Optics
Dual-state control of lasing and absorption via conjugate exceptional points
Arnab Laha, Somnath Ghosh, Adam Miranowicz et al. (+1 more)
Lasing and coherent perfect absorption (CPA) are time-reversed manifestations of non-Hermitian light-matter interactions. While exceptional points (EPs) have been extensively explored for controlling lasing dynamics, their role in the concurrent manipulation of lasing and absorption remains largely unexplored. Here, we demonstrate the emergence of a pair of conjugate second-order EPs (EP2s) in a gain-loss-engineered Fabry-Pérot microcavity that enables dual-state operation involving both coherent amplification and absorption. By spatially tailoring gain and loss, we realize two such EP2s: one associated with the coalescence of coupled scattering-
matrix poles and the other, its conjugate, arising from the coalescence of corresponding zeros, thereby directly linking the amplifying and absorbing branches of the system. Leveraging the branch-point
topology of these conjugate EP2s, we adiabatically encircle them in the gain-loss parameter space and achieve deterministic state permutation, enabling multiple reconfigurable switching schemes for the controlled generation and manipulation of threshold lasing and CPA. Notably, simultaneous encirclement of these conjugate EP2s yields a coordinated dual-state switching protocol, resulting in a frequency-matched coexistence of lasing and absorption responses within the same cavity. Our results establish an EP-based framework for unified and flexible control of lasing and absorption in non-Hermitian photonic systems.
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All authors Arnab Laha, Somnath Ghosh, Adam Miranowicz, Lin Wu
Applied Physics
A Promising Ohmic Contacts Approach for High-Al AlxGa1-xN (x>0.6) Channel HEMTs with AlN/GaN Digital Alloy Channel
Tariq Jamil, Abdullah Al Mamun Mazumder, S M Tazbiul Hasan et al. (+9 more)
In this paper we report a novel ohmic contact formation scheme for Extreme Bandgap (EBG) AlxGa1-xN (x>0.6) channel HEMTs with undoped barrier layers. Our approach consists of using a new low temperature (LT) pulsed metal-organic chemical vapor deposition (PMOCVD) doping scheme for the n++-GaN regrown contacts and an AlxGa1-xN digital
alloy (DA) channel layer comprising short period super
lattices (SPSL) of AlN and GaN. Pulsed growth and doping yield a sheet resistivity which is a factor of 3-5 lower than that of conventional doped n++-GaN layers grown under identical conditions. Moreover, the regrown n++-GaN layer has no hetero-barrier with the GaN layers of the AlxGa1-xN DA channel. These innovations led to MOCVD regrown linear ohmic contacts and a record-low contact resistance Rc ~6.5 {\Omega}-mm to the Al0.62Ga0.38N DA channel layer of a HEMT with AlN barrier layer.
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All authors Tariq Jamil, Abdullah Al Mamun Mazumder, S M Tazbiul Hasan, Mafruda Rahman, Muhammad Ali, Ankit Malik, Kamal Hussain, Chandan Joishi, Mansura Sadek, James G. Fiorenza, Grigory Simin, Asif Khan
Medical Physics · Cross-listed from cs.CV
Radiuma: A Unified Zero-Code Executable Graphical Workflow Generator for Reproducible and Shareable Medical Image Analysis and Machine Learning
Mohammad Salmanpour, Mehrdad Oveisi, Isaac Shiri et al. (+1 more)
Medical image computing software is essential for identifying imaging biomarkers that can support diagnosis, prognosis, treatment planning, and clinical research. However, the lack of standardized, user-friendly, and reproducible software environments has limited the broader adoption of advanced medical image analysis workflows. We present Radiuma, a freely available modular platform designed to support reliable and reproducible medical image analysis across multiple modalities and file formats. Radiuma integrates image reading, visualization, registration, fusion, processing,
segmentation, radiomics feature extraction, and machine learning modules for classification, regression, and clustering. Its modular design allows users to execute each component independently or connect modules through a visual workflow system, where the output of one step can be graphically passed to the next. This enables the creation of custom, executable, and reproducible multi-step pipelines without requiring extensive programming expertise. Results from each module can be inspected directly in the visualization window, providing immediate feedback on processing quality and workflow accuracy. Radiuma also supports saving and sharing customized workflows, promoting transparency, reusability, and consistency across collaborative studies. By combining flexibility, usability, and standardized analysis tools, Radiuma provides a practical environment for radiomics and machine learning research in clinical and translational settings. The platform is designed to be accessible to users with diverse expertise, including radiologists, physicists, clinicians, and data scientists.
