Physics Education
The Physics Show – A community college delivers BIG on community outreach
Frank Cascarano and David Marasco
Founded in 2007, the Foothill College Physics Show has served nearly a quarter of a million attendees in the two decades that have followed. This demo show features both performances for the public and field trips for students from local Title 1 schools. The college's students play an important role, acting as both on-stage talent, leading tours of the college, and helping build equipment. From a small beginning, it now hosts over twenty-five thousand attendees a year, and is an important part of the college's outreach efforts.
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Chemical Physics
Resolving the Marcus-Rehm-Weller Paradox in Electron Transfer
Ethan Abraham
Marcus theory famously predicts that electron-transfer rates decrease once the thermodynamic driving force exceeds the reorganization energy. Yet many systems instead exhibit Rehm-Weller kinetics characterized by rate saturation rather than decrease. Here we show that these apparently contradictory phenomenologies emerge as opposite physical limits of the same two-state
quantum Hamiltonian. In the normal region, the model recovers both Marcus and Rehm-Weller behavior. In the inverted region, however, it predicts Marcus's decreasing rate in the nonadiabatic limit but Rehm-Weller saturation in the adiabatic limit. Using physically realistic reorganization energies and electronic coupling values, we show that Rehm-Weller's data can be quantitatively reproduced within a microscopic
quantum model without invoking phenomenological corrections.
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Instrumentation and Detectors
Rapid Inference of Logic Gate Neural Networks for Anomaly Detection in High Energy Physics
Lino Gerlach, Elliott Kauffman, Liv Helen Våge et al. (+1 more)
The increasing data rates and complexity of detectors at the Large
Hadron Collider (LHC) necessitate fast and efficient machine learning models, particularly for rapid selection of what data to store, known as triggering. Building on recent work in differentiable logic gates, we present a public implementation of a Convolutional Differentiable Logic Gate Neural Network (CLGN). We apply this to detecting anomalies at the Level-1 Trigger at CMS using public data from the CICADA project. We demonstrate that the CLGN achieves physics performance on par with or superior to conventional quantized neural networks. We also synthesize an LGN for a Field-Programmable Gate Array (FPGA) and show highly promising FPGA characteristics, notably zero Digital Signal Processor (DSP) resource usage. This work highlights the potential of logic gate networks for high-speed, on-detector inference in High Energy Physics and beyond.
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All authors Lino Gerlach, Elliott Kauffman, Liv Helen Våge, Isobel Ojalvo
Chemical Physics
Benchmarking Proton Tunneling Splittings with a Wavefunction-Based Double-Well Model: Application to the Formic Acid Dimer
Krishna Kingkar Pathak
Proton tunneling across hydrogen bonds is a fundamental
quantum effect with implications for spectroscopy, catalysis, and biomolecular stability. While state-of-the-art instanton and path-integral methods provide accurate multidimensional tunneling splittings, simplified one-dimensional models remain valuable as conceptual and benchmarking tools. Here we develop a wavefunction-based framework for tunneling splittings using a Cornell-type double-well potential and apply it as a
benchmark for hydrogen-bond tunneling. Analytical WKB estimates and numerical finite-difference solutions are compared across a range of barrier parameters, showing consistent agreement. As a test case, we map the formic acid dimer (FAD) barrier onto a quartic double-well model parameterized to reproduce the reported barrier height of $V_b \\approx 2848~\\text{cm}^{-1}$. The resulting tunneling splitting of about $0.037~\\text{cm}^{-1}$ matches the reduced-dimensional calculations of Qu and Bowman. The close agreement between numerical and semiclassical results highlights the pedagogical and diagnostic value of one-dimensional models, while comparison with molecular
benchmarks clarifies their limitations relative to full multidimensional
quantum treatments.
