Peer Revenue Fully funded

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Physics Physics

physics Watch Physics

Friday 21 November

Optics · Cross-listed from quant-ph

Spectroscopy on a single nonlinear mode recognizes quantum states

Wouter Verstraelen, Stanisław Świerczewski, Andrzej Opala et al. (+7 more)

Characterising optical quantum states is essential for the development of quantum technologies. While traditional approaches to perform full quantum state tomography are often experimentally demanding, neuromorphic architectures may provide an effective alternative. In this work, we demonstrate how a quantum nonlinear driven-dissipative mode is sufficient to act as a quantum reservoir. By analyzing the occupations at different frequencies in the emission spectrum, a linear regression suffices in many cases to recognize the relevant parameters of incident squeezed states. Beyond highlighting the general potential of this approach under continuous driving, we illustrate its effectiveness in an explicit nontrivial example where the source is a degenerate optical parametric oscillator (OPO), coupled to a nonlinear polariton microcavity.
Read the full abstract

All authors Wouter Verstraelen, Stanisław Świerczewski, Andrzej Opala, Andrew Haky, Matteo Gadani, Huawen Xu, Oleksandr Kyriienko, Michał Matuszewski, Alberto Bramati, Timothy C.H. Liew

Data Analysis and Statistics · Cross-listed from astro-ph.CO

Exploring the Cosmological Model Degeneracy with a new evaluate factor G

Yuan-bo Xie, Yun-dong Wu, Wei Hong et al. (+1 more)

In the context of fitting cosmological models, parameter degeneracy remains a central issue. This paper critically examines traditional methods for constraining parameters and focuses on the G factor as a tool for evaluating the quality of observational data. To ensure analytical independence, two datasets–Cosmic Chronometers (CC) and Baryon Acoustic Oscillations (BAO)–were utilized as samples for parameter fitting, supplemented by Markov Chain Monte Carlo (MCMC) simulations. The Figure of Merit (FoM) matrix served as the final criterion for assessing fitting performance. The results show that the G factor of the CC dataset increases linearly with redshift z, whereas the G factor of the BAO dataset follows a cubic relationship. Further analysis indicates that the FoM value for datasets with high G factors is significantly higher than that for datasets with low G factors, thereby validating the G factor's effectiveness as a tool for assessing observational data quality and reducing parameter degeneracy. This suggests that the G factor may serve as a diagnostic tool and selection criterion for optimizing observational datasets in future research.
Read the full abstract

All authors Yuan-bo Xie, Yun-dong Wu, Wei Hong, Tong-jie Zhang

Chemical Physics

Orbital Surface Hopping with an Electron Thermostat Yields Accurate Dynamics and Detailed Balance

Yongtao Ma and Wenjie Dou

In mixed quantum-classical simulations of molecule-metal surface interactions, the discretization of the metallic electronic continuum typically results in a closed-system representation that fails to capture the open-system nature of the true physical process. This approximation can introduce significant artifacts, including deviations in the dynamical evolution and a violation of the principle of detailed balance. To address this fundamental challenge, we introduce an electronic thermostat into our previously developed orbital surface hopping (OSH) framework, generalizing the method to efficiently handle many discrete electronic states. We first outline the derivation of electronic thermostat orbital surface hopping, where the amplitude of the electronic thermostat is well justified. We then demonstrate that this method can reproduce accurate dynamics and detailed balance in long time, whereas without electronic thermostat the detailed balance is violated. Thus, this method offers a reliable tool for studying nonadiabatic dynamics near metal surfaces.
Read the full abstract

Geophysics

Diffusion-Inversion-Net (DIN): An End-to-End Direct Probabilistic Framework for Characterizing Hydraulic Conductivities and Quantifying Uncertainty

Xun Zhang, Weijie Yang, Jiangjiang Zhang et al. (+1 more)

We propose the Diffusion-Inversion-Net (DIN) framework for inverse modeling of groundwater flow and solute transport processes. DIN utilizes an offline-trained Denoising Diffusion Probabilistic Model (DDPM) as a powerful prior leaner, which flexibly incorporates sparse, multi-source observational data, including hydraulic head, solute concentration, and hard conductivity data, through conditional injection mechanisms. These conditioning inputs subsequently guide the generative inversion process during sampling. Bypassing iterative forward simulations, DIN leverages stochastic sampling and probabilistic modeling mechanisms to directly generate ensembles of posterior parameter fields by repeatedly executing the reverse denoising process. Two representative posterior scenarios, Gaussian and non-Gaussian, are investigated. The results demonstrate that DIN can produce multiple constraint-satisfying realizations under identical observational conditions, accurately estimate hydraulic-conductivity fields, and achieve reliable uncertainty quantification. The framework exhibits strong generalization capability across diverse data distributions, offering a robust and unified alternative to conventional multi-stage inversion methodologies.
Read the full abstract

All authors Xun Zhang, Weijie Yang, Jiangjiang Zhang, Simin Jiang

Fluid Dynamics

Perspiration vapor lightens near skin air but hinders human evaporative cooling in arid heat

Shri H. Viswanathan, Ankit Joshi, Isabella DeClair et al. (+7 more)

