Computational Physics · Cross-listed from cs.LG
An Energy-Based Conservative-Dissipative Latent Neural Evolution Operator for Magnetization Dynamics
Sebastian Schaffer and Lukas Exl
We develop an energy-based reduced-order model for micromagnetic magnetization dynamics that couples a convolutional autoencoder to a structured latent neural ordinary differential equation. Motivated by the precessional-dissipative structure of the Landau-Lifshitz-Gilbert equation, the latent vector field is generated from the gradient of a learned scalar potential through an antisymmetric operator and a symmetric positive-semidefinite dissipative operator. This potential is learned in nonunique latent coordinates and is not identified with the Gibbs free energy, but decreases monotonically along autonomous continuous-time solutions, while the antisymmetric component permits motion along its level sets. The encoder, decoder, latent energy, and operators are trained jointly on short trajectory windows using latent and decoded-rollout losses alone, without time-derivative supervision, physical-energy labels, or dissipation penalties. At inference, an initial state is encoded once, evolved in latent space, and decoded only at the requested output times, enabling substantially cheaper trajectory prediction than the micromagnetic solver used to generate the training data. We compare quadratic, deep, and additive deep-quadratic latent energies on two
datasets parameterized by field amplitude and generated for the two applied-field directions of the NIST $\mu$MAG Standard Problem 4. Dissipative-only and antisymmetric-dissipative models achieve comparable accuracy on short training-style windows but differ substantially on uninterrupted rollouts, for which the antisymmetric-dissipative models provide markedly more accurate trajectory predictions. The deep-quadratic energy gives the best overall accuracy for both field directions and exhibits slower error growth when rollouts are extended to twice the training horizon.
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Chemical Physics
Learning Memory-Dependent Neural Network Correlation Potentials for Accurate Electron Dynamics
Harish S. Bhat and Christine M. Isborn
Time-dependent density functional theory, though exact in theory, is in practice applied in an adiabatic approximation using exchange-correlation functionals with only local temporal dependence. Simultaneously, the exact correlation potential formally depends on, among other quantities, the time-history of electron densities. Here we develop a framework to learn neural network models of correlation functionals that feature explicit
memory-dependence. Our framework includes two different approaches, both linked through their use of adjoint-based optimization. One approach decouples the learning of the functional from inversion to find ground truth values of the correlation potential. The other approach learns the functional directly without requiring inversion. We apply these methods to modeling the electron dynamics of two-electron systems in two spatial dimensions. In both cases, our methods yield correlation functionals with low test set propagation error, outperforming standard local density and generalized gradient approximation functionals by one to two orders of magnitude. Overall, our framework points at one strategy to move beyond the adiabatic approximation and develop
memory-dependent correlation functionals that yield accurate propagation for excited state and/or non-
equilibrium dynamics.
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Physics and Society
Crowding controls the scaling of bus frequency with demand
Siddharth Patwardhan, Şirag Erkol, Filippo Radicchi et al. (+1 more)
Cities must allocate limited resources to maintain mobility, with uncertainties about the resulting state of the system. Analyzing roughly 3,000 bus routes with more than 4 billion yearly riders across 19 metropolitan areas worldwide, we uncover a robust scaling law of the form $f \sim (d/t)^\alpha$ with exponent $\alpha \in [1/2,\,2/3]$, linking the service frequency $f$ to passenger demand $d$ and route duration $t$. We show that this scaling emerges from a simple optimization principle: cities implicitly minimize total passenger waiting time under a fixed operational budget when both schedule frequency and crowding are taken into account. This mechanism produces two universal regimes: a frequency-dominated regime with $\alpha = 1/2$ when crowding is negligible, and a capacity-dominated regime with $\alpha = 2/3$ when most routes are overloaded. Intermediate exponents arise when only part of the network operates near capacity. Furthermore, we find that the benefits of additional investment are highly uneven across systems. For instance, our model suggests that a $20\%$ budget increase yields nearly a 5-minute reduction in daily waiting time per passenger in Boston, compared to only about 1 minute in Paris. These findings place urban transit within a broader class of constrained capacity-allocation problems, while highlighting a distinct regime in which prescribed route demands shape the allocation of limited service resources. The resulting scaling laws show how simple optimization principles can generate systematic exponents in complex transport systems, beyond the dissipation-based frameworks usually considered in physical and biological flow networks.
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All authors Siddharth Patwardhan, Şirag Erkol, Filippo Radicchi, Marc Barthelemy
Fluid Dynamics
Quasi-Two-Dimensional Simulation of a Rotating Detonation Engine Combustor and Injector
S. She-Ming Lau-Chapdelaine, Matei I. Radulescu and Zekai Hong
A numerical simulation of an annular rotating detonation engine with stoichiometric hydrogen-oxygen is performed. A generic, well posed, and easily implemented approach using a quasi-two-dimensional method to model the area variations through the rotating detonation engine's injector and combustor is presented. The detonation–injector interaction is studied for the case with a ratio of four between the combustor and injector's throat areas. A shock wave is formed in the divergent portion of the injector due the high back pressure created by the detonation in the combustor. A Favre-averaged steady-state analysis of stream lines and particle paths reveals that the shock causes an irrecoverable loss of stagnation pressure. Stagnation pressure gain in the combustor is insufficient to make up for the loss and the flow leaves the engine with lower stagnation pressure than in the plenum.
