Fluid Dynamics · Cross-listed from cs.AI
PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction
Andreas Radler, Vincent Seyfried, Johannes Brandstetter et al. (+1 more)
Neural surrogates have shown great potential in simulating dynamical systems, while offering real-time capabilities. We envision Neural Twins as a progression of neural surrogates, aiming to create digital replicas of real systems. A neural twin consumes measurements at test time to update its state, thereby enabling context-specific decision-making. We argue, that a critical property of neural twins is their ability to remain on-trajectory, i.e., to stay close to the true system state over time. We introduce Parallel-in-time Neural Twins (PAINT), an architecture-agnostic family of methods for modeling dynamical systems from measurements. PAINT trains a generative neural network to model the distribution of states in parallel over time. At test time, states are predicted from measurements in a sliding window fashion. Our theoretical analysis shows that PAINT is on-trajectory, whereas autoregressive models generally are not. Empirically, we evaluate our method on a challenging two-dimensional turbulent fluid dynamics problem. The results demonstrate that PAINT stays on-trajectory and predicts system states from sparse measurements with high fidelity. These findings underscore PAINT's potential for developing neural twins that stay on-trajectory, enabling more accurate state estimation and decision-making.
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All authors Andreas Radler, Vincent Seyfried, Johannes Brandstetter, Thomas Lichtenegger
Atmospheric and Oceanic Physics
Asymptotically well-balanced geostrophic reconstruction finite volumes numerical schemes for the 2D rotating NLSWE in spherical coordinates
Alejandro González del Pino, Manuel Jesús Castro Díaz and Jorge Macías Sánchez
The dynamics of large-scale geophysical fluids is primarily governed by the balance between the Coriolis force and the pressure gradient. This phenomenon, known as geostrophic
equilibrium, is the basis for the geostrophic model, which has proven to be extremely useful for understanding and
forecasting large-scale atmospheric and oceanic dynamics. In the present work, we develop second- and third-order finite-volume numerical schemes applied to the 2D rotating shallow-water equations in spherical coordinates. These schemes are designed to preserve the geostrophic
equilibrium in the limit as the Rossby number tends to zero. The final goal is to design reliable and efficient
forecasting models for simulating meteotsunamis, long-wave events generated in the ocean by atmospheric pressure disturbances. These disturbances produce long waves of small amplitude that gradually amplify as they approach the coast. The numerical results for various analytical and real-world test cases underscore the importance of maintaining geostrophic
equilibrium over time.
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Instrumentation and Detectors
Frequency domain laser ultrasound microscopy for nanometric layer thickness imaging with GHz elastic plate resonances
Martin Ryzy, Guqi Yan, István Veres et al. (+6 more)
Nanometric layer thickness imaging is crucial for fundamental research and characterization of micro fabricated devices. Here, we assess the potential of a non-contact opto-acoustic frequency domain
laser ultrasound (FreDomLUS) microscopy technique for imaging nanometric thickness variations via GHz zero-group velocity (ZGV) elastic plate resonances. The method exploits the ZGV's lateral energy confinement that leads to sharp resonance peaks which can be effectively probed with the FreDomLUS technique at GHz acoustic frequencies. For demonstration purposes we introduced sub-10 nm height variation patterns in the topmost layer of solidly mounted bulk-acoustic wave resonators with a design frequency of around 1.7 GHz. They are raster-scanned to retrieve ZGV-frequencies from local acoustic spectra as a contrast quantity for imaging. We show how to retrieve quantitative height information by numerically calibrating the factor which inversely relates ZGV frequency change with the layer thickness change. Height variations in stacks with nominal thickness changes of 8 nm, 4 nm, and 1 nm can be resolved and indicate sub-nanometer depth resolution capabilities. The lateral resolution is studied by measuring the method's step edge function and it is found to be in the micrometer range. Atomic force microscopy imaging is used to validate the results.
