Chemical Physics
A Comprehensive Experimental and Theoretical Spectroscopic Study of Proteinogenic Amino Acids
Ann S. Y. Lu, Prajna Bhatt, Nathalie K. Fernando et al. (+2 more)
Amino acids are essential building blocks of life, yet our understanding of their chemistry and electronic structure in the solid state remains limited. This is particularly important because amino acids in the solid state are relevant to biological and pharmaceutical processes. X-ray photoelectron spectroscopy provides a powerful experimental probe of chemical states and occupied electronic structure; however, most spectroscopy studies of amino acids focus on gas-phase species or surface adsorbates, while crystalline amino acids remain underexplored, largely because of experimental challenges associated with radiation damage. Additionally, the spectra are often complex and difficult to interpret, motivating a combined experimental-theoretical approach. This study combines X-ray photoelectron spectroscopy and density functional theory calculations to systematically investigate the core, semi-core, and valence states of 20 proteinogenic amino acids as well as selenomethionine which can be incorporated during
protein synthesis and deliver the essential nutrient, Se, required by humans. Calculated relative core binding energies show excellent agreement with experiment and enable reliable assignments. Projections of the density of states provide insight into the influence of local coordination and extended
crystal structure, yielding a systematic understanding of the electronic structure and bonding in solid-state AAs. The insights gained from this study enhance the understanding of crystalline amino acids and validate the robustness of an integrated experiment–theory framework.
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All authors Ann S. Y. Lu, Prajna Bhatt, Nathalie K. Fernando, Laura E. Ratcliff, Anna Regoutz
Fluid Dynamics
A formal log(Re)-cost framework for the engineering turbulence problem
Jiaqi Li, Robert F. Kunz, George Huang et al. (+1 more)
In fluid engineering, the
turbulence problem is the longstanding challenge of obtaining accurate predictions of engineering quantities at affordable computational cost. Viewed through computational complexity, a practical
algorithm requires cost growth no worse than $O(N)$, where $N$ denotes problem size. For turbulent flows, the problem size may be approximated by the number of dynamically relevant scales and hence by the Reynolds number $Re$. We propose a multi-fidelity, physics-constrained, data-driven framework designed to meet this criterion under stated assumptions. We augment the Spalart–Allmaras model through field inversion and machine learning using a constrained formulation that preserves the law of the wall. The model is trained at a low Reynolds number, where high-fidelity data are affordable, and deployed at higher Reynolds numbers. For a mean-flow-aligned grid in a wall-bounded flow, fixed spanwise resolution, and steady-solver cost linear in grid-point count, the low-fidelity RANS prediction scales as $O(\log(Re))$. The high-fidelity calculation and learning stage each contribute $O(Re^0)$ relative to the target Reynolds number, giving an overall formal cost of $O(\log(Re))$. In plane channel flow, a model trained at $Re_\tau=1000$ corrects the wake-layer error of the baseline model and retains the improvement at $Re_\tau=5200$. In the periodic hill, a model trained at $Re_b=5600$ is tested at $Re_b=10595$, $19000$, and $37000$. The constrained formulation preserves separation and recovery behavior as Reynolds number increases, yields the lowest root-mean-square error across all tests, and exhibits nearly Reynolds-number-independent error, indicating robust extrapolation.
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All authors Jiaqi Li, Robert F. Kunz, George Huang, Xiang I. A. Yang
Fluid Dynamics
Parameter mapping and physical reconstruction of Akhmediev breathers at the interface of two fluid half-spaces
Olga Avramenko and Volodymyr Naradovyi
A methodology combining parameter mapping and physical reconstruction of Akhmediev breathers at the interface between two fluid half-spaces is developed on the basis of the Nayfeh model. Parameter maps are constructed to characterize the breather modulation period, the modulational instability growth rate, and the relative contribution of the bound second harmonic to the reconstructed interfacial profile. Their combined analysis provides a physically meaningful classification of breather regimes beyond the conventional focusing condition of the nonlinear Schrödinger equation. Reconstruction of the physical interfacial profile establishes a quantitative relation between nonlinear wave deformation and the contribution of the bound second harmonic, making it possible to identify the range of applicability of the weakly nonlinear approximation. Representative regimes from different modulational instability regions are analyzed to demonstrate the influence of resonance and focusing boundaries on breather characteristics and interfacial wave profiles. The proposed approach provides a direct link between the mathematical description of Akhmediev breathers and their physical interpretation and can be extended to other localized solutions of the nonlinear Schrödinger equation and to a broad class of stratified hydrodynamic systems.
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Fluid Dynamics
Information Transport and Observability in Compressible Aerodynamics
Bo Zhang
Pressure measurements provide sparse but direct observations of compressible aerodynamic flows, yet how information about hidden aerodynamic parameters is transported through the flow and encoded in these observations remains poorly understood. Here, we investigate information transport and observability in compressible aerodynamics using a differentiable shock-capturing immersed-boundary solver. By propagating gradients through the full unsteady flow solution, an automatic-differentiation-based observability metric is introduced to quantify the sensitivity of sparse pressure measurements to unknown aerodynamic parameters and identify informative sensing locations for inverse learning. The results reveal that aerodynamic information is transported non-uniformly through the flow field, producing localized regions of high observability. Inverse-learning experiments further demonstrate that observability and learnability are related but distinct concepts: although highly observable probes generally facilitate accurate parameter recovery, the highest-observability probe is not consistently the most effective for parameter inference. Furthermore, both the flow regime and the airfoil geometry substantially influence the distribution of observability and the convergence behavior of inverse learning. These findings establish a quantitative framework for understanding how aerodynamic information is encoded in sparse measurements and demonstrate the potential of automatic differentiation for observability analysis, informative sensor selection, and aerodynamic inverse analysis.
