Fluid Dynamics
Offline accuracy is not enough: closed-loop instability and stabilisation of a wall-sensor neural estimator in opposition control
Giorgio Maria Cavallazzi, Miguel Pérez-Cuadrado and Alfredo Pinelli
Opposition control reduces skin-friction drag by opposing the wall-normal velocity on a near-wall detection plane, but the detection-plane velocity it requires is not available from wall-mounted sensors. Wall data can reconstruct inner-flow quantities accurately when assessed offline on a fixed flow state, and we ask whether such a reconstructed field can instead serve as a live surrogate sensor inside the feedback loop. We train a recurrent
estimator to infer the detection-plane velocity from the two wall-shear-stress components in opposition-controlled
turbulence. Offline it performs extremely well, reaching a correlation of 0.99 and near-unity coherence across the energetic scales; yet the same
estimator fails in closed loop, decorrelating from the true field within a few viscous time units as the control collapses. The failure is not one of accuracy but of distribution shift induced by the controller itself: small closed-loop errors carry the flow off the attractor represented in the training data, while unresolved high-wavenumber errors enter through the wall boundary condition and return as out-of-distribution inputs. Standard remedies such as low-pass filtering and exponential averaging only delay numerical breakdown while accelerating decorrelation. Stable wall-only control is recovered by imposing spectral consistency on the deployed actuation and retraining the
estimator on its own closed-loop data, giving a controller that holds much of the drag reduction of ideal opposition control from wall quantities alone. The obstacle is not whether the near-wall flow can be reconstructed offline, but whether that reconstruction stays dynamically consistent when allowed to modify the flow it senses.
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Computational Physics · Cross-listed from cond-mat.str-el
NQS-Agent: Health-Aware Agentic Hyperparameter Optimization for Neural-Network Quantum States
Jia-Qi Wang, Xiao-Qi Han, Ze-Feng Gao et al. (+2 more)
Neural-network
quantum states (NQS) provide expressive variational representations for strongly correlated
quantum many-body systems, but their practical accuracy depends sensitively on architecture-level hyperparameters and optimization schedules. Here we develop NQS-
Agent, an implemented open-source software framework for health-aware hyperparameter optimization (HPO) in NQS calculations. Its workflow monitors energy trajectories, detects destructive optimization events, stops unstable calculations, modifies the learning-rate schedule, resumes optimization from safe checkpoints, and ranks candidates with an anomaly-aware score. We demonstrate the approach on a residual convolutional NQS for the square-
lattice Heisenberg $J_1$-$J_2$ model, using architectures with parameter counts comparable to aCNN, a convolutional NQS architecture used here as a reference. The results show that NQS-
Agent improves over the reported human-tuned aCNN baseline for the aCNN reference architecture and identifies a structurally distinct wide-and-shallow competitive candidate within the parameter-count-matched residual-CNN search space. These results show that the stability and recovery history of an optimization trajectory should be considered when assessing an NQS result. Health-aware HPO therefore provides a reproducible tuning protocol that goes beyond selecting a single lowest-energy calculation.
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All authors Jia-Qi Wang, Xiao-Qi Han, Ze-Feng Gao, Rong-Qiang He, Zhong-Yi Lu
Physics and Society
Collective cooperation without individual fidelity in LLM agents
Henrique Ferraz de Arruda, Carlos Gracia Lázaro, Alberto Aleta et al. (+1 more)
Large
language models (LLMs) are increasingly used as
agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making. Here we test LLM
agents against a direct empirical
benchmark: a large-scale networked Prisoner's Dilemma experiment with human participants. Using the same interaction protocol, payoff structure, and network
topologies, we compare nine open-weight LLMs with the human data. The selected model reproduces several macro-level features of cooperation dynamics, including the early decline and later stabilization of cooperation. This aggregate agreement, however, does not extend uniformly to finer levels of behavior. LLM populations underestimate individual-level heterogeneity and generate conditional cooperation patterns that differ from those observed in humans. Adding a fraction of random
agents improves some aspects of micro-level agreement, but does not remove the mismatch in decision rules. These findings reveal a macro–micro dissociation in LLM-based social
agents: collective outcomes can appear human-like even when the underlying behavioral distributions and mechanisms are not. They suggest that validating LLM
agents as human surrogates requires comparisons across aggregate dynamics, individual heterogeneity, and context-dependent decision rules, rather than outcome-level agreement alone.
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All authors Henrique Ferraz de Arruda, Carlos Gracia Lázaro, Alberto Aleta, Yamir Moreno
Atomic Physics
Modulation theory formulation of atomic light-matter interaction
Matteo Simoni, Ivan Rojkov and Jonathan Home
We provide a re-formulation of the light-matter interaction of trapped-atom systems in terms of classical modulation theory. We introduce commuting “mean” quadrature operators together with “deviation” operators that describe the
quantum fluctuations resulting from the uncertainty principle. From the “mean” position operator stems an accurate approximate expression for the internal transition coupling strengths in terms of Bessel functions which matches that of classical modulation theory. The error of the approximation is a direct result of
quantum fluctuations. We also show that this result can also be obtained with WKB theory. The validity of our approach is numerically verified and supported by an expansion of the exact expression using a recurrence relation between orthogonal
polynomials. Compared to the exact solution, our result is analytically more tractable, numerically more stable, and admits a transparent physical interpretation which connects the classical and
quantum pictures.