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All authors Martin Ryzy, Guqi Yan, István Veres, Thomas Berer, Ivan Alić, Clemens Grünsteidl, Georg Watzl, Georg Gramse, Susanne Kreuzer
Instrumentation and Detectors · Cross-listed from nucl-ex
CE$\nu$NS Search with Cryogenic Sapphire Detectors at MINER: Results from the TRIGA reactor data and Future Sensitivity at HFIR
D. Mondal, W. Baker, M. Chaudhuri et al. (+20 more)
We report on a search for coherent elastic
neutrino–
nucleus scattering (CE$\nu$NS) using cryogenic sapphire (Al$_2$O$_3$) detectors deployed at the Mitchell Institute
Neutrino Experiment at Reactor (MINER), located near the 1~MW$_\text{th}$ TRIGA research reactor at Texas A&M University. The experiment operated with a primary detector mass of 72~g and achieved a baseline energy resolution of $\sim 40$~eV. Using exposures of 158~g-days (reactor-on) and 381~g-days (reactor-off), we performed a statistical background subtraction in the energy region of 0.25–3~keV. A GEANT4 simulation has been performed to understand the reactor-correlated background present in the data and it agrees with our observations. The resulting best-fit ratio of the observed CE$\nu$NS rate to the Standard Model prediction after rejecting the reactor induced background from the data with the help of simulation, is $\rho = 0.26\pm 1534.74~\mathrm{(stat)} \pm 0.05~\mathrm{(sys)}$ with a significance of $0.007 \pm 0.022~\mathrm{(stat)} \pm 0.001~\mathrm{(sys)}$. This low significance indicates a high background rate at low energies. To have enhanced sensitivity, the MINER collaboration plans to relocate the experiment to the 85~MW$_\text{th}$ High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). With improved shielding, increased detector mass, and higher antineutrino flux, the upgraded setup is projected to achieve a 3$\sigma$ CE$\nu$NS detection within 30~kg$\cdot$days of exposure.
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All authors D. Mondal, W. Baker, M. Chaudhuri, J. B. Dent, R. Dey, B. Dutta, V. Iyer, A. Jastram, V. K. S. Kashyap, A. Kubik, K. Lang, R. Mahapatra, S. Maludze, N. Mirabolfathi, M. Mirzakhani, B. Mohanty, H. Neog, J. L. Newstead, M. Platt, S. Sahoo, J. Sander, L. E. Strigari, J. Walker
Instrumentation and Detectors
Frequency domain laser ultrasound for inertial confinement fusion target wall thickness measurements
Martin Ryzy, Guqi Yan, Clemens Grünsteidl et al. (+4 more)
In inertial confinement fusion experiments hollow, spherical mm-sized capsules are used as a container for
nuclear fuel. To achieve maximum implosion efficiency, a perfect capsule geometry is required. This paper presents a wall thickness measurement method based on zero-group velocity guided elastic wave resonances. They are measured with a non-destructive, contactless frequency domain
laser ultrasound microscopy system. Wall thickness measurements along the equator of a high-density carbon capsule with a diameter of around 2 mm and a wall thickness of around 80 $\unicode{x00B5}$m excellently agree with infrared interferometry reference measurements. In addition, the multi-resonant nature of a spherical shell is studied by complementing experimental observations with plate dispersion calculations and finite element wave propagation simulations. The presented method is scalable and can be applied to a broad range of target materials, including metals, or metal-doped targets.
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All authors Martin Ryzy, Guqi Yan, Clemens Grünsteidl, Georg Watzl, Kevin Sequoia, Pavel Lapa, Haibo Huang
Physics and Society
From classical to active particles: mathematical tools for social dynamics and behavioural economics
Marina Dolfin and Leone Leonida
This essay provides a critical overview of the mathematical kinetic theory of active particles, which is used to model and study collective systems consisting of interacting living entities, such as those involved in behavior and
evolution. The main objective is to study the interactions of large systems of living entities mathematically. More specifically, the study relates to the complex features of living systems and the mathematical tools inspired by statistical physics. The focus is on the mathematical description of these interactions and their role in deriving differential systems that describe the aforementioned dynamics. The paper demonstrates that studying these interactions naturally yields new mathematical insights into systems in the natural sciences and behavioral economics.