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Instrumentation and Detectors
Quantifying the Full Damage Profile of Focused Ion Beams via 4D-STEM Precession Electron Diffraction and PSNR Metrics
M.G. Masteghin, Z.P. Aslam, A.P. Brown et al. (+4 more)
Focused ion beams (FIBs) are widely used in nanofabrication for applications such as
circuit repair, ultra-thin lamella preparation, strain engineering, and
quantum device prototyping. Although the lateral spread of the ion beam is often overlooked, it becomes critical in precision tasks such as
impurity placement in host substrates, where accurate knowledge of the ion-matter interaction profile is essential. Existing techniques typically characterise only the beam core, where most ions land, thus underestimating the full extent of the point spread function (PSF). In this work, we use four-dimensional scanning transmission electron microscopy (4D-STEM) to resolve the ion beam tail at defect densities equivalent to $<$0.1 ions nm$^{-2}$. Convergent beam electron diffraction (CBED) patterns were collected in calibration regions with known ion fluence and compared to patterns acquired around static dwell spots exposed to a 30 keV Ga$^{+}$ beam for 1-10 s. Cross-correlation using peak signal-to-noise ratio (PSNR) revealed that 4D-STEM
datasets are ultra-sensitive for defect quantification and more robust against scanning artefacts than conventional dark-field imaging. This approach is complementary to image resolution methods enabling a comprehensive profiling of ion-induced damage even at low-dose regimes, offering a more accurate representation of FIB performance and supporting application tailoring beyond the conventional resolution metrics focused solely on the beam core.
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All authors M.G. Masteghin, Z.P. Aslam, A.P. Brown, M.J. Whiting, S.K. Clowes, R.P. Webb, D.C. Cox
Geophysics · Cross-listed from cs.CE
GEDICorrect: A Scalable Python Tool for Orbit-, Beam-, and Footprint-Level GEDI Geolocation Correction
Leonel Corado, Sérgio Godinho, Carlos Alberto Silva et al. (+4 more)
Accurate geolocation is essential for the reliable use of GEDI
LiDAR data in footprint-scale applications such as aboveground biomass modeling, data fusion, and ecosystem monitoring. However, residual geolocation errors arising from both systematic biases and random ISS-induced jitter can significantly affect the accuracy of derived vegetation and terrain metrics. The main goal of this study is to develop and evaluate a flexible, computationally efficient framework (GEDICorrect) that enables geolocation correction of GEDI data at the orbit, beam, and footprint levels. The framework integrates existing GEDI Simulator modules (gediRat and gediMetrics) and extends their functionality with flexible correction logic, multiple similarity metrics, adaptive footprint clustering, and optimized I/O handling. Using the Kullback–Leibler divergence as the waveform similarity metric, GEDICorrect improved canopy height (RH95) accuracy from $R^2 = 0.61$ (uncorrected) to 0.74 with the orbit-level correction, and up to $R^2 = 0.78$ with the footprint-level correction, reducing RMSE from 2.62~m ($rRMSE = 43.13\%$) to 2.12~m ($rRMSE = 34.97\%$) at the orbit level, and 2.01~m ($rRMSE = 33.05\%$) at the footprint level. Terrain elevation accuracy also improved, decreasing RMSE by 0.34~m relative to uncorrected data and by 0.37~m compared to the GEDI Simulator baseline. In terms of computational efficiency, GEDICorrect achieved a $\sim2.4\times$ speedup over the GEDI Simulator in single-process mode (reducing runtime from $\sim84$~h to $\sim35$~h) and scaled efficiently to 24 cores, completing the same task in $\sim4.3$~h – an overall $\sim19.5\times$ improvement. GEDICorrect offers a robust and scalable solution for improving GEDI geolocation accuracy while maintaining full compatibility with standard GEDI data products.
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All authors Leonel Corado, Sérgio Godinho, Carlos Alberto Silva, Juan Guerra-Hernández, Francesco Valérioa, Teresa Gonçalves, Pedro Salgueiro
Biological Physics
Confinement inhibits surficial attachment and induces collective behaviors in bacterial colonies
Vincent Hickl, Gabriel Gmünder, René M. Rossi et al. (+4 more)
Bacterial colonies are a well-known example of living active matter, exhibiting collective behaviors such as nematic
alignment and collective motion that play an important role in the spread of microbial infections. While the underlying mechanics of these behaviors have been described in model systems, many open questions remain about how microbial self-organization adapts to the variety of different environments bacteria encounter in natural and clinical settings. Here, using novel imaging and computational analysis techniques, the effects of confinement to 2D on the collective behaviors of pathogenic bacteria are described. Biofilm-forming Pseudomonas aeruginosa are grown on different substrates, either open to the surrounding fluid or confined to a single monolayer between two surfaces. Orientational ordering in the colony,
cell morphologies, and trajectories are measured using single-
cell segmentation and tracking. Surprisingly, confinement inhibits permanent attachment and induces twitching motility, giving rise to multiple coexisting collective behaviors. This effect is shown to be independent of the confining material and the presence of
liquid medium. The nematic
alignment and degree of correlation in the
cells' trajectories determines how effectively bacteria can invade the space between two surfaces and the 3D structure of the colony after several days. Confinement causes the formation of dynamic
cell layers driven by collective motion as well as collective verticalization leading to the formation of densely packed crystalline structures exhibiting long-range order. These results demonstrate the remarkable breadth of collective behaviors exhibited by bacteria in different environments, which must be considered to better understand bacterial colonization of surfaces.