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All authors Mohammad Salmanpour, Mehrdad Oveisi, Isaac Shiri, Arman Rahmim
Instrumentation and Detectors
Wideband Balanced Photodetectors for Classical and Quantum Light Detection from Optical, EUV, to X-rays
Ivan Ryger, Terry Brown, Dina S. Eissa et al. (+1 more)
The rapid development of coherent short-wavelength light sources in the extreme ultraviolet (EUV) and soft X-ray (SXR) regimes has created a growing need for advanced optoelectronic detection capabilities, particularly for
quantum-noise-limited measurements, microelectronics and
semiconductor metrology, and emerging
quantum information applications. However, extending balanced photodetection to these wavelength regimes is severely hindered by a fundamental bandwidth-noise trade-off imposed by the exceptionally large junction capacitance of EUV-SXR silicon photodiodes. Here, we report a novel wideband photoreceiver architecture that overcomes this bottleneck via a bootstrapped transimpedance amplifier design. By leveraging a low-noise junction field-effect transistor interface, we effectively isolate the photodiode capacitance and suppress the apparent input capacitance seen by the core amplifier. Combined with active compensation of parasitic feedback reactance, this architecture mitigates the conventional trade-off between detector active area and signal bandwidth. Experimentally, we achieved a system-level input-referred noise floor of 13 $fA/\sqrt{Hz}$, closely approaching theoretical thermal limits. Furthermore, we achieved a six-fold extension in signal-to-noise limited bandwidth and, through the implementation of a novel grounded field plate, demonstrated a common-mode rejection ratio (CMRR) exceeding 30 dB up to 100 kHz. This highly scalable, silicon-based architecture effectively bridges the short-wavelength detection gap, establishing a robust experimental platform for next-generation
quantum-noise-limited and
quantum-enhanced X-ray measurement, as well as ultra-sensitive inspection and metrology applications in high-numerical-aperture EUV lithography.
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All authors Ivan Ryger, Terry Brown, Dina S. Eissa, Chen-Ting Liao
Instrumentation and Detectors
Long-term stability-degradation analysis of DUNE SiPMs in Liquid Nitrogen
Thomas Tsang, Flavio Cavanna, Hucheng Chen et al. (+3 more)
We developed a cryogenic
photon readout system to monitor arrays of DUNE SiPMs operating at various over-voltages in
liquid nitrogen (LN2) for over three months. Photoelectron signals were read out simultaneously via weak capacitive coupling to the micro-FEMB, a 32-channel charge sensitive readout board designed for 77 K to 300 K operation in
liquid argon time projection chambers (LAr-TPCs). A waveform snippet acquisition scheme was implemented to capture waveform signals whenever they exceeded a predetermined trigger level; consequently, empty waveforms were discarded. Selected SiPM parameters were monitored to detect any deviations beyond statistical fluctuations. While some parameters exhibited ~1{\sigma} variations over the 3-month test period, there was no evidence of drift being enhanced when operating at 4 V or 5 V compared to a 3 V over-voltage. However, a discernible PDE drop was observed across all channels, which warrants further investigation. In addition, we demonstrated that our system can simultaneously read out single
photons from a selected group of SiPMs at room temperature.
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All authors Thomas Tsang, Flavio Cavanna, Hucheng Chen, Shanshan Gao, Lingyun Ke, Koloina Rambeloson
Optics · Cross-listed from quant-ph
Self-Generated Chiral Rotation in Whispering-Gallery Optomechanics
Mohamed Hatifi
Backscattering in whispering-gallery-mode resonators is usually a passive mode-splitting mechanism produced by a fixed defect. Here, we show that, when the backscatterer is a mechanical angular degree of freedom, the same process becomes an angular-
recoil backaction channel capable of generating chirality under reciprocal driving. A localized movable scatterer coherently converts
photons between clockwise and counterclockwise whispering-gallery modes, transferring angular
recoil in each circulation-changing event. In a weak-scattering driven-dissipative model, reciprocal bidirectional pumping gives zero net torque at rest, but rotation Doppler-shifts the two opposite scattering rates in opposite directions. For suitable detuning, this feedback produces negative angular friction, destabilizes the nonrotating reciprocal state, and selects one of two
symmetry-related steady rotations. The threshold scales inversely with the square of the WGM azimuthal index. The mechanically
chiral state produces a direction-dependent weak-probe response, visible as a Doppler splitting of the backscattered spectra, turning passive WGM mode splitting into a minimal mechanism for autonomous
chiral optomechanics.
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Optics
Second-Order Nonlinear Response in Centrosymmetric Hyperbolic Media
Evgenii E. Narimanov
We show that centrosymmetric natural
hyperbolic media such as hexagonal boron nitride exhibit a bulk second-order nonlinear response, leading to second-harmonic and sum- and difference-frequency generation with efficiencies comparable to those of established nonlinear optical
crystals such as potassium dihydrogen phosphate.
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Physics and Society
Human-AI Collaboration in Science at Scale: A Global Large-scale Randomized Field Experiment
Binglu Wang, Weixin Liang, Jiahui Xue et al. (+4 more)
Collaboration is the defining mode of modern science, yet its core mechanism – feedback – remains hard to observe, difficult to scale, and unequally distributed. Here we test whether large
language models (LLMs) can contribute to this hidden but vital practice and reallocate scientific feedback, an essential yet scarce resource for knowledge production. In a global large-scale randomized field experiment, we delivered customized LLM-generated feedback for over 31,000 arXiv preprints across 150 fields and more than 45,000 researchers from 133 geographic regions. Relative to controls, authors who received feedback had a significantly higher likelihood of revising their manuscripts, corresponding to a 12.55% relative increase over the baseline revision rate. Exposure to AI feedback also increased authors' subsequent use of LLM tools in their future papers, suggesting longer-run shifts in scientific practice. These effects were strongest among authors from non-English-dominant research regions, manuscripts less embedded in the scholarly literature, and teams with lower h-indexes and earlier career stages, consistent with the idea that AI feedback may provide the greatest benefit where access to timely critique is otherwise limited. Together, these findings provide causal evidence that structured AI-based interventions can transform access to scientific feedback from a largely private advantage into a more widely distributed resource, with broader implications for productivity, equity, and capacity across the global research system.