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Biological Physics
Low-Energy DNA Bubble Dynamics via the Quantum Coulomb Potential
Juan D. García-Muñoz, A. Contreras-Astorga and L. M. Nieto
We developed a low-energy model that can be used at any time to describe the dynamics of DNA bubbles at temperatures below the melting point. The Schrödinger equation associated with this problem is solved in imaginary time with a
quantum Coulomb potential, and we obtain an approximate expression for its more general physical solution as a linear combination of the states whose energies are close to the lower bound energy. We can then determine the probability density, the first-passage time density, and the correlation functions in terms of Bessel functions. Our findings are consistent with results obtained directly from the Fokker-Planck equation. Comparisons with the Gamma and
Diffusion models are discussed.
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Optics
Coherent Control of Wave Scattering via Minimal-Parameter Tuning of Complex Spectra
Ali H. Alhulaymi, Nazar Pyvovar, Philipp del Hougne et al. (+2 more)
We introduce and validate a theoretical framework for coherent control of multichannel linear scattering to route waves through complex geometries with multiple scattering. We show that steady-state perfect routing solutions are achievable at any frequency via tuning
geometric parameters so that multiple complex eigenfrequencies coincide on the real axis. The relevant complex spectra describe critically constrained scattering processes (CCONs), where a specific number of generically accessible outgoing channels are inaccessible due to destructive interference. Focusing on electromagnetic waves, we demonstrate in simulations routing and demultiplexing with high discrimination of signals in a multiport chaotic cavity with a small number of tunable scatterers and free-space signal routing in a grating coupler with a similar number of tunable elements in its unit
cell. The minimal number of tuning parameters required is predicted by codimension arguments and validated in simulations. A special class of perfect reflection processes are found to be enhanced in the presence of time-reversal
symmetry. A similar approach can be used to implement other interesting functionalities, such as isolation, power division, mode conversion and filtering. The method can be applied to other classical waves and also to
quantum matter waves.
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All authors Ali H. Alhulaymi, Nazar Pyvovar, Philipp del Hougne, Owen D. Miller, A. Douglas Stone
Fluid Dynamics
A Liquid-Fueled Reactor Network Model for Enhanced NOx Prediction in Gas Turbine Combustors
Philip John, Haresh Chandrasekhar, Sourav Saha et al. (+1 more)
This study introduces a
liquid-fueled reactor network (LFRN) framework for reduced-order modeling of gas turbine combustors. The proposed LFRN extends conventional gaseous-fueled reactor network methods by incorporating specialized reactors that account for spray breakup, droplet heating, and evaporation, thereby enabling the treatment of multiphase effects essential to
liquid-fueled systems. Validation is performed against detailed computational fluid dynamics (CFD) simulations of a
liquid-fueled can combustor, with parametric studies conducted across variations in inlet air temperature and fuel flow rate. Results show that the LFRN substantially reduces NOx prediction errors relative to gaseous reactor networks while maintaining accurate outlet temperature predictions. A sensitivity analysis on the number of clusters demonstrates progressive convergence toward the CFD predictions with increasing network complexity. In terms of computational efficiency, the LFRN achieves runtimes of $O(10s)$ on a single CPU core, representing speed-ups generally exceeding 1,000$\times$ compared to CFD. Overall, the findings demonstrate the potential of the LFRN as a computationally efficient reduced-order modeling tool that complements CFD to enable rapid emissions assessment and design-space exploration for
liquid-fueled gas turbine combustors.
