Accelerator Physics
pyTRAIN – a modern TRAIN implementation
Michi Hostettler, Xavier Buffat, Tobias Persson et al. (+2 more)
The TRAIN code, developed in 1995 as a post-processor for second-order transport maps from MAD, has been used extensively at the LEP and the LHC to study self-consistent closed orbits, tunes and chromaticities of bunch trains under the presence of beam-beam long-range (BBLR) and PACMAN effects.. This paper presents a modern re-implementation of the TRAIN concept in Python using well-known numeric libraries (numpy, scipy) and an optional link to MAD-X via cpymad. This greatly improves the usability, maintainability and extensibility of the code. New functionality includes the support for arbitrary particle types, an arbitrary number and distribution of beam-beam interaction points, and the extrapolation of the beam-beam induced closed-orbit effects to arbitrary points in the machine. The code is benchmarked against the classic TRAIN code, and simulation results are compared to observations from LHC physics operation.
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All authors Michi Hostettler, Xavier Buffat, Tobias Persson, Tatiana Pieloni, Jorg Wenninger
Fluid Dynamics · Cross-listed from cond-mat.soft
Diffusion compaction coupling controls pore pressure dynamics in granular fluid flows
Eric C.P. Breard, Claudia Elijas Parra and Mattia de' Michieli Vitturi
Excess pore pressure in granular–fluid mixtures can transiently suppress frictional contacts and dramatically enhance flow mobility, yet its
evolution is commonly modeled using constant effective diffusivities. Here we show that the apparent diffusivity is not intrinsic but emerges from the coupling between pore-pressure
diffusion and granular compaction. Starting from two-phase mass conservation for a deformable, gas-saturated granular assembly, we derive an
evolution equation for excess pore pressure that captures deformation of the granular skeleton. In the thin-flow, small-excess-pressure limit, this reduces to a one-dimensional
diffusion–compaction equation with a time-dependent source term controlled by porosity changes. A modal analysis yields a reduced basal equation that separates diffusive drainage from compaction-driven forcing and identifies the corresponding timescales. This framework introduces a dimensionless source-to-
diffusion ratio, $\Psi_0$, which governs the competition between these processes and collapses effective diffusivities obtained from high-resolution two-fluid simulations over nearly two orders of magnitude in bed height. This scaling implies that the apparent diffusivity, and thus flow mobility, is not intrinsic but depends on flow thickness through the competition between
diffusion and compaction. Incorporating this physics into a depth-averaged model demonstrates that the resulting closure reproduces the thickness dependence of pore-pressure decay and runout observed in experiments. These results provide a physically grounded description of pore-pressure
evolution in granular–fluid flows and clarify how
diffusion–compaction coupling controls their mobility.
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Chemical Physics · Cross-listed from quant-ph
Disorder-induced non-Gaussian states in large ensembles of cavity-coupled molecules
R. Schwengelbeck, M. Pandini, R. Daraban et al. (+1 more)
We analyze vibrational dynamics in a toy model for polaritonic chemistry under collective electronic strong coupling. In a Holstein-Tavis-Cummings model, incoherently excited by a
photon, we show that disorder leads to non-Gaussian states of vibrational modes on short time scales at the single-molecule level. Using exact
matrix product state simulations, we demonstrate that this effect can remain robust for larger molecule numbers, implying that
nuclear wave packets cannot be effectively described by thermal states. Furthermore, we compare simulations of the exact
quantum dynamics with semiclassical approximations. We find that the Ehrenfest approximation can only well reproduce ensemble-averaged observables for very large system sizes. Also simulations in the truncated Wigner approximation fail to capture the non-Gaussian effects. Our work highlights the importance of disorder and genuine
quantum effects in cavity-modified
nuclear dynamics in polaritonic chemistry.
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All authors R. Schwengelbeck, M. Pandini, R. Daraban, J. Schachenmayer
Accelerator Physics
High-power attosecond X-ray free-electron lasers: physics and design strategy
Chenzhi Xu, Jiawei Yan, Ye Chen et al. (+5 more)
Attosecond pulses from X-ray free-electron
laser (XFEL) have opened new opportunities for probing ultrafast electronic dynamics on the Angstrom–attosecond spatiotemporal scale. Most attosecond XFEL concepts rely on generating an ultrashort high-current spike through either external
laser modulation or accelerator-based beam manipulation. Despite their different implementations, these approaches share the same essential physics, namely that the XFEL amplification is confined to a short effective lasing window within the electron beam. However, existing studies are often scheme-specific and do not yet provide a unified quantitative picture of how fundamental electron-beam properties constrain high-power attosecond performance. In this work, we investigate the general physics and scheme-independent requirements for generating high-power attosecond X-ray pulses from a short current spike. From the perspective of post-saturation superradiant
evolution, we show that the effective lasing length of the electron beam governs both the attainable peak power and the pulse duration. We further examine the distinct roles of slice energy spread, slice emittance, energy chirp, undulator tapering, and transverse beam tilt. Our results reveal the trade-off between peak power, pulse shortening, and single-spike probability, and provide facility-independent guidelines for optimizing electron-beam phase-space manipulation toward terawatt-class attosecond XFEL operation.