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Fluid Dynamics
Guarantees by Construction for Learned Finite Volume Schemes on Steady Supersonic Flow
Denis Gueyffier
A second order finite volume scheme rests on two local quantities: a gradient reconstructed in each
cell, and a limiter which scales it down where the reconstruction would overshoot. Both are set by fixed formulas, and on coarse unstructured meshes a small network can supply better values. But a network is free to output anything, and the usual safeguard is a penalty in the training loss, which discourages inadmissible states without preventing them. We replace the penalty by a hard constraint. The network still sets both quantities, and every value it can produce lies inside safe bounds: its stencil weights cannot cancel a neighbour, and its limiter is capped by the local flow. The flux, the wall treatment and the time step are not learned and carry their own guarantees. Admissibility therefore holds for every value of the weights rather than as an outcome of training, and no negative density or pressure occurred in any computation reported here. Because the scheme is safe whatever the network does, we could ask what the network contributes. We test it on supersonic channel flow over an obstacle, including the forward facing step of Woodward and Colella. Learning lowers the error by 38% on an unseen geometry and 29% on an unseen obstacle
topology, measured against the same scheme with the network switched off. The method aims at the accuracy of a fine mesh for the cost of a coarse one, and refining once improves the error fourfold while multiplying the run time by eight. Learning secures half of this improvement for a sixth of this time. All of this comes from one of the two quantities the network sets. The gradient reconstruction reproduces the full effect on its own, and the limiter accounts for about a tenth as much. This also explains why the gain fades beyond the Mach numbers the weights were trained on.
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Medical Physics
A simplified reconstruction of Positron Emission Tomography image using Time of Flight simulated data in Gate 10
Marcin Balcerzyk, Santiago Jimenez-Serrano, Óscar Pietrzyk et al. (+2 more)
Background: Ultrafast Time-of-Flight (TOF) information in Gate 10 simulations enables direct 3D PET reconstruction without scanner-specific modeling. Although such picosecond timing is not achievable in current detectors, simulated data allow exploration of idealized TOF regimes and rapid evaluation of prototype PET designs. Methods: The TOF-driven reconstruction assigns each coincidence to its annihilation position using sub-picosecond timestamps, producing voxelized 3D histograms with flexible voxel size and field-of-view selection. The approach operates directly on Gate-sorted coincidences, requires no corrections, and outputs MHD/DICOM images. Timestamp precision (~10^-13 s) enables localization on the millimeter scale. Results: Full 3D images of the simulated INSPIRE PET scanner are generated in under one second. The method resolves 0.25 mm features in point-source studies and 1.2 mm rods in the Derenzo phantom, reproduces ground-truth activity distributions in image-quality tests, and maintains quantitative stability across geometries. Performance reflects the theoretical benefits of ultrafast TOF rather than current detector capabilities. Significance: This fast, geometry-agnostic reconstruction tool supports early-stage PET prototyping, allowing rapid assessment of spatial resolution, sensitivity, and uniformity without implementing complex reconstruction software. It is broadly applicable to any simulated PET system with sorted coincidences and enables systematic exploration of ultrafast TOF performance in a controlled environment.
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All authors Marcin Balcerzyk, Santiago Jimenez-Serrano, Óscar Pietrzyk, Edwing Ulin-Briseño, Marta Freire
Optics
Two Complementary Approaches to Determine the Effective Index of Dielectric Ring Resonators
Ergun Simsek
Open-source finite-element frameworks for modal analysis of dielectric ring resonators are presented based on two complementary approaches. The first computes resonant wavelengths for discrete azimuthal mode numbers and obtains the effective index at a target wavelength by interpolation; the second solves directly for the azimuthal mode number at a prescribed wavelength via a quadratic
eigenvalue formulation. Both approaches are validated against a commercial electromagnetic mode solver and yield nearly identical effective indices and dispersion curves. The fixed-mode-number approach requires slightly longer computation time but uses half as much
memory, making it the preferred choice for
memory-limited computers.
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Optics
Conjugate Gradient Unrolled Network with PSF Conditioning for Non-Diagonal Data Fidelity in CASSI Reconstruction
Xiang Li, Qiuyu Yue, Yang Zhang et al. (+1 more)
Deep unfolding methods achieve state-of-the-art performance in coded aperture snapshot spectral imaging (CASSI) reconstruction but typically rely on closed-form data-fidelity updates that assume an ideal optical system. In practical CASSI systems, the field-dependent and wavelength-dependent point spread function (PSF) introduces convolutional coupling that breaks the diagonal structure of the normal equation, rendering closed-form updates inapplicable. We propose a deep unfolding framework that addresses this fundamental algorithmic challenge through three contributions: (1)~a $K$-step conjugate gradient (CG) unrolling that explicitly solves the non-diagonal normal equation under PSF-inclusive forward operators; (2)~a learned gradient refinement module with wavelength-adaptive step sizes generated from a per-wavelength PSF
embedding; and (3)~a PSF-conditioned penalty
estimator that adapts the ADMM regularization strength to the optical degradation severity. A Monte Carlo PSF training strategy further improves robustness to manufacturing-induced PSF variations. Our method achieves 30.53~dB on the KAIST
dataset, +2.73~dB over the DPU baseline (1.27M) using a comparable number of parameters (1.42M), and +1.70~dB over a larger baseline (DPU-B+, 2.12M) using 33% fewer parameters.
