Optics
Theory of magnetic circular dichroism spectroscopy technique in reflected light using an additional mirror
Yuri Markin and Zoja Kun'kova
The theory of the method of
magnetic circular dichroism spectroscopy in reflected light (RMCD) has been developed using a simplified scheme without an analyzer, with the use of an additional mirror in the optical system and with the application of the method of phase modulation of the light wave using a photoelastic modulator. An additional mirror reflector, together with the mirror surface of the sample, forms a double mirror located between the pole pieces of a powerful laboratory electromagnet. The presence of an additional mirror leads to mixing of the amplitude ($\Delta R/R$) and phase ($\Delta \phi$) RMCD-components in the amplitudes of the signals at the first and second harmonics of the phase modulation. It is shown that the RMCD spectroscopy technique using a simplified scheme and an additional mirror requires the synchronous measurement of three electrical signals proportional to the amplitudes of the corresponding terms in the expression for the total intensity of monochromatic radiation detected by the photodetector: a constant signal $V_{dc}$ and high-frequency amplitudes $V_f$ and $V_{2f}$ at phase modulation frequencies $f$ and $2f$. The ratios $V_f/V_{dc}$ and $V_2f/Vdc$ determine a system of linear equations for $\Delta R/R$ and $\Delta \phi$ with known ellipsometric parameters $\tan\Psi$ and $\Delta$ of the additional mirror, which are found either from additional measurements using an ellipsometer or directly (in sity) using the RMCD spectrometer itself.
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Optics
First Steps towards Machine Learning for Prediction and Pre-Correction in Direct Laser Writing
Sven Enns, Julian Hering-Stratemeier and Georg von Freymann
Additive manufacturing using 2-
Photon Polymerization (2PP, aka direct
laser writing DLW) enables the fabrication of almost arbitrary complex 3D structures from the meso to the submicron scale. However, deviations between the anticipated target structure and the actual print often occur due to physico-chemical processes, limiting the accuracy and reliability of this technology. To minimize these deviations, we hereby present our latest research in developing different neural networks, targeting the above-mentioned aspect. Our networks are trained on several experimental as well as theoretical
datasets and show good results in predicting fabrication deviations and (pre-) correcting 2.5D micro-structures. Hence, we demonstrate, that besides conventional iterative correction methods, neural networks are a promising alternative to significantly improving the output quality in DLW. Furthermore, there are no fundamental limitations to transferring this machine learning approach to other 3D printing technologies, as they all face the same challenge in terms of fidelity. To our point of view, the use of neural networks has the potential to enhance the capabilities of this technology, enabling the creation of complex structures with increased accuracy and precision in the near future.
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Biological Physics
Phytoscale Transport Physics: Insights into Xylem Flow Homeostasis and Drought Stress
Jinmay Kalita, Sumit Kumar Mehta, Suraj Panja et al. (+1 more)
We investigate the flow dynamics of nutrient solution through the xylem vessels of Brassica juncea under drought stress. To this end, we perform experiments to obtain morphological traits of xylem vessels under drought-stressed conditions, and develop a mathematical framework to model the underlying flow through the xylem, considering several features relevant to the plant system. Performing experiments using state of the art instruments, we measure the morphology of xylem vessels, physicochemical and mechanical properties of xylem walls under drought stressed conditions. Our experimental results unveil that drought reduces both xylem diameter and pit aperture size, implicating hydraulic adaptations of plants to drought stress. We find that the reduced cellulose content in drought stressed xylem vessels lowers the
zeta potential and decreases elasticity of the vascular region. Additionally, drought stress alters metabolite activity, increases reactive oxygen species, reduces chlorophyll content, and limits the uptake of essential metallic nutrients. Besides, we perform three-dimensional numerical simulations to evaluate local flow field, mechanical stress, hydraulic conductivity, and radial transport efficiency of xylem vessels under drought stressed conditions. Simulated results reveal that under drought conditions, resistance to axial flow through xylem vessels increases significantly, which in turn, promotes radial transport of nutrients, allowing plants to survive even in drought stress. We show that the radial flow efficiency of xylem vessels becomes notably higher under drought stress than in well-watered control plants. Overall, results of this endeavor provide new insights into how
geometric adaptations of xylem vessels modify flow behavior under water deficient conditions, enhancing the plants ability to survive environmental stress.
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All authors Jinmay Kalita, Sumit Kumar Mehta, Suraj Panja, Pranab Kumar Mondal
Plasma Physics
High fidelity simulations of the multi-species Vlasov-Maxwell system with the Numerical Flow Iteration
Rostislav-Paul Wilhelm and Fabio Bacchini
Validity of fluid models breaks down for non-thermal or weakly collisional
plasmas which often occur e.g. in the
solar wind. In these regimes one has to resort to modelling through the first-principle Vlasov-Maxwell system, but its six-dimensional phase-space dynamics, strong filamentation, and multi-scale structure make direct numerical simulation extremely demanding. Particle-In-
Cell (PIC) methods remain the standard for ion-scale studies, yet their
memory cost and intrinsic noise hinder accurate electron-scale simulations. In this paper, we introduce an alternative method based on an iterative-in-time approximation of characteristics. The approach reconstructs the phase-space dynamics from the time history of the electromagnetic fields and the initial distribution functions, enabling extremely high effective resolution far below the phase-space grid scale without storing or advecting high-dimensional data. Earlier work demonstrated this capability for the multi-species electrostatic Vlasov system. Here we discuss an extension of the method to the full Vlasov-Maxwell equations using a
Hamiltonian splitting to advance the solution in a structure-preserving way while retaining the reduced
memory footprint.