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All authors Philip John, Haresh Chandrasekhar, Sourav Saha, Opeoluwa Owoyele
Computational Physics · Cross-listed from cs.LG
Machine Learning-Based Ultrasonic Weld Characterization Using Hierarchical Wave Modeling and Diffusion-Driven Distribution Alignment
Joshua R. Tempelman, Adam J. Wachtor and Eric B. Flynn
Automated ultrasonic weld inspection remains a significant challenge in the nondestructive evaluation (NDE) community to factors such as limited training data (due to the complexity of curating experimental specimens or high-fidelity simulations) and environmental
volatility of many industrial settings (resulting in the corruption of on-the-fly measurements). Thus, an end-to-end machine learning (ML) workflow for acoustic weld inspection in realistic (i.e., industrial) settings has remained an elusive goal. This work addresses the challenges of data curation and signal corruption by proposing workflow consisting of a reduced-order modeling scheme,
diffusion based distribution
alignment, and U-Net-based
segmentation and inversion. A reduced-order Helmholtz model based on Lamb wave theory is used to generate a comprehensive
dataset over varying weld heterogeneity and crack defects. The relatively inexpensive low-order solutions provide a robust training dateset for inversion models which are refined through a transfer learning stage using a limited set of full 3D elastodynamic simulations. To handle out-of-distribution (OOD) real-world measurements with varying and unpredictable noise distributions, i.e.,
Laser Doppler Vibrometry scans, guided
diffusion produces in-distribution representations of OOD experimental LDV scans which are subsequently processed by the inversion models. This integrated framework provides an end-to-end solution for automated weld inspection on real data.
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Optics
Phase Matching of Orbital Angular Momentum in Rare Earth Ion Doped Solid State Systems
Owen R. Wolfe, Joshua Dugre, Grant Kirkland et al. (+1 more)
In this work, we demonstrate the generation of stimulated
photon echos carrying unique topological charge that results from the temporal phase matching conditions of three independent beams spatially and spectrally overlapped in a cryogenically cooled rare earth ion doped solid state system. A sample of $Tm^{3+}:YAG$ was used to generalize the momentum phase matching condition to include the orbital angular momentum of the input fields. The input fields and corresponding
photon echo were characterized via astigmatic transform, with results mapping directly to the expected behavior of traditional stimulated
photon echos. These results demonstrate that rare earth ion doped systems are capable of spatial multiplexing, spatial filtering, and the generation of structured light, opening pathways towards real time optical multi-modal signal processing.
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All authors Owen R. Wolfe, Joshua Dugre, Grant Kirkland, R. Krishna Mohan
Computational Physics · Cross-listed from math.NA
A finite element method using a bounded auxiliary variable for solving the Richards equation
Abderrahmane Benfanich, Yves Bourgault and Abdelaziz Beljadid
The Richards equation, a nonlinear
elliptic parabolic equation, is widely used to model infiltration in porous media. We develop a finite element method for solving the Richards equation by introducing a new bounded auxiliary variable to eliminate unbounded terms in the weak formulation of the method. This formulation is discretized using a semi-implicit scheme and the resulting nonlinear system is solved using Newton's method. Our approach eliminates the need of regularization techniques and offers advantages in handling both dry and fully saturated zones. In the proposed techniques, a non-overlapping Schwarz domain decomposition method is used for modeling infiltration in layered soils. We apply the proposed method to solve the Richards equation using the Havercamp and van Genuchten models for the capillary pressure. Numerical experiments are performed to validate the proposed approach, including tests such as modeling flows in fibrous sheets where the initial medium is totally dry, two cases with fully saturated and dry regions, and an infiltration problem in layered soils. The numerical results demonstrate the stability and accuracy of the proposed numerical method. The numerical solutions remain positive in the presence of totally dry zones. The numerical investigations clearly demonstrated the capability of the proposed method to effectively predict the dynamics of flows in unsaturated soils.
