Computational Physics · Cross-listed from quant-ph
Improved lower bound for the two-way-assisted quantum capacity of the bosonic thermal-loss channel
Matthew Barber and Stefano Pirandola
The bosonic thermal-loss channel is a fundamental model for
quantum communication. This Gaussian channel models the transmission of bosonic systems subject to both loss and thermal noise. It describes many practical systems, including optical fibers, waveguides and free-space links, where background thermal noise is significant. Determining the channel's two-way-assisted
quantum capacity, the maximum rate at which
quantum information can be transmitted reliably through the channel with two-way classical assistance, remains an open problem. Here, we improve upon the best known lower bound for this capacity for a wide range of channel parameters. We achieve this by combining an improved
qubit-bosonic distribution technique with a recently introduced technique for discovering
entanglement distillation protocols.
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General Physics
Constraints on Rastall gravity from current observational data
Mahnaz Asghari and Hooman Moradpour
We study the cosmological features of Rastall
gravity, where the covariant energy-momentum conservation is modified to $\nabla_{\mu}T^{\mu}_{\nu}=\lambda \nabla_{\nu}R$ in curved spacetime. For this purpose, we obtain the modified field equations of Rastall model to linear order of perturbations, and then inspect the evolutionary behavior of cosmological observables, chiefly the matter power spectrum and the Hubble parameter, within the context of Rastall
gravity. We also compare Rastall model with cosmological probes, namely
cosmic microwave background, weak
lensing,
supernovae,
baryon acoustic oscillations, and
redshift-space distortions data. According to our numerical results, Rastall
gravity prefers lower growth of structures compared to the $\Lambda$CDM model, which indicates consistency with low-
redshift large-scale structure probes. Moreover, numerical analysis reveals that Rastall
gravity with a cosmological constant as the dark energy component suffers from the Hubble tension, like the $\Lambda$CDM model.
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General Physics
Quantum Technologies: System-Level Performance and Validation Priorities
Slava G. Turyshev
Performance claims in
quantum technology are not properties of hardware alone. They are properties of a declared task, system boundary, normalization denominator, uncertainty or security convention, and comparator. We perform a cross-domain analysis spanning
quantum computing, simulation, communication, sensing, clocks, randomness generation, and their enabling technologies. The analysis changes substantive conclusions in several representative cases. For the longest direct finite-key
quantum key distribution case analyzed here, the same final key gives rates differing by a factor of $1.91$ when normalized by complete acquisition rather than transmission-active time. In Advanced LIGO, $6.1\,\mathrm{dB}$ peak
quantum-noise reduction coexists with a $0.534$ coincident analysis-ready fraction, separating detector-level gain from delivered observing service. In
quantum computation and photonic sampling, matching the observable, error tolerance, loss model, sample count, amortization, and classical hardware moves or reverses published crossover claims. Across domains, the recurring limits are correlated error, multiplicative interface loss, thermal and nonequilibrium occupation, calibration covariance, measurement efficiency, fabrication yield, and control latency. A
quantum advantage is therefore established only for a fixed task and boundary when the accepted output outperforms the best documented alternative at matched accuracy, elapsed time, availability, and lifecycle cost. The resulting framework identifies the measurements required to convert component records into reproducible system capability. Credible progress is defined by reproduced logical workloads, prospectively validated simulations, repeater links outperforming direct transmission, long-duration calibrated sensors and clocks, integrated hardware with predictable yield and reliability.
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Plasma Physics
Real-time feedback control of ELM frequency using divertor gas puffing and its effects on tungsten-induced radiation and plasma performance in KSTAR
Minseok Kim, Young-Ho Lee, SangKyeun Kim et al. (+10 more)
The edge-localized mode (ELM) frequency ($f_{\mathrm{ELM}}$) was successfully controlled in real time on KSTAR using a proportional-integral (PI) feedback controller, employing a $\mathrm{D}_2$ divertor gas puff as the actuator under tungsten lower-divertor conditions. The controller accurately tracked a two-step target—a 30 Hz increase in $f_{\mathrm{ELM}}$ for 4 s, followed by a 30 Hz decrease for 3 s—yielding mean and median absolute percentage errors of approximately 13% and 12%, respectively. Compared to a reference discharge, the actively controlled shot did not exhibit a significant drop in volume-integrated core radiation, confirming that excessive gas use merely degrades overall
plasma performance. However, when contrasted with the exponential increase in core radiation observed in the absence of divertor gas puffing, these results underscore the critical need for real-time optimization. Specifically, divertor gas commands must be actively managed to maintain an $f_{\mathrm{ELM}}$ sufficient for flushing tungsten from the core while maximizing global
plasma performance.