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All authors Chenzhi Xu, Jiawei Yan, Ye Chen, Winfried Decking, Marc Guetg, Tianyun Long, Bingyang Yan, Haixiao Deng
Atomic Physics
Coherent terahertz field tomographic imaging in warm Rydberg vapors
Jan Nowosielski, Marcin Jastrzębski, Wojciech Wasilewski et al. (+2 more)
Rydberg atom-based sensors have emerged as highly sensitive tools for terahertz (THz) metrology, yet most current imaging techniques discard crucial phase information. In this Letter, we present a coherent THz-to-optical conversion scheme in warm Rb vapor that enables complex-amplitude field imaging. By manipulating the phase-matching conditions via an adjustable interference pattern of optical probe beams, we demonstrate the ability to perform tomographic reconstruction of the THz field distribution. We experimentally validate the spatial resolution and phase-sensitivity of the system by resolving sub-centimeter features and identifying incident angles of arrival. Our results establish a robust framework for phase-resolved THz imaging and holography using atomic vapors at room temperature.
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All authors Jan Nowosielski, Marcin Jastrzębski, Wojciech Wasilewski, Mateusz Mazelanik, Michał Parniak
Classical Physics · Cross-listed from quant-ph
Classical counterparts of shortcuts to adiabaticity in nonlinear dissipative Lagrangian systems
Jincheng Shi, Yicheng Pan, Yue Ban et al. (+1 more)
Shortcuts to adiabaticity (STA) were first developed in
quantum dynamics to realize rapid transformations with suppressed residual excitations. Here we show how the same idea can be implemented in classical nonlinear dissipative Lagrangian systems. Using a coupled $r$-$\theta$ manipulator as an illustrative model, we perform inverse engineering on the Euler-Lagrange equations with Rayleigh dissipation by prescribing endpoint-stationary trajectories, obtaining the corresponding force and torque profiles and quantifying how
geometric coupling amplifies errors and residual energy. We further compare smooth STA protocols with actuator-bounded time-optimal solutions and with proportional-integral-derivative tracking, which highlights a trade-off among smoothness, speed, and robustness. Finally, we introduce a single-shot correction based on one mid-course measurement to reduce the effect of early deviations while keeping the inputs nearly smooth. These results provide a practical bridge between
quantum STA concepts and their classical counterparts.
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All authors Jincheng Shi, Yicheng Pan, Yue Ban, Xi Chen
Physics and Society
Tell me Mr. AI, what do you see in this image?
Tommaso Giacometti, Paola Surcinelli, Mariachiara Stellato et al. (+1 more)
Background: The Rorschach inkblots are ambiguous stimuli developed to evoke subjective interpretations in humans, while modern artificial intelligence (AI) models are trained to recognize well established patterns and classes. The comparison of these two opposite systems arises a simple and provocative question: what happens when we ask an AI model to interpret an inkblot that "is not supposed to represent anything predefined"? Methods: We submitted the complete set of ten Rorschach inkblots to 61 AI models pretrained on the ImageNet
dataset, spanning multiple architectural families. Model predictions were analyzed at the level of top-ranked classes and were quantified using a selected set of psycho-semantic variables inspired by the Rorschach tradition. Statistical analyses examined the effects of model family, computational complexity, and image conditions, comparing model-generated responses with human reference profiles. Findings: Across all architectures, model responses were highly non-random and showed systematic semantic convergence and inter-model agreement. However, quantitative analyses revealed a clear and robust separation between human responses and all AI model families. Human profiles exhibited substantially higher affective load, semantic richness, projected agency, and variability, whereas AI models converged toward frequent, formally coherent, and perceptually stable interpretations. Interpretation:
Vision models consistently project the semantic organization learned, favoring consensus and formal coherence over affective or symbolic elaboration. Applying the Rorschach test to AI systems does not assess human-like cognition but provides a principled framework for exposing perceptual and semantic biases embedded in contemporary computer
vision models.
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All authors Tommaso Giacometti, Paola Surcinelli, Mariachiara Stellato, Nico Curti
Physics and Society
Care Trajectories Are Linked to Mental Health and Mortality in Cancer Patients
Simon D. Lindner, Elisabeth L. Zeilinger, Amelie Fuchs et al. (+3 more)
Treatment of cancer involves heterogeneous, complex care pathways. The relationship between these longitudinal trajectories, baseline mental health, and prognostic outcomes remains poorly understood. We introduce an interpretable time-analysis framework leveraging these temporal dynamics, analyzing care patterns spanning up to 37 years for >8,000 patients. Using Dynamic Time Warping (DTW) and Hierarchical Clustering on sequence data of healthcare encounters, we identified nine distinct, robust trajectory phenotypes. We evaluated their prognostic utility by incorporating them into generalized linear models alongside conventional clinical, demographic, and socioeconomic
covariates. The trajectory clusters significantly enhanced mortality prediction and maintained independent predictive significance. Compared to a low-utilization reference group (mortality 31.5%), all eight remaining clusters exhibited substantially higher mortality odds. We uncovered two primary high-risk trajectory patterns: long-term, complex care pathways reflecting chronic disease courses (up to 196 events; mortality OR up to 3.38, 95% CI 2.13-5.37), and shorter but intense trajectories indicating rapid progression (median 78 events; OR 2.32, 95% CI 1.82-2.97). Unexpectedly, the high-utilization complexity clusters were associated with significantly lower baseline anxiety scores, highlighting a divergent relationship between trajectory intensity, mortality risk, and initial psychological burden. These results demonstrate that incorporating temporal healthcare utilization data uncovers robust trajectory phenotypes capturing multidimensional prognostic information. This offers significant explanatory power beyond established static variables for refining risk stratification in precision oncology.