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Instrumentation and Detectors
Statistical analysis and correction of the pile-up effect in MAPMT single photoelectron counting with the SPACIROC-3 ASIC: application to the Mini-EUSO experiment
Enzio M'sihid, Etienne Parizot, Matteo Battisti et al. (+1 more)
We present a comprehensive study addressing pile-up effects in single photoelectron counting with R-11265 Hamamatsu multi-anode photomultiplier tubes (MAPMTs) equipped with the SPACIROC-3 ASIC. Extended dead time in the electronics causes saturation and quenching of the counting rate, an effect we counter by inverting the pile-up plot once the double pulse resolution is determined. Our work combines extensive numerical simulations with experimental validations to quantify the statistical uncertainties associated with the corrected event rates. We apply this methodology to the Mini-EUSO experiment onboard the International Space Station where machine learning techniques are employed to extract pixel-by-pixel double pulse resolutions from long-term
photon count histograms. This integrated approach enables the accurate recovery of true
photon fluxes essential for studying ELVES, meteors and other transient phenomena detected by Mini-EUSO.
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All authors Enzio M'sihid, Etienne Parizot, Matteo Battisti, Sylvie Blin
Fluid Dynamics
Mees M. Flapper, Detlef Lohse and Sander G. Huisman
This work investigates
chiral particles, which break mirror
symmetry, in turbulent Taylor–Couette flow. These particles generally display a translation-rotation coupling moving through a quiescent fluid. Here we performed experiments using large
chiral particles (typical size \unit{5}{mm}) in turbulent Taylor–Couette flow, for Reynolds numbers $9\cdot10^3 \leq \text{Re} \leq 1.5 \cdot 10^5$. The density-matched
chiral particles are studied in a dilute regime $(\phi = 1.7 \cdot 10^{-4})$, where their location and orientation are tracked over time to investigate the particle-fluid coupling. We investigate whether the translation-rotation coupling observed at low Reynolds numbers is still observable over the measured high Reynolds numbers, using the tracked location and orientation. Similarly, we verify whether the
chiral particles display a preferred location or orientation, and whether the left-handed and right-handed particles show different rotation statistics. The location data show that the
chiral particles closely follow the structure of Taylor
vortices. Hence, the orientation data and rotation data of the
chiral particles are split between the Taylor
vortices and particle chiralities. The results show no difference in rotation and orientation dynamics between chiralities. Rather, the particle dynamics are flow-dominated, where the flow vorticity determines the specific particle dynamics.
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Medical Physics · Cross-listed from cs.CV
Simultaneous Image Quality Improvement and Artefacts Correction in Accelerated MRI
Georgia Kanli, Daniele Perlo, Selma Boudissa et al. (+2 more)
MR data are acquired in the frequency domain, known as k-space. Acquiring high-quality and high-resolution MR images can be time-consuming, posing a significant challenge when multiple sequences providing complementary contrast information are needed or when the patient is unable to remain in the scanner for an extended period of time. Reducing k-space measurements is a strategy to speed up acquisition, but often leads to reduced quality in reconstructed images. Additionally, in real-world MRI, both under-sampled and full-sampled images are prone to artefacts, and correcting these artefacts is crucial for maintaining diagnostic accuracy. Deep learning methods have been proposed to restore image quality from under-sampled data, while others focused on the correction of artefacts that result from the noise or motion. No approach has however been proposed so far that addresses both acceleration and artefacts correction, limiting the performance of these models when these degradation factors occur simultaneously. To address this gap, we present a method for recovering high-quality images from under-sampled data with simultaneously correction for noise and motion artefact called USArt (Under-Sampling and Artifact correction model). Customized for 2D
brain anatomical images acquired with Cartesian sampling, USArt employs a dual sub-model approach. The results demonstrate remarkable increase of signal-to-noise ratio (SNR) and contrast in the images restored. Various under-sampling strategies and degradation levels were explored, with the gradient under-sampling strategy yielding the best outcomes. We achieved up to 5x acceleration and simultaneously artefacts correction without significant degradation, showcasing the model's robustness in real-world settings.