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All authors Binglu Wang, Weixin Liang, Jiahui Xue, Yuhui Zhang, Hancheng Cao, Dashun Wang, Yian Yin
Atomic Physics
Minimally Destructive Fast Imaging of Single Atoms in an Optical Tweezer Array with Coherent Excitation
Rei Yokoyama, Takumi Kashimoto, Kosuke Shibata et al. (+8 more)
Ultracold neutral atoms in an optical
lattice and an optical tweezer array offer highly-controllable
quantum many-body systems, utilized for various
quantum science and technology such as
quantum computing,
quantum metrology, and
quantum simulation. By combining high-fidelity imaging of individual atoms, one can further enhance the capability of such experimental platforms as
quantum gas microscopes, tweezer clocks, and tweezer-array-based
quantum computers. In this work, we propose a minimally destructive single-atom imaging by deterministic coherent excitation of atoms with alternately applied pi-pulses from counter-propagating directions, mitigating the fundamental heating effect associated with the
stochastic absorption process. Using ytterbium-174 atoms trapped in an optical tweezer array, we experimentally demonstrate fast and low-loss single-atom imaging with a discrimination fidelity of 99.89(5) % and a survival probability of 98.80(44) % in 17.6 microseconds. Importantly, our scheme exhibits the lower heating rate, about half of that of the former scheme utilizing the incoherent excitation. This fast and minimally destructive imaging scheme is beneficial for relaxing the requirement on the trap depth, thereby enabling scalable atom imaging across a wide range of
quantum science platforms.
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All authors Rei Yokoyama, Takumi Kashimoto, Kosuke Shibata, Yuki Kawamura, Toshi Kusano, Chih-Han Yeh, Reiji Asano, Yuma Nakamura, Tetsushi Takano, Yosuke Takasu, Yoshiro Takahashi
Applied Physics · Cross-listed from cond-mat.str-el
Charge density wave in a band insulator
Md Shafayat Hossain, Wenhao Liu, Yuqi Zhang et al. (+20 more)
Charge density wave (CDW) implies a periodic modulation of the charge density. Typically observed in metallic systems, CDWs arise from
Fermi surface instabilities, resulting in the total or partial gapping of the
Fermi surface. Here, we present experimental evidence for a CDW state emerging in a band
insulator which has no
Fermi surface. The bulk and surface of our material platform, Bi4Br4, is gapped over the entire Brillouin zone. Through topographic and spectroscopic imaging at low temperatures, we unveil an unexpected unidirectional charge modulation in Bi4Br4, breaking the
lattice translation
symmetry. The CDW develops at temperatures below 40 K and adds an energy gap atop the existing insulating gap of Bi4Br4. Furthermore, our transport measurements reveal nonlinear electrical conduction, a phenomenon conventionally associated with the sliding or phason mode of incommensurate CDWs. These highly unusual observations represent a new type of CDW and demand a new theoretical framework for CDWs.
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All authors Md Shafayat Hossain, Wenhao Liu, Yuqi Zhang, Qi Zhang, Chao Lei, Nana Shumiya, Kouta Dagnino, Maksim Litskevich, Yu-Xiao Jiang, Jia-Xin Yin, Nikhil Dhale, Zi-Jia Cheng, Byunghoon Kim, Yongkai Li, Tyler A. Cochran, Xian P. Yang, Fan Zhang, Yugui Yao, Zhiwei Wang, Bing Lv, Titus Neupert, Luis Balicas, M. Zahid Hasan
Optics
Polarization-Induced Beam Bending: Mathematical Model, Discretization, and Algorithm
Harbir Antil, Rainald Löhner and Sarswati Shah
We study a reduced hydrodynamic formulation of paraxial vector beam propagation in which the beam intensity, optical phase, and spatially-dependent polarization are coupled through a nonlinear dispersive system. While prior analytical work derived a solution for the beam path valid for short propagation distances, a fully resolved numerical treatment of the model over long ranges has not previously been available. Here we present a conservative numerical scheme for the coupled system, combining a finite-volume discretization of the intensity equation with monotone Hamilton–Jacobi (H-J) solvers for the phase dynamics and upwind transport of polarization. The method preserves the nonnegativity of the intensity and remains stable under long-distance propagation. We perform large-scale simulations over propagation distances of tens of meters, while resolving millimeter-scale transverse structure. The numerical results reproduce the analytically predicted and experimentally observed quadratic beam bending at short distances and reveal systematic deviations beyond the asymptotic regime. These deviations arise from nonlinear phase accumulation and dispersive effects captured by the full model but are neglected in the short-distance approximation.
