Plasma Physics · Cross-listed from astro-ph.HE
On Lorentz Variability of Magnetically Dominated Relativistic Outflows
V. I. Berezhiani, N. L. Shatashvili and A. G. Tevzadze
We show that magnetized relativistic
outflows can exhibit a relativistic effect in which Lorentz transformation maps spatially extended
magnetic field structure into apparent temporal variability in the observer's frame. Using a force-free Beltrami configurations as representative
equilibria of magnetically dominated
outflows, we demonstrate that Lorentz mapping of stationary helical
magnetic field produces quasi-periodic modulation of observable electromagnetic signatures, without invoking intrinsic
plasma variability. The same mechanism naturally generates temporal
evolution of the observed polarization properties, producing smooth, generally aperiodic polarization-angle swings in the observer's frame. This effect may be described as an aberration of force-free
magnetic fields under Lorentz transformation. The characteristic frequency of the time variability is determined by the helical wave-number of the
magnetic field, the viewing angle, and the bulk Lorentz factor of the jet
outflow, and scales linearly with $\Gamma$. This establishes a purely kinematic relativistic origin of variability and introduces the concept of
magnetic Lorentz seismology: the inference of
magnetic field structure in relativistic
outflows directly from observed temporal variability.
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Applied Physics
Deterministic Reconstruction of Tennis Serve Mechanics: From Aerodynamic Constraints to Internal Torques via Rigid-Body Dynamics
Sun-Hyun Youn
Most conventional studies on tennis serve biomechanics rely on phenomenological observations comparing professional and amateur players or, more recently, on AI-driven statistical analyses of motion data. While effective at describing what elite players do, these approaches often fail to explain why such motions are physically necessary from a mechanistic perspective. This paper proposes a deterministic, physics-based approach to the tennis serve using a 12-degree-of-freedom multi-segment model of the human upper body. Rather than fitting the model to motion capture data, we solve the inverse kinematics problem via trajectory optimization to rigorously satisfy the aerodynamic boundary conditions required for Flat, Slice, and Kick serves. We subsequently perform an inverse dynamics analysis based on the Principle of Virtual Power to compute the net joint torques. The simulation results reveal that while the kinematic trajectories for different serves may share visual similarities, the underlying kinetic profiles differ drastically. A critical finding is that joints exhibiting minimal angular displacement (kinematically “quiet” phases), particularly at the wrist, require substantial and highly time-varying torques to counteract gravitational loading and dynamic coupling effects. By elucidating the dissociation between visible kinematics and internal kinetics, this study provides a first-principles framework for understanding the mechanics of the tennis serve, moving beyond simple imitation of elite techniques.
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Applied Physics
Modeling and Experiments of an Injection-Locked Magnetron With Various Load Reflection Levels
Xiaojie Chen, Bo Yang, Naoki Shinohara et al. (+1 more)
In this article, we investigate the performance of an injection-locked 5.8-GHz continuous-wave magnetron with various load reflection levels. The load reflection is introduced to an equivalent magnetron model to theoretically evaluate the system performance. The effects of different load reflection levels on the magnetron's output are numerically analyzed. Experiments are performed while the load reflection is varied using an E-H tuner between a magnetron and a circulator. A narrower locking bandwidth is observed under constant injection power with increasing load reflection. The proper-mismatched system suppresses its sideband energy, thereby reducing phase noise. The experimental features qualitatively validate the theoretical analyses results. The investigation results also provide guidance for advanced applications in communication and high-energy physics based on injection-locked magnetrons.
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All authors Xiaojie Chen, Bo Yang, Naoki Shinohara, Changjun Liu
Optics
Controlling Ultrafast Excitations in Germanium:The Role of Pump-Pulse Parameters and Multi-Photon Resonances
Amir Eskandari-asl and Adolfo Avella
We employ the Dynamical Projective Operatorial Approach (DPOA) to investigate the ultrafast optical excitations of germanium under intense, ultrashort pump pulses. The method has very low resource demand relative to many other available approaches and enables detailed calculation of the residual electron and hole populations induced by the pump pulse. It provides direct access to the energy distribution of excited carriers and to the total energy transferred to the system. By decomposing the response into contributions from different multi-
photon resonant processes, we systematically study the dependence of excited-carrier density and absorbed energy on key pump-pulse parameters: duration, amplitude, and
photon energy. Our results reveal a complex interplay between these parameters, governed by resonant Rabi-like dynamics and competition between different multi-
photon absorption channels. For the studied germanium setup, we find that two-
photon processes are generally dominant, while one- and three-
photon channels become significant under specific conditions of pump-pulse frequency, duration, and intensity. This comprehensive analysis offers practical insights for optimizing ultrafast optical control in
semiconductors by targeting specific multi-
photon pathways.