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All authors Xiang Li, Qiuyu Yue, Yang Zhang, Zhenrong Zheng
Medical Physics
PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping
Busra Bulut, Maik Dannecker, Thomas Sanchez et al. (+14 more)
Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal
brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple orientations. Existing SVR methods are optimized and validated only for clinical-range echo times (TEs), limiting their use at non-clinical TEs and making them incompatible with quantitative T2 mapping, a protocol- and center-independent biomarker of fetal
brain maturation requiring HR reconstructions across multiple TEs. We present
PRIME-SVR, the first implicit neural representation (INR) framework for joint HR reconstruction from multi-echo MRI. A single fully connected network models a continuous function from spatial coordinates to signal intensities across TEs, while a second network estimates slice-specific acquisition degradations. Cross-TE coherence is enforced via a Bloch equation-derived regularization penalizing deviations from expected T2 decay, with adaptive weighting that strengthens coupling for degraded stacks. The method is fully self-supervised. We validate
PRIME-SVR on 39 in vivo fetal acquisitions (13 subjects x 3 TEs) from two centers, two vendors, and two field strengths (1.5 T and 0.55 T). Compared to state-of-the-art SVR,
PRIME-SVR improves reconstruction sharpness by 47%, anatomical accuracy by 30%, and cross-TE structural consistency by 14%. It enables reconstruction at late TEs previously inaccessible to SVR, yielding the first 0.8 mm isotropic T2 maps at 0.55 T and the first T2 maps derived from INR-based SVR.
PRIME-SVR also accelerates quantitative imaging by reducing the data needed for multi-TE reconstruction, cutting acquisition from 15 to 10 minutes while keeping T2 accuracy within 1.7% in white and deep gray matter, or to 5 minutes with a mean T2 error of 2.3% for high-quality acquisitions.
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All authors Busra Bulut, Maik Dannecker, Thomas Sanchez, Sara Neves Silva, Steven Jia, Jean-Baptiste Ledoux, Leo Pomar, Joanna Sichitiu, Yvan Gomez, Meriam Koob, Vincent Dunet, Maria Deprez, Guillaume Auzias, Francois Rousseau, Jana Hutter, Daniel Rueckert, Meritxell Bach Cuadra
Instrumentation and Detectors · Cross-listed from astro-ph.IM
Skipper CCD readout time optimization for astronomical applications
Johannes Wüthrich, Andrin Fazan, Sean MacBride et al. (+1 more)
Skipper CCDs enable the reduction of CCD readout noise by non-destructively measuring the individual pixel charge packets multiple times. This readout noise reduction has attracted considerable interest in the astronomical community, particularly in spectroscopic surveys targeting faint objects at high
redshifts. However, noise reduction via repetitive sampling leads to an unavoidable increase in readout time, often to prohibitive levels. To enable their use in astronomical applications, the optimal operation regime of Skipper CCDs must be determined, balancing noise improvement and readout time. Traditionally, such optimization has been carried out empirically for each CCD architecture. We present a general optimization scheme derived from first principles and experimentally verified in the laboratory using a Skipper CCD as used by the Oscura experiment. While the existence of an optimal combination of correlated double-sampling integration time and number of Skipper samples for reaching a given readout noise level at minimal readout time has previously been observed empirically, we model this trade-off analytically based on the intrinsic noise power spectral density of the sensor, allowing the optimal operating point to be predicted rather than determined experimentally for each architecture. We further show that the location of this optimum is governed by the per-sample charge-transfer time, whose minimization is therefore key to achieving fast Skipper CCD readout.
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All authors Johannes Wüthrich, Andrin Fazan, Sean MacBride, Marcelle Soares-Santos
Computational Physics · Cross-listed from cond-mat.soft
Let's Stalk About Membranes: Committor-Based Enhanced Sampling of Stalk Formation
Giorgia Rossi, Enrico Trizio, Davide Bochicchio et al. (+2 more)
Membrane fusion is essential for cellular communication and function, and understanding how two lipid
bilayers merge is key to informing therapeutic strategies. Functionalized nanoparticles have recently emerged as synthetic fusogens, but the molecular mechanisms driving this process remain unclear, partly because fusion involves transitions over high free-energy barriers, difficult to capture in molecular simulations. While enhanced sampling methods can address this problem, they also rely on the definition of collective variables, which are especially hard to define for fusion, as it arises from the collective rearrangement of many molecules and cannot be easily reduced to a simple intuitive coordinate. Here, we study stalk formation, the first step of fusion, mediated by an amphiphilic gold nanoparticle, by employing an enhanced sampling strategy based on the committor function, machine-learned through a self-consistent procedure. This method requires minimal prior knowledge of the system and leverages the learned committor function as an effective collective variable, enabling uniform sampling of the entire pathway. From the resulting reactive trajectories and extensive transition region sampling, we obtain converged free-energy estimates and mechanistic insight into stalk formation.
