Instrumentation and Detectors · Cross-listed from quant-ph
FAlCon: A unified framework for algorithmic control of quantum dot devices
Tyler J. Kovach, Daniel Schug, Zach D. Merino et al. (+3 more)
As
spin-based
quantum systems scale, their setup and control complexity increase sharply. In
semiconductor quantum dot (QD) experiments, device-to-device variability, heterogeneous control-electronics stacks, and differing operational modalities make it difficult to reuse characterization, calibration, and control logic across laboratories. We present FAlCon, an open-source software ecosystem for portable, automated characterization and tuning measurement workflows. FAlCon provides (i) a lightweight domain-specific
language for expressing state-based tuning logic in a hardware-agnostic form; (ii) specialized transmittable libraries of physics-informed QD data structures (“tuning vernacula”); and (iii) extensible libraries of shared measurement protocols enabling an interoperable lab-agnostic measurement stack. By separating
algorithm intent from instrument realization, while preserving traceability and supporting typed scripting, FAlCon enables researchers and engineers to exchange, adapt, and deploy characterization and autotuning routines across heterogeneous QD setups. The framework supports all users, ranging from end users running prebuilt
algorithms with custom initial conditions to developers extending instrumentation support and contributing new tuning strategies. Although the present release targets QD experiments, other
qubit modalities and scientific experiments could reuse FAlCon's modular abstractions by providing new tuning data types and instrument control templates.
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All authors Tyler J. Kovach, Daniel Schug, Zach D. Merino, Mark Friesen, Mark A. Eriksson, Justyna P. Zwolak
Physics Education
Students' reasoning in choosing measurement instruments in an introductory physics laboratory course
Micol Alemani, Karel Kok and Eva Philippaki
The aim of this study is to investigate the decisions and
reasoning of undergraduate students when choosing simple measurement instruments in an introductory physics laboratory course. For this study, we have developed a questionnaire and implemented it in a pre-/post-test manner to analyze the influence of lab instruction on both students' decisions and
reasoning. To characterize students' justifications, we have inductively developed a coding manual that captures the nuances of students'
reasoning when choosing an instrument. It shows that students consider different aspects for their decisions, such as data quality, practical and personal considerations. We have also found that laboratory instruction influenced both students' decisions and justifications, leading to a stronger emphasis on data quality. In fact, after instruction, the majority of students choose the instrument with lower uncertainty and base their justifications mainly on the aim of reducing uncertainties, avoiding systematic effects or mistakes in the instrument reading, and less often than before instruction on personal experience and intuition. These findings suggest that dedicating specific laboratory instruction sessions on measurements and data quality, and having students choose between different instrumentation and provide a justification for their decision, can positively impact students' habits in the laboratory and encourage them to base their choices on evidence rather than intuition.
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Optics
Lasing from a Quantum-Dot-Like Buried Heterostructure in an InP Nanobeam Cavity
Valdemar Bille-Lauridsen, Rasmus Jarbøl, Meng Xiong et al. (+4 more)
We report lasing from a lithographically defined buried
heterostructure with an estimated lateral footprint of (107 nm)^2, embedded in an InP photonic-
crystal nanobeam cavity. This represents the smallest laterally confined buried
heterostructure gain region from which lasing has been observed. Despite etching of the active region during cavity definition and the associated risk of surface-related nonradiative recombination, optically pumped devices exhibit a clear lasing threshold and a narrow linewidth. By systematically varying the BH size, we investigate how the lasing threshold depends on the active volume under optical pumping. The estimated intrinsic threshold under ideal carrier injection is 57 nW, comparable to values reported for single
quantum-dot nanolasers, highlighting the potential of
quantum-dot-scale buried
heterostructures as deterministic, scalable gain media for nanophotonic
lasers.
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All authors Valdemar Bille-Lauridsen, Rasmus Jarbøl, Meng Xiong, Aurimas Sakanas, Elizaveta Semenova, Kresten Yvind, Jesper Mørk
Instrumentation and Detectors
Advances in the Fabrication of On-chip Superconducting Integral Field Units for CMB and Line-Intensity Astronomy
L. G. G. Olde Scholtenhuis, D. Perez Capelo, K. Karatsu et al. (+7 more)
Studying the polarization and spectral distortion of the
Cosmic Microwave Background (CMB) in tandem with intensity fluctuations of the
Cosmic Infrared Background (CIB) allows us to verify our assumptions on
cosmic inflation and investigate the dynamics and
evolution of
galaxy clusters in the last 10 billion years. Because of its broadband emission and being an all-sky extended source, observing the entire CMB in detail is a very time-consuming and expensive exercise. Fortunately, in the last few years, the on-chip superconducting spectrometer technology has moved out of the lab and into the
telescope. With its compact size and background-limited sensitivity, this family of instruments is particularly well-suited for fast and large area observations in a relatively unexplored range of the electromagnetic spectrum. However, recent examples of this technology do not yet reach the requirements needed for large spectroscopic and polarimetric surveys of the CMB. We formulate several of these requirements and introduce novel on-chip components and fabrication techniques. We introduce a cross-over to enable distinguishing signal polarization, minimize signal loss by locally optimized lithography of a coplanar-waveguide (CPW), lower the spectral resolution of microstrip filters by deposition of a dielectric layer, and increase the yield of the spectrometer array by removing individual line shorts. These together have culminated in the successful fabrication of a fourteen-spaxel IFU.