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Fluid Dynamics
Divergence-aware adaptive prediction framework for accelerating CFD simulations of unsteady flows
Xiangrui Zou, Zhuoqun Zhao, Guillermo Barragán et al. (+1 more)
Reliable long-horizon prediction remains a challenge for data-driven CFD surrogates, because offline-trained models accumulate autoregressive errors and lose accuracy when operating conditions change. This work develops a divergence-aware adaptive CFD-surrogate framework that couples a CFD solver with a proper orthogonal decomposition-deep learning (POD-DL) surrogate in a closed-loop workflow. CFD snapshots are compressed by POD, and a neural-network predictor advances the reduced state in time. The surrogate performs autoregressive
forecasting, while its reliability is monitored online. When a prescribed update interval is reached or prediction degradation is detected, the CFD solver is automatically recalled to generate new snapshots and update the surrogate. The framework is assessed for three-dimensional flow past a circular cylinder at Re = 160-400. Baseline non-adaptive predictions exhibit progressive error growth over long forecast horizons, confirming the need for online correction. With prescribed update intervals, the adaptive framework preserves the dominant wake dynamics and reduces error growth after retraining compared with the non-adaptive model. For a representative 200-snapshot interval, the framework achieves a speed-up ratio of approximately 92 relative to CFD. An event-triggered mode is introduced using ensemble uncertainty and dynamically estimated thresholds. This mode terminates unreliable forecasts without requiring ground-truth CFD data during prediction, and the detected triggers are consistent with the onset of deterioration in the lift-coefficient
evolution. Under varying inlet conditions, the framework detects regime changes, recalls CFD, and recovers reliable predictions. These results demonstrate that divergence-aware CFD-surrogate coupling provides a robust and efficient route for adaptive long-horizon flow prediction under evolving operating conditions.
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All authors Xiangrui Zou, Zhuoqun Zhao, Guillermo Barragán, Soledad Le Clainche
Fluid Dynamics
On the Two-Dimensional Structure and Asymmetries of Ionic Liquid Electrospray Plumes
Zach Ulibarri, Giuliana Hofheins, Sophia Gessman et al. (+1 more)
Vacuum electrosprays play important roles in electrospray propulsion, a type of spacecraft electric propulsion wherein charged ions or droplets are accelerated at high velocity to provide spacecraft thrust, and they also have important implications for surface etching and mass spectrometry. Here we present the first fully two-dimensional time-of-flight (TOF) mass spectrometry survey of a
vacuum electrospray plume, generated by a tungsten needle externally-wetted with the ionic
liquid 1-Ethyl-3-methylimidazolium tetrafluoroborate (EMI-BF$_4$). We find that the plume exhibits clear two-dimensional compositional variation, structure, and asymmetry, with heavy particles and energetic neutrals being more prevalent in the center and a ring-shaped distribution for the monomers (the lightest molecular ions) peaking at 10$^\circ$ off beam-axis with a relative minima in the center. Using a new spatial baseline characterization method, we find a population of energetic neutrals firing 3.4$^\circ$ off beam-axis. In particular, we find that by comparing different parts of the plume, the estimated propulsive efficiency from any one sampled point may vary by as much as a factor of 6. We also find that high mass droplets, which are often assumed as absent in many studies of externally-wetted needles, may carry away significant propellant mass at lower effective velocity and reveal a cone-jet mode of operation at 470 nA output current. We thus find that whole-plume compositional surveys may more accurately assess plume composition and propulsive efficiency, and a significant portion of the `missing mass' in electrospray propulsion sources presumed to be operating in the pure ion regime can be potentially explained by limited sampling of the spatially non-uniform ion plume.
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All authors Zach Ulibarri, Giuliana Hofheins, Sophia Gessman, Elaine Petro
Optics
Lindell M. Williams, Yan Jin and Scott B. Papp
Generative pre-trained
transformers (GPTs) have enabled capability-rich
language models, but their operation incurs substantial costs in computation,
memory, and data movement. A major source of this cost is
attention, a nonlinear transformation that computes context-dependent weights from
token correlations. Here, we introduce Kerr-
soliton attention, harnessing driven-dissipative nonlinear dynamics in a resonator to realize, execute, and validate deep-learning
attention in physical hardware. We train a GPT
language model using an analytic representation of Kerr-
soliton attention and explore generative inference by streaming model-produced inputs through a time-multiplexed
soliton ensemble, whose nonlinear
evolution transforms the scores into
attention weights. We observe high-fidelity agreement between the experimentally produced weights and those predicted by the model. By mapping
memory and nonlinear computation to physical dynamics, Kerr-
soliton attention points toward hybrid digital-physical systems that reduce data movement in deep learning.
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Optics · Cross-listed from quant-ph
Quantum Error Correction and the Limits of Quantum Metrology
Zhuoran Bao and Daniel F. V. James
Quantum metrology has been making amazing progress in the past decades. It is always in researchers' interest to search for new optimal states that improve parameter estimation. In this paper, we point out a connection between the code's error correcting capacity and its ability to act as a sensor. We backed our claim by providing an example that relates the Absorption emission code to the sensor state for arbitrary state rotation. It is hoped that, in building such a unified theory, one can draw inspiration from error correction to develop promising
quantum sensors.