Sweat evaporation is the body's primary cooling mechanism, yet the physical factors governing it are not fully understood. We identify a dueling buoyancy effect in the context of the human body, in which perspiration vapor reduces the near skin air density, counteracting the downward flow driven by cooling of warm air upon contact with the skin. In hot, arid, stagnant environments, this opposing buoyancy suppresses free convection and can reduce sweat evaporation by more than half. As a result, commonly used thermoregulation models can substantially underpredict body temperature (e.g., by 1C after 2 hours of exposure to typical Arizona summer conditions). We develop compact, physics informed models for free convective heat transfer coefficients across wide temperature and humidity ranges, enabling improved thermoregulation modeling and thermal audits. These results enhance understanding of human heat balance and support more accurate heat stress assessment to inform behavioral, infrastructural, and policy decisions for extreme heat adaptations.
Read the full abstract

All authors Shri H. Viswanathan, Ankit Joshi, Isabella DeClair, Bryce Twidwell, Muhammad Abdullah, Lyle Bartels, Faisal Abedin, Joseph Rotella, Cibin T. Jose, Konrad Rykaczewski

Chemical Physics

Spin-adapted open-shell time-dependent density functional theory: towards a simple and accurate method for spin-flip-down excitations

Hewang Zhao and Zhendong Li

A major challenge in using spin-flip time-dependent density functional theory (SF-TD-DFT) for spin-flip-down excitations is the presence of spin contamination. While several improved methods have been developed in the past, a simple and accurate method remains elusive. Here, based on our previous development on spin-adapted open-shell TD-DFT for spin-conserving excitations (X-TD-DFT) [Z. Li and W. Liu, J. Chem. Phys. 135, 194106 (2011)], we introduce a method termed as XSF-TDA for modeling spin-flip-down excitations, and provide an in-depth comparison of different methodologies for mitigating spin contamination in SF-TDA. Pilot applications to prototype systems demonstrate the promise of XSF-TDA over existing SF-TDA methods, including unrestricted SF-TDA (USF-TDA) and mixed-reference SF-TDA (MRSF-TDA), in describing bond breakings and inverted singlet-triplet gap systems.
Read the full abstract

Optics

Exponentially enhanced sensing through non-reciprocal light propagation

Paul-Édouard Blanchard, Alexander McDonald and Philippe St-Jean

Non-reciprocity is a key resource for pushing the performance of photonic devices beyond the fundamental limits imposed by Lorentz reciprocity. Notably, it was predicted that sensor architectures implementing a bosonic analogue of the Kitaev chain can yield an enhanced signal-to-noise ratio. However, experimental demonstrations of such sensors have so far been limited to small chains. Here we show how this model can be implemented in a scalable optical platform, enabling the detection of weak perturbations with a signal-to-noise ratio, linked to the quantum Fisher information extracted per input photon, that grows exponentially with system size. Our approach encodes two coupled Hatano-Nelson chains—realizing an effective bosonic Kitaev model—within the resonant modes of an electro-optic frequency comb. Non-reciprocal propagation in frequency space is achieved via simultaneous phase and amplitude modulation of the intracavity field. We demonstrate the sensing of a weak modulation tone coupling the two chains, observing an exponential enhancement of the signal-to-noise over three orders of magnitude as the system is scaled to over 70 frequency modes per chain. These results establish a new regime of non-Hermitian sensing, highlighting how non-reciprocal dynamics can be harnessed to achieve exponentially enhanced measurement precision in photonic systems.
Read the full abstract

Optics

Broadband telecom single-photon emissions from InAs/InP quantum dots grown by MOVPE droplet epitaxy

Shichen Zhang, Li Liu, Kai Guo et al. (+6 more)

The development of quantum materials for single-photon emission is crucial for the advancement of quantum information technology. Although significant advancement has been witnessed in recent years for single photon sources in near infrared band ({\lambda}~700-1000 nm), several challenges have yet to be addressed for ideal single photon emission at the telecommunication band. In this study, we present a droplet-epitaxy strategy for O-band to C-band single-photon source based semiconductor quantum dots (QDs) using metal-organic vapor-phase epitaxy (MOVPE). Via investigating the growth conditions of the epitaxial process, we have successfully synthesized InAs/InP QDs with narrow emission lines spanning a broad spectral range of {\lambda}~1200-1600 nm. The morphological and optical properties of the samples were characterized using atomic force microscopy and micro photoluminescence spectroscopy. The recorded single-photon purity of a plain QD structure reaches (g(2)(0) = 0.16), with a radiative recombination lifetime as short as 1.5 ns. This work provides a crucial platform for future research on integrated microcavity enhancement techniques and coupled QDs with other quantum photonics in the telecom bands, offering significant prospects for quantum network applications.
Read the full abstract

All authors Shichen Zhang, Li Liu, Kai Guo, Xingli Mu, Yuanfei Gao, Junqi Liu, Fengqi Liu, Quanyong Lu, Zhiliang Yuan

Medical Physics

Double-Profile Intersection (DoPIo) Ultrasound: Pointwise Shear Elasticity Estimation using Paired Confocal Displacement Profiles

Keita Yokoyama, Murad Hossain, Sabiq Muhtadi et al. (+1 more)