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All authors Georgia Kanli, Daniele Perlo, Selma Boudissa, Radovan Jirik, Olivier Keunen
Optics · Cross-listed from quant-ph
Unrepeated White Rabbit Time Synchronisation over a 300 km Optical Fibre Link
Ben Amies-King and Marco Lucamarini
White Rabbit (WR) technology provides a commercially-available off-the-shelf solution for time synchronisation with sub-nanosecond accuracy and picosecond-level precision over optical fibre links typically spanning tens of kilometres. Such high-performance time dissemination can support a variety of applications, including position,
navigation and timing (PNT), financial transactions, metrology, as well as
entanglement and
quantum key distribution (QKD). Demonstrations of WR over significantly longer distances remain few and far between, particularly in scenarios where intermediate amplification is unavailable, such as stretches of long-haul
underwater fibre. In this work, we report the longest unrepeated deployment of WR to date, achieving time synchronisation over a 300 km (51.34 dB) single-span optical fibre link, even in highly asymmetrical configurations, with 99.86% uptime, whilst maintaining picosecond-level precision and sub-nanosecond accuracy. This was achieved through careful selection and optimisation of the components deployed at the link's end points. By leveraging standard telecom fibre and off-the-shelf hardware, our results pave the way for a scalable and standardised timing backbone for large-scale
quantum networks, offering a practical route toward time distribution in future heterogeneous
quantum communication systems.
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Biological Physics · Cross-listed from cond-mat.soft
Quantifying surfactant adsorption at fluid interfaces by combining X-ray reflectivity and simulations
Kay-Robert Dormann, Joshua Reed, Chen Shen et al. (+4 more)
Adsorption of surfactants to fluid interfaces occurs in numerous daily-life and technological contexts. The surfactant surface coverage $\Gamma$ governs interface characteristics like tension $\gamma$, viscoelastic properties, and the stability of thin foam films. Directly measuring $\Gamma$ as a function of the bulk concentration $c$ is highly desirable but challenging, particularly for non-ionic surfactants that lack easily detectable labels.
Neutron reflectometry is currently the only generally applicable method, but it is not available for routine experiments. Here, we propose a simulation-assisted approach to deduce the adsorption isotherm $\Gamma(c)$ from X-ray reflectivity data: As a first step, we use atomistic molecular dynamics simulations of surfactant-loaded air/water interfaces with prespecified $\Gamma$ to obtain interfacial electron density profiles. From these profiles, we compute theoretical X-ray reflectivity curves and compare them with experimental measurements to determine the matching bulk concentration. We focus on two non-ionic surfactants (C$_{12}$EO$_6$ and $\beta$-C$_{12}$G$_2$}) with previously established force fields to illustrate how this combined approach of experiments and simulations can determine the adsorption isotherm. Additional insights are gained through comparison with the measured surface tension isotherms $\gamma(c)$, based on the equation of state $\gamma(\Gamma)$ from simulations.
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All authors Kay-Robert Dormann, Joshua Reed, Chen Shen, Daniel Mitlewski, Matej Kanduč, Benno Liebchen, Emanuel Schneck
Atomic Physics
A permanent-magnet Zeeman slower and magneto-optical trap for calcium atoms for ultracold Rydberg physics
Eduardo Marin-Bujedo, Julien A. L. Grondin, Thomas Schiltz et al. (+3 more)
We report the construction and characterization of an experimental setup for producing a cold gas of $^{40}$Ca atoms and excite them to high Rydberg states with a resonant three-
photon-excitation scheme. The apparatus comprises four stages, each designed in-house. An oven heated to $\sim 500^\circ$C generates an atomic beam that is collimated by a capillary stack. The beam is sent into a passive, permanent-magnet-based Zeeman slower that reduces the atomic velocity to $30$ m/s. The slow atoms are captured in a magneto-optical trap (MOT) and cooled to $1.0(3)$ mK with a trapping time of $16(2)$ ms. Ground-state atoms in the cold gas are excited to high Rydberg states via resonant excitation through the intermediate $4s4p\, ^1P_1$ and $4s4d\, ^1D_2$ states. The MOT is operated at the center of an electrode stack, which serves to apply continuous and pulsed electric fields and field-ionize the Rydberg atoms for detection. We
benchmark our MOT against previous implementations and find its performance consistent with state-of-the-art results in terms of temperature and trapping lifetime. Finally, we demonstrate Rydberg spectroscopy of calcium, confirming the system's suitability for ultracold Rydberg physics experiments.
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All authors Eduardo Marin-Bujedo, Julien A. L. Grondin, Thomas Schiltz, Thomas Corbo, Xavier Urbain, Matthieu Génévriez
Optics
Deterministic quantum dot single-photon sources: operational principles and state-of-the-art specifications
J.C. Loredo, L. Stefan, B. Krogh et al. (+13 more)
Non-classical states of light play a fundamental role in
quantum technology. From photonic
quantum computers and simulators, to
quantum communication and sensing,
quantum states of light enable performing tasks that may outperform their best classical counterparts.
Semiconductor quantum dots embedded in photonic nanostructures offer the most advanced classes of
quantum light sources. Importantly, the underlying physics processes determining device performance are today fully understood, and dedicated engineering projects are currently advancing these sources towards real-world
quantum technology applications. We review the performance of deterministic single-
photon sources based on
quantum dots in photonic
crystal waveguides, the approach with the highest performance specs since it intrinsically combines suppression of leaky modes and Purcell enhancement to slow-light waveguide mode. Furthermore, we present prototype data from sources that today are commercially available and with performance metrics approaching the ideal.