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All authors Minseok Kim, Young-Ho Lee, SangKyeun Kim, Minwoo Kim, Sang-hee Hahn, Hiro J. Farre-Kaga, Ricardo Shousha, Juhyeok Jang, SooHyun Son, Yoon Seong Han, Junghoo Hwang, Boseong Kim, Egemen Kolemen
Instrumentation and Detectors
A Silicon Photonic Transmitter for High Energy Physics and Cryogenic Detectors
Adam Quinn and Sajjad Moazeni
We demonstrate a silicon photonic transmitter designed for reading out physics detectors in extreme environments. The transmitter operates at room and cryogenic temperature and consists of a micro-ring modulator and a co-designed CMOS serializer and driver.
Fluid Dynamics
A Sharp and Conservative VOF Method for Multicomponent Liquid–Gas Mass Transfer: Bubble Dissolution and Droplet Evaporation
Shuo Zhao, Jie Zhang and Ming-Jiu Ni
We present a sharp and conservative geometrical VOF–finite-volume method for multicomponent
liquid–gas mass transfer across deformable interfaces. The method solves problems in which multiple species are coupled through interfacial mass balances, latent-heat exchange, and vapor–
liquid equilibrium. The key novelty is a fully sharp two-field treatment of scalar transport: the species and temperature equations are solved separately in the
liquid and gas phases, while the one-sided Robin conditions for species and the two-sided flux jump for temperature are imposed directly on the reconstructed interface through an embedded-boundary discretization. This avoids both volumetric regularization of interfacial source terms and explicit coupling based on previous-time-step interfacial data. A consistent geometrical advection scheme is used for volume, momentum, energy, and species transport, and a sequential coupling strategy is developed to determine the partial interfacial mass fluxes, close the temperature equation, and update the thermodynamic-
equilibrium state. The method is validated through single- and multicomponent bubble dissolution, single-component droplet evaporation, non-ideal ethanol–isooctane droplet evaporation, and sessile water–glycerol droplet evaporation. The results demonstrate second-order accuracy, accurate interfacial flux prediction, good mass and energy conservation, and the ability to capture complex multicomponent effects such as gas replacement, azeotropic
volatility reversal, and composition-driven Marangoni flow.
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Fluid Dynamics
Liquid bridges between horizontal cylinders: effects of gravity and electric fields
Agnes J. Bokányi-Tóth, Andrew J. Archer, Radu Cimpeanu et al. (+3 more)
Liquid bridges suspended between two parallel horizontal cylinders are studied using experiments, reduced-order mathematical modelling and numerical simulations. Both non-electrified and electrified configurations are considered, with the cylinders acting as electrodes between which a potential difference can be applied. The initial focus is on
equilibrium bridge shapes, while the dynamics is examined only in the non-electrified case. In the experiments,
transformer-oil bridges are investigated and modelled as perfect dielectrics. The electric field counteracts the gravitational effects and causes the bridges to rise and become flatter, with shapes that are well described by Young–Laplace-type equations containing non-local electric-field contributions evaluated using a boundary-element method. Bifurcation diagrams of these solutions are constructed by pseudo-arclength continuation to characterise the dependence of bridge shapes on
liquid volume and electric-field strength. In the absence of an electric field, the transient relaxation to
equilibrium is analysed using a reduced-order model developed using Onsager's variational principle, with comparison to direct numerical simulations. The steady states predicted by the reduced-order model agree closely with those of the full formulation over a wide parameter range, and the dynamics is well captured in the overdamped regime.
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All authors Agnes J. Bokányi-Tóth, Andrew J. Archer, Radu Cimpeanu, H. C. Hemaka Bandulasena, Gyula I. Tóth, Dmitri Tseluiko
Physics and Society
Grant-Making Under Uncertainty
Daniel L. Goroff
The theory of Decision-making under uncertainty as it applies to NIH grant-making generally and to NIH research plans concerning the science of science in particular.