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All authors Simon D. Lindner, Elisabeth L. Zeilinger, Amelie Fuchs, Simone Lubowitzki, Peter Klimek, Alexander Gaiger
Optics
High precision micro-optical elements on fiber facets via focused-ion beam machining
Raman Kumar and Sebastian Will
Fiber-integrated micro-optical elements promise a scalable approach to
photon collection and beam shaping for
quantum information processing. Here, we demonstrate single-step micro-machining of micro-spherical, micro-spiral, and micro-axicon structures directly onto the core of single-mode optical fibers using focused-ion beam (FIB) machining with nanometer-scale precision. Atomic force microscopy reveals that micro-concave and micro-convex spherical surfaces achieve shape accuracies of $\lambda/50$ and $\lambda/33$ (at $\lambda = 633$~nm), respectively. Via optical characterization using a He-Ne
laser (633~nm), we verify for the micro-spiral and -axicon structures the expected far-field donut beam patterns. Via Mach-Zehnder interferometry, we verify the azimuthal and radial phase structure of the light emitted from the spiral and axicon fibers, respectively. Surface metrology further shows that FIB processing introduces only a small increase in nanoscale roughness while preserving sub-nanometer RMS surface quality. The monolithically integrated fiber micro-optic elements demonstrated here enable broad use in
quantum technology, for example, fiber micro-cavities for cavity
quantum electrodynamics, beam shaping for neutral atom trapping, and the generation of structured light for free-space
quantum network links.
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Optics · Cross-listed from quant-ph
Security Risks of VOA-Induced Luminescence in Chip-Based quantum key distribution
Zijian Li, Chenyu Xu, Xin Hua et al. (+5 more)
Integrated photonics is widely regarded as a key enabler for scalable
quantum key distribution (QKD), offering compactness, stability, and compatibility with
semiconductor fabrication. Despite rapid advances in chip-based QKD, the implementation security of integrated photonic components remains insufficiently understood. Here we present the first systematic study of an implementation-level security vulnerability associated with p-n junction-based variable optical attenuators (VOAs), a ubiquitous component in integrated QKD transmitters. We theoretically and experimentally demonstrate that electrically biased p-n junction VOAs emit spontaneous luminescence. Using a single-
photon-sensitive spectral measurement technique, we identify the emission wavelength to be centered around 1107 nm, well separated from the C-band
quantum signals. This spectral separation gives rise to a previously unrecognized wavelength-resolved side channel, enabling potential wavelength-splitting attacks without directly disturbing the encoded
quantum states. By incorporating the measured luminescence into a quantitative security analysis, we show that even extremely weak emission can lead to non-negligible information leakage. Our findings reveal a fundamental and previously overlooked security risk in photonic integrated QKD systems and highlight the necessity of security-aware device design for future integrated
quantum communication technologies.
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All authors Zijian Li, Chenyu Xu, Xin Hua, Yongqiang Du, Xin Liu, Tao Lin, Xi Xiao, Kejin Wei
Popular Physics
The Rise of Quantum Computing – Take a BITE for Built Environment and Urban Microclimate Research
Liangzhu Leon Wang, Huiheng Liu, Honghao Fu et al. (+3 more)
Quantum computing is a new approach to computation that utilizes superposition,
entanglement, interference, and tunneling to solve problems too complex for classical computers. This paper discusses the basic concepts and development of
quantum computing, exploring its potential applications in the built environment and urban microclimate research. In buildings,
quantum computing may help optimize energy management, control HVAC systems, and plan electric vehicle charging networks more efficiently. For urban microclimates, it could accelerate renewable energy planning and support multi-objective design, making it easier to balance urban building performance with
climate conditions. Since current
quantum hardware is still in the Noisy Intermediate-Scale
Quantum (NISQ) stage, we propose the "BITE" principle to guide researchers in choosing suitable problems for
quantum acceleration: B (Big search), I (Input-light), T (Tiny computation), and E (Evaluation polish). Although
quantum computing still faces challenges such as noise and hardware limits, it offers great potential for developing more
climate-resilient, sustainable, and energy-efficient cities of the future.