Current acoustic radiation force (ARF) based methods for quantifying tissue elasticity primarily rely on shear wave propagation. However, their spatial resolution is limited by the need for spatial averaging, and their accuracy is affected by shear wave guidance, out of plane reflections, and geometric dispersion, which reduce their applicability in mechanically complex tissues. This study introduces a novel technique called Double Profile Intersection (DoPIo) ultrasound, which enables pointwise estimation of shear elastic modulus within the region of ARF excitation by leveraging the scatterer shearing rate. This rate is inferred by tracking ARF induced displacement using two tracking beams with different lateral widths. The wider beam captures scatterers located outside the ARF excitation region that begin to displace as shearing propagates. The time at which the two resulting displacement profiles intersect is mapped to shear elastic modulus using an empirically derived model based on finite element simulations. In silico, DoPIo estimated shear elastic modulus with a median error of -0.02 kPa and a median absolute deviation of 1.98 kPa in elastic materials up to 35 kPa. Experimental validation in vitro and ex vivo demonstrated that DoPIo reliably distinguished softer regions from stiffer ones, and its modulus estimates remained consistent across varying ARF push amplitudes, provided sufficient displacement estimation signal to noise ratio. DoPIo offers a feasible approach for high resolution, on axis shear elasticity estimation and holds promise as a quantitative biomarker that is independent of ARF amplitude.
Read the full abstract

All authors Keita Yokoyama, Murad Hossain, Sabiq Muhtadi, Caterina Gallippi

Computational Physics · Cross-listed from cond-mat.soft

Computer Simulation of Gel Formation in Colloidal Systems of Sticky Rods

Johannes Krotz

We develop and validate a simulation framework for colloidal gelation. We first reproduce the benchmark results of Santos, Campanella, and Carignano for spherical, gel-forming particles, then extend the methodology to more complex systems of “sticky” spherocylindrical rods interacting via a Kihara-like potential. Using comprehensive parameter sweeps documented for reproducibility, we analyze the emergence of porous, percolating networks and conduct a topological characterization of the resulting microstructures. This characterization leverages Early TDA to extract multiscale connectivity features and to define topology-driven metrics for automated comparison between simulations and experiments. Our simulations reveal a clear dependence of network formation on rod aspect ratio and particle density, consistent with established theory and, to our knowledge, not previously demonstrated for spherocylindrical colloids with Kihara-type interactions. Rheological probing of the simulated systems shows signatures characteristic of gels, which supports the structural analysis. We further compare our computational results with experimental data obtained on Bastian Trepka's gels collected by Jacob Steindl. Although these first comparisons indicate that the present model is not yet sufficient to quantitatively describe those specific gelled systems, the agreement in qualitative trends and the robustness of our tools suggest strong potential. Overall, the work demonstrates functional, extensible methods for simulating gelation in rod-based colloids, provides topological data analysis based metrics that can aid automated comparison between experiments and simulations, and outlines several promising directions for future refinement and application.
Read the full abstract

Thursday 20 November

Applied Physics

Application of riblets to separating turbulent boundary layers

Amirreza Rouhi, Vishal Kumar, Oriol Lehmkuhl et al. (+3 more)

We conduct direct numerical simulations of separating turbulent boundary layers (TBLs) over triangular riblets with tip angles $90^o$ (T9) and $60^o$ (T6). Our setup follows the separating TBL study of Wu et al.\ (J. Fluid Mech., vol.\ 883, 2020, p.\ A45). An equilibrium zero pressure-gradient (ZPG) TBL is generated at a reference location, followed by imposition of a Gaussian suction profile to create a separation bubble. The ZPG TBLs over the riblets and the benchmark smooth case have matched momentum thickness Reynolds number $Re_{\theta_0} = 583$ (friction Reynolds number 224). We employ a well-validated spectral-element solver, and leverage its unstructured-grid nature to generate an optimal grid, based on the size of turbulent scales across the TBL. At the reference location, the T9 and T6 riblets respectively increase and reduce drag, with viscous-scaled spacings $52$ and $13$. We discover that for both riblet cases, the mean separation location occurs at a distance of $140\theta_0$ downstream of the reference location, $18\%$ shorter than the mean separation distance for the smooth case ($170\theta_0$). This outcome is related to the progressive enhancement of the Kelvin-Helmholtz (KH) rollers over the riblets, owing to the continuous rise in the adverse pressure-gradient. The KH rollers penetrate into the turbulent separation bubble, with significantly larger size and coherence compared to their counterparts upstream of the mean separation location.
Read the full abstract

All authors Amirreza Rouhi, Vishal Kumar, Oriol Lehmkuhl, Wen Wu, Melissa Kozul, Alexander J. Smits

Applied Physics

Non-equilibrium effects in turbulent boundary layers over riblets: DNS of step changes in surface texture

Vishal Kumar, Melissa Kozul, Wen Wu et al. (+2 more)

We computationally study the response of zero-pressure-gradient (ZPG) turbulent boundary layers (TBLs) to streamwise step changes from a smooth wall to riblets (SM_RI), and vice versa (RI_SM). To quantify the departure from equilibrium due to the step changes, we conduct reference calculations of ZPG TBLs over an entirely smooth wall, and an entirely riblet-covered surface. To save the computational cost, we generate an optimal grid for an unstructured spectral-element code, consistent with the size of turbulent scales across the TBL. By the step change, the momentum thickness Reynolds number reaches $Re_{\theta_0} \simeq 680$ (friction Reynolds number $Re_{\tau_0} \simeq 283$), and by the domain outlet downstream of the step change, $Re_{\theta} \simeq 1000$ ($Re_{\tau} \simeq 400$). The TBL departure from equilibrium due to the step change, and its subsequent relaxation, recall previous studies on step changes in surface roughness. Downstream of the step change, growth of the internal equilibrium layer thickness $\delta_\text{IEL}$, hence recovery to equilibrium, follows two stages. Stage I corresponds to the recovery up to the buffer region ($y^+ \simeq 10$), which is slower during the RI_SM step change than the SM_RI counterpart. For the RI_SM cases during Stage I, $\delta_\text{IEL} \propto (x/k)^{0.6}$, and this stage is completed by $x \simeq 100k \simeq (5\delta_0 - 20\delta_0)$ downstream of the step change, where $k$ is the riblet height. Stage II recovery i.e.\ recovery of the outer region, is quite slow. Therefore, for drag-increasing riblets with $k^+ \ge 25$, $\delta_\text{IEL}$ does not reach the boundary layer thickness, even up to $50\delta_0$ downstream of the step change, owing to the advected frozen wake from upstream. As a result, skin-friction coefficient reaches to more than $90\%$ of its equilibrium counterpart, but does not reach its $100\%$.
Read the full abstract