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All authors L. G. G. Olde Scholtenhuis, D. Perez Capelo, K. Karatsu, D. J. Thoen, A. J. van der Linden, S. O. Dabironezare, L. H. Marting, J. J. A. Baselmans, S. Vollebregt, A. Endo
Plasma Physics
Accelerating the Particle-In-Cell code ECsim with OpenACC
Elisabetta Boella, Nitin Shukla, Filippo Spiga et al. (+3 more)
The Particle-In-
Cell (PIC) method is a computational technique widely used in
plasma physics to model
plasmas at the kinetic level. In this work, we present our effort to prepare the semi-implicit energy-conserving PIC code ECsim for exascale architectures. To achieve this, we adopted a pragma-based acceleration strategy using OpenACC, which enables high performance while requiring minimal code restructuring. On the pre-exascale Leonardo system, the accelerated code achieves a $5 \times$ speedup and a $3 \times$ reduction in energy consumption compared to the CPU reference code. Performance comparisons across multiple NVIDIA GPU generations show substantial benefits from the GH200 unified
memory architecture. Finally, strong and weak scaling tests on Leonardo demonstrate efficiency of $70 \%$ and $78 \%$ up to 64 and 1024 GPUs, respectively.
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All authors Elisabetta Boella, Nitin Shukla, Filippo Spiga, Mozhgan Kabiri Chimeh, Matt Bettencourt, Maria Elena Innocenti
Plasma Physics
Gridless Quasistatic Model for Efficient Simulation of Plasma-based Accelerators
Ángel Ferran Pousa, Wilbert M. den Hertog, Severin Diederichs et al. (+4 more)
The accurate modeling of
plasma-based accelerators relies on costly numerical simulations due to the complexity of
laser-
plasma and beam-
plasma interactions. Several strategies can highly reduce the computational cost compared to 3D first-principles particle-in-
cell simulations, such as exploiting the near axial
symmetry and quasistatic nature of
plasma wakefields in many practical cases. Here, we propose a quasistatic
algorithm that enables the modeling of axially symmetric
plasma wakes without the need of a numerical grid. The gridless approach allows extremely fine features to be resolved without a dramatic increase in computational cost. This is critical, e.g., for the design of future
plasma-based
colliders with nanometer emittance beams. The proposed model has been implemented in the Wake-T code, where it is coupled to a
laser envelope solver and a particle beam pusher to enable the efficient simulation of
laser- and beam-driven
plasma accelerators.
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All authors Ángel Ferran Pousa, Wilbert M. den Hertog, Severin Diederichs, Al berto Martinez de la Ossa, Jorge L. Ordóñez Carrasco, Alexander Sinn, Maxence Thévenet
Optics
Efficient methods for wave propagation in electron microscopy
Zdeněk Nekula, Jakub Bělín and Andrea Konečná
Accurate wave-optical simulation in electron microscopy is severely constrained by the extreme sampling requirements imposed by short wavelengths and relatively large convergence angles. Conventional implementations of the angular spectrum method (ASM) rapidly become computationally intractable, often exceeding realistic
memory and time limits. We present two numerical approaches – the scaling angular spectrum method (SASM) and the no-
lensing angular spectrum method (NLASM) – that systematically reduce the sampling requirements while retaining the essential physics of wave propagation. SASM replaces the original optical system with a scaled equivalent in which lens-induced beam convergence or divergence is reduced, lowering
memory usage and computational cost by approximately the square of the scaling factor. NLASM suppresses
lensing effects altogether, enabling highly efficient propagation away from focal planes. Benchmarking against the Bluestein (chirp-z) transform reveals that the three methods are complementary and together enable wave-optical simulations of complex electron-optical systems previously considered infeasible. These results establish practical pathways toward routine wave-based modeling in electron microscope design.
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Fluid Dynamics
Fluid-Structure Interaction and Scaling Laws for Deterministic Encapsulation of Hyperelastic Cells in Microfluidic Droplets
Andi Liu and Guohui Hu
The precise encapsulation of deformable particles in multiphase flows involves complex transient Fluid-Structure Interactions (FSI) and topological interfacial changes. In the context of single-
cell analysis, a numerical framework that couples the Cahn-Hilliard phase-field model with the Arbitrary Lagrangian-Eulerian (ALE) method is employed to investigate the dynamics of deformable
cell encapsulation in flow-focusing microchannels. By resolving the coupling between the hyperelastic
cell, carrier fluid, and evolving interface, we propose a unified dimensionless scaling law to predict the operational spatial window for the deterministic encapsulation quantitatively. Furthermore, the physical presence of
cells modulates the droplet generation flow regime via a "
geometric blockage effect", shifting the transition boundary from the squeezing to the dripping regime toward lower flow-rate ratios. The droplet generation period demonstrates a non-monotonic dependence on the
cell blockage ratio $\Gamma$, which induces a competitive mechanism between shear enhancement and hydraulic resistance penalty, and consequently leads to an optimal hydrodynamic balance at $\Gamma \approx 0.32$. Finally, we find that while droplet periodicity is robust to variations in
cell stiffness, the transient stress field within the
cell is highly sensitive, particularly during the capillary pinch-off singularity. This work clarifies the fundamental interaction between hyperelastic
cells and multiphase flows, and provides a quantitative framework for optimizing damage-free
cell encapsulation systems.