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Applied Physics
Contactless micro-elastography of single cells using oscillating microbubbles as shear wave sources
Gabrielle Laloy-Borgna, Maxime Fauconnier, Sibylle Grégoire et al. (+2 more)
The mechanical properties of
cells play key roles in their physiology, function, physiological and pathological transformations. Micro-elastography has recently emerged as a promising tool to estimate cellular viscoelastic properties within a millisecond, without the need for mechanical modeling. Here, we report a fully contactless approach to single-
cell micro-elastography, using acoustically oscillating gas microbubbles positioned near individual
cells (20~\textmu m diameter megakaryocytes) as localized shear wave sources. Using this approach, we successfully performed micro-elastography on
cells up to five times smaller than those studied in previous works, establishing the smallest single-
cell elastography measurements to date. Spherical or non-spherical bubble oscillations generated 15~kHz elastic waves, which we detected using a high-speed camera coupled to a standard bright-field microscope. Noise correlation elastography enabled the measurement of average and local shear-wave velocities within single
cells. Our results demonstrate that this method is robust and reproducible across multiple
cells from the same
cell line, paving the way for real-time, label-free mechanical monitoring of single
cells during fast biological processes.
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All authors Gabrielle Laloy-Borgna, Maxime Fauconnier, Sibylle Grégoire, Stefan Catheline, Claude Inserra
Data Analysis and Statistics · Cross-listed from astro-ph.IM
Robust and scalable simulation-based inference for gravitational wave signals with gaps
Ruiting Mao, Jeong Eun Lee and Matthew C. Edwards
The
Laser Interferometer Space
Antenna (LISA) data stream will inevitably contain gaps due to maintenance and environmental disturbances, introducing nonstationarities and spectral leakage that compromise standard frequency-domain likelihood evaluations. We present a scalable Simulation-Based Inference (SBI) framework capable of robust parameter estimation directly from gapped time-series data. We employ Flow Matching Posterior Estimation (FMPE) conditioned on a learned summary of the data, optimized through an end-to-end training strategy. To address the computational challenges of long-duration signals, we propose a dual-pathway summarizer architecture: a 1D Convolutional Neural Network (CNN) operating on the time domain for high precision, and a novel wavelet-based 2D CNN utilizing asymmetric, dilated kernels to achieve scalability for
datasets spanning months. We demonstrate the efficacy of this framework on simulated
Galactic Binary-like signals, showing that our joint training approach yields tighter, unbiased posteriors compared to two-stage reconstruction pipelines. Furthermore, we provide the first systematic comparison showing that FMPE offers superior stability and coverage calibration over conventional Normalizing Flows in the presence of severe data artifacts.
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Atmospheric and Oceanic Physics
A Neural-Network Model-Measurement-Based Observation Operator For Weather Radar Reflectivity Assimilation
Marco Stefanelli, Žiga Zaplotnik and Gregor Skok
In three-dimensional variational data assimilation (3DVar) for numerical weather prediction (NWP), the observation operator $\mathcal{H}$ plays a central role by mapping model state variables to an observation equivalent. For weather radar, however, specifying $\mathcal{H}$ is particularly challenging: reflectivity is a nonlinear, microphysics-dependent diagnostic quantity that only indirectly relates to the model's prognostic variables, making traditional parameterised radar operators complex, regime-dependent and difficult to tune. In this study, we propose a neural-network (NN)-based observation operator for radar reflectivity and apply it within a 3DVar framework. Using five years (2019-2023) of radar reflectivity data from the Lisca radar and 4.4 km-resolution short-range forecasts from ALADIN model over Slovenia, we train a convolutional encoder-decoder neural network to map model temperature, humidity, horizontal wind components and surface pressure fields to radar reflectivity. Across independent test cases spanning clear-sky, stratiform, and convective regimes, the NN-based operator accurately reproduces the spatial structure and intensity of observed reflectivity, relying primarily on the model state near the observation point. In the extreme precipitation case, which caused widespread floods in Slovenia on August 4, 2023, assimilating the full radar disc reduces the domain-averaged reflectivity root-mean-square error from 5.99 dBZ to 3.47 dBZ and improves the
alignment between the analysed and observed convective bands. Embedded within 3DVar, the Jacobian of the NN observation operator allows radar reflectivity observations to inform model state variables, producing corresponding analysis increments. The proposed NN radar observation operator offers a flexible alternative to traditional parameterised radar operators for improving convective-storm forecasts.