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All authors J.C. Loredo, L. Stefan, B. Krogh, R. Jensen, I. Suleiman, S. Krüger, M. Bergamin, H. Thyrrestrup, S. Budtz, J. Roulund, Z. Liu, X. Zhao, L. Vertchenko, A. Ludwig, O. A.D. Sandberg, P. Lodahl
Data Analysis and Statistics · Cross-listed from astro-ph.IM
SlotFlow: Amortized Trans-Dimensional Inference with Slot-Based Normalizing Flows
Niklas Houba, Giovanni Giarda and Lorenzo Speri
Inferring the number of distinct components contributing to an observation, while simultaneously estimating their parameters, remains a long-standing challenge across signal processing, astrophysics, and neuroscience. Classical trans-dimensional
Bayesian methods such as Reversible Jump Markov Chain Monte Carlo (RJMCMC) provide asymptotically exact inference but can be computationally expensive. Instead, modern deep learning provides a faster alternative to inference but typically assume fixed component counts, sidestepping the core challenge of trans-dimensionality. To address this, we introduce SlotFlow, a deep learning architecture for trans-dimensional amortized inference. The architecture processes time-series observations, which we represent jointly in the frequency and time domains through parallel encoders. A classifier produces a distribution over component counts K, and its MAP estimate specifies the number of slots instantiated. Each slot is parameterized by a shared conditional normalizing flow trained via permutation-invariant Hungarian matching. On sinusoidal decomposition with up to 10 overlapping components and Gaussian noise, SlotFlow achieves 99.85% cardinality accuracy and well-calibrated parameter posteriors, with systematic biases well below one posterior standard deviation. Direct comparison with RJMCMC shows close agreement in amplitude and phase, with Wasserstein distances $W_2 < 0.01$ and $< 0.03$, indicating that shared global context captures inter-component structure despite a factorized posterior. Frequency posteriors remain centered but exhibit 2-3x broader intervals, consistent with an encoder bottleneck in retaining long-baseline phase coherence. The method delivers a $\sim 10^6\times$ speedup over RJMCMC, suggesting applicability to time-critical workflows in gravitational-wave astronomy, neural spike sorting, and object-centric
vision.
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Optics · Cross-listed from cond-mat.mes-hall
Chiral cavity-induced quantum phase transitions in a quantum ring
Loic Remolif and Ivan Iorsh
We consider a
quantum ring placed in a gyrotropic cavity characterized by the energy splitting between the left and right circularly polarized modes. We show that despite the absence of constant
magnetic field penetrating through the ring, in the regime of the ultrastrong light matter coupling, the total current in the ground state changes discontinuously with the light matter coupling in the direct analogy with the Aharonov-Bohm ring. We consider the driven-dissipative of the system and show that the discontinuous change of the total angular momentum can be directly probed via the spectral and statical properties of the radiation emitted by the system under weak coherent drive.
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Optics
A 3D-integrated BiCMOS-silicon photonics high-speed receiver realized using micro-transfer printing
Ye Gu, He Li, Tinus Pannier et al. (+24 more)
Meeting the escalating demands of data transmission and computing, driven by artificial intelligence (AI), requires not only faster optical transceivers but also advanced integration technologies that can seamlessly combine photonic and electronic components. Traditional approaches struggle to overcome the parasitic limitations arising from fabricating those components using different processes. Here, we report a novel 3D heterogeneously integrated optical receiver based on micro-transfer printing ({\mu}TP), enabling the co-integration of a compact bipolar CMOS (BiCMOS) electronic chiplet (0.06 mm2) directly onto a silicon photonic integrated
circuit (SiPIC). While previous {\mu}TP demonstrations have focused primarily on photonic integration, our work pioneers the direct integration of electronics and photonics, significantly enhancing performance and scalability. The resulting optical receiver achieves 224 Gb/s four-level pulse amplitude modulation (PAM-4) operation, delivering -5.2 dBm optical modulation amplitude(OMA) sensitivity at a bit-error rate (BER) of 2.4 x 10-4, a record-small footprint, and an excellent power efficiency of 0.51 pJ/b. This demonstration not only showcases the potential of {\mu}TP for high-density, cost-efficient integration but also represents a critical step toward next-generation optical interconnects in the AI era.
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All authors Ye Gu, He Li, Tinus Pannier, Shengpu Niu, Patrick Heise, Christian Mai, Prasanna Ramaswamy, Alex Farrel, Alin Fecioru, Antonio Jose Trindade, Ruggero Loi, Nishant Singh, Senbiao Qin, Biwei Pan, Jing Zhang, Johanna Rimbock, Kristof Dhaenens, Toon De Baere, Geert Van Steenberge, Dieter Bode, Dimitrios Velenis, Guy Lepage, Neha Singh, Joris Van Campenhout, Xin Yin, Gunther Roelkens, Peter Ossieur
Biological Physics
Nature of frontier quasi-particle states in nitrogen-base systems
Raul Quintero-Monsebaiz, Per Hyldgaard and Elsebeth Schröder
Understanding photophysical properties of DNA is important: It can help us elucidate and probe the impact of charges and free radicals in the cellular environment. For example, a photoemission at a given nucleobase means that we both charge it and place an electron right next to a neighboring part of the genetic code. Inverse photoemission means that we trap a free electron (at some empty state or resonance), and instead emit a low-energy
photon. This may reduce the damage if it happens at an already charged base, but it can cause extra damage if it arises somewhere else. Predicting the nature of sudden optically-driven excitations, termed quasi-particles (QPs), help us detail interactions and possibly control the damage that might follow. Also, these QPs contain information on the larger DNA assembly because they reflect the fingerprints of nucleobase polarity, the hydrogen bonding in Watson-Crick pairs, and the van der Waals (vdW) interactions in the Watson-Crick-pair stacking that makes up the genome. In this study, we utilize the recently developed (optimally tuned) range-separated hybrid vdW density functional, AHBR-mRSH* [JPCM 37, 211501 (2025)] to analyze the electron-attached and ionized QP states of these DNA components, with a particular focus on dipole- and multipole-trapped empty states (bound or resonances). We also evaluate critical properties such as dipole and quadrupole moments, QP HOMO-LUMO energy gaps, and transition-dipole moments. Finally, we classify the Watson-Crick stacked dimers based on their QP nature. This classification provides the foundation for proposing a model of DNA reactivity and photo-physical activity.