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Instrumentation and Detectors
Photon Calibration Techniques for High Resolution Cryogenic Detectors
William J. Matava and Michael R. Williams
Monoenergetic
photons from a pulsed
laser diode or LED are commonly used to calibrate the detector response of high-resolution calorimetric detectors. However, when the detector's resolution is larger than the energy of a single
photon, a calibration is normally derived using Poisson statistics. In this paper, we clarify the assumptions implicit in this calibration method, before considering how a more realistic model of a detector's performance will violate these assumptions, biasing the calibration. Finally, we judge the individual impact of specific detector parameters on our calibration, and conclude with discussion of both the limits of our calculations and the implications for state-of-the art detectors.
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Chemical Physics
Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs
Ihor Neporozhnii, Sjoerd Hoogland and Oleksandr Voznyy
Machine learning interatomic potentials (MLIPs) require generating computationally expensive, large-scale training
datasets to accurately simulate materials and molecules. Incorporating electronic structure information using multitask learning improves sample efficiency, however, training on full
Hamiltonian matrices, which scale quadratically with the number of atoms, is intractable for large
datasets. In this work, we show that multitask learning utilizing orbitally resolved semiempirical charges significantly improves sample efficiency and accuracy in MLIPs. To efficiently predict orbital charges, we implement a specialized equivariant model, reducing charge prediction error compared to an invariant baseline. By augmenting training with computationally inexpensive GFN1-xTB orbital charges, which scale linearly with the number of atoms, our model achieves a 58% reduction in energy mean absolute error relative to energy-only baselines, and requires five times less data to match their performance. Against the stronger baseline of joint energy-and-force training, orbital charges still reduce the energy error by 12%. Furthermore, our approach matches or outperforms models trained on expensive density functional theory (DFT) atomic charges, capturing orbitally resolved electronic complexity and forcing the network to learn a physically accurate latent space that spontaneously clusters metals by shared chemical properties. Because orbital charges are only required during training, this approach preserves inference efficiency, providing a scalable recipe for developing accurate, data-efficient foundation models for complex chemical systems.
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Atmospheric and Oceanic Physics
Seeing Inside the Storm: Improving Nowcasting by Integrating Meteorological Drivers
Minghui Qiu, Jun Chen, Lin Chen et al. (+5 more)
Most nowcasting systems, built on radar reflectivity, focus on current precipitation, ignoring the atmospheric precursors – such as low-level convergence, turbulent eddies, and latent heating – that offer a fleeting window to foresee storm birth. We introduce MeteoLogist, a physics-inspired radar intelligence framework that models the full life cycle of convection – from its precursors to organized storm
evolution. However, exploiting these precursors is non-trivial: they originate from multiple meteorological drivers – thermodynamic, kinematic, and microphysical – that evolve asynchronously (C1) and remain spatially fragmented (C2). To this end, MeteoLogist designs three tightly integrated components. The Physics-Tailored Encoders process radar echoes according to their intrinsic physical scales and semantics, forming thermodynamic, kinematic, and microphysical streams that capture distinct dynamical regimes. The Temporal-Phase Aligner addresses C1 by leveraging causal temporal
attention to capture when and how different drivers interact and activate. The Cross-Field Spatial Aggregator addresses C2 through cross-regional fusion, aligning weak and scattered precursors across neighboring
cells to expose upstream triggers and enforce spatial coherence. Evaluated on 3D-NEXRAD (2020–2022, US-wide), MeteoLogist boosts high-impact detection (CSI40) by +9.7% over strong baselines, and achieves a remarkable 37.67% gain during the storm-developing stage – demonstrating true foresight in sensing storms before they appear. The code can be found in the supplementary material.
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All authors Minghui Qiu, Jun Chen, Lin Chen, Weifeng Chen, Shuxin Zhong, Zhidan Liu, Yu Zhang, Kaishun Wu
Optics
Breaking order: Talbot effect with spinodal architectures
Robin Krüger, Jeevan Rois, Martin Bech et al. (+1 more)
The Talbot effect describes the emergence of periodic patterns in perturbed propagating wave fields. The effect is well studied for perturbations from structurally coherent optics such as diffraction gratings. The emergence of freeform and metaoptical designs raises the question of whether comparable behavior can also be observed from complex, non-periodic structures. Here we demonstrate that
stochastic structures inspired by recent metamaterial designs, display a strong Talbot-like behavior. Re-emergence of projected wavefronts through
stochastic spinodal architectures at distinct propagation distances are proven theoretically and experimentally in the visible and hard X-ray regimes. A direct application of this phenomenon is X-ray dark-field imaging for characterizing artificial and natural meso-structured materials. Our work shows that spinodal X-ray optics effectively bridge the gap between the two opposing approaches in dark-field X-ray imaging that advocate for either spatially fully coherent (i.e gratings) or incoherent (i.e diffusers) optics. This opens opportunities for exploring a new dimension in the implementation of X-ray imaging methods. Given the impact and universality of the classical Talbot effect, we expect our work to enable new opportunities for characterizing and manipulating matter.