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Fluid Dynamics
Coherent-structure dynamics in wall turbulence from state-space trajectories
Emma Lenz, Ahmed Elnahhas and H. Jane Bae
We identify dynamical processes in the near-wall region of turbulent wall-bounded flow by representing coherent-structure energy as trajectories in a low-dimensional state space and analyzing recurring trajectory patterns using network motifs. Direct numerical simulations (DNS) are performed for three configurations: a minimal flow unit (MFU) at $Re_\tau \approx 180$ to isolate the self-sustaining process (SSP), a full-scale channel at the same Reynolds number to study interactions between near-wall structures, and an MFU at $Re_\tau \approx 2200$ to investigate near-wall/outer-layer coupling. Proper orthogonal decomposition (POD) is used to identify modes corresponding to streaks, rolls, and meandering structures of the SSP, and the flow is projected onto these modes to track their energy over time. Motif analysis then identifies statistically significant dynamical pathways in the resulting state-space trajectories. Several motifs are common to all three configurations, indicating robust near-wall dynamics across different background flows. While motifs associated with the classical SSP are recovered, equally prominent motifs with no direct SSP analogue are also identified, demonstrating that preferred near-wall dynamics extend beyond the canonical regeneration cycle. Conditioning the $Re_\tau \approx 180$ MFU on outer-layer energy further shows that quiescent outer-layer states produce dynamics resembling those of the low-Reynolds-number full channel, whereas energetic outer-layer states promote high-energy bursting events. These results demonstrate that near-wall
turbulence evolves along preferred dynamical pathways whose prevalence is modified, but not eliminated, by outer-layer activity.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Triboelectrification of a dense metal-organic framework for resilient mechanical energy harvesters
Chuzhan Jin, Debayan Mondal, Jiahao Ye et al. (+1 more)
Triboelectric nanogenerators (TENGs) incorporating metal-organic frameworks (MOFs) have largely been designed around porous architectures, based on the assumption that high internal surface area is the primary driver for triboelectric enhancement. Herein, we demonstrate that a dense, nominally nonporous MOF called ZIF-zni can instead function as an effective high-loading filler within a tribopositive polyurethane (PU)
matrix, offering a design strategy for harnessing interfacial electromechanical effects. A 20 wt% ZIF-zni@PU composite delivers an output voltage of 470+/-15 V and a peak power density of 1.31+/-0.03 W m-2 against polydimethylsiloxane (PDMS). The device exhibits stable performance over ~94,000 cycles under a contact force of ~100 N, and it remains operational under higher impact beyond 500 N. This concept enables the demonstration of a proof-of-concept triboelectric floor tile. Combined experimental and theoretical studies indicate that the performance enhancement arises from favourable interfacial polarization, reduced dielectric screening, and surface accessible ZIF-zni rich domains, rather than porosity alone. These features are accompanied by reduced work of adhesion and modified surface roughness, hence improving contact electrification. These findings establish dense MOFs as an effective triboelectric filler and identify interfacial electronic structure and polarization as key design parameters for engineering mechanical energy harvesters and self-powered sensors.
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All authors Chuzhan Jin, Debayan Mondal, Jiahao Ye, Jin-Chong Tan
Geophysics
Low electrical conductivity of dry CaSiO3 perovskite under lower mantle conditions
Yoshiyuki Okuda, Bin Chen, Juliana Peckenpaugh et al. (+1 more)
Electrical conductivity (EC) provides important constraints on the composition and volatile distribution of Earth's deep mantle, yet the EC of davemaoite (CaSiO3 perovskite), a major lower-mantle phase, remains poorly constrained. We measured the EC of nominally dry CaSiO3 perovskite at pressures up to 89 GPa and temperatures up to 2200 K using impedance spectroscopy in a
laser-heated diamond anvil
cell. Conductivity increases with temperature but decreases systematically with pressure and it is substantially lower than previously reported, yet broadly consistent with recent theoretical predictions for oxygen-vacancy-mediated ionic transport. A distinct change in the temperature dependence coincides with the tetragonal-to-cubic phase boundary, revealing a modest enhancement of ionic transport across the structural transition. Along a normal lower-mantle geotherm, dry davemaoite is comparable in conductivity to bridgmanite near the top of the lower mantle but becomes progressively less conductive with depth. Under cold-slab conditions, dry davemaoite is substantially less conductive than dry subducted MORB and cannot account for the observed high-conductivity anomalies. Dry davemaoite therefore contributes little to bulk lower-mantle conductivity.
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All authors Yoshiyuki Okuda, Bin Chen, Juliana Peckenpaugh, Hirokazu Kadobayashi
Optics · Cross-listed from cs.AI
When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal Inference
Ismail Erbas, Xavier Intes and Vikas Pandey
Quantization is widely used to reduce the computational and
memory demands of neural-network inference. In recurrent networks, however, the quantized state is stored and returned at the next time step, so the rule used to store that state can alter subsequent computations. Here, we introduce recurrent-state write-back to denote this rule and isolate its effect in a compact GRU encoder–decoder for fluorescence lifetime imaging, a molecular imaging modality used in quantitative biological imaging. A central task is estimating two lifetime parameters, the short-lived component {\tau}1 and the long-lived component {\tau}2, from high-noise time-resolved fluorescence signals. Holding the trained model fixed, replacing continuous state propagation with deterministic 4-bit state storage increases estimation errors for {\tau}1 and {\tau}2 by approximately 70x and 300x, respectively. Failure occurs when repeated small updates remain below the write threshold, leaving the stored state nearly fixed while the network continues to propose change. Error feedback, residual
memory, and direction
memory carry information from these suppressed updates across time and recover accuracy without retraining. Precision sweeps show that increasing state precision can worsen a fixed recurrent solution, while matched training shows that compatibility with the state interface can be learned. To test whether this behavior extends beyond the GRU, we repeat the post-training intervention in an independently trained LSTM, where coarse write-back reproduces the failure, error feedback restores accuracy, and state-specific interventions reveal greater sensitivity of the
cell state than the hidden state. Our results establish recurrent-state write-back as a key determinant of low-precision recurrent dynamics and identify the state-storage interface as a central design consideration for quantized recurrent inference.