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All authors Giorgia Rossi, Enrico Trizio, Davide Bochicchio, Giulia Rossi, Michele Parrinello
Fluid Dynamics
Theoretical development of an operational wave-induced ice erosion model through laboratory experiments
Wenjun Lu, Behnam Ghadimi, Dominique Mouaze et al. (+6 more)
Wave-induced melting of vertical ice fronts is represented in several operational iceberg and coastal-erosion models by the rough-wall parameterization of White (1980), whose closure chain is incompletely documented and whose commonly used compact expression is stated at the waterline. We reconstruct the formulation, specify the rough-turbulent wave-friction closure using Jonsson's implicit relation and its Lambert-W solution, and extend the model to a depth-resolved melt-rate profile under linear wave kinematics. Because the horizontal and vertical orbital-velocity components are linked, we use the horizontal component as a convenient representative scale and introduce a dimensionless coefficient alpha for the remaining closure uncertainty. The reconstruction gives a waterline coefficient of 3.0 x 10^-4 with White's resultant-velocity definition and 2.09 x 10^-4 for the reference choice alpha = 1; neither reproduces White's published 1.46 x 10^-4 directly. Two monochromatic wave-flume experiments with freshwater ice are then used to calibrate alpha from profiles below the wave trough. The full Lambert-W friction coefficient is used in this calibration. Best-fit values are 0.684 and 0.612 for periods of 1.54 and 0.87 s, respectively, a relative difference of approximately 11%. Their corresponding effective waterline coefficients, 1.43 x 10^-4 and 1.28 x 10^-4, are close to White's published value but do not constitute an independent validation. The fitted profiles reproduce the observed depth dependence below the trough, while deviations near the surface expose unresolved effects of intermittent submergence, local wave impact, and uncertain thermal forcing.
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All authors Wenjun Lu, Behnam Ghadimi, Dominique Mouaze, Marianne Font, Rémi Lambert, Harvey Goodwin, Widar Weizhi Wang, Raed Lubbad, Sveinung Løset
History and Philosophy of Physics · Cross-listed from quant-ph
Schrödinger's real-valued equation revisited
Oliver Passon and Bernd Rosenow
The Schrödinger equation can be rewritten as a second-order equation for a single real-valued scalar field. Taken at face value, however, this one-field reduction obscures several local structures of
quantum mechanics: the Born density and current, the role of momentum as a generator, and the usual derivation of the uncertainty relation. We argue that these difficulties do not show that real variables fail. They show that the reduced equation is not, by itself, a complete local representation of the theory. Its coherent real form is obtained by restoring the underlying
Hamiltonian phase-space structure, where the missing component reappears as a canonical partner. In this real phase-space formulation, the standard structures reappear locally, and minimal
magnetic coupling reveals an internal \(SO(2)\) gauge structure. Thus, the symbol \(i\) can be removed, but the
symplectic and complex structure it encodes cannot.
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Physics and Society
Corruption as a self-sustained collective state in political systems
Nuno Crokidakis and Jaime L. C. da C. Filho
Political corruption is often interpreted as the result of individual misconduct or isolated institutional failures. However, persistent corruption patterns observed in real-world political systems suggest that systemic corruption may instead emerge as a self-sustaining governance arrangement supported by reinforcing interaction structures. In this work, we introduce a minimal compartmental model for the dynamics of systemic political corruption and the formation of corruption-supporting relational structures. The concentration of political power is treated as an emergent macroscopic observable arising from these coupled dynamics. Despite its simplicity and mean-field character, the model exhibits a nontrivial phase transition separating regimes of low corruption from structurally captured states sustained by self-reinforcing interaction mechanisms. The model predicts that, above a critical interaction strength, the captured state becomes dynamically stable, with small perturbations of the macroscopic variables naturally relaxing back to the stationary state. We argue that this mechanism provides a possible explanation for the persistence of corruption across successive electoral cycles and institutional crises. In particular, the political dynamics observed in the state of Rio de Janeiro, Brazil, provide a qualitative illustrative example of several mechanisms discussed by the proposed framework rather than a quantitative application of the model. More broadly, the results suggest that long-term political capture may emerge spontaneously from reinforcing interactions between systemic corruption and the relational structures that sustain it, without requiring centralized coordination or complex strategic behavior.
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Fluid Dynamics
Water entry of small hydrophobic spheres on an axisymmetric wavefield
Chase T. Gabbard, Mario Ibrahim, Joseph Quinton et al. (+3 more)
When a sphere crosses an air-water interface it can entrain a significant volume of air, a process relevant to numerous naval, industrial, and environmental settings. While air entrainment through sphere impact onto quiescent interfaces has been extensively studied, real-world interfaces are inherently unsteady, and the influence of surface waves is less understood. In this work, we systematically investigate the effect of waves on air entrainment in a highly controlled setting: small hydrophobic spheres impacting an axisymmetric standing wavefield. By analyzing the resulting cavity across a wide parameter space, including wave phase, driving amplitude, and frequency, we reveal that local interface deformation dramatically alters the maximum cavity volume. This effect is driven by a
geometric modulation of the splash curtain, which shifts the transition between cavity closure modes. We demonstrate that the influence of waves in our setting is well parameterized by the instantaneous interface slope evaluated at the radius of the sphere, successfully collapsing our experimental data alongside the traditional Weber and Bond numbers.