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Discovery of novel magnetic Y-Mn-B compounds via advanced machine learning guided framework
Weiyi Xia, Wei Shen Tee, Maxim Moraru et al. (+2 more)
Rare-earth transition-metal borides offer critical structural motifs for permanent-magnet design; however, the manganese-rich regions within these compositional phase spaces remain largely unexplored. In this work, we develop an advanced machine-learning-assisted discovery framework to explore Y-Mn-B ternary system. Starting from over one million hypothetical structures generated from known structures in databases, we filtered promising candidates by first applying graph neural networks to predict material stability, then using machine-learning-interatomic-potential to relax their structures, and finally validating the results with first-principles calculations. We identify 5 stable and near-stable Y-Mn-B phases along with 61 metastable compounds with the formation energy within 100 meV/atom with respect to the ternary convex hull. Among them, Y2Mn7B7 and YMn4B4 are structurally analogous to the previously synthesized $R_{1+\epsilon}Fe_4B_4$ 1D incommensurate composite chain compounds. In striking contrast to the strongly suppressed Fe moments reported, our first-principles calculations reveal that the predicted Mn-chain phases preserve sizable local Mn moments (approximately 1.1 $\mu_B$) and favored
ferromagnetic ordering. Electronic structure analyses elucidate the microscopic origin of moment recovery via an enhanced exchange splitting driven by a Stoner-like instability. We also perform systematic Mn-Fe substitution to confirm the thermodynamic continuity and a monotonic enhancement of the macroscopic magnetization, from Fe to Mn. These findings indicate that targeted transition-metal substitution within a one-dimensional boride family can recover transition-metal magnetism, offering a physically interpretable route for designing new
magnetic rare-earth transition-metal borides.
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All authors Weiyi Xia, Wei Shen Tee, Maxim Moraru, Ying Wai Li, Cai-Zhuang Wang
Atomic Physics · Cross-listed from quant-ph
Fast Nondestructive Readout for High-Clock-Rate Atom Array Quantum Processor
Xu-Zhao-Qiu Zeng, Chang You, Qing-Wei Wang et al. (+18 more)
Neutral-atom arrays have rapidly advanced to support thousands of
qubits and execute high-fidelity logical operations. However, these processors remain severely throttled by their slowest fundamental operation: nondestructive
qubit measurement, which requires milliseconds and fundamentally limits the system's clock rate. This bottleneck arises from both an inherent
photon-budget dilemma—sufficient fluorescence for reliable state discrimination must be collected without excessive heating or loss—and frame-based imaging, which imposes one common exposure and decision latency on intrinsically independent, site-local measurements. Here, we overcome these limitations with a fast, nondestructive readout architecture based on real-time, site-resolved adaptive protection. By integrating continuous
photon counting with a dynamic feedforward framework, we decode
qubit states with sub-microsecond latency and instantly shield atoms from redundant scattering. Demonstrated in parallel across a 100-
qubit reconfigurable atom array, with adaptive protection on a 25-site subarray, this dynamic decision protocol reduces the average probe time to just $15\ \mu\text{s}$. Model-free benchmarking yields a discrimination infidelity of $4.1 \times 10^{-5}$ and an atom loss of $2.1 \times 10^{-4}$, simultaneously setting new performance records for atom arrays. Exploiting this capability, we operate repeated
quantum circuits at an unprecedented 1.7 kHz clock rate with atoms reused over 120 consecutive rounds—nearly sevenfold higher than the previous record—and enter the sub-millisecond cycle regime for the first time. By removing nondestructive readout as the dominant cycle-time bottleneck, this work unlocks high-clock-rate mid-
circuit syndrome extraction, paving the way for high-throughput, fault-tolerant
quantum computation.
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All authors Xu-Zhao-Qiu Zeng, Chang You, Qing-Wei Wang, Zi-Feng Li, Yi Ji, Dong An, Chao Yu, Jia-Rui Liu, Zi-Mo He, Jia-Rui Gu, Yuhao Mei, Hao-Wen Cheng, Yu-Chen Zhang, Rui Lin, Zhan Wu, Jun Rui, Jun Zhang, Ming-Cheng Chen, Yu-Hao Deng, Chao-Yang Lu, Jian-Wei Pan
Fluid Dynamics
Autonomous route to the strange attractor: the many life stages of the chaotic bubblewheel
Michael Zhao and Saverio E. Spagnolie
A body floating atop a supersaturated fluid may accumulate bubbles along its underbelly, which can render the body rotationally unstable. But rotation can strip the surface of these bubbles when they make contact with the air above. To explore this coupling we perform experiments using cylinders floating on carbonated water. The cylinders exhibit an array of distinct dynamics, including constant rolling, periodic and aperiodic oscillation, chaos, and intermittent capsizing, which can be tuned by body size, mass distribution, and gas concentration. A continuum model for the bubble density dynamics, and Galerkin projection, tie the experiment to the generalized Lorenz system and its famed chaotic attractor. Even an extremely small center-of-mass offset can have a qualitative impact on the dynamics, and an offset of as little as a few percent can fully stabilize the system. The experiment represents a highly accessible physical realization of the Lorenz system, which, owing to continuous gas loss from the fluid to the air above, sweeps slowly without intervention across a classical bifurcation diagram in time.