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All authors Vincent Hickl, Gabriel Gmünder, René M. Rossi, Antonia Neels, Qun Ren, Katharina Maniura-Weber, Bruno F. B. Silva
Medical Physics
Variational Percolation Bounds for Cellular Membrane Occlusion
Cesar Mello and Fernando Medina da Cunha
Malignant membranes cluster nutrient transporters within glycan-rich domains, sustaining metabolism through redundant intake routes. A theoretical framework links interfacial chemistry to transport suppression and energetic or redox collapse. The model unites a screened Poisson-Nernst-Planck electrodiffusion problem, an interfacial potential of mean force, and a reduced energetic-redox module connecting flux to ATP/NADPH balance. From this structure, capacitary-spectral bounds relate total flux to the inverse principal
eigenvalue (J_tot <= C*exp(-beta*chi_eff)*P(theta)). Two near-orthogonal levers, geometry and field strength, govern a linear suppression regime below a percolation-type knee, beyond which conductance collapses. A composite intake index Xi = w_G*J_GLUT + w_A*J_LAT/ASCT + w_L*J_MCT dictates energetic trajectories: once below a maintenance threshold, ATP and NADPH fall jointly and redox imbalance drives irreversible commitment. Normal membranes, with fewer transport mouths and weaker fields, remain above this threshold, defining a natural selectivity window. The framework demonstrates existence, regularity, and spectral monotonicity for the self-adjoint PNP operator, establishing a
geometric-spectral transition that links molecular parameters such as branching and sulfonation to measurable macroscopic outcomes with predictive precision.
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Medical Physics · Cross-listed from cs.LG
A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis
Ciaran Bench, Oskar Pfeffer, Vivek Desai et al. (+7 more)
In principle, deep learning models trained on medical time-series, including wearable photoplethysmography (PPG) sensor data, can provide a means to continuously monitor physiological parameters outside of clinical settings. However, there is considerable risk of poor performance when deployed in practical measurement scenarios leading to negative patient outcomes. Reliable uncertainties accompanying predictions can provide guidance to clinicians in their interpretation of the trustworthiness of model outputs. It is therefore of interest to compare the effectiveness of different approaches. Here we implement an unprecedented set of eight uncertainty quantification (UQ) techniques to models trained on two clinically relevant prediction tasks: Atrial Fibrillation (AF) detection (classification), and two variants of blood pressure regression. We formulate a comprehensive evaluation procedure to enable a rigorous comparison of these approaches. We observe a complex picture of uncertainty reliability across the different techniques, where the most optimal for a given task depends on the chosen expression of uncertainty, evaluation metric, and scale of reliability assessed. We find that assessing local calibration and adaptivity provides practically relevant insights about model behaviour that otherwise cannot be acquired using more commonly implemented global reliability metrics. We emphasise that criteria for evaluating UQ techniques should cater to the model's practical use case, where the use of a small number of measurements per patient places a premium on achieving small-scale reliability for the chosen expression of uncertainty, while preserving as much predictive performance as possible.
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All authors Ciaran Bench, Oskar Pfeffer, Vivek Desai, Mohammad Moulaeifard, Loïc Coquelin, Peter H. Charlton, Nils Strodthoff, Nando Hegemann, Philip J. Aston, Andrew Thompson
Physics and Society
A New Lens on the Sustainability of the AI Revolution
Pierluigi Contucci, Godwin Osabutey and Filippo Zimmaro
We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological adoption preceded scientific understanding, initially disrupted this ratio: EPE collapsed as profits outpaced efficiency, with poorly integrated technologies, and recovered only with the rise of scientific knowledge and societal adaptation. Later industrial revolutions, such as electrification and microelectronics, grounded in established scientific theory, did not exhibit comparable declines. Today's AI revolution, highly profitable yet energy-intensive, remains pre-scientific and may follow a similar trajectory in EPE. We combine this conceptual discussion with cross-country EPE data spanning the last three decades. We find that the advanced economies exhibit a consistent linear growth in EPE: those countries are the ones that contribute most to global GDP production and energy consumption, and are expected to be the most affected by the AI transition. Therefore, we advocate for regular monitoring of EPE: transparent reporting of AI-related energy use and productivity-linked incentives can expose hidden energy costs and prevent efficiency-blind economic expansion.