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All authors Chase T. Gabbard, Mario Ibrahim, Joseph Quinton, Eli Silver, Jesse Belden, Daniel M. Harris
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Kinetically-Arrested Phase Separation leads to Tunable Domain Structures in Vapor-Deposited Glasses
A T M Mahbub Alahe, Thomas J. Ferron and Camille E. Bishop
The characteristic length scale of phase-separated organic thin film blends is a critical structural parameter governing the performance and functionality of organic electronic devices. The arrested morphologies of vapor-deposited organic thin films result from the interplay between thermodynamic driving forces and kinetic constraints during deposition. Here, we aim to isolate the role of kinetic effects in phase separation by varying the deposition rate at a constant substrate temperature for a co-deposited molecular glass blend of N,N'-bis(3-methylphenyl)-N,N'-diphenylbenzidine (TPD) and Disperse Orange 37 (DO37). The dependence of morphology on deposition rate is quantified using power spectral density (PSD) analysis of atomic force microscopy (AFM) images. Two distinct deposition rate-dependent length scales at the surface reveal how deposition kinetics directly influence domain size and film topography. Complementary Resonant Soft X-ray Scattering (RSoXS) measurements indicate that phase separation extends throughout the film thickness. These observations are consistent with the surface equilibration mechanism previously described for homogeneous vapor-deposited films, in which enhanced surface mobility allows molecules in the growing film to partially equilibrate into distinct surface-templated states during deposition. In the current work, this mechanism allows the multi-component blend to phase separate and coarsen into a structure with multiple length scales before kinetically arresting to an extent that depends on the deposition rate. The demonstration of finely tunable domain size with deposition rate provides strategies to design new organic electronic devices with desired morphologies.
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Optics
Three-dimensional field of view of remote focusing microscopy system
Dinesh Saini and Manish Kumar
Remote focusing (RF) microscopy is well known for its ability to sharply image a wide range of planes away from the working distance of the microscope objective. However, the exact nature of the three-dimensional (3D) field of view (FOV) is not known. In this letter we report an optical ray tracing based FOV study of a remote focusing microscopy system. We numerically simulate the Strehl ratio of two configurations of the remote focusing systems, and use this for the evaluation of the 3D FOV. The FOV tapers down as the sample plane moves away from the working distance. To cross-verify our simulation results, we experimentally measured the FOV at various offset planes. This rate of taper depends on the numerical aperture. We also discuss the advantages and limitations of remote focusing in microscopy.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Exploring Multifunctionality in MgO-Based Magnetic Tunnel Junctions with Coexisting Magnetoresistance and Memristive Properties
Alejandro Schulman, Elvira Paz, Tim Böhnert et al. (+2 more)
Magnetic tunnel junctions (MTJs) and memristors are two key emerging nanotechnologies that attracted significant interest for potential applications at the forefront of the digital revolution, including sensing, data storage, and non-conventional computation. The co-integration of these phenomena into a single multifunctional device is an important step toward harnessing the re-programmability of memristive systems with the high yield and varied functionality of MTJs. This study demonstrates the co-existence of magnetoresistance and memristive properties on MgO-based MTJs. These devices show a magnetoresistance with a linear response as a function of a
magnetic field and no hysteresis, which are the requirements for good
magnetic field sensors, as well as demonstrating a non-volatile and quasi-analogue memristive behavior as a function of an applied electrical field down to nanosecond pulses. Furthermore, by doping the
oxide barrier, the memristive power consumption is lowered by 20% giving the multi-functionality of the devices a promising scalability potential. This study also shows that, memristive switching can be reversibly used to completely suppress and recover the spintronic functionalities. These results can pave the way for a seamless co-integration of memristors and spintronic devices in complex reprogrammable
circuits addressing applications such as reprogrammable multifunctional field sensor arrays and neuromorphic computing.
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All authors Alejandro Schulman, Elvira Paz, Tim Böhnert, Alex Steven Jenkins, Ricardo Ferreira
Medical Physics
COBRA2026: a large-scale multicenter pelvic cone-beam computed tomography projection dataset
Adrian Thummerer, Simon Rit, Florian Kamp et al. (+11 more)
The COBRA2026
dataset is a large-scale, multicenter resource of raw radiotherapy cone-beam computed
tomography (CBCT) acquisitions created for the development and evaluation of conventional and learning-based reconstruction and image-correction methods. It contains data from 867 patients undergoing pelvic radiotherapy at six European centers, acquired using Elekta and Varian imaging systems. For each case, the
dataset includes raw projection data, acquisition geometry, calibration and correction information, clinically reconstructed CBCT images, and corresponding planning CT images. Vendor-specific files were anonymized and converted into open formats. Planning CT images were deformably registered to the daily CBCT anatomy, and matched projections were simulated using the corresponding acquisition geometry. All cases underwent visual quality control, and cases with substantial processing or registration errors were excluded. The approximately 950 GB
dataset is divided into training, validation, and test sets containing 692, 52, and 123 cases, respectively. Projection stacks and volumetric images are provided as compressed MetaImage files, with geometry and metadata supplied in XML and YAML formats. COBRA2026 supports research on full- and sparse-view reconstruction, low-dose imaging, artifact and scatter correction, motion compensation, and synthetic CT generation. The
dataset is released under the CC BY-NC 4.0 license, indexed on Zenodo (doi:10.5281/zenodo.21322350), and accompanied by openly available preprocessing and baseline reconstruction code. It also forms the basis of the COBRA2026 reconstruction challenge.