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All authors Liangzhu Leon Wang, Huiheng Liu, Honghao Fu, Zhipeng Deng, Bing Dong, Naiping Gao
Optics
NEMO: Neural Electro-Mechano-Optic Sensors for Multiplexed Neural Interfaces
Andrew Cochran, Harshvardhan Gupta, Vishal Jain et al. (+2 more)
We introduce a novel electro-optomechanic neural sensor for realizing ultra-compact neural recording probes that can detect and relay electrophysiology signals from within neural tissue. This technology addresses outstanding challenges faced by existing neural recording technologies, including the resolution trade-off with signal-to-noise-ratio (SNR) due to the high impedances of small electrodes, and lingering stimulation artifacts. The sensor employs a highly miniaturized NEMS (nano-electromechanical systems) electrostatic transducer that modulates a silicon photonic microdisk resonator to convert electrical signals to an optical signal modulation. We have been able to achieve a limit of detection down to 110 microvolts, making the sensor sensitive enough to detect neural signals. This sensitive electro-optomechanic sensor directly detects electrophysiology signals and converts them to optomechanic modulation for effective transmission to outside the
brain, which provides the unique potential for massive multiplexing of neural recordings. This design eliminates the need for bulky backend headstages that limit neural recording on awake free-roaming subjects. The ability of the device to record electrophysiological signals has been demonstrated using benchtop characterization and ex-vivo recordings from live neural tissue.
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All authors Andrew Cochran, Harshvardhan Gupta, Vishal Jain, Maysamreza Chamanzar, Gianluca Piazza
Geophysics · Cross-listed from cs.LG
Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning
Mert Can Turkmen, Eng Leong Tan and Yee Hui Lee
Most data-driven ionospheric models operate on gridded products, which do not preserve the time-varying sampling structure of satellite-based sensing. We instead model the ionosphere as a dynamic graph over ionospheric pierce points, with connectivity that evolves as satellite positions change. Because satellite trajectories are predictable, the graph
topology over the forecast horizon can be constructed in advance. We exploit this property to condition forecasts on the future graph structure, which we term ephemeris conditioning. This enables prediction on lines of sight (LoS) that appear only in the forecast horizon. We evaluate our framework on Global
Navigation Satellite System data from a co-located receiver pair in Singapore spanning 2023 to 2025. The task is
forecasting irregularities defined by the Rate of TEC Index (ROTI) up to 2 hours ahead as per-node binary classification. The resulting model, IonoDGNN, achieves a Brier Skill Score (BSS) of 0.55 and an area under the precision-recall curve (PR-AUC) of 0.77. These correspond to improvements over persistence of 53% in BSS and 58% in PR-AUC, with larger gains at longer lead times. Ablations confirm that graph structure and ephemeris conditioning each contribute meaningfully. Under simulated coverage dropout, the model retains predictive skill on affected nodes through spatial message passing from observed neighbors. Compared to interpolation baselines, the proposed model achieves better recovery, especially at higher dropout levels. These results suggest that dynamic graph
forecasting on evolving LoS is a viable alternative for ionospheric modeling. The project and the
dataset are available at https://github.com/Mert-chan/IonoDGNN.
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Instrumentation and Detectors · Cross-listed from quant-ph
Optomechanical Detection of Individual Gas Collisions
Yu-Han Tseng, Clarke A. Hardy, T. W. Penny et al. (+4 more)
We experimentally demonstrate the detection of momentum transfers from individual collisions of Kr, Xe, and SF$_6$ with an optically levitated nanoparticle, finding good agreement with theoretical expectations. The observed event rates accurately measure the gas partial pressures, while the spectral shape provides a sensitive probe of the surface properties of the nanoparticle, including its temperature. The reconstruction of impulse signals as small as 200 keV/$c$ further establishes that levitated optomechanical sensors can reach the sensitivity required for precision measurements of fundamental particle interactions, and demonstrates a proof-of-principle for a primary pressure sensor based on the detection of individual gas particle collisions.
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All authors Yu-Han Tseng, Clarke A. Hardy, T. W. Penny, Cecily Lowe, Jacqueline Baeza-Rubio, Daniel Carney, David C. Moore
Computational Physics · Cross-listed from math.OC
Momentum Stability and Adaptive Control in Stochastic Reconfiguration
Yuyang Wang and Xin Liu
Variational Monte Carlo (VMC) combined with expressive neural network wavefunctions has become a powerful route to high-accuracy ground-state calculations, yet its practical success hinges on efficient and stable wavefunction optimization. While
stochastic reconfiguration (SR) provides a geometry-aware preconditioner motivated by imaginary-time
evolution, its Kaczmarz-inspired variant, subsampled projected-increment natural gradient descent (SPRING), achieves state-of-the-art empirical performance. However, the effectiveness of SPRING is highly sensitive to the choice of a momentum-like parameter $\mu$. The original sensitivity of $\mu$ and the instability observed at $\mu=1$, have remained unclear. In this work, we clarify the distinct mechanisms governing the regimes $\mu<1$ and $\mu=1$. We establish convergence guarantees for $0\le\mu<1$ under mild assumptions, and construct counterexamples showing that $\mu=1$ can induce divergence via uncontrolled growth along kernel-related directions when the step-size is not summable. Motivated by these theoretical insights and numerical observations, we further propose Principal Range Informed MomEntum SR (
PRIME-SR), a tuning-free momentum-adaptive SR method based on effective spectral dimension and subspace overlap.