All authors Vishal Kumar, Melissa Kozul, Wen Wu, Oriol Lehmkuhl, Amirreza Rouhi

Chemical Physics

Electric-Field-Induced Tautomerism in Metal-Free Benziporphyrins Enables Aromaticity-Controlled Conductance Switching

Yenni P. Ortiz, Arnau Cortés-Llamas, Jordi Ribas-Arino et al. (+1 more)

Metal-free porphyrins can switch between hydrogen-bonded tautomers, potentially enabling reversible control in molecular electronics. However, electric field gating of porphyrin tautomerism, which is critical for device integration, has not been fully realized. We propose metal-free benziporphyrins (MFBPs), in which one pyrrole ring is replaced with a phenol group, as a new platform for tautomer-based molecular electronics. This approach introduces asymmetry, which allows for three distinct tautomers, each possessing a characteristic aromatic or antiaromatic electronic structure. Density functional theory and quantum transport calculations show that: i) experimentally realisable electric fields can selectively stabilize each tautomer, and ii) each tautomer exhibits a characteristic conductance profile. The strong switching capability of MFBPs is demonstrated by ON/OFF ratios exceeding 500 at low bias. Fused MFBPs further expand functionality by providing multiple tautomeric states for multistate molecular registers and enabling wire-like architectures with enhanced conductance. These results establish MFBPs as versatile building blocks for electric-field-responsive molecular devices and open new research opportunities for molecular-scale logic and memory.
Read the full abstract

All authors Yenni P. Ortiz, Arnau Cortés-Llamas, Jordi Ribas-Arino, Stefan T. Bromley

Space Physics

High-Energy Atmospheric Radiation: From Thunderstorm Ground Enhancements to Terrestrial Gamma-Ray Flashes

A. Chilingarian, L. Hovhannisyan, B. Sargsyan et al. (+1 more)

This work presents a unified conceptual and observational framework that reinterprets these radiation bursts as manifestations of the same runaway processes happening at different atmospheric depths (Dual-stage model, DSM). We review recent results from satellite (ASIM), aircraft (ALOFT), balloon (HELEN), and ground-based (SEVAN and KANAZAWA) experiments to demonstrate the advantages of this integrated approach. This study addresses key contradictions in the field, introduces new classification criteria based on physics rather than detector location, and enhances our understanding of particle acceleration in thunderstorms.
Read the full abstract

All authors A. Chilingarian, L. Hovhannisyan, B. Sargsyan, M. Zazyan

Physics and Society

Predicting Healthcare Provider Engagement in SMS Campaigns

Daanish Aleem Qureshi, Rafay Chaudhary, Kok Seng Tan et al. (+5 more)

As digital communication grows in importance when connecting with healthcare providers, traditional behavioral and content message features are imbued with renewed significance. If one is to meaningfully connect with them, it is crucial to understand what drives them to engage and respond. In this study, the authors analyzed several million text messages sent through the Impiricus platform to learn which factors influenced whether or not a doctor clicked on a link in a message. Several key insights came to light through the use of logistic regression, random forest, and neural network models, the details of which the authors discuss in this paper.
Read the full abstract

All authors Daanish Aleem Qureshi, Rafay Chaudhary, Kok Seng Tan, Or Maoz, Scott Burian, Michael Gelber, Phillip Hoon Kang, Alan George Labouseur

Classical Physics · Cross-listed from cs.SD

Is Phase Really Needed for Weakly-Supervised Dereverberation ?

Marius Rodrigues, Louis Bahrman, Roland Badeau et al. (+1 more)

In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating to what extent information can be retrieved from the sole knowledge of reverberant (wet) speech becomes critical. This work investigates the role of the reverberant (wet) phase in the time-frequency domain. Based on Statistical Wave Field Theory, we show that late reverberation perturbs phase components with white, uniformly distributed noise, except at low frequencies. Consequently, the wet phase carries limited useful information and is not essential for weakly supervised dereverberation. To validate this finding, we train dereverberation models under a recent weak supervision framework and demonstrate that performance can be significantly improved by excluding the reverberant phase from the loss function.
Read the full abstract

All authors Marius Rodrigues, Louis Bahrman, Roland Badeau, Gaël Richard

Chemical Physics

The seeding method: A test case for classical nucleation theory in small systems

Thomas Philippe, Yijian Wu and Aymane Graini

Molecular dynamics simulations are widely used to investigate nucleation in first-order phase transitions. Brute-force simulations, though popular, are limited to conditions of high metastability, where the critical cluster and the nucleation barrier are small. The seeding method has recently emerged as a powerful alternative for exploring lower supersaturation regimes by initiating simulations with a pre-formed nucleus. In confined systems (NVT ensemble), the seeded simulations are particularly effective for determining stable cluster properties and provide a stringent test case for classical nucleation theory (CNT). In this work, we perform NVT seeded simulations of Lennard-Jones condensation in small systems and compare them with CNT predictions based on several thermodynamic models, including equations of state, perturbation theory, and ideal gas approximation. We find that CNT accurately predicts stable cluster radii across a wide range of conditions. Notably, even the very simple ideal gas approximation proves useful for initializing seeded simulations. Furthermore, seeded simulation results correspond to the critical cluster radii of infinite systems: CNT predictions with good equations of state show very good agreement with simulations, while the perturbation theory and the ideal gas approximation perform well at low temperatures but deviate significantly at high temperatures.
Read the full abstract