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Applied Physics
Nanostructuring SiC by sequential plasma oxidation and reactive ion etching
Joonas Isometsä, Auguste Bieleviciute, Xiaolong Liu et al. (+2 more)
Silicon carbide (SiC) is a highly promising material for the rapidly growing UV detection industry due to its visible-blindness, low dark current, and exceptional thermal and chemical stability. Despite these advantages, the performance of state-of-the-art SiC UV detectors remains limited due to high reflectance losses, even with the use of anti-reflection coatings. Here, we develop a reactive ion etching process for nanostructuring SiC to eliminate the reflectance losses. The process is based on consecutive oxidation and etching cycles. Consequently, a reflectance below 0.5% is achieved from deep UV (200 nm) to close to the SiC cut-off (~360 nm). The nanostructures are effective even at large incident angles as the reflectance remains practically unchanged up to 60 degrees. Furthermore, it is confirmed that the process consumes only ~1 um of SiC and is compatible with Al2O3 masking, thereby facilitating straightforward integration into device fabrication. The developed cyclical etching process could also prove useful for SiC etching in general.
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All authors Joonas Isometsä, Auguste Bieleviciute, Xiaolong Liu, Ville Vähänissi, Hele Savin
Chemical Physics
Multi-GPU MBE(3)-OSV-MP2 for Performant Large-Scale ab initio Calculations
Qiujiang Liang and Jun Yang
The computational acceleration of orbital-invariant local correlation methods on graphics processing units (GPUs) has remained largely unexplored due to substantial algorithmic complexities. The runtime efficiency of GPU-implemented local correlation theories can be significantly constrained by the parallelizable degree of the orbital localization procedure, the iterative solution of the local wave function, and the adaptation of CUDA kernels to inherently local or sparse operations. Using the second-order Møller-Plesset perturbation (MP2) theory, we present a multi-GPU implementation for large-scale third-order many-body expansion orbital-specific virtual MP2 (MBE(3)-OSV-MP2) energy calculations. Accordingly, our
algorithms and implementation address the GPU parallelization ability for peak utilization and parallelism of local MP2 computation in several aspects, including Jacobi-Pipek-Mezey localization, randomized OSV generation, direct MP2 integral regeneration, as well as CUDA kernel adaptation to local operations. The GPU-based MBE(3)-OSV-MP2 energy computation achieves $O(N^{1.9})$ scaling and 84% parallel efficiency up to 24 GPUs distributed on multiple nodes. The present implementation delivers 40-fold wall-time speedup of the canonical RI-MP2 and 10-fold speedup of the CPU-based MBE(3)-OSV-MP2 for (H$_2$O)$_{128}$/cc-pVDZ and (H$_2$O)$_{190}$/cc-pVDZ, respectively. A large scale computation of 784-atom insulin peptide yields the full MBE(3)-OSV-MP2 energies in 24 minutes with cc-pVDZ (7571 basis functions) and 6.4 hours with cc-pVTZ (17448 basis functions) on 8 NVIDIA A800 GPUs. Our work opens up new possibilities for performing fast GPU-based local correlation calculations on real-life macromolecules.
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Applied Physics
Applicability of Radiowave Anechoic Chambers for Acoustic Free-Field Measurements on the Example of the Chamber at ITMO University
Farid Bikmukhametov, Ksenia Razrezova, Roman Smolnitsky et al. (+3 more)
Acoustic anechoic chambers allow free-field measurements required for the verification of effects under investigation and for the characterization of developing devices. However, the construction of an anechoic chamber is a labor-intensive process that requires a lot of resources, which is why these facilities are rather rare. An analogous statement can be made about radiowave chambers. At the same time, it is known that materials for radiowave absorption might absorb acoustic waves as well, and it can be expected that some chambers can be utilized for both electromagnetic and acoustic free-field measurements. This work examines the feasibility of the radiowave anechoic chamber of ITMO University (Saint-Petersburg, Russia) for acoustic measurements. The acoustic properties of the chamber coatings are estimated via measurements and numerical calculations. Characterization of sound pressure level, background noise level, and reverberation time is performed in accordance with the ISO 3745:2012 standard. The key conclusion is that the chamber can be considered as an acoustic anechoic chamber, but only for specific frequency ranges and distances from the source, which depend on the measurement directions.
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All authors Farid Bikmukhametov, Ksenia Razrezova, Roman Smolnitsky, Yuri Shchelokov, Nikolay Kanev, Mariia Krasikova
Optics
Scattering Symmetries in Diffraction Gratings
Karim Achouri
Metasurfaces enable powerful control of electromagnetic waves using subwavelength planar structures, but their deeply subwavelength periodicity typically suppresses propagating diffraction orders, which limits the number of available scattering channels. Diffraction gratings and metagratings overcome this limitation by supporting multiple propagating diffraction orders, thus providing additional degrees of freedom for controlling wave propagation. However, when several diffraction channels are present, it becomes nontrivial to predict how spatial
symmetries combined with reciprocity affect the overall scattering response. For this purpose, we develop a formalism to determine the scattering
symmetries of diffraction gratings supporting multiple diffraction orders. The approach is based on constructing a global scattering
matrix that connects all incident and scattered diffraction channels and on introducing
matrix representations of spatial
symmetry operations acting on the field amplitudes. From these representations, we derive an invariance condition that directly constrains the sub-scattering
matrices associated with each pair of diffraction orders. This provides a rigorous approach for computing the grating scattering coefficients imposed by
symmetry and reciprocity. We illustrate the application of this approach via several examples and show how metagratings may be used to achieve, for instance, angle-asymmetric transmission and extrinsic
chiral effects.