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Plasma Physics
Growth of Phaseolus vulgaris in Response to Seed Priming by Plasma-Activated Water in Laboratory Screening and Outdoor Pot Trial
Mustafa Ghulam, Ramin Mehrabifard, Adriana Mišúthová et al. (+4 more)
This study explores
plasma-activated water (PAW) effects on Common bean growth in laboratory and pot trials. Three treatments were assessed: PAW priming, spraying, and their combination. Laboratory trials showed no germination improvement. However, pot trials revealed notable increases in seedling length, biomass, and antioxidant enzyme activity. Enzymes SOD, G-POX, CAT, APX, and GR showed significantly higher activity in PAW-treated plants. These effects were linked to reactive oxygen and nitrogen species in PAW. Findings suggest PAW enhances bean growth and physiology, supporting field farming applications.
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All authors Mustafa Ghulam, Ramin Mehrabifard, Adriana Mišúthová, Zuzana Lukačová, Pratik Doshi, Zdenko Machala, Božena Šerá
History and Philosophy of Physics
What is Stochastic Supervenience?
Youheng Zhang
Standard formulations of supervenience typically treat higher level properties as point valued facts strictly fixed by underlying base states. However, in many scientific domains, from statistical mechanics to machine learning, basal structures more naturally determine families of probability measures than single outcomes. This paper develops a general framework for
stochastic supervenience, in which the dependence of higher level structures on a physical base is represented by Markov kernels that map base states to distributions over macro level configurations. I formulate axioms that secure law like fixation, nondegeneracy, and directional asymmetry, and show that classical deterministic supervenience appears as a limiting Dirac case within the resulting topological space of dependence relations. To connect these metaphysical claims with empirical practice, the framework incorporates information theoretic diagnostics, including normalized mutual information, divergence based spectra, and measures of tail sensitivity. These indices are used to distinguish genuine structural stochasticity from merely epistemic uncertainty, to articulate degrees of distributional multiple realization, and to identify macro level organizations that are salient for intervention. The overall project offers a conservative extension of physicalist dependence that accommodates pervasive structured uncertainty in the special sciences without abandoning the priority of the base level.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Multi-Functional Properties of Manganese Pnictides: A First-Principles Study on Magneto-Optics and Magnetocaloric Properties
Jayendran S, Abhishek K G, Suresh R et al. (+2 more)
Magnetic refrigeration presents an energy-efficient and environmentally benign alternative to traditional vapour-compression cooling technologies. It relies on the magnetocaloric effect, in which the temperature of a
magnetic material changes in response to variations in an applied
magnetic field. Optimal magnetocaloric materials are characterized by a significant change in
magnetic entropy under moderate
magnetic field. In this study, we systematically investigated the inter-atomic exchange interactions,
magnetic anisotropy energy and magnetocaloric properties of MnX (X = N, P, As, Sb, Bi) using a combination of density functional theory and Monte-Carlo simulations. Additionally, the magneto-optical Kerr and Faraday spectra were computed using the all-electron, fully relativistic, full-potential linearized muffin-tin orbital method. The largest Kerr effect observed in MnBi can be inferred as a combined effect of maximal exchange splitting of Mn 3d states and the large
spin-orbit coupling of Bi. To extract site-projected
spin and orbital moments,
spin-orbit coupling and orbital polarization correction are accounted in the present calculation, which shows good agreement between the moment obtained from the X-ray
magnetic circular dichroism sum rule analysis,
spin-polarized calculation, and experimental studies. The
magnetic transition temperatures predicted through Monte-Carlo simulations were in good agreement with the corresponding experimental values. Our results provide a unified microscopic understanding of magnetocaloric performance and magneto-optical activity in Mn-based pnictides and establish a reliable computational framework for designing next-generation
magnetic refrigeration materials.