Embedding EPE within sustainability frameworks would help align technological innovation with energy productivity, a critical condition for sustainable growth.
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Fluid Dynamics
Jet drop production from bubbles with neighbors
Tristan Aurégan, Noé Daniel, Megan Mazzatenta et al. (+1 more)
Bubbles bursting at the surface of the ocean produce drops that heavily influence ocean-atmosphere interactions. One of the mechanisms through which drops are formed is called jet drop production, where the collapse of the bubble cavity leads to the formation of a fast upwards jet that breaks to form drops. While isolated bubble bursting has been extensively studied, bubbles are often found in rafts (for instance in the ocean surface or a sparkling wine glass) and the understanding of collective effects remains more limited. We investigate experimentally how jet drop formation is modified by the presence of neighboring bubbles during the collapse. With the help of multiple high speed views of the collapsing bubble, we show how a change of cavity shape during collapse leads to the selection of smaller, faster, and more numerous drops. The size of the emitted drops is monotonically reduced with increasing number of neighboring bubbles (up to six for hexagonal packing) with the size reduction reaching a factor 5. The drop size distribution associated with bubbles arranged in rafts of various sizes is therefore much wider than in the case of isolated bubbles, and with a peak shifted to smaller sizes.
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All authors Tristan Aurégan, Noé Daniel, Megan Mazzatenta, Luc Deike
Atmospheric and Oceanic Physics
Benchmarking Regional Thermodynamic Trends in an AI emulator, ACE2, and a hybrid model, NeuralGCM
Katharine Rucker, Ian Baxter, Pedram Hassanzadeh et al. (+2 more)
AI models have emerged as potential complements to physics-based models, but their skill in capturing observed regional
climate trends with important societal impacts has not been explored. Here, we
benchmark satellite-era regional thermodynamic trends, including extremes, in an AI emulator (ACE2) and a hybrid model (NeuralGCM). We also compare the AI models' skill to physics-based land-atmosphere models. Both AI models show skill in capturing regional temperature trends such as Arctic Amplification. ACE2 outperforms other models in capturing vertical temperature trends in the midlatitudes. However, the AI models do not capture regional trends in heat extremes over the US Southwest. Furthermore, they do not capture drying trends in arid regions, even though they generally perform better than physics-based models. Our results also show that a data-driven AI emulator can perform comparably to, or better than, hybrid and physics-based models in capturing regional thermodynamic trends.
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All authors Katharine Rucker, Ian Baxter, Pedram Hassanzadeh, Tiffany A. Shaw, Hamid A. Pahlavan
Fluid Dynamics · Cross-listed from cs.LG
Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning
Michiel Straat, Thorben Markmann, Sebastian Peitz et al. (+1 more)
Chaotic convective flows arise in many real-world systems, such as microfluidic devices and chemical reactors. Stabilizing these flows is highly desirable but remains challenging, particularly in chaotic regimes where conventional control methods often fail. Reinforcement Learning (RL) has shown promise for control in laminar flow settings, but its ability to generalize and remain robust under chaotic and turbulent dynamics is not well explored, despite being critical for real-world deployment. In this work, we improve the practical feasibility of RL-based control of such flows focusing on Rayleigh-Bénard Convection (RBC), a canonical model for convective heat transport. To enhance generalization and sample efficiency, we introduce domain-informed RL
agents that are trained using Proximal Policy Optimization across diverse initial conditions and flow regimes. We incorporate domain knowledge in the reward function via a term that encourages Bénard
cell merging, as an example of a desirable macroscopic property. In laminar flow regimes, the domain-informed RL
agents reduce convective heat transport by up to 33%, and in chaotic flow regimes, they still achieve a 10% reduction, which is significantly better than the conventional controllers used in practice. We compare the domain-informed to uninformed
agents: Our results show that the domain-informed reward design results in steady flows, faster convergence during training, and generalization across flow regimes without retraining. Our work demonstrates that elegant domain-informed priors can greatly enhance the robustness of RL-based control of chaotic flows, bringing real-world deployment closer.
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All authors Michiel Straat, Thorben Markmann, Sebastian Peitz, Barbara Hammer
Instrumentation and Detectors
Light Monitoring System for the Lead Tungstate Calorimeter in Hall D at Jefferson Lab
A. Somov, V. V. Berdnikov, H. Voskanyan et al. (+11 more)
A new electromagnetic calorimeter composed of 1596 lead tungstate (PbWO$_4$) scintillating
crystals has been constructed for the GlueX detector in
Hall D at Jefferson Lab. The calorimeter is equipped with a light monitoring system that uses light-emitting diodes. The light monitoring system was fabricated, installed, and integrated into the GlueX trigger system. It was successfully operated during detector commissioning and data collection. The paper describes the design, installation, and performance of the light monitoring system.