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Medical Physics
What is Implementation Science; and Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application Gap in Medical Imaging
Ahmad Fayaz-Bakhsh, Janice Tania, Syaheerah Lebai Lutfi et al. (+2 more)
The transformative potential of artificial intelligence (AI) in medical Imaging (MI) is well recognized. Yet despite promising reports in research settings, many AI tools fail to achieve clinical adoption in practice. In fact, more generally, there is a documented 17-year average delay between evidence generation and implementation of a technology. Implementation science (IS) may provide a practical, evidence-based framework to bridge the gap between AI development and real-world clinical imaging use, to shorten this lag through systematic frameworks, strategies, and hybrid research designs. We outline challenges specific to AI adoption in MI workflows, including infrastructural, educational, and cultural barriers. We highlight the complementary roles of effectiveness research and implementation research, emphasizing hybrid study designs and the role of integrated KT (iKT), stakeholder engagement, and equity-focused co-creation in designing sustainable and generalizable solutions. We discuss integration of Human-Computer Interaction (HCI) frameworks in MI towards usable AI. Adopting IS is not only a methodological advancement; it is a strategic imperative for accelerating translation of innovation into improved patient outcomes.
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All authors Ahmad Fayaz-Bakhsh, Janice Tania, Syaheerah Lebai Lutfi, Abhinav K. Jha, Arman Rahmim
Physics and Society · Cross-listed from stat.AP
Trajectory-based real-time pedestrian crash prediction at intersections: A novel non-linear link function for block maxima led Bayesian GEV framework addressing heterogeneous traffic condition
Parvez Anowar, Nazmul Haque, Md Asif Raihan et al. (+1 more)
This study develops a real-time framework for estimating pedestrian crash risk at signalized intersections under heterogeneous, non-lane-based traffic. Existing approaches often assume linear relationships between
covariates and parameters, oversimplifying the complex, non-monotonic interactions among different road users. To overcome this, the framework introduces a non-linear link function within a
Bayesian generalized extreme value (GEV) structure to capture traffic variability more accurately. The framework applies extreme value theory through the block maxima approach using post-encroachment time as a surrogate safety measure. A hierarchical
Bayesian model incorporating both linear and non-linear link functions into GEV parameters is estimated using Markov Chain Monte Carlo simulation. It also introduces a behavior-normalized Modified Crash Risk (MRC) formula to account for pedestrians' habitual risk-taking behavior. Seven
Bayesian hierarchical models were developed and compared using deviance information criterion. Models employing non-linear link functions for the location and scale parameters significantly outperformed their linear counterparts. The results revealed that pedestrian speed has a negative relationship with crash risk, while flow and speed of motorized vehicles, pedestrian flow, and non-motorized vehicles conflicting speed contribute positively. The MRC formulation reduced overestimation and provided crash predictions with 93% confidence. The integration of non-linear link functions enhances model flexibility, capturing the non-linear nature of traffic extremes. The proposed MRC metric aligns crash risk estimates with real-world pedestrian behavior in mixed-traffic environments. This framework offers a practical analytical tool for traffic engineers and planners to design adaptive signal control and pedestrian safety interventions before crashes occur.
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All authors Parvez Anowar, Nazmul Haque, Md Asif Raihan, Md Hadiuzzaman
Accelerator Physics
Master Oscillator and Phase Reference Line Design for the PIP-II Linac
A. Syed, B. Vaughn, P. Varghese et al. (+8 more)
The phase averaging reference line system provides the RF phase reference, LO and clock signals to the LLRF and other accelerator sub-systems. The PIP-II linac has RF systems at three frequencies - 162.5 MHz, 325 MHz and 650 MHz. A temperature-stabilized, low-phase-noise oscillator is used as the master oscillator. Phase reference signals at 162.5 MHz, 325 MHz, and 650 MHz, along with LO signals at 182.5 MHz, 345 MHz, 670 MHz and LLRF clocks at 1320 MHz and 1300 MHz, are generated in temperature-controlled RF modules at each frequency section. A phase reference from each module travels to the next section, where it is doubled to produce required frequencies. The reference also travels alongside the accelerating cavities in the tunnel, allowing cavity probe and reference cables to temperature track and reduce measurement errors from temperature changes or phase drift.