PRIME-SR achieves performance comparable to optimally tuned SPRING while significantly improving robustness in VMC optimization.
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Optics
Fast dynamic wavefront correction for multi-photon microscopy with a high resolution MEMS phase-only modulator
Juan David Muñoz-Bolaños, Eva Ernst, Maria Borozdova et al. (+6 more)
Multi-
photon microscopy is a powerful technique for deep-tissue imaging, providing high spatial resolution at increased penetration depth. Nevertheless, imaging remains largely restricted to superficial tissue layers well below 1 mm. Adaptive optics based on indirect wavefront sensing can significantly extend the accessible imaging depth, but their iterative operation is typically time-consuming and therefore limits the correction of strong, spatially complex aberrations. Until recently, this limitation was partly due to the lack of high-speed phase modulators offering sufficiently large numbers of actuators. Recent technological advances have addressed this bottleneck with the emergence of megapixel phase modulators operating at kilohertz rates. Here, we demonstrate wavefront correction in multi-
photon microscopy using a high-speed phase-only MEMS modulator combined with a rapidly converging scatter-compensation
algorithm. We experimentally correct complex aberrations comprising 144 spatial modes in less than one second, resulting in signal enhancements exceeding a factor of two. Benchmarking against a fast
liquid-
crystal spatial light modulator reveals a sixfold increase in correction speed. We further demonstrate adaptive optics imaging in mouse
brain tissue using both two-
photon and three-
photon excitation fluorescence microscopy. These results indicate that high-resolution MEMS-based spatial light modulators enable efficient indirect wavefront sensing and represent a promising platform for high-speed wavefront shaping in non-linear microscopy.
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All authors Juan David Muñoz-Bolaños, Eva Ernst, Maria Borozdova, Florian Harrasser, Daniel Grünbacher, Alexander Knapp, Simon Moser, Monika Ritsch-Marte, Alexander Jesacher
Optics
Notes on Images and Communication
Denis Martynov
This is an active module taught to Bachelor's and Master's students at the University of Birmingham since 2021, covering selected topics in applied optics with an emphasis on imaging,
lasers, and classical and
quantum communication. The module covers both the theoretical foundations and experimental aspects of these topics, and explores a range of instrumentation examples, including optical and
radio telescopes, adaptive optics,
laser cutting systems, optical tweezers,
laser interferometers, optical atomic clocks, optical coatings, coaxial cables and optical fibres, frequency combs, and
quantum key distribution technologies.
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Plasma Physics
Autoregressive prediction of 2D MHD dynamics inferred from deep learning modeling
David Kivarkis, Waleed Mouhali, Sadruddin Benkadda et al. (+1 more)
We develop two deep learning surrogate autoregressive models for the prediction of the temporal
evolution of two-dimensional ideal magnetohydrodynamic (MHD) Kelvin-Helmholtz instabilities across a range of
magnetic field strengths. Using two neural network architectures, a Koopman-based
Transformer model and a ConvLSTM-UNet, our approach enables simultaneous prediction of vorticity and current density directly from high-resolution simulations. The models are trained in an autoregressive manner and are able to reproduce key features of the multiscale dynamics over several instability growth and nonlinear saturation phases. Beyond accurate field reconstruction, the surrogates preserve essential physical structures of ideal MHD dynamics, including the conservation trends of global invariants and the propagation of Alfvénic fluctuations. Compared to direct numerical simulations, the proposed surrogates offer substantially reduced computational cost while maintaining good agreement with the reference dynamics. These results suggest that deep learning based surrogate models can provide a promising complementary tool for the efficient and physically consistent exploration of high-fidelity
plasma and fluid simulations.
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All authors David Kivarkis, Waleed Mouhali, Sadruddin Benkadda, Kai Schneider
Biological Physics
Noise-Driven Differentiation via Gene Frustration and Epigenetic Fixation
Davey Plugers and Kunihiko Kaneko
Gene expression in
cells is
stochastic, yet differentiation can display reproducible timing and stable fate commitment. We develop an analytical theory for a previously identified mechanism in which weakly stable intermediate, or frustrated, gene-expression states are perturbed by
stochastic fluctuations and subsequently fixed by slow epigenetic feedback. By eliminating the fast expression dynamics, we show that the differentiation of the slow epigenetic variable is driven by the noise of gene-expression, which can be amplified by regulatory interactions. We derive the logarithmic dependence of onset time for differentiation upon the effective noise intensity, and the input-dependent probability of reaching either fate. We further construct a Waddington-inspired time-dependent probability landscape that visualizes population branching and progressive fate fixation.