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All authors A. Somov, V. V. Berdnikov, H. Voskanyan, A. Asaturyan, L. Gan, S. Taylor, F. Barbosa, C. Stanislav, V. Popov, I. Somov, I. Jaegle, A. Smith, H. Egiyan, B. Bunton
Fluid Dynamics
Uncertainty Quantification in Resolvent Analysis of Experimental Wall-Bounded Turbulent Flows
Salvador Rey Gomez and Tomek Jaroslawski
Experimental mean flows are commonly used to study wall-bounded
turbulence. However, these measurements are often unable to resolve the near-wall region and thus introduce ambiguity in the velocity closest to the wall. This poses a source of uncertainty in equation-based approaches that rely on these mean flow measurements such as resolvent analysis. Resolvent analysis provides a scale-dependent decomposition of the linearized Navier Stokes equations that identifies optimal gains, response modes and forcing modes that has been used to great effect in turbulent wall-bounded flows. Its potential in the development of predictive tools for a variety of wall-bounded flows is high but the limitations of the input data must be addressed. Here, we quantify the sensitivity of resolvent analysis to common sources of experimental uncertainty and show that this sensitivity can be quantified with minimal additional computational cost. This approach is applied to both local and biglobal resolvent analysis by using artificial disturbances to mean profiles in the former and particle image velocimetry measurements with differing near-wall fits in the latter. We also highlight an example where poor near-wall resolution can lead to erroneous conclusions compared to the full-resolution data in an adverse pressure gradient turbulent boundary layer.
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Fluid Dynamics
Blast wave induced unsteady flow at the shock tube opening
Saini Jatin Rao, Akhil Aravind and Saptarshi Basu
Shock tubes have been a crucial device, facilitating studies across a wide range of practical applications. An open-ended shock tube employing the wire-explosion technique with a rectangular cross section is used in the present study to generate blast waves over a Mach number range of 1.2-1.8, enabling detailed investigation of unsteady compressible flow at the tube opening. The blast wave produces a complex flow field comprising a compressible
vortex ring with a trailing jet, and several transient structures, including embedded shocks, inward-moving shock or reverse shocks, shear layers, and Prandtl-Meyer expansion fans. An approximate model based on a power-law density profile describes the blast
evolution inside and outside the tube, with the equivalent source deduced from measured shock trajectories. The blast wave-tube exit interaction is analyzed using the method of characteristics with alternate exit boundary conditions. A steady-pressure outlet best reproduces experimental observations, predicting supersonic efflux, embedded shocks, expansion waves, and circulation production. Several previously unreported unsteady features, including reverse shock or "reshock" formation and embedded shock shedding, are documented. The findings highlight the intricate dynamics of various features associated with such highly transient, blast-driven flows emanating from an open-ended shock tube.
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Fluid Dynamics · Cross-listed from cond-mat.soft
Emergent clusters in strongly confined systems
Pamud Akalanka Bethmage, Ryker Fish, Brennan Sprinkle et al. (+1 more)
Driven suspensions, where energy is input at a particle scale, are a framework for understanding general principles of out-of-
equilibrium organization. A large number of simple interacting units can give rise to non-trivial structure and hierarchy. Rotationally driven colloidal particles are a particularly nice model system for exploring this pattern formation, as the dominant interaction between the particles is hydrodynamic. Here, we use experiments and large-scale simulations to explore how strong confinement alters dynamics and emergent structure at the particle scale in these driven suspensions. Surprisingly, we find that large-scale (many times the particle size) density fluctuations emerge as a result of confinement, and that these density fluctuations sensitively depend on the degree of confinement. We extract a characteristic length scale for these fluctuations, demonstrating that the simulations quantitatively reproduce the experimental pattern. Moreover, we show that these density fluctuations are a result of the large-scale recirculating flow generated by the rotating particles inside a sealed chamber. This surprising result shows that even when system boundaries are far away, they can cause qualitative changes to mesoscale structure and ordering.