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All authors Adrian Thummerer, Simon Rit, Florian Kamp, Matteo Maspero, Martjin P.W. Intven, Thomas G. Boné, Christopher Kurz, Guillaume Landry, Thomas Baudier, Mustafa Kadhim, Julius Arnold, Michael Rauter, Barbara Knäusl, Lukas Zimmermann
Fluid Dynamics
Discrete Boltzmann model at Burnett level for compressible multicomponent flows under external forces
Demei Li, Huilin Lai, Chuandong Lin et al. (+1 more)
This work extends the Burnett-level discrete Boltzmann model (DBM) from single-component to multicomponent compressible flows under external forces, building on the fundamental framework of the high-precision discrete kinetic method. A high-isotropy 25-discrete-velocity set is adopted to guarantee numerical stability and spatial
symmetry, while a rigorous moment-matching strategy is developed to construct the
equilibrium distribution function and external force term. Different from the single-component counterpart, the present model intrinsically incorporates interspecies mass
diffusion effects and multi-component thermodynamic nonequilibrium behaviors, which are critical for complex compressible multicomponent systems. The Chapman–Enskog expansion verifies that the proposed model can exactly recover the Burnett-level governing equations for forced multicomponent compressible flows in the continuum limit. Five canonical
benchmark cases, including multicomponent mass
diffusion, compressible Sod shock tube, thermal Couette flow, Kelvin–Helmholtz instability, and Rayleigh–Taylor instability, are systematically performed. Numerical results demonstrate that the developed Burnett-level multicomponent DBM achieves high accuracy and robustness in capturing both hydrodynamic
evolution and multicomponent nonequilibrium characteristics under external forces.
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All authors Demei Li, Huilin Lai, Chuandong Lin, Suni Chen
Chemical Physics
Investigating aqueous mineral interfaces using sum frequency generation spectroscopy
Moritz Zelenka and Ellen H.G. Backus
Sum frequency generation spectroscopy can be utilized to address the challenge of studying solid-
liquid interfaces under ambient pressure and with macroscopic amounts of
liquid, while still retrieving molecular level information. By utilizing a non-linear optical process, this method allows to selectively measure vibrations of interfacial species. Within this chapter sum frequency generation spectroscopy is first briefly introduced and then comprehensively evaluated as an investigative tool for solid-
liquid interfaces, with mineral-water interfaces as model system. Additionally, it is explained how the actual probing depth of this method depends on both the experimental setup and the properties of the investigated system, such as surface charges.
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Optics
Dirac-vortex modes beyond the continuum limit
Jiayu Fan, Jiusi Yu, Aoning Luo et al. (+7 more)
Dirac-
vortex modes (DVMs) in Kekule-modulated
lattices provide a topological route to wave confinement and are commonly described by the continuum Jackiw-Rossi model, in which the initial phase acts as a redundant gauge degree of freedom and does not affect observables of the mode. Here we show that this picture breaks down in discrete
lattices when the complex mass texture that induces the DVMs no longer satisfies the slowly varying envelope approximation. In this regime,
lattice discreteness turns the initial phase into a physically observable parameter that shifts the DVM center. By further introducing a sublattice-antisymmetric perturbation, we convert this phase-dependent center motion into a continuous spectral response of the DVM, enabling its frequency tuning across nearly the entire topological bandgap. Our simulation and experimental results agree well with a revised continuum model accounting for the mode-center motion. Within this perturbative framework, the model shows that the frequency shift exhibits a sinusoidal-like dependence on the initial phase. These findings reveal initial phase-sensitivity of the DVMs realized in
lattices, an important and basic feature absent from the ideal continuum Jackiw-Rossi model, and demonstrate initial phase engineering as a potential pathway towards reconfigurable photonic devices.
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All authors Jiayu Fan, Jiusi Yu, Aoning Luo, Yiyi Yao, Shijie Kang, Xiexuan Zhang, Haitao Li, Biye Xie, Xiao-Dong Chen, Xiaoxiao Wu
Accelerator Physics
Exploiting Non Redundant Aperture Interferometry as a Diagnostics Tool for Synchrotron Light Characterization
C. Carilli, U. Iriso, B. Nikolic et al. (+2 more)
We recently introduced a novel interferometric method inspired by
radio astronomy, utilizing a Non-Redundant Aperture (NRA) mask with self-calibration to fully characterize the two-dimensional transverse shape of electron beams from a single-shot interferogram. This paper reports the latest advancements in this technique, including a new data analysis approach based on closure amplitudes, which removes the need for self-calibration. We also demonstrate the method's applicability to wavefront sensing, and we explore its potential for resolving beam
halos superimposed on well-defined Gaussian beam core.