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Computational Physics · Cross-listed from cs.LG
Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions
Xiao Wang, Tomohiro Hashizume, Pia Siegl et al. (+1 more)
Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise evaluation of elementary and nonlinear filtering functions on data encoded as tensor trains (TTs), a class of tensor networks. Our approach operates entirely in the compressed domain, enabling efficient computation on exponentially large
datasets while maintaining a controlled computational cost. We demonstrate its power in two key areas: (I) evaluating highly nonlinear elementary and filtering functions on a 3D reactive flow field, enabling high-fidelity reaction rate computation and region filtering, and (II) finding extrema in complex optimization problems, such as solving Max-SAT instances on spaces up to $2^{70}$ configurations. These results establish ITNT as a foundational tool that provides tensor network methods with the capability for general-purpose data science and large-scale optimization.
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All authors Xiao Wang, Tomohiro Hashizume, Pia Siegl, Dieter Jaksch
Computational Physics · Cross-listed from cs.CE
Reduced-Order Physics-Informed Neural Network with Adaptive Basis Refinement for Structural Identification
Rui Zhang, Konstantinos Vlachas and Eleni Chatzi
Physics-informed neural networks (PINNs) provide a flexible framework for solving forward and inverse problems. However, their direct application to structural dynamics remains limited by high system dimensionality and model-form errors arising from incomplete physics. Reduced-order models (ROMs) can alleviate the dimensionality bottleneck, yet existing PINN-ROM couplings typically rely on fixed reduced subspaces, target forward simulations, or assume complete physics, restricting their use for inverse identification under parametric variability or incomplete system knowledge. To address these limitations, this work proposes a Reduced-Order Physics-Informed Neural Network (RO-PINN) framework with adaptive basis refinement for structural identification under known and incomplete physics. Via projection, reduced governing equations are embedded directly into the PINN loss, facilitating learning in a low-dimensional latent space. An adaptive scheme updates the projection basis during training so that the latent space is progressively realigned with evolving structural parameters or learned residual restoring forces. This realignment reduces basis-mismatch errors and limits their influence on the inferred residual force. The method is validated on a four-story steel frame with nonlinear hysteretic braces under sparse and noisy measurements. Results show parameter identification comparable to or more accurate than
Bayesian model updating with lower computational cost in the considered cases, recovery of unmodeled nonlinear restoring forces under incomplete physics, and joint identification of residual restoring forces and structural parameters within the same framework. Overall, RO-PINN provides a unified framework for structural identification by integrating reduced-order modeling, adaptive basis refinement, and physics-informed learning within a single formulation.
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Applied Physics · Cross-listed from cond-mat.supr-con
Demonstration of a scalable all-solid-state refrigerator exploiting diffusion geometries and limiting interfacial conductances at temperatures below 1 kelvin
Robert M. Young, Zachary Stegen, John X. Przybysz et al. (+32 more)
Solid-state refrigerators using Normal-metal/
Insulator/
Superconductor (NIS) junctions have previously demonstrated excellent electron cooling but limited ability to cool
phonons. The energy gap of the
superconductor is used as an energy filter to allow higher than average energy electrons to preferentially tunnel from the normal-metal through the
insulator into the
superconductor where they travel as quasi-particles. Typically, the heat is moved and work is done to deposit hot quasi-particles into a normal-metal quasi-particle trap for rejection to the next refrigeration stage. Realizing that (1) the quasi-particles flow diffusively, driven by a concentration gradient in the electric field-free
superconductor, and (2) that the undesirable backwards leaking of heat from the hot-side trap can be reduced by engineering the geometry and materials at the
superconductor-to-trap interface, enhanced cooling can be achieved. Fabrication of the refrigerator was accomplished using a tungsten and titanium-tungsten
alloy as the cold-side normal-metal, aluminum
oxide as the
insulator, aluminum as the
superconductor, and gold as the trap, with the cold-side NIS portion being attached to the hot-side gold trap by bump bonding. The refrigerator consisted of 1121 junction pairs, each pair being an SINIS unit, all electrically connected in series. Using this we have measured the effective
phonon temperature of a 3.9 mm x 3.9 mm x 0.65 mm silicon chip driven down to 70 mK from a bath temperature of 120 mK, and down to 174 mK from a 271 mK rejection temperature (a cooling of -97 mK). This is the first demonstration of the sub 1 K cooling of an entire silicon chip using NIS junctions.