Fluid Dynamics

Hierarchical Bayesian constitutive model selection for high-strain-rate soft material characterization

Victor Sanchez, Sawyer Remillard, Bachir A. Abeid et al. (+5 more)

The high-fidelity characterization of soft, tissue-like materials under ultra-high-strain-rate conditions is critical in engineering and medicine. Still, it remains challenging due to limited optical access, sensitivity to initial conditions, and experimental variability. Microcavitation techniques (e.g., laser-induced microcavitation) have emerged as a viable method for determining the mechanical properties of soft materials in the ultra-high-strain-rate regime (higher than 10^3 s^{-1}); however, they are limited by measurement noise and uncertainty in parameter estimation. A hierarchical Bayesian model selection method is employed using the Inertial Microcavitation Rheometry (IMR) technique to address these limitations. With this method, the parameter space of different constitutive models is explored to determine the most credible constitutive model that describes laser-induced microcavitation bubble oscillations in soft, viscoelastic, transparent hydrogels. The target data/evidence is computed using a weighted Gaussian likelihood with a hierarchical noise scale, which enables the quantification of uncertainty in model plausibility. Physically informed priors, including range-invariant, stress-based parameter priors, a model-redundancy prior, and a Bayesian Information Criterion motivated model prior, penalize complex models to enforce Occam's razor. Using a precomputed grid of simulations, the probabilistic model selection process enables an initial guess for the Maximum A Posteriori (MAP) material parameter values. Synthetic tests recover the ground-truth models and expected parameters. Using experimental data for gelatin, fibrin, polyacrylamide, and agarose, MAP simulations of credible models reproduce the data. Moreover, a cross-institutional comparison of 10% gelatin indicates consistent constitutive model selection.
Read the full abstract

All authors Victor Sanchez, Sawyer Remillard, Bachir A. Abeid, Lehu Bu, Spencer H. Bryngelson, Jin Yang, Jonathan B. Estrada, Mauro Rodriguez Jr

History and Philosophy of Physics

The 59th Karl Jansky Lecture: Discovering the Radio Universe

K. I. Kellermann

The NRAO 59th Karl Jansky Lecture was presented on 24 October 2024, 22 November 2024, and 4 December 2024 in Charlottesville, Virginia, Socorro, New Mexico, and Green Bank, West Virginia, respectively. The lecture covered the circumstances of the author's start in radio astronomy, the demographics of radio astronomers, discussions of the outstanding, mostly serendipitous, discoveries made by radio astronomers over the past century, and concluded with reflections on the prospects for further new discoveries.
Read the full abstract

Fluid Dynamics

On the Poisson-Source Basis of Logarithmic Wall-Pressure-Variance Growth

Jonathan M. O. Massey, Joseph C. Klewicki and Beverley J. McKeon

In high-Reynolds-number wall-bounded flows, the inner-scaled wall-pressure variance \ra{is often represented as a} logarithmic increase with frictional Reynolds number. We consider the two sources of the incompressible pressure–Poisson equation: a linear (rapid) term linked to mean shear and a nonlinear (slow) term composed of quadratic velocity fluctuations. This paper establishes a link between the sources and the coefficients in \rtwo{a logarithmic} inner-scaled variance \rtwo{representation}. \rone{To leading order} we \rone{posit that} the \ra{linear source provides a Reynolds-number-independent offset, while the nonlinear source contributes the logarithmic coefficient}. The illustrative dataset is direct numerical simulation (DNS) at \rtwo{frictional Reynolds number} $\delta^+\approx 550$, although the principal contribution is the establishment of a mechanistic link to well-known high-$\delta^+$ scalings of wall-bounded turbulence. Through consideration of the sources and the integral solution method of the Poisson equation, we find that the linear source contribution sits predominantly in the buffer layer and maps to the near-wall cycle. \rone{To leading order}, this contribution becomes $\delta^+$ invariant under inner scaling, thus contributing an offset in the \rtwo{logarithmic representation}. The interfacial regions between uniform momentum zones characteristic of the inertial layer (vortical fissures) spatially localise strain and vorticity contributions and contain an increasingly large proportion of the strain and vorticity. We show that fissures act as a compact carrier for the source terms, with the nonlinear term especially prominent in these regions. By considering the inertial layer statistics, we link the changing nonlinear contribution to \rtwo{the} $\ln \delta^+$ \rtwo{growth}, in agreement with previous empirical observations.
Read the full abstract

Medical Physics

A Respiratory Motion Analysis for Guiding Stereotactic Arrhythmia Radiotherapy Motion Management

Yuhao Wang, Yao Hao, Hongyu An et al. (+4 more)