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Fluid Dynamics
Two-phase Temperature Reconstruction in Ice-Water Systems
Zhukun Wang, Daisuke Noto, Douglas J. Jerolmack et al. (+1 more)
What controls heat transport when
liquid water interacts with ice? Answering this question is essential for understanding water bodies undergoing phase change. Yet experimental progress remains limited by the lack of a minimally invasive methodology for simultaneously resolving temperature fields in coupled
liquid water and non-isothermal ice systems. Here, we introduce a physics-based data-assimilation method for reconstructing temperature fields in buoyancy-driven flows interacting with non-isothermal ice. Particle tracking velocimetry provides the
liquid velocity field, while thermal and kinematic boundary conditions constrain the inverse problem. The method couples advection–
diffusion in
liquid water with conduction in ice to reconstruct simultaneous mean temperature fields and quantify heat transport across the water–ice interface, while remaining minimally invasive and compatible with free-surface systems. We demonstrate the method in laboratory experiments in which the temperature range across the
liquid water in contact with ice drives cabbeling-induced convection. This framework enables investigation of coupled thermo-fluid dynamics in cryospheric aquatic systems, including heat exchange at the ice-water interface and
liquid-phase energetics, with broader applications to phase-change processes in food and energy industries.
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All authors Zhukun Wang, Daisuke Noto, Douglas J. Jerolmack, Hugo N. Ulloa
Fluid Dynamics
Characterizing periodic orbits in two-dimensional Rayleigh-Bénard flows
Joaquín Cullen, Melisa Y. Vinograd and Patricio Clark di Leoni
Unstable periodic orbits and steady states are believed to form the backbone of spatiotemporal chaos and
turbulence, yet their computation in thermally driven flows remains scarce for transitional regimes. In this work we compute and characterize a steady state and three families of periodic orbits in two-dimensional Rayleigh-Bénard at $\mathrm{Pr}=1$, near the transition to chaos. We find that in its route to chaos, the flow hops between several sets of orbits after becoming quasiperiodic. We use Floquet analysis to study the stability of the orbits obtained and characterize their bifurcations, showing how the appearance of primary and secondary frequencies, as well as phase-locking mechanisms, are all related to the dynamics of the orbits. We study in detail how the flow shadows the orbits found and determine in which regimes each orbit is dynamically relevant or not. Our analysis also reveals two important insights: (1) all
symmetries are broken before the onset of chaos, and (2) this onset does not alter the behavior of the heat transport.
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Computational Physics
Reduced Precision Diffusion Synthetic Acceleration for S$_N$ Neutron Transport in LLNL's ARDRA using Hypre
Joanna Piper Morgan and Mario Ivan Ortega
Fast solution times in production discrete ordinates transport codes often require the use of
diffusion synthetic acceleration (DSA). A good implementation of DSA will substantially reduce the number of iterations to converge the transport solution at the cost of solving a
diffusion-like linear system at each iteration. Often fewer transport iterations will lead to smaller times to solution. Recent work has shown the promise of reduced precision preconditioning. Time and
memory cost can be decreased as long as the reduced precision does not substantially degrade the convergence behavior of the higher precision linear system being solved. In this paper, we discuss the algorithmic implications of accelerating transport iterations in double precision (64 bit) while using a reduced precision (32 bit) DSA solve. We initially implement a stand alone code to investigate any theoretical limitations of reduced precision DSA in slabs. Then we implement reduced precision DSA in ARDRA, Lawrence Livermore National Laboratory's (LLNL) discrete ordinance neutral particle transport code, coupled with hypre, LLNL's scalable linear solver and multigrid methods library capable of mixed precision linear solves Then we solve various
neutron transport problems of interest. We find that in most circumstances, DSA acceleration in reduced precision has little to no impact on the acceleration properties of DSA when used with source iteration in most circumstances. However, as tighter tolerances are required, reduced precision DSA may fail to successfully accelerate the transport solve and increase time to solution.
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Chemical Physics
Learning multistate kinetics with a variational multistate committor network
Chenyu Tang, Cheng Giuseppe Chen, Benoît Roux et al. (+1 more)
The long-time dynamics of complex molecular systems often involves rare transitions across networks of metastable states. Building on transition-path theory, which provides a rigorous framework for describing rare transitions between two metastable states, we introduce the variational multistate committor network (VMCN), a neural framework that learns the probabilities of reaching each metastable state directly from molecular simulation data. From this representation, VMCN identifies state-specific commitment, candidate transition regions and committor-consistent pathways between state pairs, and an effective kinetic network characterized by transition rates. The model is trained using finite time-lag trajectory data together with boundary conditions defined on conservative state cores. Applications to a triple-well potential, trialanine isomerization, and the $c$–ring rotation in the V$_{\rm o}$ domain of a vacuolar ATPase show that VMCN recovers metastable organization, provides committor-consistent descriptions of transition mechanisms, and estimates state-to-state kinetics. VMCN further provides diagnostics for incomplete state decompositions and enables adaptive exploration of candidate metastable states and their connecting regions. By integrating VMCN with generative committor-guided path sampling (Gen-COMPAS) for chignolin, we start from two end point structures, identify a misfolded state and a candidate folding intermediate, and we direct subsequent sampling toward the resulting multistate transition network.