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All authors C. Carilli, U. Iriso, B. Nikolic, N. Thyagarajan, L. Torino
Instrumentation and Detectors · Cross-listed from astro-ph.IM
Noise reduction in suspension control with photon-pressure actuator for CHRONOS gravitational wave detector
Daiki Tanabe, Yuki Inoue and Mario Juvenal S. Onglao III
Improving sub-Hz sensitivity of gravitational wave (GW) detectors is important to detect heavier binary black hole mergers and study phenomena in stronger
gravity fields. Torsion-bar-based GW detectors have been projected to focus on low-frequency GW. Among noise sources of GW detector, actuation noise induced by vibration of force sources and fluctuation of environmental
magnetic fields is one that increases in low frequency. In this study, we propose
photon-pressure actuator as a solution to isolate an actuator from seismic and
magnetic noise. It can also be used as a
photon calibrator. We designed an optical layout of the
photon-pressure actuator having four beams independently controlled and applied it to CHRONOS experiment. Based on a realistic power control system, we estimated its maximum torque amplitude around yaw rotation axis as $1.0\times 10^{-8}$ N$\cdot$m and actuation efficiency as $6.6\times 10^{-13}$ rad/V, which are sufficiently large for controlling the CHRONOS torsion bar. The actuation noise was estimated as $5.3\times 10^{-19}$ rad ${\rm Hz}^{-1/2}$ at 1 Hz, lower than the target sensitivity of CHRONOS. Assuming its usage as a
photon calibrator, the estimated systematic error was 1.14%.
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Chemical Physics
Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models
Steffen Wedig, Felix Burton, Rokas Elijošius et al. (+2 more)
Foundation machine-learned interatomic potentials (MLIPs) are trained on large
quantum-mechanical
datasets and generalise across broad regions of chemical and configurational space. Beyond their usual role in accelerating sampling-based simulations, their internal representations encode chemically rich local atomic environments. Here, we introduce Rem3Di, a representation-learning framework that repurposes latent features from atomistic foundation models as transferable molecular descriptors for property prediction and virtual screening. Rem3Di combines a potential's per-atom features into a single fixed-length descriptor of the whole molecule that varies smoothly with three-dimensional structure and is invariant to the ordering of the atoms. The descriptor can be used directly or fine-tuned for specific prediction tasks. To capture molecular handedness, Rem3Di constructs pseudoscalar features, which are unchanged by rotation but reverse sign under mirror reflection. This lets the descriptor distinguish enantiomers, which can differ in activity and toxicity. The
transformer is pretrained on large molecular
datasets by reconstructing corrupted atom features, so no experimental labels are required. Across public drug-property
benchmarks, Rem3Di matches or exceeds published baselines without relying on classical 2D fingerprints. Additionally, the same descriptor yields chemically meaningful differentiation of transition-metal complexes without predefined bonding rules or handcrafted representations. Rem3Di therefore provides a route from simulation-trained atomistic representations to transferable, chirality-aware molecular representations for chemical machine learning.
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All authors Steffen Wedig, Felix Burton, Rokas Elijošius, Christoph Schran, Lars L. Schaaf
Accelerator Physics
Capabilities and Limitations of Non-Redundant Aperture Interferometry for Beam Size Measurements
C. Carilli, U. Iriso, B. Nikolic et al. (+3 more)
Non-Redundant Aperture Interferometry (NRAI) is a beam characterization technique developed at ALBA in collaboration with
radio-astronomy institutes. It enables the single-acquisition measurement of the full 2D transverse profile of the electron beam using visible synchrotron radiation. To better understand the technique limitations and performance, we performed extensive SRW simulations and compare them with experimental data. This paper presents the results of these studies, which define the capabilities and limits of NRAI applied to the current ALBA machine.
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All authors C. Carilli, U. Iriso, B. Nikolic, N. Thyagarajan, L. Torino, A. Turull Barot
Physics and Society
When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets
Takahiro Ezaki, Naoto Imura and Katsuhiro Nishinari
Shippers are beginning to delegate carrier selection to large
language model (LLM)
agents. We ask what such delegation does to a freight matching
market, and which platform design choices contain it. We carried out
agent-based simulations in which fifty shipper
agents, built on commercial LLMs from OpenAI (GPT), Anthropic (Claude), and Google (Gemini), procure truckload capacity for thirty days. The
market implements the rules of digital freight matching: each load is offered down the shipper's ranked list of carriers (waterfall tendering), carriers have daily capacity limits, spot prices respond to congestion, and carrier ratings accumulate with transactions. We found three risks and one remedy that works.
Agents converged at once: for a fixed sampled carrier population, the same carrier was the modal first choice of every model on day one, attracting up to 76% of requests. Because each
agent picks from its own randomly drawn list of displayed candidates, the platform controls how many options each shipper sees; concentration rose steeply once lists exceeded about ten carriers, with the onset differing across models. Which carriers ended up dominant varied widely from one sampled
market to another, and displaying true quality instead of estimated ratings changed neither the level nor this variability (by design, quality affects only what
agents see, never delivery outcomes). Against these risks, disclosing each carrier's remaining daily capacity cut concentration by a third and doubled shipper surplus, while vendor diversification, list-order randomization, and popularity display showed no clearly detectable effect. Platform information design, ahead of model choice or model regulation, is the lever that works.