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Optics · Cross-listed from quant-ph
Connecting Density Matrix Spectroscopy to Biexciton Entanglement Dynamics
Yusuke Masaki, Takashi Otaki and Hiroaki Matsueda
Quantum entanglement is one of the most intriguing features of
quantum mechanics. To investigate the
entanglement between two
excitons in a biexciton, an experimental technique called density
matrix spectroscopy (DMS) has recently been developed. DMS combines stimulated emission
tomography and pump-probe techniques to obtain a time-resolved density
matrix of the polarization state of a
photon pair emitted from the biexciton. The reconstructed density
matrix is expected to encode information about the biexciton state and its
entanglement dynamics, but the precise nature of this connection has remained unclear. In this paper, we derive an analytical relationship between the density
matrix obtained by DMS and the biexciton state. In addition, we perform numerical simulations to compare the
entanglement dynamics obtained by DMS with the biexciton's
entanglement dynamics in a two-dimensional electron-hole system using an extended ionic Hubbard model. We find that DMS can partially capture the
entanglement in the biexciton, in particular, the dynamics of the difference $S_{\mathrm{bi}} - S_k$, where $S_{\mathrm{bi}}$ is the
entanglement entropy of the biexciton and $S_k$ is the
entanglement in terms of the wavevectors of the
excitons that constitute the biexciton. These results demonstrate the validity of DMS for obtaining information about the
entanglement dynamics of the biexciton.
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Chemical Physics · Cross-listed from quant-ph
Quantum Computations on Fusion Blanket Molten Salts
Susanta Das, Thiago J. Pinheiro Dos Santos, Subhamoy Bhowmik et al. (+15 more)
Molten salts such as FLiBe (2LiF–BeF$_2$) are leading blanket materials for breeding and recovering tritium in fusion reactors. Predicting tritium speciation requires accurate electronic ground-state energies for representative molten-salt clusters, a demanding task for correlated electronic-structure methods. Here we report the first application of heterogeneous
quantum–classical computing to tritium binding in FLiBe. Clusters drawn from ab initio molecular dynamics are partitioned by an embedded-wavefunction (EWF) method into atom-centered fragments, and the largest fragments are solved on IBM
quantum hardware using extended sample-based
quantum diagonalization (ext-SQD). Across nine clusters, the heterogeneous
quantum–classical workflow reproduces fragment ground-state energies with agreement to full configuration interaction within 0.7~kcal/mol and a mean absolute deviation of 0.3~kcal/mol. In contrast, fragmented and unfragmented conformational energy differences and tritium binding energies differ by 12~kcal/mol and 110~kcal/mol on average, respectively, identifying fragment construction rather than fragment solution as the dominant source of algorithmic bias. To the best of our knowledge, this is the first such demonstration for a charged ionic system and in particular an inorganic molten salt, where electrostatic and polarization effects make the accurate treatment of electronic correlation particularly challenging. These results also identify areas of future research towards an accurate and scalable
quantum–classical workflow to compute free-energy estimates of tritium speciation in fusion blankets.
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All authors Susanta Das, Thiago J. Pinheiro Dos Santos, Subhamoy Bhowmik, Milana Bazayeva, Zhen Li, Akhil Shajan, Danil Kaliakin, Fangchun Liang, Vyacheslav S. Bryantsev, Al Geist, Abigail McClain Gomez, Thaddeus Pellegrini, Robert Walkup, Seetharami R. Seelam, Mario Motta, Kenneth M. Merz, Jr., Thomas Beck
Biological Physics · Cross-listed from cond-mat.soft
Stress tensor field and mesoscopic stresses in the vertex model for tissues
Paulo C. Godolphim, Leonardo G. Brunnet and Rodrigo Soto
Mechanical stresses are fundamental regulators in biological tissues, where the vertex model (VM) is pivotal for theoretical and force-inference studies. Yet, no uniform expression for the stress tensor exists for the VM. Here we provide a microscopic derivation of it, linking mesoscopic stresses to the VM forces. The stress field presents a freedom on how tensions are distributed across
cells, which allows previous expressions to emerge as particular realizations of the field and suggests a link between mesoscopic stresses and cytoskeletal force-transmission architectures in real
cells.
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Atomic Physics · Cross-listed from quant-ph
Blueprint for a fault-tolerant compound photon-atom quantum architecture
Geva Arwas, Doron Azoury, Daniel Azses et al. (+11 more)
Fault-tolerant
quantum computing requires architectures that simultaneously address scalability, connectivity, and error correction under realistic noise constraints. We present a compound photonic-atomic
quantum computing platform that uses cavity QED to realize near-deterministic entangling operations between flying photonic
qubits and stationary atomic
qubits.