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All authors Chenyu Tang, Cheng Giuseppe Chen, Benoît Roux, Christophe Chipot
Applied Physics · Cross-listed from quant-ph
Proof-of-principle long-distance Sagnac twin-field quantum key distribution network
Reem Mandil, Yen-An Shih, Abhay Verma et al. (+2 more)
Twin-field (TF)
quantum key distribution (QKD) offers a promising approach to long-distance QKD networks due to its superior performance over large channel losses. Due to specialized hardware requirements, nearly all long-distance TFQKD demonstrations have only two users exchanging keys, rather than a network with three or more users. In this work, we experimentally demonstrate a proof-of-principle three-user-pair Sagnac TFQKD network spanning 127-km using single-
photon avalanche detectors without any active phase stabilization or postcompensation. We implement efficient procedures for maintaining polarization stability and circumventing Rayleigh backscattering noise to achieve a stable Sagnac interference visibility of $93\pm1$% over one hour. A secure key rate of $1.398\times10^{-5}$ bits per pulse is achieved over an asymmetric communication channel with 102-km fiber and 45-dB overall loss. To our knowledge, this is the first TFQKD network without active phase stabilization or postcompensation achieved over long fibers. Our results represent a highly practical and cost-effective approach to long-distance QKD networks.
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All authors Reem Mandil, Yen-An Shih, Abhay Verma, Li Qian, Hoi-Kwong Lo
Physics and Society
Publish or Perish: A population-dynamics model of the corruption of peer review
Shyam Ranganathan
Peer review is commonly treated as a mechanism of quality control. This article examines what happens when participants are split between two groups. Explicators assess whether submissions present logic-based arguments (whether deductive, inductive or abductive), independently of whether they agree with the reasons or the conclusion. Interpreters assess submissions according to their outlook. Their employment of logic is opportunistic, as a means of presenting their own views, but not as a means of assessing others' views. Using a deterministic population-dynamics model of iterative games of "Publish or Perish," this article shows how this asymmetry permits interpreters to increase their population share even when they begin as a small minority. It compares fixed and survivor-drawn reviewer panels, alternative replenishment rules, hostile explicator responses, generational differences in ratios, and different panel sizes. Under survivor-drawn review, explicator hostility to interpreters produces an unstable
equilibrium at an explicator share of $q^* = 2 - \sqrt{3} \approx 0.2679$: below this threshold, interpreters can survive without explicator cooperation and continue toward dominance. The threshold is not universal, however. Exogenous replenishment in favour of explicators can eliminate the point of no return, and sufficiently lagged, generational replenishment can permit explicators to recover after it has apparently been crossed. The model therefore identifies not only a mechanism by which peer review becomes corrupted, but also the institutional conditions determining whether that corruption becomes self-sustaining or reversible.
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Chemical Physics
Recovering molecules from coarse-grained beads: free-energy-conditioned generative backmapping across chemical space
Luis Itza Vazquez-Salazar and Tristan Bereau
Transferable coarse-grained (CG) force fields compress chemical space: by aggregating atoms into a reduced set of interaction beads, models such as MARTINI reduce the number of distinguishable compounds by roughly three orders of magnitude, making high-throughput screening of thermodynamic properties tractable across soft matter, with drug–membrane permeability as a well-developed example. The compression is lossy and, so far, one-way: a screen returns a combination of beads, with no established route back to the compounds it stands for. Recovering those compounds–compositional backmapping–is a one-to-many inverse map, distinct from the better-studied conformational problem of rebuilding atomic coordinates from a known mapping. Here we formulate compositional backmapping as conditional graph generation by introducing juniper, a discrete denoising
diffusion model over molecular graphs conditioned on the octanol–water partition free energy $\Delta G_{\mathrm{W} \mapsto \mathrm{O}}$, the principal driver of MARTINI bead type assignment and hence a proxy for bead identity. Trained on molecules of up to 9 heavy atoms mapped onto one or two beads, juniper generates molecules that are 93% valid and 92% unique for two-bead targets, and whose $\Delta G_{\mathrm{W} \mapsto \mathrm{O}}$ distributions track the target $\Delta G^{\mathrm{CG}}_{\mathrm{W} \mapsto \mathrm{O}}$ linearly ($r^{2} \geq 0.96$), departing only in the hydrophobic and hydrophilic tails. Although the model receives no chemical information beyond a single scalar, the functional groups shift systematically with the imposed free energy, from branched hydrocarbons at the apolar end to amides, imides, and isocyanates at the polar end. A bead combination flagged by a CG screen can therefore be turned into candidate molecules for atomistic study or synthesis.
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Atmospheric and Oceanic Physics
Exploring the role of input data on hail nowcast skill using spatiotemporal neural networks
George Pacey, Ophélia Miralles, Ulrich Hamann et al. (+1 more)
Hail can cause large financial losses and poses risks to public safety, making reliable nowcasts essential for timely warnings. Deep-learning approaches have emerged as a strong alternative to conventional methods, but how input data choices affect performance has not been deeply explored. We investigate how the skill of a deep-learning hail nowcasting model can be improved without changing the model architecture. Sensitivity experiments assess the impact of training-data volume, random data augmentation (mirroring and rotation) and the number of input timesteps. Increasing the years of training data substantially improves forecast skill, by up to 25 minutes at later lead times. Augmentation improved performance for larger
datasets but interestingly degraded performance for smaller ones. Sensitivity to input timesteps was weaker than sensitivity to training years. These findings show that improvements in data selection and preprocessing can yield substantial gains even with a fixed architecture, offering guidance for future deep-learning nowcasting development.