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History and Philosophy of Physics
Comment on "Specific heat of an ideal Bose gas above the Bose condensation temperature," [Am. J. Phys. 72(9), 1193–1194 (2004)]
Frank Wang
We examine the English translation of Albert Einstein's groundbreaking 1925 paper on Bose-Einstein condensation. We guide readers to execute the calculations Einstein outlined for the specific heat above the condensation temperature, correct some numerical errors, and compare his formula with a different one published in the American Journal of Physics in 2004. The history of the acceptance of Einstein's theory will be summarized.
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Optics
Programmable recirculating bricks mesh architecture for photonic neural networks
Jacek Gosciniak
General-purpose programmable photonic processors are considered a crucial technology because they combine the ultra high-speed, massive bandwidth, and energy efficiency of light-based computing with the flexibility of software-defined hardware. Unlike application-specific photonic integrated
circuits (ASPIC) designed for one task, these processors use reconfigurable waveguide meshes to implement various functions, such as switching, filtering, or AI computation, on a single chip, allowing for rapid prototyping and versatile, on-demand hardware redefinition. Here we report a recirculating bricks mesh architecture that can be easily implemented in photonic neural networks. It will be shown that a single programmable optical system is capable of performing various functions depending on the requirements. In particular, we will show that the same network, after being reprogrammed, can perform many different functions, ranging from a crossbar network to optical interference
circuits with variable structures, which can then be subjected to Singular Value Decomposition. Furthermore, the "bricks" mesh serves as an excellent foundation for implementing a monitoring system capable of monitoring the power in each location of the
circuit and, subsequently, sel-fcalibrating and stabilizing the
circuit using a feedback loop.
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Optics
Enhanced Mid-Infrared Single-Photon Detection with Antenna-Coupled Superconducting Nanowires
Dip Joti Paul, Stewart Koppell, Gregor G. Taylor et al. (+7 more)
Scaling the
photon-detection area of superconducting nanowire single-
photon detectors (SNSPDs) has traditionally been achieved by nanowire meandering. However, material inhomogeneities and fabrication-induced defects, such as line-edge roughness, increase with nanowire length, leading to reduced internal
photon-detection efficiency and elevated dark-count rates. This trade-off becomes increasingly pronounced as nanowires are scaled to sub-100 nm widths and sub-5 nm thicknesses required for mid- to far-infrared sensitivity. Here, we demonstrate an
antenna-coupled SNSPD architecture that enhances the effective
photon-detection area without increasing nanowire length. A crossed bowtie
antenna integrated with an 80 nm-wide, 3 nm-thick WSi nanowire yields 15.7$\times$ increase in effective detection area at 7.4 $\mu$m compared to a bare nanowire of identical
geometric footprint, while maintaining the same internal detection efficiency and dark-count rate.
Antenna coupling provides a scalable approach to increasing
photon-detection area while reducing the noise-equivalent power, offering performance benefits for applications in astronomy, biological imaging, and molecular spectroscopy.
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All authors Dip Joti Paul, Stewart Koppell, Gregor G. Taylor, Boris Korzh, Sahil R. Patel, Andrew D. Beyer, Emma E. Wollman, Matthew D. Shaw, Phillip D. Keathley, Karl K. Berggren
Accelerator Physics
Bridging Metal Additive Manufacturing and RF Accelerator Design: Development of a 704.4 MHz Crossbar H-Mode Linac for Efficient Beam Acceleration
Chuan Zhang, Eduard Boos, Roland Boehm et al. (+3 more)
The development of Ultra-High Frequency (UHF) linear accelerators via Metal Additive Manufacturing (MAM) is a strategic research focus of the RACERS team at GSI. The 704.4 MHz Crossbar H-mode (CH) cavity, proposed in 2021 to facilitate efficient frequency jumps and downsize accelerator footprints, represents both the highest-frequency CH structure to date and the first of its kind fabricated entirely through MAM. This study demonstrates the structure's capability for efficient beam acceleration in both Continuous Wave (CW) applications (e.g., accelerator-driven systems) and pulsed operations (e.g., spallation
neutron sources). By operating in the UHF regime, the cavity inherently enhances sparking resistance, shifting the physical bottleneck away from surface electric field constraints to enable higher accelerating gradients. To manage the resulting thermal loads within compact dimensions, this study utilizes the design freedom of MAM to integrate a sophisticated "lotus-root-like" cooling network, which is a geometry unachievable through conventional subtractive machining. In combination with a kind of high-strength and high-conductivity
alloy, CuCr1Zr, the cavity can achieve energy gain rates of 1.4-1.5 MeV/m (CW) and 4.6-4.8 MeV/m (pulsed), while maintaining a peak surface temperature of approximately 60 degrees Celsius. These results indicate that bridging additive manufacturing with advanced RF design provides a robust framework for next-generation UHF linac structures to go beyond current accelerating-gradient limits.