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All authors A. Syed, B. Vaughn, P. Varghese, E. Cullerton, S. Stevenson, D. Pieper, D. Peterson, J. Holzbauer, R. Madrak, A. Mosher, D. Klepec
Optics · Cross-listed from quant-ph
Quantum Key Distribution in the Iberian Peninsula
Vicky Domínguez Tubío, Mario Badás Aldecocea, David L. Bakker et al. (+3 more)
A promising use of
quantum networking is
quantum key distribution (QKD), which can provide information-theoretic security unattainable by classical means. While optical fiber-based QKD networks suffer from exponential loss, satellite-assisted
quantum communication offers a scalable solution for long-distance secure key exchange. In this work, we propose and evaluate a satellite-based QKD setup covering the Iberian Peninsula, linking Madrid with Barcelona, Bilbao, and Lisbon. Our proposed setup uses a Low-Earth-Orbit (LEO) state-of-the-art satellite equipped with a spontaneous parametric down-conversion (SPDC) source to distribute entangled
photon pairs to ground stations. Considering vibrations in the satellite, we optimize the beam waist to enhance the transmission probability and improve the secret key rate (SKR). Our results show that key rates sufficient for real-world applications, such as secure communication between hospitals, using hybrid classical-
quantum protocols are feasible with existing protocols. Our results highlight the viability of near-term satellite-based QKD networks for national-scale secure communications.
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All authors Vicky Domínguez Tubío, Mario Badás Aldecocea, David L. Bakker, Gustavo C. Amaral, Diego López, Johannes Borregaard
Physics Education
Experimental Skills for Non-PhD Roles in the Quantum Industry
Shams El-Adawy, A.R. Piña, Benjamin M. Zwickl et al. (+1 more)
As the
quantum information science and engineering (QISE) workforce grows, there is an anticipated need for professionals with bachelor's and master's degrees who can fill a wide range of roles in the
quantum industry. This report identifies the experimental skills needed for individuals with bachelor's or master's degrees to succeed in
quantum industry roles. Through semi-structured interviews with
quantum industry employers, we gathered data on 22 distinct positions spanning hardware, software, and business functions. While employers describe varying expectations of
quantum expertise, the unifying requirement across these roles is proficiency in experimental skills, which fall into four key categories: instrumentation, computation and data analysis, experimental and project design, as well as communication and collaboration. Positions open to bachelor's and master's graduates use all four skill areas, but the balance of experimental skill set needed differs. Bachelor's roles lean toward instrumentation, computation and data analysis, as well as experimental and project design skills. Individuals in these roles build, operate, and troubleshoot hardware, and they gather and interpret data to design and carry out experiments. Master's roles stand out with the communication and collaboration skills needed on top of the other three skill categories. Individuals in these roles oversee experiments, coordinate teams, and align efforts with company and client needs. By articulating experimental skills needed for bachelor's and master's roles in the
quantum industry, this report provides actionable insights for educators developing QISE courses and programs.
