is generic and can be implemented for any gridded variable provided meaningful tolerances are defined, offering a pragmatic complement to existing spatial verification tools in general
forecasting workflows.
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Classical Physics · Cross-listed from astro-ph.GA
The Initial Mass Function as the Equilibrium State of a Variational Process: why the IMF cannot be sampled stochastically
Eda Gjergo, Zhiyu Zhang and Pavel Kroupa
The
stellar initial mass function (sIMF) is often treated as a
stochastic probability distribution, yet such an interpretation implies Poisson noise that is inconsistent with growing observational evidence. In particular, the observed relation between the mass of the most massive
star formed in an embedded cluster and the cluster's total
stellar mass supports a deterministic sampling process, known as optimal sampling. However, the physical origin of optimal sampling has not been formally established in the literature. In this work, we show that the
stellar mass distribution implied by optimal sampling emerges from applying the Maximum
Entropy principle to the fragmentation of
star-forming clumps, whose structure is set by density-dependent cooling in the optically thin regime. Here, the maximum
entropy leads to unbiased distributions. By applying calculus of variations to minimize the
entropy functional obtained assuming fragmentation, we recover the power-law form of the sIMF, and we show that any distribution deviating from the sIMF violates the Maximum
Entropy principle. This work provides a first-principles foundation for the deterministic nature of
star formation. Thus, the sIMF is the distribution resulting from a maximally unbiased system.
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Accelerator Physics
From Beam to Bedside: Reinforcing Domestic Supply of $^{99}$Mo/$^{99m}$Tc using Novel High-Current D+ Cyclotrons for Compact Neutron Generation and $^{99}$Mo Production
Jarrett Moon, Daniel Winklehner, Jose Alonso et al. (+3 more)
Technetium-99m ($^{99m}$Tc) is essential to more than 16 million diagnostic procedures performed annually in the United States. It is typically acquired on-site from generators containing $^{99}$Mo, in turn produced at
nuclear reactor facilities. This supply chain involves multiple points of vulnerability, which can lead to shortages and delays with potentially negative patient outcomes. We report on the development of a new family of cyclotrons originally designed for the IsoDAR
neutrino experiment, capable of operating at much higher current than typical cyclotrons. When operated with deuterons at 1.5 MeV/amu and an anticipated continuous beam current of 5 mA, simulations project that such a system would yield $\sim$10$^{13}$
neutrons per second using a thin beryllium target. This
neutron yield is sufficient, in principle, to support $^{99}$Mo production without the use of highly enriched uranium or reliance on foreign reactors. Simulations and conceptual design studies suggest that the system's beam dynamics could make it a viable pathway toward decentralized, hospital-based isotope generation. The relatively low energy of the deuterons minimizes activation and safety concerns. This work presents the physics motivation, technical design considerations, and projected
neutron yields, outlining a pathway from a
neutrino-physics prototype to a biomedical isotope production platform.
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All authors Jarrett Moon, Daniel Winklehner, Jose Alonso, Claire Huchthausen, David McClain, Janet Conrad
Instrumentation and Detectors
Performance of the Particle-Identification Silicon-Telescope Array Coupled with the VAMOS++ Magnetic Spectrometer
L. Bégué-Guillou, A. Lemasson, P. Morfouace et al. (+32 more)
The Particle-Identification Silicon-
Telescope Array (PISTA) is a new detection system designed for high-resolution studies of the fission process induced by multi-nucleon transfer in inverse kinematics. It is specifically optimized for experiments with the VAMOS++
magnetic spectrometer at GANIL (Grand Accélérateur National d'Ions Lourds). The array comprises eight trapezoidal $\Delta$E-E silicon
telescopes arranged in a corolla configuration. Each
telescope integrates two single-sided stripped silicon detectors, enabling target-like
recoil identification, energy loss measurements, and trajectory reconstruction. Positioned in close proximity to the target, PISTA's compact geometry achieves high-efficiency tracking of target-like
recoils produced in multi-nucleon transfer reactions at Coulomb barrier energies. The spatial
segmentation of the array allows precise determination of the mass and charge of the target-like
nucleus, and excitation energy of fissioning systems. This work presents the particle identification and excitation energy reconstruction performances for the interactions of $^{238}$U beam with $^{12}$C target. An excitation energy resolution of 800 keV (FWHM) was determined together with mass resolution of 1.1% (FWHM). The combination of PISTA and VAMOS++
magnetic spectrometer enables unprecedented investigations of the fission process as a function of the excitation energy of the fissioning
nucleus, particularly for exotic systems produced in transfer-induced reactions.
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All authors L. Bégué-Guillou, A. Lemasson, P. Morfouace, D. Ramos, J. Taieb, J.D. Frankland, M. Rejmund, G. Fremont, P. Gangnant, A. Cobo-Zarzuelo, N. Kumar, T. Efremov, A. Chatillon, E. Clément, G. De France, A. Francheteau, I. Jangid, C. Lenain, D. Mauss, T. Tanaka, L. Audoin, M. Caamano, B. Errandonea, M. Godio, D. Gruyer, B. Jacquot, M. Lalande, R. C. Malone, A. Munoz, A. P. D. Ramirez, J. L. Rodríguez-Sánchez, C. Schmitt, O. Syrett, C. Surrault, A. P. Tonchev
Plasma Physics
Compressible Turbulence as a Source of Particle Beams and Ion Bernstein Waves in Collisionless Plasmas
Chuanpeng Hou, Huirong Yan and Siqi Zhao
Unraveling the origin of
proton beams and ion Bernstein waves is important to understanding kinetic dissipation in the
solar wind. Here we focus on their generation mechanisms, rather than their well-studied roles in instabilities and particle heating. We investigate their formation in collisionless
plasmas using high-resolution particle-in-
cell simulations of compressible
turbulence. At magnetohydrodynamic (MHD) scales, compressive fluctuations are damped via transit-time damping (TTD), naturally producing suprathermal electrons and
proton beams. At sub-ion scales, quasi-perpendicular fast modes excite multiple branches of ion Bernstein waves, whose properties agree with predictions from the
plasma dispersion relation solver. Under
solar wind conditions, TTD remains efficient and provides a natural explanation for the super-Alfvénic
proton beams measured in situ. Our results demonstrate that compressive fluctuations play a central role in driving cross-scale energy transfer and kinetic dissipation in collisionless
plasma turbulence.
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