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All authors Robin Krüger, Jeevan Rois, Martin Bech, Matias Kagias
Fluid Dynamics
Particle Image Velocimetry of 3D printed vascular fluidic phantom devices
Job van Essen, Ahmed Sharaf, Denzel Hopman et al. (+2 more)
Altered hemodynamics play a key role in cerebrovascular diseases such as aneurysms and stenosis. However, in vivo imaging lacks the spatial resolution required to resolve flow dynamics in small vessels. This study presents an experimental framework to investigate microscale hemodynamics using transparent 3D printed vascular models and particle image velocimetry (PIV). Optically transparent microfluidic models with straight and pathological (aneurysmal and stenotic) geometries were fabricated via additive manufacturing up to a minimum diameter size of 500 microns and characterized using optical microscopy. Flow experiments were conducted under steady laminar conditions, and local velocity fields and wall shear stress (WSS) were measured using microPIV. Measured velocities have been compared with analytical Hagen Poiseuille predictions, obtaining mean relative errors of 5 to 17 percent. The platform reliably captured key flow features and spatial variations in velocity. Overall, the results demonstrate that transparent 3D printed vascular models combined with microPIV provide a robust experimental approach for studying microscale cerebrovascular hemodynamics.
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All authors Job van Essen, Ahmed Sharaf, Denzel Hopman, Selene Pirola, Paola Fanzio
Atmospheric and Oceanic Physics
Atmosphere as a steam engine
Anastassia Makarieva and Andrei Nefiodov
Earth's atmosphere operates a steam cycle in which water vapor evaporates from the surface, expands, condenses, and returns as precipitation. The Clausius-Clapeyron law relates the incremental expansion work of saturated water vapor to latent heat converted at a Carnot efficiency corresponding to the temperature difference between evaporation and condensation. We generalize this relation to an atmospheric column with condensation occurring over a range of heights and derive the expansion work per mole of precipitated water. This includes the gravitational work associated with lifting moist air to the mean condensation height, the expansion work generated by condensation, and a correction for incomplete condensation. Using GPCP v3.3 precipitation and observational constraints on condensation height, we estimate the global steam-engine power as $W_v=4.4\pm0.9$ W/m2, close to an independent estimate of total atmospheric power, $W=W_P+W_K\simeq4.3\pm0.6$ W/m2, obtained from the gravitational power of precipitation and kinetic energy generation by horizontal pressure gradients diagnosed from MERRA-2. Kinetic energy generation is $W_K\simeq3.2\pm0.3$ W/m2, of which at least two thirds is generated in the lower atmosphere. The smaller upper-atmospheric contribution, dominated by temperature-related pressure gradients, is comparable to Lorenz available potential energy generation. The agreement between steam-engine and atmospheric power is linked to condensation and precipitation fallout. By removing water from the atmospheric gas phase and enabling column-mass redistribution, precipitation maintains surface pressure gradients that drive cross-isobaric flow in the frictional lower atmosphere. The steam-engine framework thus provides a thermodynamic basis for condensation-induced atmospheric dynamics and identifies a major lower-atmospheric power pathway associated with water phase transitions.
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Atomic Physics
Vapor-Cell-Induced Uncertainty in Rydberg Atom Measurements via the Electric-Field Volume-Integral-Equation Method
Martin Stumpf, William J. Watterson, Rajavardhan Talashila et al. (+5 more)
Electromagnetic scattering effects of a vapor
cell on electric-field measurements using Rydberg atom-based sensors are analyzed with the aid of the volume integral equation method. In a manner similar to measurement, this computational approach determines the electric field over grid points within the vapor
cell. Its relatively high computational efficiency makes it suitable for use in optimization routines and statistical uncertainty studies. We apply this method to compare uncertainty contributions arising due to the presence of the vapor
cell, such as uncertainty in the glass relative permittivity or standing wave formation inside the
cell, to those arising from the atomic spectroscopic measurement, such as uncertainty in the atomic dipole moment. For vapor
cell dimensions less than half a wavelength, the dominant uncertainty source arises from uncertainty in the glass relative permittivity, resulting in a total uncertainty of $\sim$3.5% – comparable to the best uncertainties obtained with traditional field generation methods at national metrology institutes. Precise permittivity measurements have the potential to further reduce measurement uncertainty to $<1$%.
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All authors Martin Stumpf, William J. Watterson, Rajavardhan Talashila, Matt T. Simons, Alexandra Artusio-Glimpse, Lawrence Carslake, Tian Hong Loh, Christopher L. Holloway
Fluid Dynamics
Soft Mobility Theory
Christophe Eloy
Predicting how a deformable body moves and deforms in a viscous flow underlies problems ranging from microorganism locomotion to soft microrobotics, yet existing frameworks are either problem-specific or ill-suited to inverse design. We propose the soft mobility theory: applying the principle of virtual power and the Lorentz reciprocal
theorem to a hyperelastic body in a background Stokes flow yields a configuration-dependent ordinary differential equation for the generalized coordinates of the body. This soft mobility equation extends classical rigid-body mobility theory in that the mobility, elastic, body-force, and flow-coupling tensors all depend explicitly on the instantaneous deformation. We specialize the framework to assemblies of hydrodynamically interacting spheres connected by elastic springs, using the Rotne-Prager-Yamakawa approximation to compute the mobility, and validate it on canonical problems spanning rigid and flexible bodies in quiescent and shear flows. An open-source JAX implementation makes entire simulations end-to-end differentiable. This allows efficient gradient-based inverse design: as proofs of concept, we recover the asymptotic optimum of a three-sphere swimmer and design a soft gyrotactic "surfer" that exploits passive deformation to ascend faster than its rigid counterpart in a Taylor-Green flow.