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All authors Pamud Akalanka Bethmage, Ryker Fish, Brennan Sprinkle, Michelle M. Driscoll
Computational Physics · Cross-listed from quant-ph
Closed-loop calculations of electronic structure on a quantum processor and a classical supercomputer at full scale
Tomonori Shirakawa, Javier Robledo-Moreno, Toshinari Itoko et al. (+18 more)
Quantum computers must operate in concert with classical computers to deliver on the promise of
quantum advantage for practical problems. To achieve that, it is important to understand how
quantum and classical computing can interact together, and how one can characterize the scalability and efficiency of hybrid
quantum-classical workflows. So far, early experiments with
quantum-centric supercomputing workflows have been limited in scale and complexity. Here, we use a Heron
quantum processor deployed on premises with the entire supercomputer Fugaku to perform the largest computation of electronic structure involving
quantum and classical high-performance computing. We design a closed-loop workflow between the
quantum processors and 152,064 classical nodes of Fugaku, to approximate the electronic structure of chemistry models beyond the reach of exact diagonalization, with accuracy comparable to some all-classical approximation methods. Our work pushes the limits of the integration of
quantum and classical high-performance computing, showcasing computational resource orchestration at the largest scale possible for current classical supercomputers.
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All authors Tomonori Shirakawa, Javier Robledo-Moreno, Toshinari Itoko, Vinay Tripathi, Kento Ueda, Yukio Kawashima, Lukas Broers, William Kirby, Himadri Pathak, Hanhee Paik, Miwako Tsuji, Yuetsu Kodama, Mitsuhisa Sato, Constantinos Evangelinos, Seetharami Seelam, Robert Walkup, Seiji Yunoki, Mario Motta, Petar Jurcevic, Hiroshi Horii, Antonio Mezzacapo
Plasma Physics · Cross-listed from cond-mat.mes-hall
Dragging of electric current by hydrodynamic flow at charge neutrality
Dmitry Zverevich, Alex Levchenko and A. V. Andreev
We develop a theory of drag in
graphene double layers near charge neutrality. We work in the regime of electron hydrodynamics and account for interlayer correlations of charge puddle disorder. The drag resistivity is expressed in terms of the viscosity, intrinsic conductivity of the electron
liquid, and the correlation function of the puddle disorder. The contributions of the interlayer transfer of momentum and energy to drag have opposite signs. This leads to a nonmonotonic dependence of the drag resistivity on the carrier density. For layer-symmetric doping, the drag resistivity changes sign as a function of the carrier density. At interlayer separations shorter than the disorder correlation length, the transconductivity saturates to the disorder-induced enhancement of the intralayer conductivity. We provide quantitative estimates of the effect for Dirac electron
liquids in monolayer
graphene and
bilayer graphene double-layer devices.
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Physics and Society
Detectability threshold in weighted modular networks
Filippo Radicchi, Filipi N. Silva, Alessandro Flammini et al. (+2 more)
We study the necessary condition to detect, by means of spectral modularity optimization, the ground-truth partition in networks generated according to the weighted planted-partition model with two equally sized communities. We analytically derive a general expression for the maximum level of mixing tolerated by the
algorithm to retrieve community structure, showing that the value of this detectability threshold depends on the first two moments of the distributions of node degree and edge weight. We focus on the standard case of Poisson-distributed node degrees and compare the detectability thresholds of five edge-weight distributions: Dirac, Poisson, exponential,
geometric, and signed Bernoulli. We show that Dirac distributed weights yield the smallest detectability threshold, while exponentially distributed weights increase the threshold by a factor $\sqrt{2}$, with other distributions exhibiting distinct behaviors that depend, either or both, on the average values of the degree and weight distributions. Our results indicate that larger variability in edge weights can make communities less detectable. In cases where edge weights carry no information about community structure, incorporating weights in community detection is detrimental.
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All authors Filippo Radicchi, Filipi N. Silva, Alessandro Flammini, Santo Fortunato, Sadamori Kojaku
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Transfer learning discovery of molecular modulators for perovskite solar cells
Haoming Yan, Xinyu Chen, Yanran Wang et al. (+10 more)
The discovery of effective molecular modulators is essential for advancing perovskite
solar cells (PSCs), but the research process is hindered by the vastness of chemical space and the time-consuming and expensive trial-and-error experimental screening. Concurrently, machine learning (ML) offers significant potential for accelerating materials discovery. However, applying ML to PSCs remains a major challenge due to data scarcity and limitations of traditional quantitative structure-property relationship (QSPR) models. Here, we apply a chemical informed transfer learning framework based on pre-trained deep neural networks, which achieves high accuracy in predicting the molecular modulator's effect on the power conversion efficiency (PCE) of PSCs. This framework is established through systematical benchmarking of diverse molecular representations, enabling lowcost and high-throughput virtual screening over 79,043 commercially available molecules. Furthermore, we leverage interpretability techniques to visualize the learned chemical representation and experimentally characterize the resulting modulator-perovskite interactions. The top molecular modulators identified by the framework are subsequently validated experimentally, delivering a remarkably improved champion PCE of 26.91% in PSCs.