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All authors George Pacey, Ophélia Miralles, Ulrich Hamann, Olivia Martius
Accelerator Physics
HELIX: a hybrid envelope-multiparticle linac code with differentiable space-charge optimization
Abhishek Pathak
HELIX (Hybrid Envelope-multiparticle LInac eXplorer) is a Python
hadron-linac framework developed at Fermilab for the PIP-II superconducting H- linac. It combines exact-gradient matching through a self-consistent nonlinear particle-in-
cell (PIC) space-charge solve with the
lattice-card workflow used for machine design. One TraceWin-format
lattice drives an rms-envelope solver with 3-D linearized space charge, a multiparticle tracker with a 3-D FFT PIC solve, and linear
matrix analysis over one data model, and the constraint cards that steer six classical matching
algorithms also steer a differentiable seventh, which re-expresses a fixed-energy subset of the tracking-plus-PIC map as a single PyTorch graph and returns reverse-mode Jacobians exact to floating-point precision at a cost set by the number of constraints rather than of knobs. On a six-quadrupole manufactured verification problem the exact-Jacobian run reaches a tolerance-normalized residual of $3.2\times10^{-10}$, some 200 times below the finite-difference endpoint at the same forward-equivalent budget; the Jacobian cost is flat in the knob count, with the crossover between ten and twelve knobs and reverse mode 1.45 times faster at sixteen. Verification is hierarchical, from analytic envelope references through $10^{-9}$-$10^{-13}$ cross-implementation PIC parity to TraceWin
benchmarks on the PIP-II low-energy, medium-energy and accelerating lines: with space charge on, the rms moments along the MEBT and half-wave-resonator line agree within 0.7%, and over the 186-m linac the energy agrees within 0.03% and transmission within the combined statistical uncertainty. HELIX also provides machine-learned field-map surrogates, Pareto exploration, error Monte Carlo with orbit correction, failure compensation, and RFQ transport, with NumPy, C++/OpenMP, CUDA and Metal backends.
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Optics
Ultrafast Control of Lifetime in High Q Anisotropic Plasmon Polaritons
Romain Tirole, Giacomo Venturi, Emroz Khan et al. (+6 more)
Losses are a major roadblock in the technological implementation of surface plasmons at optical frequencies. The recent emergence of MoOCl2, a correlated van-der-Waals material with strongly anisotropic optical properties, offers new avenues to circumvent this limit. We report the far-field observation of high-Q surface plasmon polaritons in this material, arising from the anisotropic hybridisation of surface plasmons and dielectric modes. We then explore nonlinear pumping of intraband electrons to the conduction band in these structures, leading, contrary to intuition, to an abrupt increase in the lifetime of the polariton resonance, despite the injection of hot electrons. This counterintuitive phenomenon stems from the competition between
photon and plasmon excitations in a lenticular polariton resonance, yielding a largely tuneable lifetime at ultrafast speeds.
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All authors Romain Tirole, Giacomo Venturi, Emroz Khan, Lin Jing, Lin Nan, Nicola Melchioni, Andrea Mancini, Antonio Ambrosio, Andrea Alù
Physics Education
Why the better conductor surges in impedance: a low-cost demonstration of the skin effect at line frequency
Florian Platten and Helmut Wenz
When alternating current flows through a conductor, induced internal eddy currents suppress current flow in the core and restrict it to a thin surface layer. Although this effect, known as the skin effect, is covered in virtually every undergraduate electrodynamics course, it is rarely explored experimentally without specialized and expensive equipment, and is sometimes even considered a mere high-frequency phenomenon. Here, we present a readily accessible, quantitative demonstration of the skin effect at line frequency using only standard laboratory supplies: an AC/DC power supply, basic multimeters, and laboratory support rods as cylindrical conductors. Based on a precise four-point Kelvin sensing scheme capable of resolving milliohm-range resistances, we exploit relative
magnetic permeability rather than high frequency as the governing parameter to control skin depth. For non-
magnetic stainless steel, line frequency represents an electrodynamically low frequency, resulting in identical AC and DC behaviour; whereas for
ferromagnetic structural steel, high permeability compresses the skin depth to the millimeter scale, causing a sharp surge in AC impedance. This produces a counterintuitive outcome where the statically superior conductor suffers a marked relative impedance increase, whereas the poorer conductor remains unaffected, challenging common student expectations and catalyzing cognitive activation. Depending on the instructional setting, the approach can be implemented either as a quick lecture demonstration or as a quantitative experiment within an undergraduate laboratory course.
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History and Philosophy of Physics
Niels Bohr as a Physicist of Principle
Mauro Dorato and Jan Faye
Our main claim is that Bohr adopted a principle-theoretic approach to
quantum mechanics in order to reconcile two principles: the distinction principle, which asserts the necessity of a clear epistemic distinction between a classical apparatus and a
quantum system, and the non-separability principle, which asserts that the two systems are ontologically non-separable in any measurement process. The latter principle is a consequence of Bohr's argument that, during a measurement interaction, the
quantum system and the measurement apparatus form a 'new kind of individuality'. To support our argument, we first demonstrate the overlooked fact that, according to Bohr, the measurement interaction is a physical, irreversible process and not merely an epistemic acquisition of new information. In this respect,
quantum mechanics must be regarded not only as a universal theory, but also as revealing no non-arbitrary difference between a
quantum realm and a classical realm; the distinction is purely pragmatic. The tension between the epistemic necessity to rely on a neatly distinguishable classical realm and the ontic non-separability of the
quantum system and the classical apparatus inclined Bohr to refuse to provide a constructive account of the measurement process, such as that later proposed, for instance, by spontaneous dynamical reduction models.