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Applied Physics · Cross-listed from hep-ex
Precision Measurements of Higgs Hadronic Decay Modes at the FCC-ee
Andrea Del Vecchio, Jan Eysermans, Loukas Gouskos et al. (+4 more)
The expected precision at the FCC-ee on the product $\sigma\times\mathcal{B}(H\rightarrow b\bar{b}, c\bar{c},s\bar{s},gg)$ of
Higgs boson production cross sections times branching ratios of hadronic decays is presented. This study provides the first comprehensive determination of all major hadronic
Higgs decay modes in a combined fit at future $e^+ e^-$
colliders, using both
Higgs-strahlung ($ZH$) and Vector
boson fusion ($\nu\bar{\nu} H$) production processes, with a full treatment of interference effects in the $\nu\bar{\nu} jj$ final state. It assumes four identical IDEA detectors collecting $e^+e^-$ collisions at $\sqrt{s}=240$ and $365\,$GeV. The combination of all channels across both energies, with full covariance between production and decay modes, yields a production cross-section times branching-ratio precision at the percent to per-mil level for the dominant hadronic final states ($b\bar{b}, c\bar{c},gg$). These results provide a comprehensive input to the determination of
Higgs coupling projections at the FCC-ee, and they establish for the first time sensitivity to the rare decay $H\rightarrow s\bar{s}$, demonstrating that FCC-ee has the potential to provide evidence of the strange-
quark Yukawa coupling.
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All authors Andrea Del Vecchio, Jan Eysermans, Loukas Gouskos, George Iakovidis, Alexis Maloizel, Giovanni Marchiori, Michele Selvaggi
Medical Physics · Cross-listed from eess.SP
Strategies to Minimize Out-of-Distribution Effects in Data-Driven MRS Quantification
Julian P. Merkofer, Antonia Kaiser, Anouk Schrantee et al. (+2 more)
This study systematically compared data-driven and model-based strategies for metabolite quantification in
magnetic resonance spectroscopy (MRS), focusing on resilience to out-of-distribution (OoD) effects and the balance between accuracy, robustness, and generalizability. A neural network designed for MRS quantification was trained using three distinct strategies: supervised regression, self-supervised learning, and test-time adaptation. These were compared against model-based fitting tools. Experiments combined large-scale simulated data, designed to probe metabolite concentration extrapolation and signal variability, with 1H single-voxel 7T in-vivo human
brain spectra. In simulations, supervised learning achieved high accuracy for spectra similar to those in the training distribution, but showed marked degradation when extrapolated beyond the training distribution. Test-time adaptation proved more resilient to OoD effects, while self-supervised learning achieved intermediate performance. In-vivo experiments showed larger variance across the methods (data-driven and model-based) due to domain shift. Across all strategies, overlapping metabolites and baseline variability remained persistent challenges. While strong performance can be achieved by data-driven methods for MRS metabolite quantification, their reliability is contingent on careful consideration of the training distribution and potential OoD effects. When such conditions in the target distribution cannot be anticipated, test-time adaptation strategies ensure consistency between the quantification, the data, and the model, enabling reliable data-driven MRS pipelines.
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All authors Julian P. Merkofer, Antonia Kaiser, Anouk Schrantee, Oliver J. Gurney-Champion, Ruud J. G. van Sloun
History and Philosophy of Physics
Einstein's 1935 Letters to Schrödinger and Popper and the Boundaries of the PBR $\psi$-Epistemic Framework
Galina Weinstein
Einstein's 1935 critique of
quantum mechanics is often associated with the Einstein-Podolsky-Rosen (EPR) argument, yet his private correspondence from that year reveals a more exact conceptual structure guiding his claim that the $\psi$-function is incomplete. This paper reconstructs Einstein's
reasoning in his letters to Schrödinger and Popper and examines how it engages, and fails to engage with contemporary $\psi$-ontic/$\psi$-epistemic distinctions. Recent scholarship, most notably by Ben-Menahem, has interpreted Einstein as an early representative of the modern $\psi$-epistemic tradition within the Harrigan-Spekkens ontological models framework and the Pusey-Barrett-Rudolph (PBR)
theorem. I argue, however, that this retrospective classification is undermined by Ben-Menahem's own distinction between realist and radical epistemic interpretations: Einstein's 1935 view lacks the structural assumptions - defined ontic state space, preparation distributions, and overlap structure - required for membership in the HS/PBR class of $\psi$-epistemic models. Any such identification, therefore, requires importing formal machinery foreign to Einstein's original argument.