Photons provide long-range connectivity and scalability via measurement-based
quantum computing (MBQC), while atoms supply reusable, near-deterministic resources for
photon generation and
entanglement, overcoming the inefficiency of purely photonic platforms. The core primitive is a symmetrized Duan-Kimble
photon-atom controlled-phase (CZ) gate, robust to experimental imperfections and high-fidelity. Using single $^{87}$Rb atoms coupled to optical cavities, we give protocols for state preparation, measurement,
photon generation, and entangling gates on tens-of-nanosecond timescales, and show how large-scale cluster states with effectively unrestricted connectivity and reduced overhead can be generated through atomic reuse. We analyze fault tolerance on the Raussendorf-Harrington-Goyal (RHG)
lattice with a hardware-aware noise model capturing asymmetric loss and correlated photonic-atomic errors. Logical
memory simulations yield a
photon-loss threshold near $2.6\%$ per physical gate ($\sim$15% total per trajectory). The full Clifford set – Hadamard, phase, CNOT – is implementable transversally or fold-transversally at thresholds matching the identity channel, and we propose two non-Clifford resource-state routes (code teleportation and magic state cultivation) within the foliated cluster-state architecture.
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All authors Geva Arwas, Doron Azoury, Daniel Azses, Orel Bechler, Dana Ben Porath, Barak Dayan, David Dentelski, Yaron Jarach, Nadav Kandel, Aviad Landau, Yair Margalit, Alexander Poddubny, Michael Slutsky, Konstantin Yavilberg
Data Analysis and Statistics · Cross-listed from cs.LG
Scalar Representations of Neural Network Training Dynamics
Pedro Jiménez-González, Miguel C. Soriano and Lucas Lacasa
Training in artificial neural networks can be viewed as a trajectory evolving through a high-dimensional loss landscape. However, the large number of trainable parameters makes the direct analysis of these dynamics challenging. In this work, we treat such training trajectories as temporal networks and apply recently proposed strategies for the scalar
embedding of temporal networks. We investigate whether such a scalar
embedding provides a meaningful low-dimensional representation of neural network training dynamics. Using a multilayer perceptron trained on the MNIST classification task, we show that the
embedding preserves the main dynamical features observed in the original parameter space, including the emergence of sensitivity to initial conditions for specific learning rate regimes and an accurate reconstruction of the network's maximum Lyapunov exponent. We then use the embedded scalar trajectory to define a characteristic time, analogous to a Lyapunov time, after which the exponential separation between initially close embedded trajectories saturates. This characteristic time captures the typical decorrelation time between initially close network trajectories in the original high-dimensional system. Finally, we investigate the statistical organization of asymptotic training states through a spacing observable defined in the embedded space. We find that the distributions of rescaled asymptotic spacings collapse onto a common form across initial conditions and are compatible with a skew lognormal distribution. Altogether, our results suggest that scalar low-dimensional
embeddings provide a useful framework for studying and visualizing the dynamical properties of neural network optimization trajectories.
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Applied Physics
Decision-support strategies for photovoltaic self-consumption under declining electricity prices and limited remuneration of surplus generation
Ana B. Cristóbal, Daniel Sierra, Laura Palomino et al. (+2 more)
The success of distributed photovoltaics may be undermining its own future. As
solar penetration increases, electricity prices decline during periods of peak generation, reducing the value of surplus photovoltaic production. This raises a critical question: can citizen-led energy systems remain economically viable in electricity
markets dominated by renewable generation? Rather than exploring technically optimal but institutionally unrealistic solutions, we examine the options available under current regulatory and
market conditions. Using high-resolution consumption data from a rural community sharing a PV facility among 24 users, we identify pathways for long-term sustainability. The study makes two contributions. First, it shows that effective internal coordination can mobilize participation and investment as successfully as external subsidies. Second, it compares static, dynamic, and hybrid energy-sharing models, with and without storage, providing a flexible framework that balances efficiency,
fairness, and governance. Results show that collective self-consumption reduces required PV capacity, lowers investment costs, and increases annual savings compared with individually operated systems. Alternative allocation schemes further improve benefit distribution and local electricity use, although gains depend on trade-offs between efficiency,
fairness, and governance complexity. Under current electricity prices and remuneration schemes, battery storage provides limited additional economic value and becomes attractive only under specific
market conditions. Overall, the long-term viability of citizen-led photovoltaic initiatives depends less on technological sophistication than on collective coordination and adaptive governance.
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All authors Ana B. Cristóbal, Daniel Sierra, Laura Palomino, Luis Miguel Carrasco, Luis Narvarte
Computational Physics · Cross-listed from math.OC
Local-Minima-Preserving Continuous Relaxation of Ising Problems
Debraj Banerjee, Santanu Mahapatra and Kunal N. Chaudhury
The generalized Ising problem captures a broad spectrum of hard combinatorial problems, including MAX-CUT, Number Partitioning (NPP), and Maximum Independent Set. In this work, we consider the notion of one-flip local minima for this problem. We construct a
polynomial relaxation and prove the landscape equivalence
theorem: there exists a one-to-one correspondence between the local minima of the relaxation and the one-flip minima of the original Ising problem. This guarantee reduces the Ising problem to finding the local minima of a smooth function, allowing us to leverage gradient-based optimizers such as ADAM. We demonstrate that our method is scalable and it achieves strong performance across challenging
benchmarks, including
spin-glass models, MAX-CUT, and NPP.