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Instrumentation and Detectors
Gamma irradiation of ATLAS18 ITk strip sensors affected by static charge
M. Mikestikova, V. Fadeyev, P. Federicova et al. (+13 more)
Construction of the new all-silicon ITk, developed by the ATLAS collaboration to be able to track charged particles produced at the HL- LHC, started in 2021 and is expected to continue until 2028. The ITk detector will include ~18,000 highly segmented and radiation hard n+-in-p silicon strip sensors, which are being manufactured by HPK. Upon their delivery, the ATLAS ITk strip sensor collaboration performs detailed measurements of sensors to monitor quality of all fabricated pieces. QC electrical tests include IV and CV tests, full strip tests, and a measurement of the long-term stability of the sensor leakage current. While most sensors demonstrate excellent performance during QC testing, we have nevertheless observed that a number of sensors from several production batches failed the electrical tests. Accumulated data indicates a strong correlation between observed electrical test failures and high electrostatic charge measured on the sensor surface during initial reception tests. This electrostatic charge enhances the risk of "Local trapped charge" events during manufacturing, shipping, and handling procedures, resulting in failed electrical QC tests. In the presented study, we have investigated whether the TID expected in the real experiment can effectively resolve early breakdown or low interstrip isolation caused by the electrostatic charge. Selected charge-affected sensors were irradiated with gamma rays from a 60Co source for a number of TID values. The results of this study indicate that the negative effects of the electrostatic charge on the critical sensors characteristics disappear after a very small amount of an accumulated TID, which actually corresponds to one or two days in the experiment. This finding gives us confidence in mitigating the issue of electrostatic charge during the operation of the ITk strip sensors in the real experiment.
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All authors M. Mikestikova, V. Fadeyev, P. Federicova, J. Fernandez-Tejero, C. Fleta, P. Gallus, C. Jessiman, J. Keller, C. Klein, T. Koffas, J.Kroll, J. Kvasnicka, E. Staats, P. Tuma, M. Ullan, Y. Unno
Chemical Physics
Spin-Consistency Constraints in Noncollinear Tensor TDA
Wenxian Qin, Tai Wang, Yue Yu et al. (+3 more)
TDDFT for open-shell systems, whether
spin-conserving or
spin-flip, has long suffered from
spin contamination. This problem arises because the single-excitation space built upon a single Kohn-Sham determinant is not
spin-complete. Adopting
spin tensor reference states therefore offers an elegant and promising route to resolving this issue. In this work, we revisit the tensor TDDFT equations within the Tamm-Dancoff approximation (TDA) from a noncollinear perspective. We show that, for S = 1/2 reference states, the internal consistency of the
spin tensor formulation can, with the aid of the zero-excitation-energy
theorem, be recast as a set of constraints that the exchange-correlation kernel must satisfy. Standard noncollinear functionals, however, generally fail to meet these constraints. To address this, we propose a kernel reconstruction scheme that is independent of the specific functional form and free of empirical parameters. This scheme enforces the required constraints, restoring internal consistency in the full
spin tensor structure, with
spin adaptation following as a natural consequence. Furthermore, when extended to tensor reference states with other values of S, such as S = 1 for the oxygen molecule, the scheme eliminates the so-called artifact states, namely solutions with severely underestimated excitation energies. In addition, the scheme allows the target states that ROKS reference states aim to describe to be expressed and computed, at the TDA level, within the same unified framework as other states, a capability that
spin-adapted
spin-conserving TDDFT has so far lacked.
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All authors Wenxian Qin, Tai Wang, Yue Yu, Yuanyang Liu, Yiqin Gao, Yunlong Xiao
Plasma Physics
Reduced-order non-self-consistent Monte Carlo simulation of a planar magnetron discharge: electron heating, recapture and racetrack formation
Franz F. Locker and Georg Strauß
A reduced-order non-self-consistent Monte Carlo model is presented for a circular planar magnetron discharge in argon. The model combines two
magnetic-field representations, namely a superposition of
magnetic dipoles and a numerically integrated field of the finite permanent magnets, with a prescribed one-dimensional sheath-bulk potential, adaptive fourth-order Runge-Kutta orbit integration, and a null-collision treatment of electron-argon collisions. The collision module reproduces the dependence of the electron drift velocity on the reduced electric field, but overestimates its absolute value by approximately a factor of 1.5. The resulting transport predictions are therefore interpreted semi-quantitatively. Applied to a magnetron geometry based on published Langmuir-probe measurements, the simulations reproduce the qualitative emergence of a cold electron population away from the cathode while retaining a hotter near-cathode component. Electrons returning to the cathode are reflected with a prescribed probability RC, which controls their availability for further ionising collisions. For racetrack calculations initiated with at least 2 x 10^4 cathode-emitted electrons and RC = 0.5, the finite-magnet field produces a more sharply localised erosion profile whose full width at half maximum is close to a
geometric racetrack-width estimate. The dipole approximation yields a broader profile. The model is not a replacement for self-consistent PIC-MCC simulations, but is a computationally light tool for comparing
magnetic-field representations and analysing electron heating, ionisation localisation, and racetrack formation.
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