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All authors Jayendran S, Abhishek K G, Suresh R, Helmer Fjellvåg, Ravindran P
Applied Physics
Feedforward Compensation of Piezo Nonlinearity for High-Precision High-Speed Atomic Force Microscopy
Kenichi Umeda and Noriyuki Kodera
Atomic force microscopy (AFM) enables nanoscale characterization and has been widely applied to a broad range of systems. Over the past two decades, advances in high-speed AFM have enabled not only the imaging of static structures but also the direct observation of nanoscale dynamics in real time. However, because the tip or sample is typically scanned using piezoelectric actuators, nonlinearities in their response to the input signal can introduce image-scaling errors of up to 20-30%. Consequently, there is a strong demand for a method to correct piezoelectric nonlinearity that can reliably support quantitative dynamic structural measurements. Here, we propose a simple software-based feedforward method to generate scan waveforms that can be readily implemented. We identify four distinct sources of positioning error in piezo scanners and demonstrate that these errors can be compensated, achieving an order-of-magnitude improvement in positioning accuracy compared with uncompensated operation. Because the proposed method is software-based and requires no additional hardware, it preserves imaging speed and is well suited for high-speed AFM. It is also compatible with a wide range of AFM and other scanning probe systems.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction
Zhendong Cao, Shigang Ou and Lei Wang
Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable
crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization. Reinforcement fine-tuning can be employed to further boost the accuracy of the framework. We demonstrate the effectiveness of CrystalFormer-CSP on
benchmark problems and showcase its usage via web interface and
language model integration.
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Chemical Physics
Super-Poissonian Squeezed Light in the Ground State of Strongly Coupled Light-matter Systems
Cankut Tasci, Mohammad Hassan, Leon Orlov-Sullivan et al. (+2 more)
Strong light-matter coupling enables hybrid states in which photonic and electronic degrees of freedom become correlated even in the ground state. While many-body effects in long-range dispersion interactions are known to reshape electronic properties under such conditions, their impact on
quantum-optical observables remains largely unexplored. Here, we address this problem using
quantum electrodynamical density-functional theory (QEDFT) combined with the recently developed
photon-many-body dispersion (pMBD) functional, which can capture higher-order electron-
photon correlations and multi-
photon processes. We compute ground-state photonic observables including
photon number fluctuations, second-order correlations, and quadrature variances, and find squeezing and super-Poissonian
photon statistics emerging from light-matter interactions in the strong coupling regime. Our results demonstrate that capturing the full hierarchy of many-body, electron-
photon and multi-
photon correlations is essential for a consistent description of
quantum-optical properties in strongly coupled molecular systems, establishing QEDFT as a first-principles framework for predicting nonclassical photonic features in the ground state of complex systems.
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All authors Cankut Tasci, Mohammad Hassan, Leon Orlov-Sullivan, Leonardo A. Cunha, Johannes Flick
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Symmetry breaking transforms strong to normal correlation and false metals to true insulators
Alex Zunger, Jia-Xin Xiong and John P. Perdew
Material scientists and condensed matter physicists have long been divided on the issue of choosing the conceptual framework for explaining why open-shell transition-metal
oxides tend to be
insulators, whereas otherwise successful theories such as DFT often predict them to be (false) metals. Strong correlation becomes the recommended medicine. We point out that strong correlation can be mitigated by allowing DFT to lower the energy by breaking structural,
magnetic or dipolar
symmetries. Such local motifs are observed experimentally by local probes beyond the 'average structure' determined by X-Ray diffraction. Observed broken
symmetries can arise from slow fluctuations that persist over the observation time or longer. The surprising fact is that when
symmetry breaking motifs are used as input to electronic structure calculations, false metals are converted into real
insulators without the recommended medicine of strong correlation. Consistently, DFT calculations that show energy lowering
symmetry breaking correct most cases where DFT, even with advanced exchange-correlation functionals, previously missed the correct metal vs
insulator designation. Total energy calculations distinguish systems that support energy-lowering
symmetry breaking from those that do not. This approach distinguishes between paramagnetic insulating and metallic phases and shows mass enhancement in Mott metals. The reason is that
symmetry breaking removes many of the degeneracies that exist in a
symmetry-unbroken system, reducing significantly the need for strong correlation. If one chooses to ignore
symmetry breaking, the persistent degeneracies often call for strong correlation treatment. Thus,
symmetry breaking transforms strong to normal correlation and false metals to true
insulators. This view sheds light on the historic controversy between Mott and Slater that still reverberates today.