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Accelerator Physics
Design optimization, commissioning, and uncertainty analysis of the quadrupole resonator system at Jefferson Lab for SRF material characterization
Mingqi Ge, Sarra Bira, Kristof Brunner et al. (+14 more)
A quadrupole resonator (QPR) provides a sample-based platform for characterizing materials for superconducting
radio-frequency (SRF) applications under controlled field, frequency, and temperature conditions. This paper presents the design optimization, commissioning, and validation of the Jefferson Lab QPR system, including a quantitative assessment of measurement uncertainty. The resonator geometry was re-optimized from the CERN version-II design to improve quadrupole-mode separation and enable four usable modes at 400, 806, 1221, and 1640 MHz. The measurement system combines self-excited-loop RF operation, cable-loss-corrected power calibration, decay-based external-Q calibration, and RF-DC thermal-substitution calorimetry to determine the peak surface
magnetic field Bpk and sample surface resistance Rs. Commissioning measurements on bulk Nb and Nb3Sn-Ta-Cu samples validated the system response over a broad range of frequency, temperature, and RF field. The extracted superconducting energy-gap parameters are consistent with the reported values for Nb and Nb3Sn, as well as with those obtained from single-
cell cavity measurements. The commissioned system operates from 1.8 K to near the superconducting transition temperature of the sample, with accessible Bpk values from approximately 5 mT to a sample- and temperature-dependent heater-power-budget limit; a maximum field of 60 mT was demonstrated for bulk Nb at 400 MHz and 4 K. The combined relative standard uncertainties are 8.3% for Bpk and below 18% for Rs when r = PDC2/PDC1 is less than 0.9. The worst-case Bpk resolution at the 95% confidence level is approximately 1.35 mT, while the Rs resolution is below 1 nOhm at 10 mT and 2 K. These results establish the JLab QPR as a calibrated, multi-frequency platform with quantified measurement uncertainty for SRF material characterization.
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All authors Mingqi Ge, Sarra Bira, Kristof Brunner, Natalie Gale, Marco Garlasche, Valentin Giglia, Oleksandr Hryhorenko, Justin Kent, Peter Owen, Uttar Pudasaini, Guillaume Rosaz, Karol Scibor, Pramita Tiwari, Anne-Marie Valente-Feliciano, Lorena Vega Cid, Walter Venturini, Haipeng Wang
Optics
Twin-photon generation in a silicon nitride microresonator
Franz Pacher, Haochen Yan, Alekhya Ghosh et al. (+8 more)
Photonic chips with silicon nitride ($\mathrm{Si_3N_4}$) microring resonators are well established as heralded single-
photon sources, but their operation as frequency-degenerate twin-
photon sources has not previously been demonstrated. Here, we realise a twin-
photon source at telecommunication wavelengths in a $\mathrm{Si_3N_4}$ ring microresonator via an inverse four-wave mixing (FWM) process, in which two
photons from spectrally distinct pumps are converted into a pair of identical twin
photons. The measurements show a maximum coincidence-to-accidental ratio (CAR) of $5.4\pm0.6$. In addition, the microresonator functions as a heralded single-
photon source through pump-degenerate spontaneous four-wave mixing (SFWM), exhibiting a spectral purity of $P=0.67\pm0.05$ and a heralded anti-bunching of $g^{(2)}_h(0)=0.0042\pm0.0015$. Together, these results demonstrate both
photon-generation schemes on a single integrated $\mathrm{Si_3N_4}$ platform, highlighting its potential for scalable, tailored
quantum light generation.
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All authors Franz Pacher, Haochen Yan, Alekhya Ghosh, Arghadeep Pal, Toby Bi, Hao Zhang, Lixing You, Hao Li, Daniela Salvoni, Shuangyou Zhang, Pascal Del'Haye
Classical Physics · Cross-listed from quant-ph
Spurious quantum correlations
Shashaank Khanna, Matthew F. Pusey and Roger Colbeck
In his seminal paper, Bell [Physics Physique Fizika 1, 195 (1964)] considers the correlations that result from space-like separated measurements on a pair of entangled particles. He uses relativity theory to motivate the Bell causal structure, then shows the existence of
quantum correlations that cannot be explained classically within this causal structure. Classical explanations of such
quantum correlations are possible in alternative causal structures, for instance, those that allow superluminal causal influences, but, as shown in [New Journal of Physics 17 033002 (2015)], all such alternative explanations require fine tuning (causation without correlation). Here we discuss the existence of spurious
quantum correlations — correlations that look
quantum in one causal structure, but have a natural classical explanation in another. More precisely, there are causal structures that admit non-classical
quantum correlations, but for which the same correlations have a classical explanation in another causal structure without fine tuning. The realisation in the other causal structure can also be achieved without breaking any natural constraints on the causal structure that follow from relativity theory. However, similarly to non-classical
quantum correlations in the Bell causal structure, we find other causal structures with non-classical
quantum correlations that do not have a classical causal explanation in any alternative causal structure without fine tuning.