Stereotactic Arrhythmia Radiotherapy (STAR) treats ventricular tachycardia (VT) but requires internal target volume (ITV) expansions to compensate for cardiorespiratory motion. Current clinical r4DCT imaging methods are limited, and the reconstructed r4DCTs suffer from unmanaged cardiac motion artifacts that affect the quantitative assessment of respiratory motion. A groupwise surface-to-surface deformable image registration (DIR) algorithm, named gCGF, was developed. A novel principal component filtering (PCF) mechanism and a spatial smoothing mechanism were developed and incorporated into gCGF to iteratively register heart contours from an average respiratory-phase CT to ten r4DCT phases while removing random cardiac motion from the cyclic respiratory motion. The performance of the groupwise DIR was quantitatively validated using 8 digital phantoms with simulated cardiac artifacts. An ablation study was conducted to compare gCGF to another comparable state-of-the-art groupwise DIR method. gCGF was applied to r4DCTs of 20 STAR patients to analyze the respiratory motion of the heart. Validation on digital phantoms showed that gCGF achieved a mean target registration error of 0.63+-0.51 mm while successfully achieving phase smoothness and reducing cardiac motion artifacts. Among all STAR patients, the heart's maximum and mean respiratory motion magnitudes ranged from 3.6 to 7.9 mm and 1.0 mm to 2.6 mm. The peak-to-peak motion range was from 6.2 to 14.7 mm. For VT targets, the max and mean motion magnitude ranges were 3.0 to 6.7 mm and 0.8 to 2.9 mm, respectively. The peak-to-peak range was from 4.7 to 11.8 mm. Significant dominance of the first principal component of the motion direction was observed (p = 0).
Read the full abstract

All authors Yuhao Wang, Yao Hao, Hongyu An, H Michael Gach, Clifford Robinson, Phillip S Cuculich, Deshan Yang

Optics · Cross-listed from cond-mat.mtrl-sci

Light-Induced Lattice Coherence and Emission Enhancement in PTM-Passivated CsSnI3 Perovskites

Thomas Y. Adams, Bruce Barrios, Michael Ziegenfus et al. (+2 more)

Metal halide perovskites continue to lead in optoelectronic applications, but the toxicity of lead has driven efforts to identify environmentally benign alternatives. Cesium tin iodide is one such, with a direct bandgap and near-infrared emission, though its performance is limited by instability. We show that phthalimide (PTM) passivation during single crystal growth enhances optical output and ambient stability. Under continuous excitation, PTM-passivated microscale crystals show up to a nearly one order of magnitude increase in photoluminescence (PL) quantum yield, accompanied by reversible sharpening of a low-frequency Raman mode associated with Cs rattling. This reveals dynamic, light-induced lattice reordering that passivates trap states and enhances radiative recombination. Mechanical grinding yields nanocrystals with redshifted, narrowed PL, consistent with a relaxed polymorph and reduced inhomogeneous broadening. Despite increased surface area, PTM remains effective in preserving near-infrared emission in nanocrystals as well. Power-dependent PL reveals distinct carrier dynamics, where microcrystals show redshift due to bandgap renormalization, while nanocrystals show blueshift and elevated carrier temperatures (300 to 1900 K), consistent with hot-carrier recombination. Extended illumination reveals reversible optical changes, including PL modulation, reflecting dynamic light-matter interactions and evolving defect landscapes. These results identify PTM-passivated Cesium tin iodide as an ideal platform for probing morphology-dependent carrier relaxation and light-induced vibrational coherence in lead-free perovskites.
Read the full abstract

All authors Thomas Y. Adams, Bruce Barrios, Michael Ziegenfus, Hui Cai, Sayantani Ghosh

Chemical Physics · Cross-listed from quant-ph

Scalable Quantum Computational Science: A Perspective from Block-Encodings and Polynomial Transformations

Kevin J. Joven, Elin Ranjan Das, Joel Bierman et al. (+3 more)

Significant developments made in quantum hardware and error correction recently have been driving quantum computing towards practical utility. However, gaps remain between abstract quantum algorithmic development and practical applications in computational sciences. In this Perspective article, we propose several properties that scalable quantum computational science methods should possess. We further discuss how block-encodings and polynomial transformations can potentially serve as a unified framework with the desired properties. Recent advancements on these topics are presented including construction and assembly of block-encodings, and various generalizations of quantum signal processing (QSP) algorithms to perform polynomial transformations. The scalability of QSP methods on parallel and distributed quantum architectures is also highlighted. Promising applications in simulation and observable estimation in chemistry, physics, and optimization problems are presented. We hope this Perspective serves as a gentle introduction of state-of-the-art quantum algorithms to the computational science community, and inspires future development on scalable quantum computational science methodologies that bridge theory and practice.
Read the full abstract

All authors Kevin J. Joven, Elin Ranjan Das, Joel Bierman, Aishwarya Majumdar, Masoud Hakimi Heris, Yuan Liu

Computational Physics · Cross-listed from quant-ph

Navigating the Quantum Resource Landscape of Entropy Vector Space Using Machine Learning and Optimization

Nothando Khumalo, Aman Mehta, William Munizzi et al. (+1 more)

We present a machine learning framework to study the dynamics of entropy vectors and quantum resources, including entanglement and magic, focusing on violations of entropy inequalities. Using a reinforcement learning agent formulated as a Markov decision process, we identify quantum circuits that optimally navigate the entropy vector space to generate violations of Ingleton's inequality. We complement this approach with a classical optimization algorithm to produce arbitrary numbers of Ingleton-violating states, with tunable degrees of violation, and empirically determine the maximal attainable violation for Ingleton's inequality. Our analysis reveals characteristic patterns of quantum resources that accompany Ingleton violation. A comprehensive statistical analysis shows that Ingleton-violating states occupy sharply-defined, isolated regions of the Hilbert space, and are extremely rare. Together, these results establish a unified computational toolkit for studying entropy vector dynamics, tracking quantum resource evolution, and engineering circuits with controlled information-theoretic features.
Read the full abstract