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All authors Chuan Zhang, Eduard Boos, Roland Boehm, Ramy Cherif, Alexander Japs, Stefan Wunderlich
Plasma Physics · Cross-listed from astro-ph.HE
Equation of State for warm Neutron Star outer crusts
David Barba-González, Conrado Albertus and M. Ángeles Pérez-García
We describe the equation of state (EoS) of a warm ion
plasma as obtained by performing microscopic many-body simulations using Molecular Dynamics computational techniques. Using the cold one-component
plasma (OCP) composition in the
Neutron Star (NS) outer crust assumed in \cite{Murarka_2022} with a representative heavy
nucleus for each density, we refine previous calculations. We include electron screening and modeling of ions as finite-size Gaussian distributions in the interaction potential, together with an efficient Ewald energy summation procedure. From this, the EoS relation $P(n_B,T)$ is obtained as a function of baryonic number density $n_B\in[7.48\times 10^{-10},2.09\times10^{-4}]$ $ \rm fm^{-3}$ and temperature $k_{B}T\in[1,5]$ MeV in the NS outer crust conditions. In order to improve the usability of our results we provide tabulated data values along with a neural network parametrization available in the Zenodo repository, see https://doi.org/10.5281/zenodo.15348712. We find that even at moderate temperatures, thermal effects of ions are key in the higher density region closer to the inner crust, when described using a thermal effective parametrization based on the thermal adiabatic index $\Gamma_{th}$. We compare our results with other EoS in the literature performing a critical discussion.
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Optics
A Slow-Time Receiver Interface for Turbulent Free-Space Quantum Polarization Links
Heyang Peng, Seid Koudia and Symeon Chatzinotas
Atmospheric
turbulence makes free-space
quantum polarization links intrinsically time varying, whereas receiver-side reduced interfaces are often treated as static. This paper develops a slow-time receiver interface by extending an aperture-conditioned static model to the temporal domain. The receiver-plane phase field, beam-centroid displacement, and scintillation are modeled as hidden slow-time
stochastic processes, from which the reduced interface is generated at each instant. A leading-order closure maps coarse-grained phase roughness to an effective polarization-mixing variance while preserving the inherited local polarization-channel family. Aperture conditioning then yields time-dependent effective depolarization, coherence, and detection descriptors. In a representative weak-
turbulence case, the polarization branch remains close to the near-ideal regime, with effective depolarization on the order of \(10^{-3}\) and effective coherence close to unity, whereas the detection branch exhibits visibly stronger fluctuations and a longer correlation time. These results show that a single static receiver-side parameterization is insufficient to characterize the temporal behavior of turbulent free-space
quantum links. The resulting interface is intended for receiver-side characterization of time-varying
quantum links, with MDI-QKD as one representative downstream application.
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Optics · Cross-listed from quant-ph
Dissipative dynamics and superradiant countinuous time crystal in a Rydberg-interacting Dicke system
Haohang Zhou, Xianfeng Chen and Luqi Yuan
The interplay between many-body interactions and controlled dissipation provides a rich setting for nonequilibrium
quantum phases. We explore the superradiant phase transitions in a Rydberg-interacting open Dicke model with cavity loss and local dissipation. We find that the nonlinear feedback from atomic interactions stabilizes the atomic collective excited state and induces resonant collective motion, which can be further converted into robust superradiant oscillations with the inclusion of local dissipation. Beyond the mean-field approximation, we prove this system can support the finite-size local dissipative temporal order, i.e., continuous time
crystal, from evidences of the decreasing damping of a finite-frequency Liouvillian mode and the linear-size scaling of
quantum fluctuations. The correlation between the atomic colletive dynamics and the cavity
photon number makes our findings easy to detect from output optical signal. By bridging interacting atomic matter and driven-dissipative cavity QED, our Rydberg-interacting Dicke model provides a unique
spin-1/2 platform for exploring dynamical phase transitions and macroscopic temporal order in open
quantum matter.
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Applied Physics · Cross-listed from cs.RO
Muscle-inspired magnetic actuators that push, pull, crawl, and grasp
Muhammad Bilal Khan, Florian Hofmann, Kilian Schäfer et al. (+2 more)
Functional
magnetic composites capable of large deformation, load bearing, and multifunctional motion are essential for next-generation adaptive soft
robots. Here, we present muscle-inspired
magnetic actuators (MMA), additively manufactured from a thermoplastic/permanent magnet polyurethane/Nd2Fe14B (TPU/MQP-S) composite using
laser powder bed fusion (LPBF). By tuning the
laser-energy scale between 1.0 and 3.0, both mechanical stiffness and
magnetic response are precisely controlled: the tensile strength increases from 0.28 to 0.99 MPa while maintaining 30-45% elongation at break. This process enables the creation of 0.5 mm-thick flexural hinges, which reversibly bend and fold under moderate
magnetic fields without damage. Two actuator types are reported showing the system versatility. The elongated actuator with self-weight of 1.57 g, magnetized in its contracted state, achieves linear contraction under a 500 mT field, lifting 50 g (32x its own weight) and sustaining performance over at least 50 cycles. Equipped with anisotropic frictional feet, it supports movement of a
magnetic crawling
robot that achieves up to 100% locomotion success on textured substrates. The expandable actuator exhibits reversible opening and closing under a 300 mT field, reliably grasping and releasing different objects, including soft berries and rigid 3D printed geometries. It can also anchor in a tube while holding suspended 50 g loads. This work demonstrates a LPBF-based strategy to program both stiffness and magnetization within a single material system, enabling remotely driven, reconfigurable, and fatigue-resistant soft actuators. The approach opens new possibilities for force controlled, multifunctional
magnetic soft
robots for adaptive gripping, locomotion, and minimally invasive manipulation of biomedical tools.