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Chemical Physics
Size Effects in the Strong-Field Ionization and Dissociation Dynamics of (H$_2$O)$_n$ (n=1-4)
Chen Jiang, Cody L. Cavington and Kalman Varga
The size-dependent strong-field ionization and dissociation dynamics of (H$_2$O)$_n$ (n=1-4) are investigated using real-time time-dependent density functional theory (RT-TDDFT) coupled to Ehrenfest molecular dynamics under a common few-cycle near-infrared
laser pulse. It is found that the net ionization per monomer varies only weakly on cluster size, whereas the protonic and oxygen response is changed much more strongly once the cluster size grows beyond the dimer. In particular, H-ejection activity is observed to rise sharply from the dimer to the trimer/tetramer regime, while stable H-transfer is essentially absent in the dimer under the present criterion but becomes substantial in the trimer and is further amplified in the tetramer. Through timing analyses, it is shown that the dimer exhibits a weak and temporally broad response, whereas the larger clusters display a much stronger early-time protonic response concentrated within and immediately after the
laser pulse window. By endpoint oxygen statistics, a systematic increase in dissociation propensity with cluster size is likewise shown. For a clean subset of direct two-body dimer breakup trajectories, the asymptotic kinetic energy release is estimated to be 4.47 $\pm$ 1.03 eV, in reasonably good agreement with the experimental value for the unprotonated two-body Coulomb-explosion channel. Overall, it is shown by the results that increasing water-cluster size primarily reshapes the strong-field response through
proton-mediated and
topology-level
nuclear dynamics rather than through a large change in net ionization alone.
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Optics · Cross-listed from quant-ph
All-optical switching of continuous-variable entanglement in an absorption-suppressed plasmonic nanodimer
Elif Ozturk, Mehmet Gunay, Ramazan Sahin et al. (+1 more)
A subwavelength
quantum-photonic
circuit element should simultaneously generate nonclassical light, suppress plasmonic loss, and remain dynamically tunable. We show that an orthogonal plasmonic nanorod dimer can satisfy all three requirements. A phase-locked control polarization induces plasmonic refractive-index enhancement, driving the probe response toward a near-zero-extinction regime while simultaneously tuning the local second-harmonic parametric interaction. The resulting nonlinear plasmonic source operates in an absorption-suppressed regime and enables all-optical control of
quantum correlations. We demonstrate switchable logarithmic negativity and single-mode nonclassicality, establishing a route toward actively tunable
quantum-plasmonic
circuit elements operating well below the diffraction limit.
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All authors Elif Ozturk, Mehmet Gunay, Ramazan Sahin, Mehmet Emre Tasgin
Optics
Exact Helicity-Orbital Coupled Dynamics in Chiral Media: An Optical Dirac Framework for Photonic Rabi Oscillations
Xuhui Cheng and Longlong Feng
We demonstrate that light propagation in reciprocal
chiral photonic media admits a unified description in terms of an emergent Dirac structure in helicity space. Starting from Maxwell's equations, we reformulate the electromagnetic field as a four-component spinor governed by an effective non-Hermitian optical Dirac equation. In this representation, the magnetoelectric response of the
chiral medium appears as a helicity-dependent background that modifies the spectrum and eigenmodes, while the breaking of the
spin-degenerate condition generates the intrinsic
spin-orbit coupling between helicity and orbital degrees of freedom. After projection onto the positive-frequency sector, the theory reduces to an exact two-level helicity-orbital model. This model is found to have an analytical solution and describes coherent Rabi-like oscillations between
spin-orbit-coupled vector modes. Chirality controls the helicity splitting and detuning, whereas the electromagnetic mismatch of the medium determines the coupling strength responsible for oscillatory
spin-orbit conversion. The resulting dynamics is constrained by exact conservation of the total angular momentum, leading to reversible conversion between
spin and orbital angular momentum with well-defined selection rules. Our work establishes an optical Dirac framework for structured light in
chiral media, and provides experimentally accessible predictions for chirality-controlled oscillations, polarization dynamics, and orbital angular momentum conversion in structured optical fields.
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Fluid Dynamics
Sudden expansion stability thresholds modified by lateral flows
T. Salamon, R. Debuysschère, A. Chafaï et al. (+2 more)
We study the flow in a symmetric three-dimensional confined sudden expansion with lateral inflow at Reynolds number below 300 and varying lateral-to-central flow rate ratio, using experiments, linear stability analysis, weakly nonlinear theory, and direct numerical simulations. Three distinct flow regimes are identified. Outside an intermediate band of lateral-to-central flow rate ratio, the flow undergoes a steady
symmetry-breaking bifurcation above a critical Reynolds number, deflecting the central jet toward one side wall; weakly nonlinear analysis shows this bifurcation to be supercritical, excepting a very narrow parametric range. Within the intermediate band, no such critical Reynolds number exists and direct numerical simulations confirm that residual velocity asymmetries reflect the imposed
geometric imperfections rather than intrinsic amplification. Fluctuations observed experimentally in the intermediate band of lateral-to-central flow rate ratio remain unexplained and warrant further investigation.