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Computational Physics
Spectral and Eigenvector Crossovers in Random Mixed Graphs
Himanshu Shekhar, Hrishidev Unni, Santosh Kumar et al. (+1 more)
We study the spectral and eigenvector properties of random mixed graphs, combining undirected and directed interactions, using random
matrix theory (RMT). The network is represented by a Hermitian adjacency
matrix, with undirected links as real entries and directed links as purely imaginary conjugate pairs, ensuring a real spectrum. Network density is controlled by the connection probability, while directionality sets the fraction of directed edges. We focus on the GOE-to-GUE crossover: at fixed connectivity, increasing directionality breaks time-reversal
symmetry and drives spectral statistics from GOE to GUE. We show that this transition requires sufficient connectivity. In sparse networks, weak level repulsion produces Poisson statistics regardless of directionality. At fixed directionality, increasing connectivity drives a Poisson-to-GUE crossover; only above a sparsity threshold does the directionality-induced GOE-to-GUE transition emerge. In the dense regime, where the spectral density follows the Wigner semicircle law, the crossover is characterized using spacing distributions, spacing ratios, and spectral
rigidity. In sparse networks, where unfolding is unreliable, spacing-ratio statistics provide an unfolding-free characterization. Eigenvector structure is examined through multifractal dimensions and component distributions, while Kullback–Leibler divergence confirms the robustness of the transitions. Applied to S&P 500 mixed graphs, the framework reveals a GOE-to-GUE crossover across four major
market crashes. Denser crisis periods show sharper crossovers than sparser recovery periods. The results provide a unified picture of how connectivity and
symmetry breaking govern spectral and eigenvector universality, while providing a transparent probe of changing financial-
market organization.
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All authors Himanshu Shekhar, Hrishidev Unni, Santosh Kumar, Anirban Chakraborti
Atomic Physics
Sub-barrier peaks in atom-atom-ion three-body recombination
Fabian Thielemann, Joachim Siemund, Tobias Schaetz et al. (+1 more)
Hybrid atom-ion experiments have recently entered the few-partial wave regime in which magnetically tunable Feshbach resonances dominate the three-body loss dynamics. This necessitates a treatment of three-body recombination that can incorporate the role of the resonant dimer channel. Here, we develop a two-step Lindemann-type model for resonant atom-atom-ion three-body recombination that combines multichannel
quantum-defect theory for the long-range polarization potential with a universal
quantum-capture treatment of the inelastic atom-dimer step. We show that within our model, rate competition can lead to a peak in the three-body-recombination cross section of higher partial-wave channels at energies well below their corresponding centrifugal barrier. Investigating the resulting loss rates under numerically obtained non-thermal collision-energy distributions, we find that these effects survive the energy averaging and can be observed in state-of-the-art hybrid experimental setups. Our findings pave the way for a detailed characterization of atom-ion Feshbach resonances via the analysis of inelastic three-body-recombination processes.
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All authors Fabian Thielemann, Joachim Siemund, Tobias Schaetz, Krzysztof Jachymski
Instrumentation and Detectors
Monitoring antiproton numbers with a CMOS detector in a dense-track environment
C. Regenfus, P. Adrich, I. Belosevic et al. (+42 more)
The production of antihydrogen by the GBAR experiment at AD/ELENA requires good knowledge of the number of incident keV antiprotons, which can be problematic. We have used a commercial CMOS digital camera mounted around the experimental
vacuum chamber to determine antiproton numbers from ionising particles created in the annihilation process on the surface of microchannel plate detectors which are used for beam imaging. We show that the multiplicity of emerging charged particles is as expected for individual annihilations of antiprotons with nucleons at rest, taking into account the surrounding material budget. Most of those particles are in the minimal ionising regime, but can be detected with nearly 100% efficiency in the CMOS pixel detector, while due to the thin depletion layer the device is insensitive to background gammas. Thanks to the high granularity and small pixel size millions of antiproton annihilations can be reconstructed in a dense tracking environment over a large dynamic range with good resolution. From cluster length studies of non perpendicular tracks the thickness of the depletion zone and effective detection area was estimated. The cluster length also allows for a monitoring of track angles. Antiproton numbers are determined from the number of reconstructed clusters in the CMOS sensor by means of the covered solid angle relative to a calibration measurements with well known beam intensities at the most upstream location of the GBAR apparatus. Material effects on the emerging annihilation products were estimated by Monte Carlo (Geant4) calculations, while annihilation artefacts on the complex surface of a microchannel plate are cancelled out in this approach. This method minimises largely systematic uncertainties, leading to a final error of roughly 10% for the reconstruction of absolute antiproton numbers.
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All authors C. Regenfus, P. Adrich, I. Belosevic, F. Benkel, M. Chung, P. Cladé, P. Comini, P. Crivelli, P. Debu, A. Douillet, S. Geffroy, S. Guellati-Khelifa, P. Guichard, P.-A. Hervieux, L. Hilico, P. Indelicato, S. Jonsell, J.-P. Karr, B. Kim, S. Kim, E.-S. Kim, N. Kuroda, B. Lee, L. Liszkay, D. Lunney, G. Manfredi, B. Mansoulié, V. Martimort, M. Matusiak, V. Nesvizhevsky, F. Nez, K. Park, N. Paul, E. Perez, P. Pérez, C. Roumegou, J.-Y. Roussé, F. Schmidt-Kaler, K. Szymczyk, T. A. Tanaka, B. Tuchming, D.-P. van der Werf, D. Won, S. Wronka, P. Yzombard
Optics · Cross-listed from quant-ph
Quantum-Enhanced Phase Estimation with Photon-Added Even and Odd Coherent States in an SU(1,1) Interferometer
Abdelmajid El Maaroufi, Mouad Ait Maskour, Bouchra Maroufi et al. (+2 more)
We investigate phase estimation in an SU(1,1) interferometer employing $m$-
photon-added even and odd coherent states as nonclassical input resources. The phase sensitivity is evaluated through intensity detection and the error propagation method, while the ultimate precision limit is determined from the
quantum Cramér-Rao bound with the
quantum Fisher information serving as the relevant metrological quantity. Our results demonstrate that
photon addition significantly enhances the phase sensitivity, increases the
quantum Fisher information, and reduces the
quantum Cramér-Rao bound, leading to a clear improvement over the corresponding even and odd coherent states. Furthermore, the achievable sensitivity exceeds the standard
quantum limit and gradually approaches the Heisenberg scaling with increasing
photon-addition number. We also find that the $m$-
photon-added even coherent states exhibit a modest advantage over their odd counterparts. As $m$ increases, however, this distinction becomes progressively weaker, suggesting that
photon addition diminishes the role of the initial parity of the coherent state in determining the interferometric performance.