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All authors Muhammad Bilal Khan, Florian Hofmann, Kilian Schäfer, Matthias Lutzi, Oliver Gutfleisch
Chemical Physics · Cross-listed from quant-ph
Shannon and Rényi entropies of molecular densities: insights into extensivity and the incomplete description of electron correlation
Diogo J. L. Rodrigues, Evelio Francisco and Ángel Martín Pendás
In this work, we investigate the reliability of information-theoretic measures based on the electron-density and shape-function, specifically Shannon and Rényi
entropies, as descriptors of electronic correlation. By establishing a rigorous decomposition of these entropic measures into additive and nonadditive contributions, supported on a Mulliken-like atomic partition of molecules, we systematically analyze the asymptotic behavior of the
entropies at the infinite-internuclear-distance limit to assess the problem of static correlation and extensivity. Our algebraic and numerical analysis reveals several flaws in the use of these density-based descriptors. We demonstrate that for minimal-basis and different theoretical levels, the Shannon and Rényi
entropies fail to encode the amount of static correlation conveyed by the underlying wavefunction. Conversely, shape-function Shannon
entropies and Rényi
entropies (for $\alpha \neq 1$) violate extensivity. In larger basis sets, uncorrelated Hartree-Fock densities consistently overestimate
entropy compared to sufficiently correlated (e.g., full-valence-CAS) densities. Moreover, the
entropies for insufficiently correlated methods violate extensivity. These findings indicate that electron-density-based measures are insufficient for capturing static correlation, suggesting that robust entropic descriptors should be constructed from higher-dimensional
Hilbert-space objects.
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Atomic Physics · Cross-listed from cond-mat.quant-gas
Preparation of quasi-two-dimensional Bose mixture of ultracold $^{23}$Na and $^{87}$Rb atoms
Ji-Kai Liao, Hao-Ran Zhang, Xiao-Rong Yu et al. (+5 more)
Quantum gases confined in reduced dimensions have enabled the observation of many exotic
quantum phenomena. While existing experiments primarily focus on homonuclear systems, we report here on the efficient preparation of a quasi-two-dimensional (2D) heteronuclear
quantum degenerate mixture of ultracold $^{23}$Na and $^{87}$Rb. We describe the design of the
vacuum system and detail the experimental procedures for preparing the 2D
quantum mixture. The designed apparatus has several unique features, including compact and modular 2D-MOT sources, a science chamber that accommodates various
lattice geometries, a precision in-
vacuum electrode assembly, and high-resolution imaging for both atomic species. After loading the dual-species condensate into a single layer of a vertical optical
lattice, we prepare a 2D gas mixture and observe
quantum immiscibility in the in-situ
equilibrium density profiles. The observed density profiles agree well with mean-field theories. The apparatus provides a versatile platform for investigating several interesting problems, including
quantum impurities,
quantum droplets, or polar molecules in low dimensions.
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All authors Ji-Kai Liao, Hao-Ran Zhang, Xiao-Rong Yu, Ya-Qun Qi, Yi-Cheng Guo, Bo Zhao, Jun Rui, Jian-Wei Pan
Fluid Dynamics · Cross-listed from cs.LG
Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design
Yunjia Yang, Babak Gholami, Caglar Gurbuz et al. (+2 more)
Accurate machine-learning models for aerodynamic prediction are essential for accelerating shape optimization, yet remain challenging to develop for complex three-dimensional configurations due to the high cost of generating training data. This work introduces a methodology for efficiently constructing accurate surrogate models for design purposes by first pre-training a large-scale model on diverse geometries and then fine-tuning it with a few more detailed task-specific samples. A
Transformer-based architecture, AeroTransformer, is developed and tailored for large-scale training to learn aerodynamics. The methodology is evaluated on transonic wings, where the model is pre-trained on SuperWing, a
dataset of nearly 30000 samples with broad
geometric diversity, and subsequently fine-tuned to handle specific wing shapes perturbed from the Common Research Model. Results show that, with 450 task-specific samples, the proposed methodology achieves 0.36% error on surface-flow prediction, reducing 84.2% compared to training from scratch. The influence of model configurations and training strategies is also systematically studied to provide guidance on effectively training and deploying such models under limited data and computational budgets. To facilitate reuse, we release the
datasets and the pre-trained models at https://github.com/tum-pbs/AeroTransformer. An interactive design tool is also built on the pre-trained model and is available online at https://webwing.pbs.cit.tum.de.
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All authors Yunjia Yang, Babak Gholami, Caglar Gurbuz, Mohammad Rashed, Nils Thuerey