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All authors T. Salamon, R. Debuysschère, A. Chafaï, B. Scheid, F. Gallaire
Biological Physics · Cross-listed from q-bio.SC
Pathway variability, coat stiffening and mechanical adaptation during clathrin-mediated endocytosis
Johannes H. H. Dreckhoff, Ulrich S. Schwarz and Leon Lettermann
Clathrin assemblies in
cells can persist as flat plaques, abort after partial invagination, or close into clathrin-coated vesicles, but the determinants of these different fates remain unresolved. To investigate the
stochastic and complex dynamics of clathrin assemblies, we have developed a kinetic Monte Carlo simulation framework that couples individual clathrin
agents to an adaptive continuum membrane. In this hybrid discrete-continuum description, the effective coat bending
rigidity and the preferred coat
curvature emerge during growth, rather than being prescribed as material parameters. Once connected, curved
lattices stiffen from molecular bending modes to coat-level
rigidities, because
curvature changes require increased stretching or compression, while newly incorporated triskelia hardcode a history-dependent preferred
curvature. An analytical theory for non-Euclidean elasticity identifies the relevant internal variables and predicts growth laws that are validated by the simulations. The same microscopic assembly rules yield flat, stalled, and closed coats through two sequential gates in the effective membrane-coat energy landscape. Comparisons with experimentally observed coat geometries and nanodissection-induced
curvature changes agree with our theoretical predictions without any fitting parameters. The clathrin coat thus emerges as an adaptive assembly with prestress and
memory, whose fate and material parameters reflect the environment in which it has been growing.
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Physics and Society · Cross-listed from cond-mat.stat-mech
Robust secret storage in networks
Vinko Zlatić
The problem of storing secure information on a network is studied. A formal framework for distributed secret storage is introduced, and possible applications in technological and social systems are discussed. The problem is formulated as the optimization of a robustness functional in which two competing requirements are balanced: survivability under network-degrading processes and resistance to
adversarial compromise. An exact representation of survivability is derived in terms of minimal information-carrying subgraphs (MICS), which provide a reduced description of the reconstruction events relevant to the stored information. This representation is then used to construct semi-local optimization methods whose dynamics do not require global knowledge of the network structure. Finally, it is shown that, in a limiting case, the robustness functional can be mapped naturally to an effective
spin Hamiltonian.
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Physics Education · Cross-listed from quant-ph
Photonic Violation of Wigner's Inequality
Maximilian Rottensteiner, Dorian Schiffer, Tobias Pausch et al. (+2 more)
Teaching
quantum mechanics is challenging, not least because the theory often conflicts with our classical worldview.
Quantum correlations in particular are notoriously counter-intuitive. Their non-classical behavior is typically revealed through Bell-type inequalities. Among these, Wigner's Inequality constitutes a particularly accessible test, as it relies on minimal set-theoretic assumptions. In this pedagogical paper, we derive Wigner's Inequality, describe a
quantum-optical setup to experimentally violate it, and provide access to the raw data, enabling students and instructors to perform their own analyses. Our measured data shows clear violations of Wigner's Inequality, directly illustrating the non-classical nature of
quantum correlations. By connecting theory, experiment, and data analysis, this paper equips educators with a resource for engaging students in authentic scientific practice and developing a deeper understanding of
quantum systems.
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All authors Maximilian Rottensteiner, Dorian Schiffer, Tobias Pausch, Alois Mair, Anton Zeilinger
Chemical Physics · Cross-listed from cond-mat.stat-mech
Surviving the Attack of the Clones
Denis S. Grebenkov
We consider a population dynamics model in which each diffusing particle that hits a catalytic surface can split into two independent copies (clones). The particles of such a growing-in-size population search in parallel for a hidden partially reactive target to trigger a reaction event (e.g., a viral attack). We investigate the statistics of the fastest first-reaction time (FRT) among all the particles. We establish a nonlinear integral equation for the survival probability and then analyze the associated probability density of the FRT and its moments. Lower and upper bounds on the mean FRT are then deduced in terms of the system parameters (target reactivity, catalytic rate, diffusivity, etc.). Because autocatalytic replication can rapidly increase the number of searchers, it can substantially accelerate the diffusive search. We solve the nonlinear equations numerically in a basic
geometric setting and reveal advantages and limitations on the autocatalytic search.
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Chemical Physics
How is Water released in Hydrogen-Based Metal Oxide Reduction? Unraveling the Kinetic Bottleneck in Sustainable Metal Production
Zhaoxi Chen, Yulu He, Zhuoran Yao et al. (+11 more)
Hydrogen-based direct reduction of metal
oxides is a ubiquitous solid-gas redox process central to geophysics, sustainable metallurgy, redox energy cycles and catalysis. During this process, hydrogen removes
lattice oxygen to form water, yet product water has long been regarded as a passive exhaust, and its nanoscale formation, trapping and removal remain poorly understood. Here, we directly observe redox-product water release from iron
oxide during hydrogen-based direct reduction. Because water removal emerges from coupled structural, chemical and crystallographic
evolution across multiple length-scales under realistic non-
equilibrium reaction-conditions, we establish a correlative multiscale in-situ approach that links pore
evolution, molecular water signatures, phase transformation and chemical-state
evolution during hematite reduction. We uncover a mechanism in which oxygen removal induces closed nanopores spatially delocalized from reaction surfaces, causing transient trapping of water vapor. Water is released only when these pores coalesce into a percolating network connected to the surface, coinciding with and accelerating the onset of the hematite-to-magnetite transformation. These findings show that dynamically evolving pore
topology governs mass transport and redox kinetics in solid-gas reactions, closing a critical mechanistic gap in product-water removal and providing nanoscale guidance for hydrogen-based metal extraction, reactor design, and sustainable redox energy technologies under practical conditions.