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Fluid Dynamics
Flamelet Model with Epsilon Tracking in a Turbine Stator
Sylvain L. Walsh, Yalu Zhu, Feng Liu et al. (+1 more)
Combustion within a two-dimensional turbine stator passage is numerically investigated in the context of the turbine-burner concept using a Reynolds-Averaged Navier-Stokes framework coupled with a novel flamelet model. The formulation links resolved-scale
turbulence quantities with subgrid flamelet dynamics through the local turbulent kinetic energy dissipation rate, $\epsilon$, which determines the flamelet inflow strain rate. For the first time, combustion of JP-5 is considered in a turbine stator passage as a practical fuel. This is achieved by solving transport equations for 14 major species on the resolved scale, while chemical source terms are obtained from precomputed flamelet libraries based on the HyChem A3 mechanism comprising 119 species and 841 elementary reactions. Model performance is assessed against methane combustion using both a one-step kinetics model and an $\epsilon$-based flamelet formulation employing a 13-species skeletal mechanism. The $\epsilon$-based formulation predicts lower peak flame temperatures due to dissociation effects and approximately 50% lower net chemical energy addition per unit mass compared with the one-step model, as a result of flame stand-off and downstream strain-rate-induced quenching. For JP-5, the simulations capture combined endothermic pyrolysis and exothermic oxidation processes, leading to vertically displaced reaction zones, increased near-wall temperatures, and larger resolved-scale reaction regions due to the higher flamelet flammability limit relative to methane.
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All authors Sylvain L. Walsh, Yalu Zhu, Feng Liu, William A. Sirignano
Fluid Dynamics
Performance of Flamelet Models with Epsilon Tracking for Diffusion Flame Simulations
Sylvain L. Walsh, Yalu Zhu, Feng Liu et al. (+1 more)
This work examines the physical consistency of the conventional Flamelet Progress Variable (FPV) model for
diffusion flame simulations and and introduces a new compressible flamelet formulation that employs the turbulent kinetic energy dissipation rate, $\epsilon$, as the tracking variable. Two-dimensional Reynolds-averaged Navier-Stokes (RANS) simulations are conducted for a reacting, transonic, turbulent mixing layer to assess the coupling between resolved-scale and subgrid flamelet quantities, with emphasis on the role of strain rate. The FPV model is found to decouple resolved-scale and subgrid strain rates, leading to the preferential selection of
equilibrium flamelet solutions in regions of high strain and resulting in nonphysical predictions of heat release and species composition. The proposed $\epsilon$-based formulation restores physical consistency by relating the subgrid flamelet strain rate to $\epsilon$, allowing the flamelet to respond to the local resolved-scale strain field. The inclusion of resolved-scale species transport enables advective and diffusive redistribution of products across locally quenched regions. The results indicate that $\epsilon$ offers a physically consistent tracking variable that connects the sub-grid flamelet model to resolved-scale RANS computations.