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All authors Robert M. Young, Zachary Stegen, John X. Przybysz, Edward R. Engbrecht, Aaron A. Hathaway, Justin C. Hackley, Kirby B. Myers, Christian C. Thorpe, Aurelius L. Graninger, Robert Miller, Diego A. Morales, Roberto D. Carcamo, Glen Walters, Jeric P. Sarad, Nicholas F. Pleim, Seth Whitsitt, Joshua T. Shipman, Anil Erol, Melissa G. Loving, Evan Donohue, Corey A. Kegerreis, Benjamin Dalfort, Moe S. Khalil, Christopher Pinion, Randi Jaramillo, John Milinichik, Sandro J. Di Giacomo, Thomas Zodda, Nilesh Tralshawala, Gregory R. Boyd, Jonathan M. Cochran, Katherine A. Maddock, Michael P. De Feo, Aaron A.Pesetski, Marc E. Sherwin
Accelerator Physics
Bayesian Optimization of Molybdenum-99 Production by Laser Wakefield Acceleration Using Coupled PIC and Monte Carlo Simulations
Bruno Silveira Nunes, Nilson Dias Vieira Junior, Mirko Salomón Alva Sánchez et al. (+2 more)
This work applies
Bayesian optimization to a loop composed of PIC simulations of
laser electron acceleration and Monte Carlo (MC) simulations of bremsstrahlung-induced
nuclear reactions, to maximize the production of molybdenum-99, the precursor of the most used radiopharmaceutical in
nuclear medicine, metastable technetium-99. PIC and MC simulations are computationally intensive, and besides reducing the time spent, the
Bayesian optimization coupling both simulations resulted in an improvement of an order of magnitude in the $^\text{99}$Mo yield over a previous work, in which the output of an optimization loop based solely on PIC simulations was used a posteriori to estimate $^{99}\mathrm{Mo}$ production through a MC simulation.
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All authors Bruno Silveira Nunes, Nilson Dias Vieira Junior, Mirko Salomón Alva Sánchez, Alexandre Bonatto, Ricardo Elgul Samad
Fluid Dynamics
Turbulent Microscale Flow Field Prediction In Porous Media Using Convolutional Neural Networks
Vishal Srikanth, Ching-Wei Huang, Ryan Harradine et al. (+1 more)
Turbulence modeling in porous media can be greatly improved by combining high-resolution numerical methods with modern data-driven techniques. The development of accurate macroscale models (length scale greater than the pore size) will enable real-time systemic simulations of porous media flow. We consider the case of turbulent flow in homogeneous porous media, typically encountered in engineered porous media (heat exchangers, metamaterials, combustors, etc.). The underlying microscale flow field is inhomogeneous and determined by the geometry of the porous medium. Neural Networks are able to resolve the geometry-dependence and the non-linearity of porous media turbulent flow. We are proposing to separate the macroscale model into individual blocks that predict a unique aspect of the microscale flow, such as microscale spatial flow distribution and
vortex dynamics. In the present work, we determine the feasibility of the prediction of the Reynolds-averaged microscale flow patterns by using Convolutional Neural Networks (CNN). The porous medium is represented by using a square
lattice arrangement of circular cylinder solid obstacles. The pore-scale Reynolds number of the flow is 300. The porosity of the porous medium is varied from 0.45 to 0.92 with 60 steps. The microscale flow field is simulated by using Large Eddy Simulation (LES) with a compact sixth-order finite difference method. We demonstrate satisfactory prediction of the microscale flow field using the CNN with a global error less than 10%. We vary the number of training samples to study the deterioration of the model accuracy. The CNN model offers a O(106) speedup over LES with only 10% loss in accuracy.