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Fluid Dynamics
UrbanFlow-3K: A Dataset of 3,000 Lattice-Boltzmann Simulations of Random Building Layouts
Hojin Lee, Andreas Lintermann, Sangseung Lee et al. (+1 more)
The analysis of flow around buildings has gained significant research interest across various domains, including pedestrian safety, pollutant dispersion, natural ventilation, and building energy efficiency. While these domains frequently include high-resolution computational fluid dynamics (CFD) data, predicting urban flow fields with machine learning (ML) models has emerged as a promising approach to overcome the prohibitive costs of CFD simulations. However, the availability of open-source
datasets for training such ML models remains scarce. In particular, publicly available two-dimensional
datasets of urban flow fields are nearly non-existent, despite their potential value for early development and debugging stages of data-driven models, before scaling to computationally expensive three-dimensional
datasets. To bridge this gap, this study presents a comprehensive
dataset consisting of 3,000 two-dimensional urban flow simulations conducted using a
lattice-Boltzmann method across three distinct Reynolds numbers. The
dataset contains the time-averaged velocity fields. A key feature of this
dataset is its high
geometric diversity: each layout incorporates between three and six buildings with randomized sizes, positions, and rotation angles ranging from 0{\deg} to 90{\deg}. This extensive variability enables the
dataset to capture several critical flow characteristics, including wake formation, flow acceleration, shielding effects, and recirculation zones, across a wide range of orchestrated urban canopies. The large sample size and consistent simulation setup make the
dataset particularly suitable for developing and benchmarking ML architectures. In addition, the
dataset can support transfer-learning strategies in which models trained on large two-dimensional
datasets are adapted to smaller and more computationally expensive three-dimensional
datasets.
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All authors Hojin Lee, Andreas Lintermann, Sangseung Lee, Mario Rüttgers
Computational Physics · Cross-listed from cs.CV
An Approximate Graph Elicits Detonation Lattice
Vansh Sharma and Venkat Raman
This study presents a novel
algorithm based on graph theory for the precise
segmentation and measurement of detonation
cells from 3D pressure traces, termed detonation
lattices, addressing the limitations of manual and primitive 2D edge detection methods prevalent in the field. Using a
segmentation model, the proposed training-free
algorithm is designed to accurately extract cellular patterns, a longstanding challenge in detonations research. First, the efficacy of
segmentation phase on two synthetic
datasets is evaluated with an error of 2%. Next, 3D simulation data is used to establish performance of the graph-based workflow. The results of statistics and joint probability densities show oblong
cells aligned with the wave propagation axis with 17% deviation, whereas larger dispersion in volume reflects cubic amplification of linear variability. Although the framework is robust, it remains challenging to reliably segment and quantify highly complex cellular patterns. However, the graph-based formulation generalizes across diverse cellular geometries, positioning it as a practical tool for detonation analysis and a strong foundation for future extensions in triple-point collision studies.
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Biological Physics · Cross-listed from cond-mat.soft
Time reversal breaking of colloidal particles in cells
Gabriel Knotz, Till M. Muenker, Timo Betz et al. (+1 more)
We investigate signatures of broken time reversal
symmetry in
stochastic trajectory data, employing the previously introduced three point correlation called mean back relaxation. We specifically investigate data from a simple driven model, as well as from colloidal particles within living or passivated biological
cells. Both in the model as well as in
cell data, MBR detects broken time reversal
symmetry, and furthermore, allows to determine relevant time and length scales of activity. For the
cells, we show, by applying various drugs, that it is predominantly the presence of microtubules which is needed for a time reversal
symmetry breaking. We employ a bound for
entropy production, finding that it is in striking relation to previously determined active energies that quantify violation of the fluctuation dissipation
theorem.
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All authors Gabriel Knotz, Till M. Muenker, Timo Betz, Matthias Krüger
Plasma Physics
Empirical impact of near-separatrix plasma and neutral transport on the pedestal in the transition between EDA and ELMy H-modes on Alcator C-Mod
M.A. Miller, J.W. Hughes, S. Saarelma et al. (+16 more)
The transition between the ELMy H-mode and the EDA H-mode is studied on Alcator C-Mod using an experimental database and predictive pedestal models. High-resolution Thomson scattering measurements are used to compare the pedestal density, $n_{e}^\mathrm{ped}$, and the separatrix density, $n_{e}^\mathrm{sep}$ with main chamber neutral measurements. $n_{e}^\mathrm{ped}$ is sensitive to neutral sources only in the ELMy H-mode regime and not in the EDA H-mode regime. Density fluctuation spectra reveal that quasi-coherent structures become stronger at higher densities and more coherent in the EDA relative to the inter-ELM phases of ELMy H-modes, before weakening again at the highest values of $n_{e}^\mathrm{ped}$. The Saarelma-Connor pedestal density prediction model is validated for ELMy H-modes up to $n_{e}^\mathrm{ped} = 2.0 \times 10^{20}$ m$^{-3}$. An additional transport channel driven by resistive ballooning modes (RBM), $D_\mathrm{RBM}$, scaling directly with $\alpha_{t}$ and inversely with $k_\mathrm{RBM}^{2}\hat{q}_\mathrm{cyl}$ is shown to improve the prediction for EDA H-modes, finding good model agreement up to $n_{e}^\mathrm{ped} = 3.0 \times 10^{20}$ m$^{-3}$. EPED scans in $n_{e}^\mathrm{ped}$ are then performed at three values of $n_{e}^\mathrm{sep}/n_{e}^\mathrm{ped}$. Increasing this ratio moves the peeling-ballooning branch transition to lower $n_{e}^\mathrm{ped}$, increasing $p^\mathrm{ped}$ in the peeling branch and decreasing it in the ballooning branch. Agreement is found for large ELM H-modes. SPARC pedestal density predictions for an ELMy and an EDA/QCE-like H-mode are performed and found consistent with assumptions used in previous EPED modeling. Inclusion of $D_\mathrm{RBM}$ significantly weakens the density gradient near the separatrix, lowering $n_{e}^\mathrm{ped}$ by approximately 20%.