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Plasma Physics
Experimental study of argon gas breakdown with symmetric and asymmetric electrode configurations
Hridya P and Mangilal Choudhary
Paschen law relates the breakdown voltage of a gas to the product of gas pressure and inter-electrode distance, predicting a characteristic minimum voltage at a specific pd value. In this study, the role of electrode configurations (symmetric and asymmetric) and inter-electrode spacing on the gas breakdown processes (or Paschen Law) is examined. Numerous sets of experiments are performed with both symmetric and asymmetric electrode configurations of different sizes to obtain Paschen curves at different inter-electrode distances. The experimentally obtained Paschen's curves for different electrode configurations are fitted using a proposed modified empirical relation for breakdown voltage, incorporating variable power-law dependencies and fitting parameters to better capture the observed deviations. Upon closer inspection, we observed that the breakdown voltage and the corresponding pd value are influenced by both electrode configurations and the inter-electrode discharge gap. The variation in breakdown voltage and pd minima for different electrode configurations is explained by analyzing the electric field distributions between the electrodes (cathode and anode) for an applied voltage.
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Computational Physics
Discontinuity-aware physics-informed neural network for phase-field method in three-phase flow with phase change
Guoqiang Lei, Zhihua Wang, Lijing Zhou et al. (+2 more)
Physics-informed neural networks (PINNs) have been applied to simulate multiphase flows, yet they are limited in modeling phase changes and sharp interfaces due to optimization conflicts in the strongly coupled Allen-Cahn, Cahn-Hilliard, and Navier-Stokes equations and the intrinsic smoothness bias of neural representations near discontinuities. To mitigate these limitations, this study presents a discontinuity-aware physics-informed neural network (DPINN) based on the phase-field method to resolve sharp interfaces and phase changes in multiphase flows. It incorporates a discontinuity-aware network architecture to mitigate spectral bias and automatically detect and model sharp interfacial dynamics, and a learnable local artificial viscosity term to stabilize the calculation near steep gradients. During optimization, adaptive time-marching and loss-balancing strategies are employed to reduce long-horizon errors and mitigate gradient conflicts, ensuring accurate capture of phase changes. Numerical experiments on two-phase reversed single-
vortex and bubble-rising problems demonstrate that DPINN accurately resolves sharp interfacial dynamics, while conventional PINNs fail to converge. The method is further extended and tested in a three-phase droplet-icing case, where the viscosity and density ratios between ice, water, and air exceed seven and three orders of magnitude, respectively. The predicted phase change dynamics and sharp pointy-tip formation show excellent agreement with the reference, highlighting the robustness of the proposed approach.
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All authors Guoqiang Lei, Zhihua Wang, Lijing Zhou, D. Exposito, Xuerui Mao
Computational Physics · Cross-listed from math.NA
A Path-Conservative Method for a Weakly Compressible Two-Phase Model with Surface Tension
Ashley Melvin and J. C. Mandal
When extended to two-phase flows, weakly compressible models lead to a non-conservative system, which precludes its treatment using standard finite volume techniques. In this paper, a novel HLLC-type path-conservative scheme is formulated for the weakly compressible two-phase model. Furthermore, capillary effects are included in the proposed path-conservative scheme, eliminating the need to discretize surface tension terms separately. The first-order path-conservative formulation is combined with a local solution reconstruction technique to obtain high-order spatial accuracy. The solver is tested on several
benchmark two-phase flow problems to demonstrate its efficacy.
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Physics and Society
From First Principles to Multi-scale Decomposition: Mutual Information as a Segregation Index
Rohit Sahasrabuddhe and Renaud Lambiotte
Segregation operates across demographic and spatial scales and requires careful and principled measurement. In this work, we derive mutual information from well-known general approaches to constructing segregation indices and interpret the mathematical properties that are unique to it. The properties relate mainly to its behaviour when demographic identities and spatial units are coarse-grained. Mutual information decomposes into the contributions of regions and supergroups, which reveals insights that would be inaccessible otherwise. We demonstrate this with an exploratory case study of residential ethnic segregation in England and Wales. Overall, we help researchers identify when mutual information is the only suitable segregation index and provide open source software to support multi-scale analysis.