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Instrumentation and Detectors
TCAD + Allpi$\text{x}^2$ Simulation study of MALTA2, a Depleted Monolithic Active Pixel Sensor for future tracking
L. Li, P. Behera, D.V. Berlea et al. (+22 more)
In this work, a hybrid simulation framework combining TCAD and Allpi$\text{x}^2$ is presented to investigate the sensor properties of MALTA2, a depleted monolithic active pixel sensor designed for future tracking. The study starts from 3D modeling and transient simulations in TCAD, with generic doping profiles and simple well structures. The resulting doping profiles and electric field are extracted and fed into Allpi$\text{x}^2$ for high-statistics Monte Carlo simulations in both DUT-only and full-
telescope mode. Simulations reveal a strong dependence of sensor performance, specifically the detection efficiency and cluster size, on the doping concentration of the N-type blanket at the sensor surface. The doping concentration is then optimized by comparing simulations with measurement data. The active depth of the depleted region of the MALTA2 sensor is estimated in both simulations and measurements using a grazing angle method, in which the sensor is positioned at various inclinations relative to the beam, covering angles from 0 to 60 degrees. Excellent agreement on active depth is obtained with the optimal doping concentration, showing a deviation of 2% from the measured value at a threshold of 450\,$\text{e}^-$. Consequently, the framework offers a generic toolkit for sensor studies without requiring proprietary information.
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All authors L. Li, P. Behera, D.V. Berlea, D. Bortoletto, C. Buttar, T. Chembakan, V. Dao, G. Dash, Y. Enari, L. Fasselt, S. Haberl, T. Inada, F.K. Isik, C. Issever, X. Li, Y. Okazaki, H. Pernegger, P. Riedler, W. Snoeys, C.A Solans Sanchez, A. Swoboda, I. Turk Cakir, M. van Rijnbach, A. Vijay, S. Worm
Optics · Cross-listed from math-ph
Exact versus tight-binding models in longitudinally modulated $\mathcal{PT}$-symmetric coupled waveguides
Alonso Contreras-Astorga and José Israel Galindo-Rodríguez
The tight-binding (TB) model is a widely adopted approximation scheme for describing light propagation in waveguide arrays. Despite its success, its validity in $\mathcal{PT}$-symmetric systems characterized by strong longitudinal modulation has not been rigorously benchmarked against exact analytical solutions. In this work, we address this gap by performing a comparative analysis between exact continuous solutions derived from $z$-dependent supersymmetric (SUSY) transformations and their corresponding discrete TB approximations. To achieve this, we develop a theoretical model for two PT-symmetric coupled waveguides subject to longitudinal modulation. We then evaluate the performance of the TB framework against the exact SUSY
benchmark. Our results delineate the specific validity range of the TB approximation, demonstrating its proficiency in reproducing spatial intensity distributions. However, we also identify its limitations in accurately capturing the complex oscillatory phase dynamics inherent to this non-Hermitian
evolution.
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Fluid Dynamics
An ensemble-variational approach for open-loop flow control
Riccardo Maranelli, Vincent Mons, Jean-Camille Chassaing et al. (+2 more)
The design of effective control strategies for unsteady flows governed by complex, nonlinear dynamics remains a central challenge in fluid mechanics. Adjoint-based optimisation methods, while efficient for high-dimensional problems, require the derivation and implementation of adjoint equations and can exhibit numerical sensitivities in systems that are not smoothly differentiable. This work proposes an ensemble-variational (EnVar) framework as a non-intrusive alternative, in which cost-function gradients are approximated through a finite ensemble of perturbed control vectors, requiring no modification of the forward solver. The present approach is tailored to address high-dimensional control problems, here considering the case where the actuator is represented as a spatially distributed forcing field. The methodology is assessed on two-dimensional open-cavity flows across Reynolds regimes spanning from quasi-periodic to chaotic dynamics. In the quasi-periodic regime, the EnVar framework recovers control strategies consistent with adjoint-based optimisation, achieving significant reductions in kinetic energy fluctuations and driving the flow toward a periodic limit cycle. In the chaotic regime, the framework remains effective in estimating gradient information and mitigating flow fluctuations. Taken together, these results demonstrate that the EnVar method constitutes a computationally efficient, parallelisable, and non-intrusive alternative to adjoint-based approaches for high-dimensional flow optimisation across a broad range of unsteady and chaotic regimes.