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All authors Shams El-Adawy, A.R. Piña, Benjamin M. Zwickl, H.J. Lewandowski
Computational Physics · Cross-listed from quant-ph
Probing Qubit Noise with a Channel-Resolved Post-Markovian Master Equation
Chun-Tse Li, Jingming Tan, Vasil Gucev et al. (+1 more)
Accurate noise characterization is essential for scaling
quantum processors toward fault-tolerant operation. Although reduced
qubit dynamics are often modeled with Markovian master equations, present-day devices can exhibit
memory effects generated by residual
qubit-
qubit couplings, structured environments, and finite bath correlation times. Here we develop a channel-resolved, Post-Markovian Master Equation model for non-Markovian noise and test it in superconducting
qubits. Using idle-
evolution tomography on IBM
Quantum processors, we identify complementary operational signatures of non-Markovianity, including violations of CP-divisibility and revivals of distinguishability-based information-backflow measures. We further derive a closed-form spectator-$ZZ$ model with local dissipation and show that it captures the observed transverse Bloch-vector revivals while leaving the longitudinal relaxation mode Markovian within the model. The fitted closed-form dynamics enable an analytical reconstruction of the transverse
memory kernel, whose damped oscillatory structure captures the non-Markovian correction beyond the fitted Markovian baseline. Two-
qubit tomography shows buildup and revivals of
quantum mutual information on comparable timescales, supporting spectator-induced crosstalk as an important contributor to the observed
memory effects. Our results connect operational non-Markovianity diagnostics, microscopic crosstalk modeling, and reduced
memory-kernel reconstruction in a single experimental framework for superconducting
quantum hardware.
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All authors Chun-Tse Li, Jingming Tan, Vasil Gucev, Daniel A. Lidar
Instrumentation and Detectors · Cross-listed from hep-ph
New Spallation Background Rejection Techniques to Greatly Improve the Solar Neutrino Sensitivity of JUNO
Obada Nairat, John F. Beacom and Shirley Weishi Li
While the potential of the Jiangmen Underground
Neutrino Observatory (JUNO) to measure
solar neutrinos is known, realizing this potential requires new techniques to reduce detector backgrounds. One of the most serious backgrounds is due to the beta decays of unstable
nuclei produced through
muon breakup (spallation) of
nuclei. This background is much more significant in JUNO compared to Super-Kamiokande due to JUNO's shallower depth and its lack of directional information. We present the first detailed theoretical calculations of spallation backgrounds in JUNO, showing the underlying physical processes and new ways to cut backgrounds while preserving signals. A key point is showing the importance of
neutron tagging to identify hadronic showers, which are rare but produce almost all of the dangerous isotopes. With our new techniques, JUNO will be able to reduce deadtime (signal loss) by a factor of about 4.5 and to reduce the running time needed to meet sensitivity goals by a factor of two. This will give JUNO greatly improved sensitivity to $^8$B and $hep$
solar neutrinos, as we will explore in a separate paper.
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Plasma Physics · Cross-listed from astro-ph.HE
Unified kinetic theory of induced scattering: Compton, Brillouin, and Raman processes in magnetized electron and positron pair plasma
Rei Nishiura, Shoma F. Kamijima and Kunihito Ioka
We extend a unified theoretical framework for induced (stimulated) scattering-parametric instabilities of electromagnetic waves, including induced Compton, stimulated Brillouin, and stimulated Raman scattering (SRS) in strongly magnetized electron-positron pair
plasma. By solving the dispersion relations derived from kinetic theory, taking into account the ponderomotive force due to the beat of incident and scattered waves, we obtain analytical expressions for the linear growth rates of the ordinary, neutral, and charged modes of density fluctuations. Our results clarify which type of scattering dominates under different thermal coupling, resonance, and density conditions. In strong
magnetic fields, scattering of perpendicularly polarized waves is generally suppressed, but by different powers of the cyclotron frequency. Moreover, SRS, which is forbidden in unmagnetized electron and positron pair
plasma, becomes possible in the charged mode. This framework enables a comprehensive evaluation of induced scattering in extreme astrophysical and laboratory
plasma, such as fast
radio burst (FRB) emission and propagation in magnetar magnetospheres.