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Physics and Society
The impact of anticonformity on the diffusion of innovation – insights from the q-voter model
Angelika Abramiuk-Szurlej
Anticonformity, behaving in deliberate opposition to the group of influence, has long been recognized as a distinct social response, differing both from conformity and from independence. While often treated as a source of noise or contrarianism, anticonformity can play a constructive role in social dynamics by counterbalancing majority pressure and influencing collective outcomes. Recently, it was shown in laboratory experiments that evaluation may induce strategic anticonformity when rewards are anticipated. Moreover, using
agent-based modeling, it has been demonstrated that anticonformity can depolarize highly polarized social groups and prevent social hysteresis. These findings encouraged us to extend the q-voter model with asymmetric independence, an
agent-based model of the
diffusion of innovation, by introducing anticonformity, so that
agents can act independently, follow the group, or oppose it. Using a mean-field approximation (MFA), we investigate how these behavioral tendencies influence the
diffusion of innovation. Our results show that anticonformists can accelerate early adoption and enable successful
diffusion even in cases where
diffusion would otherwise fail. The model exhibits two stable adoption levels separated by an unstable branch, giving rise to hysteresis and a critical mass effect. We also demonstrate that increasing independence lowers the threshold of anticonformity needed for widespread adoption. These results highlight how anticonformist behavior can facilitate innovation
diffusion, with practical implications for decision-makers and policy design.
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Atmospheric and Oceanic Physics
High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid
Even Marius Nordhagen, Håvard Homleid Haugen, Magnus Sikora Ingstad et al. (+9 more)
We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length. The model uses a global stretched grid, dedicating 2.5 km resolution to our Nordic region of interest and 31 km resolution elsewhere, with 6-hour temporal resolution. Unique ensemble members are generated by a
stochastic model architecture, and we train it using a loss function based on the Continuous Ranked Probability Score (CRPS) evaluated in grid-point and spectral space. The spectral loss component is shown to be necessary to create fields that are spatially coherent, which is not the case when training with mean-squared error loss, nor CRPS in grid-point space only. We evaluate the forecasts against observations from surface weather stations and compare them to high-resolution operational numerical weather prediction forecasts from the MetCoOp Ensemble Prediction System (MEPS). The model shows lower CRPS than MEPS for 2 m temperature and mean sea-level pressure, with average improvements of 13% and 10%, respectively, while differences for wind speed and precipitation are smaller. For Storm Dave, the model captures the location and structure of strong-wind systems, but underestimates the peak winds.
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All authors Even Marius Nordhagen, Håvard Homleid Haugen, Magnus Sikora Ingstad, Aram Farhad Shafiq Salihi, Thomas Nils Nipen, Ivar Ambjørn Seierstad, Inger-Lise Frogner, Mariana Clare, Simon Lang, Matthew Chantry, Peter Dueben, Jørn Kristiansen
Optics · Cross-listed from quant-ph
Tunable dual-band atomic mirror based on subwavelength atomic arrays under electromagnetically induced transparency
Shiwen Sun, Yi-Xin Wang, Xiao Liu et al. (+1 more)
Subwavelength atomic arrays offer a powerful platform for engineering cooperative light-matter interactions and enabling
quantum metasurfaces. We demonstrate that a two-dimensional array of three-level atoms operating under electromagnetically induced transparency can function as a tunable dual-band atomic mirror, where two independently controllable reflection bands emerge from the collective optical responses mediated by dipole-dipole interactions. These resonances yield dual reflection bands with asymmetric linewidths, whose spectral positions and bandwidths can be tuned through the control-field parameters, dipole orientation, incident geometry, and
lattice constant. We further identify the conditions under which additional diffraction orders emerge, which delineate the operational and tunable range of the atomic mirror via its collective-mode structure. This scheme provides a fully tunable dual-band atomic mirror operating across broad frequency and angular ranges, offering a practical and experimentally accessible pathway toward reconfigurable photonic elements in atomic-array platforms at low energy levels.
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All authors Shiwen Sun, Yi-Xin Wang, Xiao Liu, Yan Zhang
Optics
DIC Displacement Measurement Method Based on Improved White Shark Optimizer
Jun Li and Zongyu Lei
The traditional integer-pixel displacement search
algorithm of digital image correlation method has low computational efficiency and has been gradually eliminated, and some intelligent optimization
algorithms have their own strengths and weaknesses. The white shark optimizer has excellent global search capabilities. However, its calculation is cumbersome, programming complex and inefficient. In order to improve the computational efficiency of the white shark optimizer, it is improved by using the Tent map, introducing the dynamic nonlinear time factor, setting the automatic termination condition and adding the three-step search method. The improved white shark optimizer is applied to the integer-pixel displacement search. In order to improve the accuracy and efficiency of sub-pixel displacement calculation, an improved surface fitting method is proposed by combining bicubic interpolation, improved white shark optimizer and surface fitting method. Through grayscale interpolation, the distance between the fitting points is reduced, and the accuracy of search of the surface fitting method is further improved. The performance of the improved white shark optimizer and the improved surface fitting method is tested by simulated speckle pattern. Test results revealed that the computational efficiency of the improved white shark optimizer is comparable to that of the particle swarm optimization, and the search success rate is as high as 100%. The calculation accuracy of the improved surface fitting method is comparable to that of the Newton-Raphson
algorithm, but the computational efficiency is much higher than that of the Newton-Raphson
algorithm. Finally, the tensile experiment of low-carbon steel is used to verify the feasibility of the improved surface fitting method in actual measurement.