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All authors Vishal Srikanth, Ching-Wei Huang, Ryan Harradine, Andrey V. Kuznetsov
Space Physics · Cross-listed from astro-ph.SR
Tracking nonlinear solar-wind dynamics over three solar cycles using Wind observations
Dario Javier Zamora and Facundo Maximo Abaca
The
solar wind is a turbulent, weakly collisional
plasma characterized by non-Gaussian fluctuations, long-range correlations, and multifractal dynamics. We investigate the long-term
evolution of these properties using hourly
proton density measurements obtained directly by the Wind spacecraft over 1995–2025. The use of a single-spacecraft
dataset provides an independent test of previous results derived from the multi-spacecraft OMNI database. The three components of the nonextensive $q$-triplet are estimated within one-year sliding windows shifted monthly: $q_{stat}$ from the distribution of density increments, $q_{rel}$ from the decay of the autocorrelation function, and $q_{sens}$ from the multifractal spectrum. Their mean values, $q_{stat}=1.71\pm0.07$, $q_{rel}=4.62\pm0.52$, and $q_{sens}=-0.45\pm0.27$, confirm the persistent presence of heavy-tailed statistics, slow relaxation, and weakly chaotic multifractal dynamics. The parameters nevertheless exhibit distinct temporal variability and relationships with
solar activity. The
Fourier spectrum of $q_{stat}$ contains a dominant period of approximately $10.1$ years, while its correlation with the sunspot number is positive and moderate ($r=0.611$). Correlation and mutual-information analyses show that $q_{stat}$ is primarily associated with
solar proxies, whereas $q_{sens}$ displays stronger relationships with geomagnetic indices and $q_{rel}$ exhibits weaker dependencies. An anomalous enhancement of $q_{stat}$ around 2004 coincides with a sequence of intense interplanetary disturbances, although possible effects related to Wind's orbital transition must also be considered. These findings demonstrate the robustness of the nonextensive description and show that the statistical and dynamical properties of
solar wind
proton density show evidence of modulation by
solar activity.
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Computational Physics
Validating direct solvers for Newton's gravitational N-body problem, and the systematic comparison between IEEE floating point and Posits
Simon Portegies Zwart
We present a systematic comparison between arbitrary precise arithmetic and integration, IEEE-754 compliant floating point arithmetic (fp16, bfp16, fp32, double precision fp64, and quadruple precision fp128), and two implementations of Posits (type III unum) for solving Newton's chaotic N-body problem. Each implementation is benchmarked with arbitrary precise calculations to objectively evaluate their performance in precision as well as speed. We rely on hardware and compiler implementations for fp64, and software implementations for arbitrary-precision arithmetic and Posits. Half precision arithmetic (fp16, bfp16, and Posits$<16,1>$) are insufficiently precise for solving Newton's equations of motion. Single precision (fp32, and Posits$<32,2>$) could be used for statistical ensemble calculations, but lead to relatively large errors in any individual strong encounter. All 64-bit implementations fp64 as well as Posits (Posits$<64,3>$) experience difficulty in our tests. One of the implementations of Posits (Universal) gives precision comparable to fp64 but is slow (by at least an orders of magnitude compared to fp64 after correcting for the more efficient hardware support for the latter). The other (CPPPosits) has a speed comparable to fp64 but has systematically larger errors (by about an order of magnitude compared to fp64 with excesses exceeding two orders of magnitude). As a consequence, this implementation leads to a systematic drift in the result space and has difficulty resolving close encounters. Posits and fp64 have difficulty when integrating a dynamical system in a moving reference frame; testing Galileo invariancy. In their current implementation, Posits do not seem to be the ideal alternative for fp64 when integrating chaotic or stiff ordinary differential equations, such as Newton's equations of motion.
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Optics
Erbium-implanted tellurite waveguides with low-temperature post-implantation activation and signal enhancement
Yuxuan Gao, Batoul Hashemi, Bruno L. Segat Frare et al. (+7 more)
In this paper, we demonstrate erbium ion implantation and signal enhancement in tellurium
oxide hybrid waveguides. Silicon nitride strips with a width of 2 $\mu$m and a height of 100 nm were clad with a 110-nm-thick tellurium
oxide layer to form hybrid waveguides, followed by erbium ion implantation at an energy of 200 keV and a dose of $1\times10^{15} ions/cm^{2}$, with a projected peak implantation depth of approximately 50 nm into the tellurium
oxide layer. After low-temperature annealing at 150 {\deg}C for 30 minutes, the propagation loss decreased from 1.7 to 0.9 dB/cm, while the erbium lifetime increased from 40 $\mu$m to over 800 $\mu$m. We measure a small-signal enhancement of 9 dB in an 11-cm-long waveguide at a wavelength of 1550 nm. These results demonstrate progress towards a low-temperature post-ion implantation process for incorporating erbium and other rare earth ions into tellurium
oxide films for integrated photonic applications.