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All authors Sylvain L. Walsh, Yalu Zhu, Feng Liu, William A. Sirignano
Atmospheric and Oceanic Physics
HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model
W. Andre Perkins, Anna Kwa, Jeremy McGibbon et al. (+5 more)
Kilometer-scale simulations of the atmosphere are an important tool for assessing local weather extremes and
climate impacts, but computational expense limits their use to small regions, short periods, and limited ensembles. Machine learning offers a pathway to efficiently emulate these high-resolution simulations. Here we introduce HiRO-ACE, a two-stage AI modeling framework combining a
stochastic version of the Ai2
Climate Emulator (ACE2S) with
diffusion-based downscaling (HiRO) to generate 3 km precipitation fields over arbitrary regions of the globe. Both components are trained on data derived from a decade of atmospheric simulation by X-SHiELD, a 3 km global storm-resolving model. HiRO performs a 32x downscaling–generating 3 km 6-hourly precipitation from coarse 100 km inputs by training on paired high-resolution and coarsened X-SHiELD outputs. ACE2S is a $1^\circ \times 1^\circ$ ($\sim$100 km)
stochastic autoregressive global atmosphere emulator that maintains grid-scale precipitation variability consistent with coarsened X-SHiELD, enabling its outputs to be ingested by HiRO without additional tuning. HiRO-ACE reproduces the distribution of extreme precipitation rates through the 99.99th percentile, with time-mean precipitation biases below 10% almost everywhere. The framework generates plausible tropical cyclones, fronts, and convective events from poorly resolved coarse inputs. Its computational efficiency allows generation of 6-hourly high-resolution regional precipitation for decades of simulated
climate within a single day using one H100 GPU, while the probabilistic design enables ensemble generation for quantifying uncertainty. This establishes an AI-enabled pathway for affordably leveraging the realism of expensive km-scale simulations to support local
climate adaptation planning and extreme event risk assessment.
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All authors W. Andre Perkins, Anna Kwa, Jeremy McGibbon, Troy Arcomano, Spencer K. Clark, Oliver Watt-Meyer, Christopher S. Bretherton, Lucas M. Harris
Chemical Physics
Benchmarking the Impact of Active Space Selection on the VQE Pipeline for Quantum Drug Discovery
Zhi Yin, Xiaoran Li, Zhupeng Han et al. (+6 more)
Quantum computers promise scalable treatments of electronic structure, yet applying variational
quantum eigensolvers (VQE) on realistic drug-like molecules remains constrained by the performance limitations of near-term
quantum hardwares. A key strategy for addressing this challenge which effectively leverages current Noisy Intermediate-Scale
Quantum (NISQ) hardwares yet remains under-benchmarked is active space selection. We introduce a
benchmark that heuristically proposes criteria based on chemically grounded metrics to classify the suitability of a molecule for using
quantum computing and then quantifies the impact of active space choices across the VQE pipeline for
quantum drug discovery. The suite covers several representative drug-like molecules (e.g., lovastatin, oseltamivir, morphine) and uses chemically motivated active spaces. Our VQE evaluations employ both simulation and
quantum processing unit (QPU) execution using
unitary coupled-cluster with singles and doubles (UCCSD) and hardware-efficient ansatz (HEA). We adopt a more comprehensive evaluation, including chemistry metrics and architecture-centric metrics. For accuracy, we compare them with classical
quantum chemistry methods. This work establishes the first systematic
benchmark for active space driven VQE and lays the groundwork for future hardware-
algorithm co-design studies in
quantum drug discovery.
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All authors Zhi Yin, Xiaoran Li, Zhupeng Han, Shengyu Zhang, Xin Li, Zhihong Zhang, Runqing Zhang, Anbang Wang, Xiaojin Zhang
Optics
A Review of Theory and Practical Considerations of Tunable Diode Laser Absorption Spectroscopy Diagnostics
Jose Guerrero and Mirko Gamba
Tunable Diode
Laser Absorption Spectroscopy (TDLAS) has emerged as a versatile and reliable diagnostic tool for measuring temperature, pressure, gas composition, and velocity in power generation and propulsion systems. This paper provides a comprehensive review of TDLAS principles and practical considerations for sensor design and implementation. The discussion begins with a mathematical introduction to the theory of gas absorption including: lineshape modeling and broadening mechanisms, quantitative measurements and challenges, and practical line selection rules. The analysis progresses to wavelength-modulation spectroscopy (WMS), highlighting its advantages in noise rejection and robustness in harsh environments. Furthermore, the calibration-free WMS model and the connection between WMS harmonics and lineshape derivatives is derived. Quantitative measurements through use of multiple harmonics is discussed and challenges surrounding measurement rate are presented. The end of the discussion focuses on practical aspects regarding the implementation of scanned-WMS sensors including
laser characterization, background subtraction, and hardware debugging.