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All authors M.A. Miller, J.W. Hughes, S. Saarelma, T. Eich, J. Dunsmore, J. Han, P. Manz, J.W. Connor, G.R. Tynan, A.E. Hubbard, A. Ho, T. Body, D. Silvagni, O. Grover, S. Mordijck, E.M. Edlund, B. LaBombard, M. Wigram, A. Cavallaro
Biological Physics · Cross-listed from cond-mat.stat-mech
Fractal and Spectral Dimensions as Determinants of Thermal Ablation Outcomes in Cancer Tissues
Mario Olmo-Fajardo, Alexander López, Malte Henkel et al. (+1 more)
Clinical thermal ablation outcomes display significant variability that classical bio-heat models cannot fully explain. One reason may lie in the fractal architecture of biological tissues, which has been identified as a robust biomarker directly correlated with cancer grades. This structural heterogeneity, together with
memory effects (e.g., thermotolerance), causes heat transfer in living tissues to differ from
Fourier diffusion, resulting in anomalous biological transport. In this work, we implemented a realistic fractal-fractional bio-heat model, with non-linear perfusion and PI-controlled power delivery, to quantify the role of tissue fractality in ablation outcomes. Our results reveal that the expansion of coagulation zones is jointly controlled by fractal geometry and its associated topological connectivity. These findings highlight spectral dimension as a key driver of clinical variability, successfully reproducing the reduced ablative efficacy in liver metastases compared to primary carcinomas, and provide evidence for topologically informed treatment strategies for the thermal ablation of malignant neoplasms.
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All authors Mario Olmo-Fajardo, Alexander López, Malte Henkel, Sébastien Fumeron
Chemical Physics
Disentangling Single- and Biexciton Dynamics with Photoelectron-Detected Two-Dimensional Electronic Spectroscopy
Luisa Brenneis, Matthias Hensen, Julian Lüttig et al. (+1 more)
Action-detected two-dimensional (2D) spectroscopy resolves the time-dependent nonlinear optical response of a
quantum system by recording incoherently detected observables such as fluorescence, photoelectrons, or photocurrents which reflect the system's excited-state population. Processes such as
exciton-
exciton annihilation alter this population and obscure, for instance, energy transfer processes. This limits the information available from action-detected 2D spectra compared to their coherently detected counterparts. Here we investigate time gating and kinetic-energy filtering in photoelectron-detected 2D spectroscopy to disentangle various processes. We implement a numerical simulation protocol that allows us to calculate photoelectron-detected 2D spectra for various systems, demonstrating that time gating can extract the same information as coherently detected 2D spectroscopy, even when annihilation is present. Furthermore, we can directly infer annihilation dynamics. Kinetic-energy filtering additionally enables the isolation of specific excited-state dynamics. Our simulations demonstrate that time gating and kinetic-energy filtering are promising extensions for photoelectron-detected 2D spectroscopy.
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All authors Luisa Brenneis, Matthias Hensen, Julian Lüttig, Tobias Brixner
Instrumentation and Detectors
A Novel Approach for Fault Detection and Failure Analysis of CMOS Copper Metal Stacks
Gregor Hieronymus Eberwein, Gianluca Aglieri Rinella, Daniela Bortoletto et al. (+16 more)
For the Inner Tracking System 3 (ITS3) upgrade, the ALICE experiment at CERN requires monolithic active pixel sensors of dimensions up to 97~mm$\,\times\,$266~mm, occupying a large fraction of a 300 mm wafer. To manufacture such a wafer-scale device, larger than the single design reticle size, stitching is employed. The MOnolithic Stitched Sensor (MOSS) is a prototype silicon pixel sensor of 14~mm$\,\times\,$259~mm size with the primary goal of understanding the stitching technique and yield. Given the large size, high yield is paramount for the ITS3 sensors, and an in-depth yield characterization was performed on these MOSS sensors. In a collaborative effort, the foundry adapted the metal stack to the requirements of the project, but recurrent fault signatures were discovered with various frequencies across all 20 wafers tested, and correlated through dedicated measurements and analyses. Following these findings, the foundry implemented a mitigation strategy to avoid the issue in the future. This article does not describe process details but concentrates on the measurements and analysis method.