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All authors Yuxuan Gao, Batoul Hashemi, Bruno L. Segat Frare, Niloofar Majidian Taleghani, Pooya Torab Ahmadi, Henry C. Frankis, Ponnambalam Ravi Selvaganapathy, Jonathan D. B. Bradley, Peter Mascher, Andrew P. Knights
Data Analysis and Statistics · Cross-listed from cond-mat.mtrl-sci
PowderLine: a programmatic powder diffraction analysis application
Adam A. Corrao, Jennifer A. Perez, John D. Langhout et al. (+3 more)
Whole-pattern fitting methods, such as Rietveld refinement, excel at extracting detailed structural, chemical, and microstructural information from powder diffraction data. Obtaining reliable results requires both considerable expertise and software-specific knowledge, and applying these methods at scale typically relies on custom scripts written for each application. High-throughput experiments and autonomous self-driving laboratories increasingly utilize powder diffraction analysis to proceed programmatically and to return structured, machine-readable results. Here, we introduce PowderLine, a Python application that encapsulates a complete refinement into a single declarative recipe, validates that recipe against a versioned schema, and executes it through refinement software to return structured results. The refinement recipe is an all-inclusive, machine-readable and -writable description of either Rietveld or single peak analysis that users, scripts, and automated
agents can specify and run in the same way. As a result of PowderLine's composability, it naturally fits into interactive, scripted, and autonomous workflows alike.
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All authors Adam A. Corrao, Jennifer A. Perez, John D. Langhout, Megan M. Butala, Thomas A. Caswell, Daniel Olds
Atomic Physics · Cross-listed from cond-mat.quant-gas
Hydrodynamization in 1D Bose gases at nonzero temperature
Jeff Leiberton and Marcos Rigol
Hydrodynamization refers to the remarkably rapid process in relativistic heavy-ion collisions by which hydrodynamic descriptions become applicable. Following the observation of analogous behavior in ultracold one-dimensional (1D) Bose gases, hydrodynamization has been conjectured to be a universal dynamical phenomenon in
quantum systems following high-energy quenches. Theoretical studies in this cold-atom setting have so far been restricted to quenches from ground states. Here we study how nonzero temperatures affect hydrodynamization. Specifically, using a homogeneous 1D gas of hard-core
bosons, we explore how the initial temperature affects the timescales associated with hydrodynamization and prethermalization following a Bragg-pulse quench. We find that while the hydrodynamization coherence time remains unchanged, increasing temperature shortens both the damping time of the hydrodynamization oscillations and the prethermalization time. We argue that this is mainly the result of the broadening of the initial rapidity distribution, and introduce a nonzero-temperature dephasing time defined in terms of the extent of the rapidity distribution.
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Computational Physics · Cross-listed from math.NA
Meshfree Snow Modelling using a Modified Cam-Clay Approach
Erik Schlesinger, Chaitanya Sanghavi, Jörg Kuhnert et al. (+2 more)
Snow is a complex geomaterial whose macroscopic response is governed by density, temperature, and the
topology of its evolving microstructure. Its mechanical behavior spans elastic, plastic, viscous, and failure dominated regimes, imposing significant challenges for numerical methods, which intends to simulate large deformations, evolving free surfaces, and complex boundary interactions. This work presents the first integration of a Modified Cam-Clay constitutive formulation for snow into a purely meshfree strong-form collocation framework based on the Generalized Finite Difference Method. The main methodological contribution is a numerical coupling that combines a global implicit mixed formulation for pressure and velocity with a constitutive return-mapping
algorithm. The hydrostatic pressure contribution is obtained from a Poisson equation and subsequently corrected through the Modified Cam-Clay return-mapping procedure, while the deviatoric response is treated semi-implicitly using a numerical viscosity formulation. This partitioned treatment of the volumetric and deviatoric stress contributions enables stable simulations with comparatively large time steps while producing smooth spatial pressure fields. As a result, forces on complex boundary geometries can be evaluated accurately. Numerical results of this coupling illustrate the algorithmic stability of the framework, the effective imposition of boundary conditions, and the suitability of local spatial refinement. The feasibility of applying the framework to vehicle-snow interaction through rigid-body coupling is also illustrated. The presented formulation provides a robust basis for future simulations of dynamic snow loading on vehicle structures.