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All authors Zhaoxi Chen, Yulu He, Zhuoran Yao, Chunwen Wang, Lei Lei, Jun Cai, Martin Aaskov Karlsen, Claudio Pistidda, Wu Zhou, Marc-Georg Willinger, Yan Ma, Dierk Raabe, Zhi Liu, Zhu-Jun Wang
Atomic Physics
All-Optical Control of Birefringence in a Cold Atomic Ensemble
Apoorva Apoorva, Naudson Lucas Lopes Matias, Bérengère Pinoche et al. (+3 more)
We demonstrate all-optical control of birefringence in a cold atomic cloud of ytterbium. By optically dressing the excited $^{3}\mathrm{P}_1$ state via an off-resonant coupling to the $^{3}\mathrm{D}_1$ level, we induce polarization-dependent light shifts of the Zeeman sublevels, resulting in a tunable polarization-dependent refractive index. For a circularly polarized dressing beam, we observe a rotation of the probe linear polarization, characteristic of the Faraday effect, in the absence of any
magnetic field. In addition, for a linearly polarized dressing beam, the probe acquires ellipticity without rotation, corresponding to linear birefringence. More generally, the polarization of the dressing beam controls the axis of rotation of the probe polarization on the Poincaré sphere. Our results establish cold atoms as a versatile platform for engineering and controlling light-induced birefringence and open new perspectives for the fast and reconfigurable control of optical response of resonant media.
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All authors Apoorva Apoorva, Naudson Lucas Lopes Matias, Bérengère Pinoche, Daniel Benedicto Orenes, Robin Kaiser, Raphaël Saint-Jalm
Optics · Cross-listed from astro-ph.IM
Improving Beam Quality in Gravitational-Wave Interferometers Illuminated by Higher-Order Laguerre-Gaussian Modes
Liu Tao, Yuefan Guo, Alberto Gatto et al. (+5 more)
Higher-order Laguerre-Gaussian (LG)
laser modes have been proposed to reduce test-mass thermal noise in
laser interferometric gravitational-wave detectors, owing to their more homogeneous intensity profiles compared to the currently employed fundamental Gaussian beam. However, LG beams such as the LG$_{3,3}$ mode suffer significant beam quality degradation in Fabry-Perot arm cavities in GW detectors with realistic state-of-the-art mirror surface figure errors, due to scattering into degenerate modes of the same order, which are resonantly enhanced by shared cavity resonance conditions. In this work, we investigate an alternative ”donut-shaped” LG$_{0,\ell}$-like mode, specifically the LG$_{0,6}$ mode, and demonstrate strategies to improve its performance. These include the introduction of a tailored circular mirror mask with anti-reflective coating in the central region, which selectively increases the losses of parasitic degenerate modes while minimally impacting the LG$_{0,6}$ mode due to its limited overlap with the masked area. We further assess the marginal benefits of anticipated improvements in mirror surface figure errors and the potential reduction of cavity finesse. We demonstrate that these strategies can reduce the average contrast defect by more than two orders of magnitude and lower the mode loss by nearly a factor of five, achieving performance at or below the typical values observed in current detectors. This work opens up new research and development pathways for employing LG$_{0,\ell}$-type modes that achieve significant thermal noise reduction while maintaining beam quality and optical performance comparable to current gravitational-wave interferometers.
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All authors Liu Tao, Yuefan Guo, Alberto Gatto, Eleonora Capocasa, Jérome Degallaix, Massimo Granata, Matteo Tacca, Matteo Barsuglia
Optics
Comb-enabled spectral-domain image transport through perturbation-prone multimode fibers
Maohan Li, Zijian Wang, Bowen Sun et al. (+9 more)
Multimode fibers (MMFs) offer a compact platform for imaging, sensing, and information transport, but their practical deployment is hindered by sensitivity to fiber perturbations, which alter modal coupling and invalidate conventional speckle-based calibrations. Here, we demonstrate perturbation-resilient image transport through MMFs by combining image-to-spectrum encoding with dual-comb spectroscopy. Two-dimensional images are converted into comb-line-resolved spectral signatures before fiber transmission, allowing spatial information to be carried in the spectral domain rather than in the output speckle field. After propagation, dual-comb heterodyne detection maps the encoded spectrum into the
radio-frequency domain, enabling massively parallel spectral readout with a single photodetector. Neural-network-assisted compressive reconstruction further enables high-fidelity imaging from sparse, noisy, and spectrally aliased measurements. Our approach achieves Pearson correlation coefficients exceeding 0.9 under strong fiber perturbations and supports frame rates up to 2.5 MHz, allowing the observation of transient switching dynamics in a digital micromirror device. These results establish a powerful tool for robust, real-time image transport through flexible MMFs, with potential applications in remote sensing and fiber-based optical instrumentation.