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All authors Haoming Yan, Xinyu Chen, Yanran Wang, Zhengchao Luo, Weizheng Huang, Hongshuai Wang, Peng Chen, Yuzhi Zhang, Weijie Sun, Jinzhuo Wang, Qihuang Gong, Rui Zhu, Lichen Zhao
Atmospheric and Oceanic Physics
IoT- and AI-informed urban air quality models for vehicle pollution monitoring
Jan M. Armengol, Vicente Masip, Ada Barrantes et al. (+7 more)
With the rise of intelligent Internet of Things (IoT) systems in urban environments, new opportunities are emerging to enhance real-time environmental monitoring. While most studies focus either on IoT-based air quality sensing or physics-based modeling in isolation, this work bridges that gap by integrating low-cost sensors and AI-powered
video-based traffic analysis with high-resolution urban air quality models. We present a real-world pilot deployment at a road intersection in Barcelona's Eixample district, where the system captures dynamic traffic conditions and environmental variables, processes them at the edge, and feeds real-time data into a high-performance computing (HPC) simulation pipeline. Results are validated against official air quality measurements of nitrogen dioxide (NO2). Compared to traditional models that rely on static emission inventories, the IoT-assisted approach enhances the temporal granularity of urban air quality predictions of traffic-related pollutants. Using the full capabilities of an IoT-edge-cloud-HPC architecture, this work demonstrates a scalable, adaptive, and
privacy-conscious solution for urban pollution monitoring and establishes a foundation for next-generation IoT-driven environmental intelligence.
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All authors Jan M. Armengol, Vicente Masip, Ada Barrantes, Gabriel M. Beltrami, Sergi Albiach, Daniel Rodriguez-Rey, Marc Guevara, Albert Soret, Eduardo Quiñones, Elli Kartsakli
Optics
A Case Study on the Performance Metrics of Integrated Photonic Computing
Frank Brückerhoff-Plückelmann, Jelle Dijkstra, Julian Büchel et al. (+7 more)
Photonic processors use optical signals for computation, leveraging the high bandwidth and low loss of optical links. While many approaches have been proposed, including in
memory photonic
circuits, most efforts have focused on the physical design of photonic components rather than full architectural integration with electronic peripheral circuitry. In this work, we present a microarchitecture level study that estimates the compute efficiency and density of three prominent photonic computing architectures. Our evaluation accounts for both the photonic
circuits and the essential peripheral electronics required for optoelectronic and analog to digital conversions. We further demonstrate a heterogenous photonic electronic in
memory processing approach for low latency neural network inference. These results provide a better understanding of the design aspects of photonic computing.
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All authors Frank Brückerhoff-Plückelmann, Jelle Dijkstra, Julian Büchel, Bottyan Batkai, Falk Ebert, Luis Mickeler, Urs Egger, Abu Sebastian, Wolfram Pernice, Ghazi Sarwat Syed
Instrumentation and Detectors · Cross-listed from astro-ph.IM
StarDICE IV: correcting visible photometry from atmospheric gray extinction using thermal infrared observations
Kélian Sommer, Bertrand Plez, Johann Cohen-Tanugi et al. (+11 more)
Ground-based surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time require photometric calibration that is both long-term stable and spatially uniform at the sub-per cent level, even during non-photometric conditions. Achieving this precision motivates new approaches to characterize atmospheric transmission, particularly to mitigate grey extinction from clouds. The StarDICE experiment aims to establish a metrology chain linking laboratory standards to astrophysical fluxes with 1 mmag accuracy in the $\textit{griz}$ bands, a goal for which controlling variable atmospheric effects is essential. We present a method that corrects photometric measurements using simultaneous radiometric information from an infrared thermal camera. The grey-extinction model is fit on an image-by-image basis using thermal radiance excess and the difference between synthetic and instrumental fluxes of calibration
stars, without requiring assumptions about the spatial structure of extinction. The method relies on a forward model that incorporates environmental monitoring, radiative-transfer simulations, and
Gaia DR3
stellar catalogues. Using data from a remote observing system that repeatedly monitored two fields under diverse atmospheric conditions, we show that the corrections reduce residuals between corrected and reference magnitudes and produce extinction maps with 2-arcmin resolution and ~0.01 mag accuracy. Using this technique, we can recover data acquired under non-photometric conditions with a precision comparable to data obtained under photometric conditions. For the most affected exposures, the mean absolute error improves from 0.64 to 0.11 mag, and temporal extinction variations can be reduced to 0.025 mag per source. We discuss the implications of this technique for future surveys and outline directions for further refinement.