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All authors Erik Schlesinger, Chaitanya Sanghavi, Jörg Kuhnert, Carsten Schilde, Pratik Suchde
Physics and Society · Cross-listed from cs.GT
The New Mathematics of Democracy
Bailey Flanigan and Ismar Volic
This article surveys emerging directions in the mathematics of democracy. It uses three case studies — voting theory, participatory budgeting, and deliberative democracy — to highlight how contemporary challenges motivate rigorous mathematical research that incorporates real-world data, institutional constraints, and implementation feasibility. Within each case, we highlight active and promising research frontiers, evidence of real-world impact, practical applications, and opportunities for getting involved.
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Fluid Dynamics
Volume-preserving Lagrangian averaging using polar factorization
Abhijeet Minz, Lois E. Baker and Jacques Vanneste
The generalised Lagrangian mean (GLM) theory of Andrews & McIntyre provides a powerful framework to study the interactions between waves and flows. A drawback of this theory is that the Lagrangian mean velocity is divergent even for incompressible fluids because the mean flow map, which sends the Lagrangian labels of fluid parcels to their mean positions, does not preserve volume. This results, for instance, in
vortices shrinking under Lagrangian averaging. We overcome this drawback by revising the definition of the mean flow map, choosing it as the volume-preserving map closest to the "bare" GLM mean map. A standard result of optimal-transport theory then shows that the new mean map is the volume-preserving factor in the polar factorization of the GLM mean map. We develop and implement a numerical method for the computation of the corresponding Lagrangian mean fields from simulation data. The implementation builds on recently developed
algorithms for the on-the-fly computation of Lagrangian means using the exponential and Butterworth filters. We demonstrate the value of volume-preserving Lagrangian averaging in simulations of the two-dimensional incompressible and shallow-water models. We compare the Lagrangian-mean fields obtained with and without the volume-preservation constraint.
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Atomic Physics · Cross-listed from quant-ph
Classical-limit formula for matrix elements between bound states of distinct one-dimensional potentials
K. Beloy
A classical-limit formula is derived for
matrix elements between bound states of distinct one-dimensional potentials. We leave open the physical interpretation of the potentials, but generally envision them to be Born-Oppenheimer-type potentials with the position variable $x$ identifying with a slowly evolving degree of freedom of the system. For instance, $x$ could represent the internuclear separation in a diatomic molecule, with the potentials being potential energy curves for different electronic states. In this scenario, the
matrix elements could be, e.g., conventional Franck-Condon factors. To test the derived formula, we assume functional forms for the potentials and operator that afford analytical solutions for the
matrix elements. As the classical limit is approached, the computed
matrix elements exhibit a clear tendency towards the classical-limit formula, providing strong validation for the formula. In future work, we anticipate using the formula to model inhomogeneous excitation spectra of atoms in one-dimensional optical
lattices, with an eye towards improved optical
lattice clock performance.
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Optics
Fabrication-Aware Inverse Design of Nanophotonic Devices for 3D Laser-Nanoprinting
Oliver Kuster, Tim Alletzhäusser, Carsten Rockstuhl et al. (+2 more)
Advances in 3D
laser-nanoprinting enable us to fabricate 3D nanophotonic devices with a wide range of functionalities on demand. By exploiting all three spatial dimensions, an enormous design space becomes available for these nanophotonic devices. However, such an immense design space is impossible to explore efficiently by intuition alone, especially when designing free-form nanophotonic devices. Density- based
topology optimization offers a natural tool for 3D nanophotonic design by allowing the efficient design of devices with millions of degrees of freedom. Traditional density-based
topology optimization relies on heuristic measures to account for limitations imposed by the fabrication method. Indeed, the fabrication method is rarely considered as part of the forward model in the design pipeline. In this work, we introduce an inverse design method that explicitly models the direct-
laser-writing process used in 3D nanoprinting. Incorporating a differentiable formulation of the direct-
laser-writing model allows us to design 3D nanophotonic devices within the experimentally available design space and to precompensate for fabrication-specific effects. Optimizing inside the experimentally available design space ensures that the constraints we put on the optimization are given by our parametrization of the fabrication method and not by heuristic methods, which might over- or underconstrain the optimization problem. Furthermore, modeling the 3D
laser-nanoprinting process explicitly allows us to not only take fabrication-specific effects, such as the proximity effect, into account but also enables the optimization to actively make use of these fabrication-specific effects to increase the functionality of the device.
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All authors Oliver Kuster, Tim Alletzhäusser, Carsten Rockstuhl, Martin Wegener, Thomas Jebb Sturges