All authors Nothando Khumalo, Aman Mehta, William Munizzi, Prineha Narang

General Physics

Unblockable Communication With Gravity

Andrew J. Groszek, Charles W. Woffinden, Michael D. Harvey et al. (+2 more)

All modern wireless communication technologies are based on electromagnetism. However, electromagnetic signals are susceptible to screening and blocking, so their availability cannot be guaranteed in adverse environments. This raises a fundamental question: Can information be transmitted through a truly unblockable channel? Here we show that gravity, unlike electromagnetism, offers such a path. We propose and implement a wireless communication protocol in which a broadcaster encodes a binary message by moving a mass, while a receiver detects the resulting gravitational signal with a gravimeter. We validate this scheme experimentally, successfully transmitting a gravitational message a distance of $\approx$ 0.7 m through a brick wall at a rate of 1 bit min$^{-1}$. These results establish gravity as a viable platform for unblockable communication.
Read the full abstract

All authors Andrew J. Groszek, Charles W. Woffinden, Michael D. Harvey, Andrew G. White, Matthew J. Davis

Optics

Eigenvalue-accelerated LDOS optimization of high-Q optical resonances

George Shaker, Beñat Martinez de Aguirre Jokisch, Pengning Chao et al. (+1 more)

We demonstrate a new method that yields orders-of-magnitude acceleration in inverse design (e.g. topology optimization) of high-$Q$ resonant cavities to maximize the local density of states (LDOS), and which is also applicable to other resonant-response metrics. The key idea is that, once conventional LDOS optimization has identified a strong resonance, subsequent optimizations can exploit a fast shift-invert eigensolver to ensure that the LDOS remains centered at the resonance peak. We show that this eliminates ill-conditioning at sharp resonances that otherwise dramatically slows LDOS (and similar) optimization for $Q \gg 100$. Our method is demonstrated by design of $Q > 10^6$ resonant cavities in 1d and 2d dielectric systems.
Read the full abstract

All authors George Shaker, Beñat Martinez de Aguirre Jokisch, Pengning Chao, Steven G. Johnson

Medical Physics

The First In Vitro and In Vivo Validation of the Hessian-Free Ray-Born Inversion for Quantitative Ultrasound Tomography

Ashkan Javaherian

This study presents the first experimental validation of a Hessian-free ray-Born inversion technique for quantitative reconstruction of sound speed from transmission ultrasound data. The method combines single-scattering theory with high-frequency approximations, yielding an inversion framework well suited to the frequency ranges used in clinical ultrasound applications. Unlike previous singly-scattered inversion approaches that account for medium heterogeneities only in the scattering potential, the proposed ray-Born method employs Green's functions approximated along ray trajectories determined by high-frequency assumptions. The associated objective function is linearized and minimized sequentially across increasing frequency bands. At each frequency set, the linearized subproblem is solved using a weighting scheme applied to both the solution and data spaces, which diagonalizes the Hessian and enables its inversion in a single step. The method, previously reported and released as an open-source package, was applied to in-vitro and in-vivo datasets provided by the University of Rochester Medical Center. The reconstructed images were evaluated by comparison with those obtained using a full-wave inversion approach based on a frequency-domain Helmholtz solver. The results demonstrate the strong potential of the Hessian-free ray-Born inversion as a computationally efficient and accurate method suitable for clinical translation.
Read the full abstract

Fluid Dynamics · Cross-listed from astro-ph.SR

Excitation of Inertial Modes in 3D Simulations of Rotating Convection in Planets and Stars

J. R. Fuentes, Ankit Barik and Jim Fuller

Thermal convection in rotating stars and planets drives anisotropic turbulence and differential rotation, both capable of feeding energy into global oscillations. Using 3D simulations of rotating convection in spherical shells, we show that inertial modes–oscillations restored by the Coriolis force–emerge naturally in rotationally constrained turbulence, without imposing any external forcing other than thermal/buoyancy driving. By varying the rotation rate at fixed Rayleigh number, we find that coherent modes appear only when the convective Rossby number, the ratio of the rotation period to the convective turnover time, falls below about one-half, where rotation dominates the dynamics. These modes are mostly retrograde in the rotating frame, equatorially symmetric, and confined to mid and high latitudes, with discrete frequencies well below twice the background rotation rate. At lower viscosities, or smaller Prandtl number, mode excitation becomes more efficient and a broader spectrum of inertial modes emerges. While the precise excitation mechanism remains uncertain, our results suggest that the modes are driven by instabilities due to differential rotation rather than stochastic forcing by convection. We conclude that similar inertial modes are likely to exist in the interiors of giant planets and stars, though their low frequencies will make them difficult to detect.
Read the full abstract

Atomic Physics

Adiabatic passage of $^{205}$TlF with microwaves in a cryogenic beam

Olivier Grasdijk, Jakob Kastelic, Jianhui Li et al. (+7 more)