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All authors Kélian Sommer, Bertrand Plez, Johann Cohen-Tanugi, Marc Betoule, Sébastien Bongard, Thierry Souverin, Sylvie Dagoret-Campagne, Marc Moniez, Jérémy Neveu, Fabrice Feinstein, Claire Juramy, Laurent Le Guillou, Eduardo Sepulveda, Eric Nuss
Chemical Physics
Generative Modeling Enables Molecular Structure Retrieval from Coulomb Explosion Imaging
Xiang Li, Till Jahnke, Rebecca Boll et al. (+12 more)
Capturing the structural changes that molecules undergo during chemical reactions in real space and time is a long-standing dream and an essential prerequisite for understanding and ultimately controlling femtochemistry. A key approach to tackle this challenging task is Coulomb explosion imaging, which benefited decisively from recently emerging high-repetition-rate X-ray free-electron
laser sources. With this technique, information on the molecular structure is inferred from the momentum distributions of the ions produced by the rapid Coulomb explosion of molecules. Retrieving molecular structures from these distributions poses a highly non-linear inverse problem that remains unsolved for molecules consisting of more than a few atoms. Here, we address this challenge using a
diffusion-based
Transformer neural network. We show that the network reconstructs unknown molecular geometries from ion-momentum distributions with a mean absolute error below one Bohr radius, which is half the length of a typical chemical bond.
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All authors Xiang Li, Till Jahnke, Rebecca Boll, Jiaqi Han, Minkai Xu, Michael Meyer, Maria Novella Piancastelli, Daniel Rolles, Artem Rudenko, Florian Trinter, Thomas J.A. Wolf, Jana B. Thayer, James P. Cryan, Stefano Ermon, Phay J. Ho
Plasma Physics
Observation of Ion-wave Satellites to Laser Harmonics in Intense Picosecond Laser-Solid Interaction
R.S. Marjoribanks, L. Zhao, F.W. Budnik et al. (+5 more)
Detailed spectra of harmonics produced from ultra-intense, sub-picosecond, high-contrast
laser pulses incident on solid targets have shown the first observation of regular red- and blue-shifted satellites. Their frequency shift is slightly less than the frequency of a nominal, pure ion-
plasma wave associated with electron critical density, where an ion-acoustic wave would be expected. We explain this as the result of a substantial reduction of Debye shielding as the intense optical fields compete to drive electrons in large-amplitude oscillations. This general effect leads to a larger, dynamical and anisotropic Debye length, which should have a broad impact on
plasma physics in this regime.
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All authors R.S. Marjoribanks, L. Zhao, F.W. Budnik, G. Kulcsar, R. Wagner, D. Umstadter, R.P. Drake, M. C. Downer
Physics and Society
Incentives for self-isolation based on incidence rather than prevalence could help to flatten the curve: a modelling study
Giulia de Meijere and Hugo Martin
In recent years, numerous advances have been made in understanding how epidemic dynamics is affected by changes in individual behaviours. We propose an SIS-based compartmental model to tackle the simultaneous and coupled
evolution of an outbreak and of the adoption by individuals of the isolation measure. The compliance with self-isolation is described with the help of the imitation dynamics framework. Individuals are incentivised to isolate based on the prevalence and the incidence rate of the outbreak, and are tempted to defy isolation recommendations depending on the duration of isolation and on the cost of putting social interactions on hold. We are able to derive analytical results on the
equilibria of the model under the homogeneous mean-field approximation. Simulating the compartmental model on empirical networks, we also do a preliminary check of the impact of a network structure on our analytical predictions. We find that the dynamics collapses to surprisingly simple regimes where either the imitation dynamics no longer plays a role or the
equilibrium prevalence depends on only two parameters of the model, namely the cost and the relative time spent in isolation. Whether individuals prioritise disease prevalence or incidence as an indicator of the state of the outbreak appears to play no role on the
equilibria of the dynamics. However, it turns out that favouring incidence may help to flatten the curve in the transient phase of the dynamics. We also find a fair agreement between our analytical predictions and simulations run on an empirical multiplex network.
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