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Plasma Physics
Transfer Learning for Analysis of Collective and Non-Collective Thomson Scattering Spectra
T. Van Hoomissen, J. Alhuthali, A.M. Ortiz et al. (+9 more)
Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental
plasma parameters, such as electron density ($n_e$) and electron temperature ($T_e$). Deep neural networks can provide accurate estimates of $n_e$ and $T_e$ when conventional fitting
algorithms may fail, such as when TS spectra are dominated by noise, or when fast analysis is required for real-time operation. Although deep neural networks typically require large training sets, transfer learning can improve model performance on a target task with limited data by leveraging pre-trained models from related source tasks, where select hidden layers are further trained using target data. We present five architecturally diverse deep neural networks, pre-trained on synthetic TS data and adapted for experimentally measured TS data, to evaluate the efficacy of transfer learning in estimating $n_e$ and $T_e$ in both the collective and non-collective scattering regimes. We evaluate errors in $n_e$ and $T_e$ estimates as a function of training set size for models trained with and without transfer learning, and we observe decreases in model error from transfer learning when the training set contains $\lessapprox$ 200 experimentally measured spectra.
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All authors T. Van Hoomissen, J. Alhuthali, A.M. Ortiz, D.A. Mariscal, R.S. Dorst, S. Eisenbach, H. Zhang, J.J. Pilgram, C.G. Constantin, L. Rovige, C. Niemann, D.B. Schaeffer
Plasma Physics
Electron Density Depletion in Reentry Plasma Flows Using Pulsed Electric Fields
Felipe Martin Rodriguez Fuentes and Bernard Parent
Communication blackout due to the
plasma layer creates a critical telemetry gap for re-entry vehicles. To mitigate this, we present the first fully-coupled simulation of high-voltage pulsed discharges interacting with a Mach 24 flowfield using an advanced numerical framework. The results demonstrate that the applied electric field generates a large, non-neutral
plasma sheath near the cathode, depleting electron density by several orders of magnitude over a distance commensurate with the height of the shock layer. This depletion window effectively reduces the attenuation of a 4 GHz signal from 60% to 4% with a manageable power requirement of 66 W per cm$^2$ of exposed cathode surface. Feasibility analysis indicates that this system can be powered by a battery pack weighing less than 3 kg for a typical re-entry trajectory, with further mass reductions possible through intermittent transmission. A sensitivity analysis reveals that the sheath
topology is governed principally by ion kinetics; specifically, corrections to ion mobility at high reduced electric fields lead to enhanced space-charge shielding and a subsequent contraction of the sheath. Conversely, the sheath structure is largely insensitive to the electron mobility model. Finally, we argue that the present drift-
diffusion model likely yields a conservative lower bound for mitigation performance. A kinetic approach accounting for ballistic ion transport and non-local ionization would likely predict thicker sheaths and lower attenuation for equivalent power deposition.
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Computational Physics · Cross-listed from cond-mat.stat-mech
Estimating Solvation Free Energies with Boltzmann Generators
Maximilian Schebek, Nikolas M. Froböse, Bettina G. Keller et al. (+1 more)
Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to ensure sufficient overlap between phase-space distributions of a solute in the gas phase and in solution. Here, we introduce a computational framework based on normalizing flows that directly maps solvent configurations between solutes of different sizes, and compare the accuracy and efficiency to conventional free energy estimates. For a Lennard-Jones solvent, we demonstrate that this approach yields acceptable accuracy in estimating free energy differences for challenging transformations, such as solute growth or increased solute-solute separation, which typically demand multiple intermediate simulation steps along the transformation. Analysis of radial distribution functions indicates that the flow generates physically meaningful solvent rearrangements, substantially enhancing configurational overlap between states in configuration space. These results suggest flow-based models as a promising alternative to traditional free energy estimation methods.
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All authors Maximilian Schebek, Nikolas M. Froböse, Bettina G. Keller, Jutta Rogal