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All authors Gregor Hieronymus Eberwein, Gianluca Aglieri Rinella, Daniela Bortoletto, Szymon Bugiel, Francesca Carnesecchi, Antonello Di Mauro, Pedro Vicente Leitao, Hartmut Hillemanns, Marc Alain Imhoff, Antoine Junique, Alex Kluge, Magnus Mager, Paolo Martinengo, Iaroslav Panasenko, Ivan Ravasenga, Felix Reidt, Valerio Sarritzu, Walter Snoeys, Miljenko Šuljić
Optics
Reconfigurable and Recyclable Low-Threshold Quasi-BIC Lasers via a Tunable polymer Coating
Xiaolin Wang, Jiayao Liu, Zimeng Zeng et al. (+5 more)
Reconfigurable and sustainable microcavity
lasers are highly desirable for next-generation integrated photonics. Here, we report a recyclable, low-threshold quasi-bound state in the continuum (q-BIC)
laser fabricated via low-cost, high-throughput interference lithography. By introducing a polyvinyl alcohol (PVA) coating on a dye-doped photonic
crystal, we suppress out-of-plane
symmetry breaking, which reinforces optical confinement and reduces the lasing threshold. The q-BIC modes are further tuned through tailoring the refractive-index of the PVA layer by using Kramers-Kronig relation via Rhodamine 6G doping, demonstrating a wavelength shift of 7.14 nm and a sensitivity of 215 nm RIU as a sensing prob. More importantly, lasing modes are reversibly tuning via precisely controlling the coating thickness. Exploiting the dissolving and re-coating process, the
laser is repeatedly reconfigured while maintaining performance. This work provides a sustainable and adaptive platform for sensing and reconfigurable photonic systems.
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All authors Xiaolin Wang, Jiayao Liu, Zimeng Zeng, Hongyu Yuan, Zhuoyang Li, Zelong He, Wenping Gong, Zhaona Wang
Chemical Physics · Cross-listed from quant-ph
Bridging Classical Sensitivity and Quantum Scrambling: A Tutorial on Out-of-Time-Ordered Correlators
Stephen Wiggins
In classical dynamical systems, chaotic behavior is often associated with exponential sensitivity to initial conditions together with global phase-space structure. Translating this
geometric concept to the strictly linear framework of
quantum mechanics presents a conceptual puzzle. The out-of-time-ordered correlator (OTOC) is often motivated as the
quantum analogue of the classical butterfly effect, but this slogan can hide important mathematical distinctions. This tutorial bridges the gap between applied mathematics and
quantum information by detailing the mathematical machinery of the OTOC. We explore how classical sensitivity translates to operator non-commutativity, why standard two-point correlation functions fail to cleanly detect this sensitivity, and how the delocalization of
quantum observables relates to classical notions of mixing. Crucially, we outline what the OTOC can and cannot diagnose, distinguishing between local instability and global chaos. Ultimately, we provide a precise and usable conceptual map, exploring how the Koopman-von Neumann formalism offers a framework to view classical and
quantum dynamics through a shared linear perspective.
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Optics
Regular and irregular revivals of quasi-periodic random waves
Yanlin Bai, Yangjian Cai, Chunhao Liang et al. (+1 more)
Paraxial wave packets with discrete spatial, temporal, or spatiotemporal spectra are known to undergo periodic axial revivals on propagation in either free space or linear transparent, weakly dispersive media. Such spectacular revivals, ubiquitously encountered in physics, from optics and acoustics to condensed matter physics, are distinguished by their strict periodicity. We show theoretically and verify experimentally that ensembles of quasi-periodic random wave packets exhibit a unique revival network composed of regular (periodic) and irregular (aperiodic) revivals. Moreover, individual realizations of a statistical ensemble self-reconstruct, in general, at different propagation distances than do ensemble averages. Our results shed new light on the fundamental physics of self-reconstruction of random wave packets with structured correlations.
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All authors Yanlin Bai, Yangjian Cai, Chunhao Liang, Sergey A. Ponomarenko
Computational Physics
Tuning Cu/Diamond Interfacial Thermal Conductance via Nitrogen-Termination Engineering
Guang Yang, Xinling Tang, Zhongkang Lin et al. (+4 more)
Cu-diamond composites are recognized as promising high-thermal-conductivity candidates for electronic cooling, offering tunable properties and competitive cost. However, their performance is significantly limited by the poor Cu/diamond interfacial thermal conductance (ITC). Here, we propose a nitrogen-termination strategy to tune the ITC of Cu/diamond interfaces and unravel atomistic mechanisms by which nitride interlayers tailor
phonon transport. Based on the MACE machine-learning interatomic potential (MLIP) framework, we fine-tune the pre-trained MACE-MPA-0 foundation model by incorporating customized C-N-Cu training
datasets. Through MLIP-driven
lattice dynamics simulations, we demonstrate that an atomically flat N-termination on diamond enhances the ITC by 21% compared to the bare Cu/diamond interface. Mode-resolved
phonon spectroscopy reveals that the LA
phonons with frequency above 4 THz and wavevectors near {\Gamma}-X and {\Gamma}-U directions are selectively modulated by N-termination engineering. Analyses of local vibrational states and interfacial bonding further indicate that the N-termination on diamond tunes the interfacial heat conduction via surficial mass modification and bonding regulation, as evidenced by variations in LDOS overlap and COHP spectra. These findings open venues for tuning heat transfer across Cu/diamond interfaces via non-metallic modification, which avoids the graphitization issues associated with metallic coatings, and provide novel guidelines for upgrading the
phonon-mediated heat transfer in Cu-diamond composites.