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All authors Maohan Li, Zijian Wang, Bowen Sun, Zhuoren Wan, Xiangze Ma, Xiuxiu Zhang, Yuan Chen, Mei Yang, Qi Wen, Zhaoyang Wen, Ming Yan, Heping Zeng
Optics
Arduino based interferometer stabilizer equipped with a digital lock-in amplifier
Giuseppe Emanuele Lio, Saravanan Rajamani, Alessandro Pitanti et al. (+2 more)
Interferometric sensors are ubiquitous in precision metrology, yet their performance is fundamentally limited by environmental noise and thermal drift. To achieve maximum sensitivity, these systems must be actively stabilized at the quadrature point of the interference fringe. Commercial stabilization solutions, typically based on analog lock-in amplifiers or FPGA architectures, are often prohibitively expensive and do not not offer the flexibility needed for custom experimental setups. In this work, we present an open-source, compact and low-cost digital stabilization system built upon the dual-core Arduino Giga R1 microcontroller. The system features a custom analog front-end with programmable gain amplifiers (PGAs) and active signal conditioning, enabling direct integration with standard amplified photodiodes. We implement a firmware-based digital lock-in amplifier running at a 100 kHz sampling rate, which performs real-time demodulation and PID feedback control without the latency bottlenecks of PC-based loops. Experimental characterization demonstrates that the system effectively suppresses long-term thermal drift and actively rejects external perturbations. The resulting device provides a standalone, Arduino-based alternative for
laser frequency stabilization and interferometric control in educational and research laboratories.
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All authors Giuseppe Emanuele Lio, Saravanan Rajamani, Alessandro Pitanti, Stefano Roddaro, Simone Zanotto
Instrumentation and Detectors
Test of a partly instrumented highly compact and granular electromagnetic calorimeter in an electron beam of 1 to 6 GeV
Adrián Irles
Highly compact and finely segmented silicon-tungsten electromagnetic calorimeters are being developed within the FCAL collaboration for applications in the LUXE experiment at DESY and future electron-positron
collider facilities. These detectors combine tungsten absorber plates with thin silicon pad sensors, providing a small effective Molière radius and high spatial granularity, which are essential for resolving nearby electromagnetic showers in high-occupancy environments. The fundamental active unit of this calorimeter concept is the Compact Silicon Sandwich (CSIS), integrating a silicon pad sensor together with signal routing, high-voltage distribution and mechanical support in a highly compact structure. The assembly of these CSIS modules is performed within a dedicated infrastructure for silicon detector integration. A partially instrumented prototype of such a calorimeter has been tested in an electron beam with energies between 1 and 6~GeV. First results from the 2025 test beam campaign are presented, including minimum-ionizing particle calibration and preliminary event displays illustrating the shower development in the highly granular detector. These results constitute an important step towards the validation of this technology for LUXE and future
collider experiments.
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Classical Physics · Cross-listed from hep-th
Quadratic Gauge Transformation
Sunita Singh and Akshit Sharma
Symmetries plays a significant role in understanding the conservation laws in
Quantum field theories. Here, we attempted a quadratic type dimensionless gauge transformation to achieve the invariance in QFTs. We have shown the extensive study of invariance of complex scalar, Abelian and Non- Abelian theories and established the conservation laws. We included an explicit graphical analysis to invoke the invariance. This is studied in a physical context, where different field configurations correspond to the same physical state. The necessity of the covariant derivative is studied in detail, highlighting how it ensures consistent transformation under local
symmetry operations. The meaning of covariance is clarified as the preservation of the form of physical laws under transformations.
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Chemical Physics · Cross-listed from cs.LG
Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
Matthias Blaschke, Daniel Kienzle, Zsuzsanna Koczor-Benda et al. (+3 more)
Generative molecular design is shaped by simple proxy
benchmarks for drug-like properties and models pretrained on large pharmaceutical
datasets. This combination yields strong
benchmark metrics but limits transferability to domains structurally distinct from drug discovery. To overcome this limitation and drive discovery toward real, scientifically grounded targets, we introduce the Nanotechnology Molecular Optimization (NMO)
Benchmark, which bridges machine learning (ML) and
quantum materials science. NMO acts simultaneously as a rigorous testbed for the ML community and a discovery engine for nanotechnology research. The suite replaces proxy oracles with
quantum simulations and introduces strict protocols that prioritize scientific utility over leaderboard-oriented overfitting. The physics-based NMO tasks impose hard structural constraints and rugged fitness landscapes, posing fundamentally new requirements on generative models. Notably, advanced molecular optimization methods underperform much simpler approaches on the NMO tasks. We develop a new baseline method identifying the critical components to solve the NMO tasks, including a novel representation for modeling structural constraints and a domain-agnostic pretraining strategy to eliminate pharmaceutical
dataset bias. Our results surpass state-of-the-art physical properties and reveal previously unknown structural motifs, offering new insights for the nanotechnology community and demonstrating that ML can drive genuine scientific discovery.