We present a hyperfine-resolved state preparation scheme for thallium fluoride (TlF) molecules based on microwave-driven adiabatic passage (AP) in a spatially varying electric field. This method enables efficient and robust population transfer between selected $\left|J,m_J=0\right\rangle$ hyperfine sublevels of the $X\,^1\Sigma^+_0$ ground state in a cryogenic molecular beam, a key requirement for the CeNTREX search for nuclear time-reversal symmetry violation. Two sequential stages of AP are implemented. The first transfers population from $J=0$ to $J=1$ at a local field of $173~\mathrm{V/cm}$, and the second transfers from $J=1$ to $J=2$ at $110~\mathrm{V/cm}$. Transfer efficiencies are quantified through laser-induced fluorescence, and accounting for residual population in excited rotational levels after a prior stage of rotational cooling. We achieve state transfer efficiencies of $0.92(6)$ and $1.05(5)$ for the first and second states of AP, respectively. This corresponds to a total efficiency of $0.97(8)$ for population transfer from $J=0$ to $J=2$. These results demonstrate robust and high-fidelity preparation of specific rotational/hyperfine states in TlF.
Read the full abstract

All authors Olivier Grasdijk, Jakob Kastelic, Jianhui Li, Oskari Timgren, Konrad Wenz, Yuanhang Yang, Perry Zhou, David Kawall, Tanya Zelevinsky, David DeMille

Computational Physics

Deep Learning Framework for Enhanced Neutrino Reconstruction of Single-line Events in the ANTARES Telescope

A. Albert, S. Alves, M. André et al. (+137 more)

We present the $N$-fit algorithm designed to improve the reconstruction of neutrino events detected by a single line of the ANTARES underwater telescope, usually associated with low energy neutrino events ($\sim$ 100 GeV). $N$-Fit is a neural network model that relies on deep learning and combines several advanced techniques in machine learning –deep convolutional layers, mixture density output layers, and transfer learning. This framework divides the reconstruction process into two dedicated branches for each neutrino event topology –tracks and showers– composed of sub-models for spatial estimation –direction and position– and energy inference, which later on are combined for event classification. Regarding the direction of single-line events, the $N$-Fit algorithm significantly refines the estimation of the zenithal angle, and delivers reliable azimuthal angle predictions that were previously unattainable with traditional $\chi^2$-fit methods. Improving on energy estimation of single-line events is a tall order; $N$-Fit benefits from transfer learning to efficiently integrate key characteristics, such as the estimation of the closest distance from the event to the detector. $N$-Fit also takes advantage from transfer learning in event topology classification by freezing convolutional layers of the pretrained branches. Tests on Monte Carlo simulations and data demonstrate a significant reduction in mean and median absolute errors across all reconstructed parameters. The improvements achieved by $N$-Fit highlight its potential for advancing multimessenger astrophysics and enhancing our ability to probe fundamental physics beyond the Standard Model using single-line events from ANTARES data.
Read the full abstract

All authors A. Albert, S. Alves, M. André, M. Ardid, S. Ardid, J.-J. Aubert, J. Aublin, B. Baret, S. Basa, Y. Becherini, B. Belhorma, F. Benfenati, V. Bertin, S. Biagi, J. Boumaaza, M. Bouta, M.C. Bouwhuis, H. Brânzaş, R. Bruijn, J. Brunner, J. Busto, B. Caiffi, D. Calvo, S. Campion, A. Capone, F. Carenini, J. Carr, V. Carretero, T. Cartraud, S. Celli, L. Cerisy, M. Chabab, R. Cherkaoui El Moursli, T. Chiarusi, M. Circella, J.A.B. Coelho, A. Coleiro, R. Coniglione, P. Coyle, A. Creusot, A.F. Díaz, B. De Martino, C. Distefano, I. Di Palma, C. Donzaud, D. Dornic, D. Drouhin, T. Eberl, A. Eddymaoui, T. van Eeden, D. van Eijk, S. El Hedri, N. El Khayati, A. Enzenhöfer, P. Fermani, G. Ferrara, F. Filippini, L. Fusco, S. Gagliardini, J. García-Méndez, C. Gatius Oliver, P. Gay, N. Geißelbrecht, H. Glotin, R. Gozzini, R. Gracia Ruiz, K. Graf, C. Guidi, L. Haegel, H. van Haren, A.J. Heijboer, Y. Hello, L. Hennig, J.J. Hernández-Rey, J. Hößl, F. Huang, G. Illuminati, B. Jisse-Jung, M. de Jong, P. de Jong, M. Kadler, O. Kalekin, U. Katz, A. Kouchner, I. Kreykenbohm, V. Kulikovskiy, R. Lahmann, M. Lamoureux, A. Lazo, D. Lefèvre, E. Leonora, G. Levi, S. Le Stum, S. Loucatos, J. Manczak, M. Marcelin, A. Margiotta, A. Marinelli, J.A. Martínez-Mora, P. Migliozzi, A. Moussa, R. Muller, S. Navas, E. Nezri, B. Ó Fearraigh, E. Oukacha, A.M. Păun, G.E. Păvălaş, S. Peña-Martínez, M. Perrin-Terrin, P. Piattelli, C. Poiré, V. Popa, T. Pradier, N. Randazzo, D. Real, G. Riccobene, A. Romanov, A. Sánchez Losa, A. Saina, F. Salesa Greus, D.F.E. Samtleben, M. Sanguineti, P. Sapienza, F. Schüssler, J. Seneca, M. Spurio, Th. Stolarczyk, M. Taiuti, Y. Tayalati, B. Vallage, G. Vannoye, V. Van Elewyck, S. Viola, D. Vivolo, J. Wilms, S. Zavatarelli, A. Zegarelli, J.D. Zornoza, J. Zúñiga