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All authors Guang Yang, Xinling Tang, Zhongkang Lin, Yulin Gu, Wei Hao, Yujie Du, Xiaoguang Wei
Atmospheric and Oceanic Physics
Resolving the Paradox of Changing El Niño-Monsoon Relation through Synchronization of Chaotic Oscillators
Devabrat Sharma, Sagar Sedani, Shruti Tandon et al. (+3 more)
For over a century, the relationship between Indian summer monsoon rainfall and El Nino-Southern Oscillation has been the foundation of 'long-range' prediction of Indian monsoon. This relation is estimated from correlations between Indian summer monsoon rainfall and a Pacific sea surface temperature-based index of El Nino-Southern Oscillation. However, a prominent multi-decadal variability in the correlation raises doubts on the realism of El Nino-Monsoon relation and stability of Indian monsoon predictability. Previous studies discussed that Pacific-based El Nino-Southern Oscillation indices do not represent El Nino's global influence completely, making their correlation with Indian monsoon unreliable. To address this limitation, a Global El Nino-Southern Oscillation framework based on the depth of the 20 degree Celsius isotherm is developed, integrating subsurface signal from all three tropical ocean basins and maximizing Indian monsoon teleconnections. Contrary to previous findings, the 20 degree Celsius isotherm-based Global El Nino-Southern Oscillation exhibits a strong and stable correlation (greater than 0.8) with Indian monsoon at an 18-month lead. Through a re-examination of the El Nino-Monsoon relationship with the superior Global El Nino-Southern Oscillation predictor, we show that the true relationship is independent of global warming and stationary in time. We discover that the stationarity in the El Nino-Monsoon relationship emerges as a natural consequence of chaotic synchronization between Indian summer monsoon rainfall and 20 degree Celsius isotherm at an 18-month lead. Such synchronization between chaotic
climate variables provides a new physical basis for
climate predictability beyond the conventional deterministic limit set by chaos. Our findings provide the foundation and renewed confidence in long range
climate prediction.
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All authors Devabrat Sharma, Sagar Sedani, Shruti Tandon, Gaurav Chopra, R. I. Sujith, B. N. Goswami
Physics and Society
Friendship paradox disappears under degree biased network sampling
Wojciech Roga
We show that in an undirected graph under degree biased sampling the expected degree of vertices is equal to the expected degree of their neighbors. In consequence, under the biased sampling the social network result known as the friendship paradox disappears. The identity is equivalent to the existence of a stationary state of a random walk on the graph or to the conservation of the total flow defined by the difference of the degrees of the vertices.
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Atmospheric and Oceanic Physics
CNN-based forecasting of early winter NAO using sea surface temperature
Elena Provenzano, Guillaume Gastineau, Carlos Mejia et al. (+2 more)
The North Atlantic Oscillation (NAO) is the dominant mode of atmospheric variability over the North Atlantic sector, influencing temperature and precipitation across Europe. While the NAO's impact on North Atlantic sea surface temperatures (SSTs) is well understood, the NAO can also be driven by SST anomalies. However, this NAO response to SST anomalies is believed to be weak and nonlinear. Former studies highlight that during early winter (November-December), El Nino Southern Oscillation (ENSO) events modulate the NAO, with El Nino (La Nina) events being linked to positive (negative) NAO phases, and an opposite effect observed in late winter (January-February). Indian Ocean SSTs and the North Atlantic Horseshoe SST anomaly have also been suggested as contributors to early winter NAO variability. However,
climate models often struggle to capture these SST-NAO teleconnections, particularly in early winter. To address this, a statistical framework based on convolutional neural networks (CNNs) is developed to predict the early winter NAO using observed SST fields one-, two-, and three-month before. A linear model serves as a
benchmark, and both models are trained on ERA5 reanalysis data from 1940 to 2023. A sensitivity analysis is used to interpret the CNN's decision-making process, revealing that it focuses on regions such as the tropical Pacific and North Atlantic, confirming results from previous works. The CNN outperforms the linear model, highlighting the value of capturing nonlinear SST-NAO relationships. Prediction skill appears to be linked to ENSO, with strong ENSO events associated with greater skill in
forecasting the NAO than neutral events. These findings underscore the potential of deep learning to build medium-range NAO prediction.
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All authors Elena Provenzano, Guillaume Gastineau, Carlos Mejia, Didier Swingedouw, Sylvie Thiria
Optics
Superballistic transport of thermal photons in confined many-body systems
Jian Dong, Junming Zhao, Philippe Ben-Abdallah et al. (+1 more)
Ballistic transport, realized when the system size is smaller than the mean free path of energy carriers, is traditionally regarded as the ultimate limit for energy transfer. Here, we predict a superballistic radiative heat transport regime that surpasses this limit in dilute chains of plasmonic nanoparticles confined within cavities. This anomalous regime exhibits superlinear scaling of the effective thermal conductivity (k ~L^1.5) and originates from the amplification of long-range interactions mediated by cavity-guided modes. Our results establish a framework for ultrafast photonic heat transport and open pathways for thermal management, information processing and energy transfer in
quantum and nanoscale systems.