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All authors Matthias Blaschke, Daniel Kienzle, Zsuzsanna Koczor-Benda, Julian Lorenz, Rainer Lienhart, Fabian Pauly
Instrumentation and Detectors · Cross-listed from astro-ph.IM
Radiation effects and noise evolution in NewAthena WFI flight-production sensors
Valentin Emberger, Johannes Müller-Seidlitz, Luisa Ostler et al. (+7 more)
The Wide Field Imager (WFI), one of the two instruments on ESA's next large X-ray observatory NewAthena, is designed for imaging spectroscopy in the 0.2-15 keV range, combining a large field of view with high count-rate capability. Its focal plane is equipped with back-illuminated DEPFET (Depleted p-channel field-effect transistor) sensors that offer high radiation tolerance and provide near Fano-limited energy resolution. Achieving this performance requires an exceptionally low readout noise, with about 3 electrons ENC expected at beginning of life. Consequently, the devices are highly sensitive to radiation-induced changes in noise behavior. In this work, we investigate the impact of both total non-ionizing dose (TNID) and total ionizing dose (TID) on the relevant noise components, including their temperature dependence. A detector module containing a 64x64-pixel sensor from a flight-production wafer was irradiated with 62.4 MeV
protons at the MedAustron accelerator facility in Wiener Neustadt to a total dose equivalent to 2.6 $\cdot$ 10$^9$ 10-MeV-
protons/cm$^2$. The detector was fully biased and operated throughout the irradiation and subsequent measurements, maintaining the nominal operating temperature of 213 K. To study short-term annealing behavior at low temperature, a second, identical module was exposed to a comparable
proton dose within a much shorter timescale by exploiting the available high beam flux. TID effects were investigated separately by irradiating another device with 17.4 keV Mo-K_alpha X-rays to a total dose of 15 Gy. We report the resulting changes in readout noise, dark current, and threshold voltage, and compare them with results from an earlier irradiation campaign using pre-flight sensors. Implications for the instrument's required operating temperature and its expected end-of-life performance are discussed.
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All authors Valentin Emberger, Johannes Müller-Seidlitz, Luisa Ostler, Wolfgang Treberer-Treberspurg, Robert Andritschke, Annika Behrens, Günter Hauser, Peter Lechner, Astrid Mayr, Leonie Sommer
Medical Physics
Compact deep learning pipeline for particle track reconstruction in the pCT detector system
B. Dudás, G. Papp, G. G. Barnaföldi et al. (+1 more)
Proton computed
tomography (pCT) requires both fast and accurate reconstruction of particle trajectories and kinetic energies to achieve clinically viable image formation. Traditional distance-based matching
algorithms often fail under the combined effects of multiple Coulomb scattering and track crossings and most importantly many of them take too much computation time, motivating the use of lightweight deep learning models that can be evaluated rapidly. In this work, we develop a two-stage reconstruction pipeline consisting of (i) a neural-network-assisted tracking module and (ii) a kinetic-energy estimation model. For the tracking task, compact multilayer perceptrons are trained to predict the expected hit position in the subsequent detector layer, providing a physically informed prior that substantially reduces ambiguities in bipartite matching. Furthermore, ambiguous tracks are flagged and excluded from the final analysis. Our training data is provided by OpenGATE simulation toolkit, both for tracking and energy estimation, where we designed a fully connected network that processes detector hit information. This model predicts the incoming
proton kinetic energy with sufficient accuracy for current pCT image reconstruction methods. The entire pipeline benefits from deep-learning parallelism and evaluates particle tracks fast enough for clinical time constraints. Together, these results demonstrate that compact deep learning models can reliably reconstruct particle trajectories and energies in a realistic pCT detector system, offering a computationally efficient and highly accurate alternative to traditional matching and tracking methods.
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All authors B. Dudás, G. Papp, G. G. Barnaföldi, G. Bíró
Medical Physics
Physically-Constrained Harmonic Separation for Robust Heart and Respiratory Rate Estimation from Wrist Photoplethysmography
Nouhaila Fraihi, Ouassim Karrakchou and Mounir Ghogho
Wrist-worn photoplethysmography (PPG) enables continuous monitoring of cardiopulmonary physiology, but reliable heart rate (HR) and respiratory rate (RR) estimation in free-living conditions remains challenging due to non-stationary motion artifacts that spectrally overlap with physiological dynamics. Existing signal-processing methods degrade under strong motion, while unconstrained deep learning approaches often lack physiological interpretability and identifiable structure. We propose a Physically-Constrained Harmonic Separation (PCHS) framework that formulates HR and RR estimation from wrist PPG as an analysis-by-synthesis problem, where accelerometer measurements condition artifact separation rather than directly regressing vital signs. A physics-guided harmonic generator decomposes the observed signal into quasi-periodic physiological components and a motion-related residual, enabling HR recovery from the fundamental frequency and RR prediction from respiratory-driven modulations of the harmonic parameters. Robust reconstruction objectives, separation constraints, and uncertainty-aware weighting stabilize the decomposition under motion. Experiments on the motion-intensive PPG-DaLiA
dataset demonstrate that PCHS outperforms state-of-the-art methods while yielding interpretable signal decompositions that effectively disentangle physiological activity from motion artifacts.
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