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Medical Physics
Scalable covalently functionalized black phosphorus hybrids for broadspectrum virucidal activity
Na Xing, Jasmin Er, Ricardo M. Vidal et al. (+14 more)
At the onset of viral outbreaks, broad-spectrum antiviral materials are crucial before specific therapeutics become available. We report scalable, biodegradable black phosphorus (BP) hybrids that provide mutation-resilient virucidal protection. BP sheets, produced via an optimized mechanochemical process, are covalently functionalized with 2-azido-4,6-dichloro- 1,3,5-triazine to form P=N bonds. Fucoidan, a sulfated polysaccharide with intrinsic antiviral activity, and hydrophobic chains are then incorporated to achieve irreversible viral deactivation. The material exhibits strong antiviral inhibition and complete virucidal activity against multiple viruses, including recent severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants. It maintains high biocompatibility, remains effective against viral mutations, and is shelf stable for at least five month. The combination of biodegradability, scalable synthesis, and synergistic antiviral and virucidal mechanisms establishes BP-conjugates as a new class of highly efficient antivirals. They offer a broad spectrum antiviral solutions that could bridge the gap between antiviral medicines and general antiseptics.
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All authors Na Xing, Jasmin Er, Ricardo M. Vidal, Sandhya Khadka, Robert Schusterbauer, Maik Rosentreter, Ranen Etouki, Rameez Ahmed, Taylor Page, Philip Nickl, Obida Bawadkji, Anja Wiesner, Joerg Radnik, Vasile-Dan Hodoroaba, Kai Ludwig, Jakob Trimpert, Ievgen S. Donskyi
Chemical Physics
Quantum Mechanical Analysis of Neutron Wavefunction Overlap and Nuclear Interaction Probability with Carborane Cage ($^{10}$B10) in Boron Neutron Capture Therapy
Hung-Te Henry Su, Chih-Hsueh Lin and Po-Han Lee
Boron
neutron capture therapy (BNCT) leverages the
nuclear reaction between thermal
neutrons and boron-10 (B-10) atoms to induce selective tumor
cell death. The spatial and
quantum mechanical overlap between the
neutron wavefunction and B-10
nuclei encapsulated in carborane cages (C2B10H12) is fundamental to optimizing therapeutic efficacy. This study presents a
quantum mechanical framework to evaluate the
neutron-boron interaction probability by modeling
neutron wavefunctions and the spatial distribution of boron atoms within the carborane cages. Using spherical harmonics and density functional theory (DFT)-derived geometries of carborane, the
neutron scattering amplitudes and reaction cross sections are quantitatively analyzed. The implications of
neutron spin states,
nuclear magnetic moments, and external perturbations on the
neutron capture probability are discussed. The computed
neutron scattering amplitudes and reaction cross sections provide a basis for evaluating
neutron-boron interaction probabilities under varied
quantum conditions. The proposed model offers theoretical insights into molecular-level design considerations for enhancing the efficiency of BNCT.
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Optics
Wavefront Coding for Accommodation-Invariant Near-Eye Displays
Ugur Akpinar, Erdem Sahin, Tina M. Hayward et al. (+3 more)
We present a new computational near-eye display method that addresses the vergence-accommodation conflict problem in stereoscopic displays through accommodation-invariance. Our system integrates a refractive lens eyepiece with a novel wavefront coding diffractive optical element, operating in tandem with a pre-processing convolutional neural network. We employ end-to-end learning to jointly optimize the wavefront-coding optics and the image pre-processing module. To implement this approach, we develop a differentiable retinal image formation model that accounts for limiting aperture and chromatic aberrations introduced by the eye optics. We further integrate the neural transfer function and the contrast sensitivity function into the loss model to account for related perceptual effects. To tackle off-axis distortions, we incorporate position dependency into the pre-processing module. In addition to conducting rigorous analysis based on simulations, we also fabricate the designed diffractive optical element and build a benchtop setup, demonstrating accommodation-invariance for depth ranges of up to four diopters.
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All authors Ugur Akpinar, Erdem Sahin, Tina M. Hayward, Apratim Majumder, Rajesh Menon, Atanas Gotchev