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Chemical Physics
Toward Unified Interphase Engineering: The Solid-Electrolyte Interphase in Batteries and Supercapacitors
Mehedi Hasan, Ishtiaq Murshed, Khayrul Islam et al. (+1 more)
The development of next-generation electrochemical energy storage requires devices that combine the high energy density of batteries with the power capability and long cycle life of supercapacitors. However, the interfacial phenomena governing performance in these systems remain poorly unified. The solid-electrolyte interphase (SEI), a nanoscale film formed by electrolyte decomposition, is well studied in batteries but its counterpart in supercapacitors has received limited systematic investigation despite growing experimental evidence. This review argues that SEI formation is a universal electrochemical process that occurs whenever electrode potentials drive electron transfer into electrolyte orbitals beyond their stability limits, independent of whether charge storage is Faradaic or non-Faradaic. Differences between battery SEIs and supercapacitor interphases arise mainly from operating conditions, not fundamental chemistry. Engineered interphases created through electrolyte additives, protective coatings, or surface functionalization suppress leakage currents, improve capacitance retention, and enable stable high-voltage operation. By identifying shared mechanisms and establishing transferable design rules, this unified framework provides a foundation for predictive interphase engineering that supports long-lived, high-performance energy-storage technologies.
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All authors Mehedi Hasan, Ishtiaq Murshed, Khayrul Islam, A.K.M. Masud
Computational Physics · Cross-listed from cond-mat.mes-hall
Interaction-Driven Chern Insulator at Zero Electric Field in ABCB-Stacked Tetralayer Graphene
Yulu Ren, Yang Shen, Chengyang Xu et al. (+2 more)
ABCB-stacked tetralayer
graphene, with intrinsic spontaneous polarization, offers a unique platform to explore electron correlation effects, whose interplay with
spin-orbit coupling may engender topological phases. Here, employing a $\mathbf{k}\cdot\mathbf{p}$ model with self-consistent Hartree-Fock calculations, we investigate its electronic ground states. Remarkably, we find that the intrinsic polarization, in conjunction with strong interactions ($U=8 \text{ eV}$) and SOC, is sufficient to drive a $C=3$
quantum anomalous
Hall state, obviating the need for an external electric field typical in ABCA stacks. Conversely, at moderate interactions ($U=6 \text{ eV}$), a minimal electric field is necessary. Furthermore, calculations predict other correlation-driven metallic phases such as quarter- and three-quarter-filled states. These results establish that the synergy of intrinsic polarization, correlations, and SOC governs the rich topological phenomena, suggesting ABCB-stacked
graphene as a highly tunable platform for exploring emergent topological phenomena.
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All authors Yulu Ren, Yang Shen, Chengyang Xu, Wanfei Shan, Weidong Luo
Atmospheric and Oceanic Physics
Technical Report: Towards Unified Diffusion Models for Multi-Model Climate Emulation at Scale
Francesco Immorlano, Elijah Tavares, Felix Draxler et al. (+3 more)
Large ensembles of
climate projections are essential for characterizing uncertainty in future
climate and extreme weather events, yet computational constraints of numerical
climate models limit ensemble sizes to a small number of realizations per model. We present a unified conditional
diffusion model that dramatically reduces this computational barrier by learning shared distributional patterns across multiple Coupled Model Intercomparison Project phase 6 models and emission scenarios. Rather than training separate emulators for each model-scenario combination, our approach captures the common statistical structures underlying nine CMIP6 models, generating daily temperature maps with a global coverage for historical and future periods. This unified framework enables: (i) efficient probabilistic sampling for comprehensive uncertainty quantification across models and scenarios; (ii) rapid generation of large ensembles that would be computationally intractable with traditional
climate models; (iii) variance-reduced treatment effect analysis via fixed-seed generation that disentangles forced
climate responses from internal variability. Evaluations on held-out models demonstrate reliable generalization to unseen future climates, enabling rapid exploration of different emission pathways.
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All authors Francesco Immorlano, Elijah Tavares, Felix Draxler, Padhraic Smyth, Pierre Gentine, Stephan Mandt
Computational Physics
MDcraft – a modern molecular dynamics simulation package with machine learning potentials support
I. S. Galtsov, R. V. Muratov, G. V. Vyskvarko et al. (+3 more)
Molecular dynamics is widely used to study various phenomena, such as
diffusion, shock wave propagation, and
plasma dynamics. A wide range of software packages supports the expanding scope of molecular dynamics applications. However, the quality of simulations depends on force field approximations, ranging from simple models to direct
quantum solutions. Recently, machine learning approaches for constructing accurate interatomic potentials have received significant
attention. In MDcraft, we integrate these advances into a scalable, physically accurate framework. MDcraft is a comprehensive, modern molecular dynamics platform. It offers a high-level Python API with a user-friendly, script-based interface. The core simulation
algorithms are implemented in C++ to ensure robustness and computational efficiency. MDcraft is built for high-performance computing on modern clusters and supports dynamic domain decomposition and load balancing via the Message Passing Interface (MPI) for scalable parallelization. Additionally, MDcraft leverages multithreading within nodes through standard C++ parallelism, enabling efficient use of heterogeneous architectures. We demonstrate the code's capabilities through several examples, including the shock response in aluminum, the shock Hugoniot in argon, and the cold curve of copper.
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All authors I. S. Galtsov, R. V. Muratov, G. V. Vyskvarko, S. A. Murzov, S. A. Dyachkov, P. R. Levashov