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All authors Abdelmajid El Maaroufi, Mouad Ait Maskour, Bouchra Maroufi, Mohammed Daoud, Saeed Haddadi
Biological Physics · Cross-listed from cond-mat.soft
Topological Mixing and Braiding Universality in Polar Active Matter
Wei Feng, Tianyu Ren, Zhihan Ye et al. (+2 more)
Connecting the autonomous kinematics of active matter to its emergent macroscopic transport is constrained by the requirement for high-resolution Eulerian velocity fields. Here, we use confined bacterial suspensions as a model active fluid, mapping the sparse Lagrangian trajectories of fluorescent spy
cells into (2+1)-dimensional
geometric braids that directly encode the spatiotemporal
entanglement of the flow. We use the finite-time braiding exponent (FTBE) as a proxy to quantify the topological
entropy and chaotic mixing of confined bacterial suspensions. In moderately confined wet systems, we find that hydrodynamic coupling drives a structural transition from a dilute active gas to coherent
vortices, and ultimately to active
turbulence, revealing three distinct density-dependent regimes of topological mixing. Conversely, truncating the hydrodynamic screening length via extreme confinement drives the system toward a dry active matter limit. In this limit, dense in-plane steric collisions suppress irreducible
entanglement and substantially reduce the FTBE at high particle densities. By evaluating the topological complexity generated per encounter, we reveal a transition from a discrete
geometric encounter regime to an areal escape mechanism. Finally, we establish a square-root scaling between the FTBE and the effective diffusivity, placing the self-sustained mixing of active fluids into the pathline braiding universality class.
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All authors Wei Feng, Tianyu Ren, Zhihan Ye, Jonas Berx, Guangyin Jing
Instrumentation and Detectors
Testbeam characterization of a 3D silicon sensor read out by Timepix4
E. Chatzianagnostou, M. J. Madurai, K. Akiba et al. (+15 more)
Testbeam results from a $300\,\mu\mathrm{m}$-thick 3D silicon sensor bump-bonded to a Timepix4 ASIC, are presented. The hit detection efficiency, spatial resolution, and timing performance of the 3D sensor are studied for several track angles, bias voltages and charge thresholds. The time measurements are corrected for Timepix4 clock-frequency variations and timewalk, while for the spatial studies, nonlinear charge-sharing corrections are determined. At perpendicular incidence, the time resolution of the 3D detector is equal to $245\,\mathrm{ps}$ with a $97\%$ hit detection efficiency. An optimal angle of $8^\circ$ with respect to the beam direction of the 3D detector was found, in which the time resolution improves by $6\%$ compared to normal incidence, while the hit detection efficiency reaches above $99\%$ and the spatial resolution is approximately $7\,\mu\mathrm{m}$. Intrapixel studies show that a time resolution of $153\,\mathrm{ps}$ at the most probable value of the signal charge can be achieved for hits between the electrodes at normal incidence, while the timing performance of these best-performing regions deteriorate upon sensor rotation. The timing properties at different depths of the 3D sensor have been investigated with tracks at grazing-angle incidence, revealing a dependence of the time resolution along the sensor depth.
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All authors E. Chatzianagnostou, M. J. Madurai, K. Akiba, D. Bacher, R. Bates, M. van Beuzekom, T. Bischoff, V. Coco, R. Dumps, T. Evans, K. Heijhoff, D. Johnson, U. Krämer, E. Lemos Cid, D. Oppenhuis, T. Pajero, E. Rodríguez Rodríguez, D. Rolf
Computational Physics · Cross-listed from quant-ph
Discrete time crystals in disordered anisotropic Heisenberg chains
Francesco Formicola, Grazia Di Bello, Antonio De Candia et al. (+2 more)
A discrete time
crystal is an out-of-
equilibrium phase of matter characterized by the spontaneous breaking of discrete time-translation
symmetry. Using extensive numerical simulations based on
matrix-product-state methods, we provide evidence for discrete time-crystalline behavior in strongly disordered
spin chains with Heisenberg interactions, including the isotropic point, subject to periodic driving. Starting from a many-body localized regime, we observe that rotations induced by delta kicks produce a pronounced subharmonic response at half the drive frequency in
spin observables. We investigate the stability of this response against rotation-angle errors through
entanglement entropy,
quantum Fisher information, short-range
spin correlations, and restricted-control ergotropy. Increasing the rotation error reveals an intermediate dynamical regime separating the time-crystalline and Floquet-localized responses. In this regime, most observables exhibit signatures of weakly correlated dynamics reminiscent of Anderson localization.
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All authors Francesco Formicola, Grazia Di Bello, Antonio De Candia, Giulio De Filippis, Carmine Antonio Perroni