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All authors Jian Dong, Junming Zhao, Philippe Ben-Abdallah, Linhua Liu
Computational Physics · Cross-listed from cond-mat.soft
Towards the Multiscale Design of Pressure Sensitive Adhesives
Nicolas Moreno, Elnaz Zohravi, Shaghayegh Hamzehlou et al. (+6 more)
Pressure-sensitive adhesives (PSAs) are soft polymeric materials that exhibit complex rheological and mechanical behavior gov- erned by the interplay between
polymer architecture, crosslink density, and
entanglement constraints. Predicting their rheological properties from underlying microstructure remains a central challenge in adhesive design. In this work, we adopt a multiscale com- putational framework based on the Lagrangian Heterogeneous Multiscale Method (LHMM), coupling a macroscopic continuum description with a mesoscale
polymer network model featuring breakable bonds embedded in a viscous medium. The approach enables consistent information transfer across scales and captures both elastic network response and viscous dissipation. The framework is calibrated using experimental rheological data and tensile measurements for four PSA formulations with varying gel fractions and crosslink densities. The simulations reproduce key experimental trends in storage modulus (G'), loss modulus (G"), and tensile stress-strain behavior under planar extension, while differentiating the distinct mechanical signatures of each formula- tion. The results elucidate how crosslink density and effective network connectivity control stiffness, stress localization, and failure characteristics. Overall, the proposed multiscale methodology provides a predictive platform for linking microstructural design pa- rameters to macroscopic mechanical properties and offers a rational basis for the formulation and optimization of next-generation PSAs.
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All authors Nicolas Moreno, Elnaz Zohravi, Shaghayegh Hamzehlou, Edgar Patino-Narino, Malavika Raj, Mercedes Fernandez, Nicholas Ballard, Jose M. Asua, Marco Ellero
Fluid Dynamics · Cross-listed from cs.LG
Physics-integrated neural differentiable modeling for immersed boundary systems
Chenglin Li, Hang Xu, Jianting Chen et al. (+1 more)
Accurately, efficiently, and stably computing complex fluid flows and their
evolution near solid boundaries over long horizons remains challenging. Conventional numerical solvers require fine grids and small time steps to resolve near-wall dynamics, resulting in high computational costs, while purely data-driven surrogate models accumulate rollout errors and lack robustness under extrapolative conditions. To address these issues, this study extends existing neural PDE solvers by developing a physics-integrated differentiable framework for long-horizon prediction of immersed-boundary flows. A key design aspect of the framework includes an important improvement, namely the structural integration of physical principles into an end-to-end differentiable architecture incorporating a PDE-based intermediate velocity module and a multi-direct forcing immersed boundary module, both adhering to the pressure-projection procedure for incompressible flow computation. The computationally expensive pressure projection step is substituted with a learned implicit correction using ConvResNet blocks to reduce cost, and a sub-iteration strategy is introduced to separate the embedded physics module's stability requirement from the surrogate model's time step, enabling stable coarse-grid autoregressive rollouts with large effective time increments. The framework uses only single-step supervision for training, eliminating long-horizon backpropagation and reducing training time to under one hour on a single GPU. Evaluations on
benchmark cases of flow past a stationary cylinder and a rotationally oscillating cylinder at Re=100 show the proposed model consistently outperforms purely data-driven, physics-loss-constrained, and coarse-grid numerical baselines in flow-field fidelity and long-horizon stability, while achieving an approximately 200-fold inference speedup over the high-resolution solver.
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All authors Chenglin Li, Hang Xu, Jianting Chen, Yanfei Zhang
Atmospheric and Oceanic Physics
Probabilistic reconstruction of global sea surface temperature using generative diffusion models
Haijie Li, Ya Wang, Kai Yang et al. (+11 more)
Accurate reconstruction of global Sea surface temperature (SST), which dominates the air-sea coupling and global
climate variability, underpins
climate monitoring and prediction. Existing SST reconstruction products primarily provide one deterministic field derived from heterogeneous satellite data and in situ observations, limiting their ability to represent observation uncertainty and to support probabilistic
forecasting. Here, we introduce Satellite and in situ Adaptive Guided Estimation (SAGE), a
diffusion-based uncertainty-aware generative framework for probabilistic SST reconstruction. SAGE learns a physically consistent prior from historical SST data and performs observation-conditioned posterior sampling without requiring satellite or in situ data during training, enabling flexible state inference from heterogeneous observations. Through a progressive data-fusion strategy, observations from two FengYun-3D polar-orbiting satellites constrain basin-scale structures, while sparse in situ measurements serve to refine local anomalies and extremes. The resulting ensemble SST fields well capture observational uncertainty and scale-dependent variability. Validation against independent in situ observations shows that SAGE substantially reduces reconstruction errors compared with widely used operational products. When used to initialize
forecasting systems, SAGE-generated SST fields substantially reduce 10-day SST forecast errors relative to current operational analyses. At the
climate scale, SAGE-driven forecasts of the 2023-2024 El Nino event show added value in capturing its onset and intensity
evolution compared to conventional approaches. Our results demonstrate that SAGE represents a step toward a new paradigm for ocean state estimation and
climate prediction.
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All authors Haijie Li, Ya Wang, Kai Yang, Gang Huang, Xiangao Xia, Ziming Chen, Weichen Tao, Chenglin Lyu, Lin Chen, Miao Zhang, Kaiming Hu, Hainan Gong, Disong Fu, Lin Wang