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All authors Riccardo Maranelli, Vincent Mons, Jean-Camille Chassaing, Matthieu Queguineur, Taraneh Sayadi
Chemical Physics · Cross-listed from cond-mat.stat-mech
Chirality-sensitive mobility and dissipation of Brownian motion on a helical landscape
Debankur Bhattacharyya and Abraham Nitzan
We study the Brownian dynamics and linear response of a particle with inertia moving in a 2-dimensional helical landscape imprinted on a cylindrical surface. In the harmonic well approximation, the deterministic motion separates into free propagation along the screw direction and harmonic motion in the transverse screw-normal direction. We show that for isotropic damping this simplification survives in the Langevin description, whereas anisotropic damping along the axial and angular directions couples the
stochastic dynamics and destroys separability. The resulting anisotropic model is formulated as a linear Ornstein-Uhlenbeck process in phase space with a zero mode associated with
diffusion along the screw coordinate, so that in an infinite system the full phase-space dynamics does not relax to a stationary distribution. To treat transport in this setting, we construct the stationary dynamics in the stable subspace obtained after projecting out the zero mode. This leads to a linear response theory for this system and yields closed analytical expressions for stationary time-correlation functions and the dynamical mobility tensor in both the time and frequency domains. The off-diagonal elements of the mobility tensor describe cross-response between axial forcing and angular motion, and between applied torque and axial transport. Consistent with time reversal
symmetry, these cross mobilities are equal and provide a direct dynamical signature of the helical geometry. In addition, a simultaneous application of driving in both the axial and angular direction reveals asymmetry in energy dissipation rate due the helical landscape.
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Fluid Dynamics
A derivation of viscous thin film flow equations on curved surfaces
J. A. Hanna and R. S. Hutton
General equations are derived for slow viscous thin fluid film flows on curved surfaces through an extension of Leal's pedagogical approach, which leaves the characteristic velocity scale unspecified and employs a direct through-thickness integration of the continuity equation. The derivation neglects inertia, and includes gravitational, capillary, and Marangoni effects, the latter coupling the thickness dynamics to free-surface transport of a dilute, non-diffusing surfactant. The resulting general expression incorporates the leading order terms of each type, as well as additional terms that become leading order for nongeneric cases. A few examples are briefly presented and literature comparisons made. The importance of gradients in
curvature is emphasized, and it is suggested that nondimensionalization of
geometric features might lead to further useful generalizations. This relatively simple formulation is intended as a starting point for exploring interactions between geometry,
gravity, and surface tension.
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Optics
Correlation visibility and generalized Siegert relation for random light beams
Yi Cui, Wanting Hou, Jun Xiong et al. (+1 more)
Phase difference is central to classical coherence theory. With the advancement of various light-field modulation techniques, artificially generated pseudo-thermal light sources or random light beams can exhibit exotic wavefront correlation properties. However, such spatial wavefront correlations cannot be fully characterized using the phase difference alone. For instance, for a pair of conjugate pseudo-thermal beams, the spatial wavefronts exhibit a significant anti-correlation, meaning that the sum of their wavefronts tends to be constant. In this work, we propose the concept of degree of wavefront correlation $p^{(1)}$, ranging symmetrically from $-1$ to $+1$, for numerically calculating the wavefront correlation properties among various pseudo-thermal light sources, and the sign (positive or negative) can be used to determine the tendency-whether it leans toward wavefront-difference or wavefront-sum correlation. Numerical results demonstrate that the classical Siegert relation does not apply to pseudo-thermal light sources that exhibit wavefront-sum correlation properties. To address this, we propose a generalization valid for all Gaussian pseudo-thermal light. Experimentally, we introduce the measurable quantities of correlation visibility $\mathcal{V}_g$ and correlation background $\mu_g$, which form a two-dimensional classification framework $\{\mu_g,\mathcal{V}_g\}$ that enables the experimental characterization of diverse Gaussian pseudo-thermal light using a common-path interferometer and intensity correlation measurement. Furthermore, the correlation visibility $\mathcal{V}_g$ can serve as an observable criterion for a zero-mean, non-circularly symmetric, and jointly Gaussian distribution.
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All authors Yi Cui, Wanting Hou, Jun Xiong, Zhiyuan Ye
Atmospheric and Oceanic Physics
The physics of AI weather models
George Craig, Tobias Selz, Matthias Beylich et al. (+1 more)
Could it be that AI weather models are solving physical equations, although they may not be the equations used by conventional NWP models? We compute correlations of forecast skill and Centered Kernel
Alignment, providing evidence that different AI weather models represent the atmosphere in similar ways, despite differences in architecture and capacity. We argue that the architecture and training of the AI models constrains the form of the physical laws that they might simulate. In particular, we propose that the models implement a particle description of the atmosphere, where the latent variables at each mesh point correspond to the position of a particle in the high dimensional latent space. We hypothesize that the movement of the particles follows a gradient flow in the latent space towards a minimum of a learned free energy functional. Analysis of the GraphCast and Aurora models show that they make changes on large spatial scales in the early processor layers and move to smaller scale with increasing layer depth, consistent with the gradient flow hypothesis.
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All authors George Craig, Tobias Selz, Matthias Beylich, Kirsten I. Tempest