Computational Physics · Cross-listed from hep-ex
Simulation of Muon-induced Backgrounds for the Colorado Underground Research Institute (CURIE)
Dakota K. Keblbeck, Eric Mayotte, Uwe Greife et al. (+5 more)
We present a comprehensive Monte Carlo simulation of
muon-induced backgrounds for the Colorado Underground Research Institute (CURIE), a shallow-underground facility with $\approx 415$~m.w.e. overburden. Using coupled mute and geant4 frameworks, we characterize the production and transport of
muon-induced secondaries through site-specific rock compositions and geometries, establishing a proof-of-concept for high-precision, end-to-end simulations. Our simulations employ angular-dependent
muon energy distributions, which improve secondary flux accuracy. For the Subatomic Particle Hideout and Cryolab I research spaces, we predict total
muon-induced
neutron fluxes of $(8.52 \pm 1.30_{\text{sys}}) \times 10^{-3}$~m$^{-2}$s$^{-1}$ and $(8.86 \pm 1.62_{\text{sys}}) \times 10^{-3}$~m$^{-2}$s$^{-1}$, respectively. Additionally, we develop a Depth-Intensity Relation (DIR) to predict the
muon-induced
neutron flux as a function of facility depth, which is consistent with measurements across a broad range of underground depths. These results provide quantitative background predictions for experimental design and sensitivity projections at shallow- and deep-underground facilities. They further demonstrate that local geology and overburden geometry influence
muon-induced secondary yields and energy spectra, emphasizing the need for site-specific simulations for accurate underground background characterization. Therefore, the simulation framework has been made publicly available at \href{https://doi.org/10.5281/zenodo.17196581}{https://doi.org/10.5281/zenodo.17196581}, for the broader low-background physics community to enable meaningful inter-facility comparisons.
Read the full abstract ▾
All authors Dakota K. Keblbeck, Eric Mayotte, Uwe Greife, Kyle G. Leach, Wouter Van De Pontseele, Caitlyn Stone-Whitehead, Luke Wanner, Grace Wagner
Optics
General diffraction properties of aperiodic slit arrays
Thiago de Souza Ferreira, Daniel Jonathan, Antonio Z. Khoury et al. (+1 more)
Fraunhofer diffraction plays a vital role in experimental physics not only because it accurately describes the behaviour of light in the usual propagation limit, but also because it links the diffracted light with the scattering object through one of the most important mathematical transformations in physics: the
Fourier transform. Acting as a probe in material characterisation as well as used as a tool for particle trapping or sensing, the pattern of interference maxima resulting from the Fraunhofer diffraction through periodic scattering is an ubiquitous routine. In this paper we analyse the Fraunhofer diffraction resulting from the much less studied aperiodic scatter of the light. We provide general conditions for the experimental observation of the peaks of interference maxima featured into patterns that display periodic structures on a number of distance scales. Our theoretical analysis is supported by thorough experimental demonstrations.
Read the full abstract ▾
All authors Thiago de Souza Ferreira, Daniel Jonathan, Antonio Z. Khoury, Daniel S. Tasca
Chemical Physics
Connections between Richardson-Gaudin States, Perfect-Pairing, and Pair Coupled-Cluster Theory
Paul A. Johnson, Charles-Émile Fecteau, Samuel Nadeau et al. (+2 more)
Slater determinants underpin most electronic structure methods, but orbital-based approaches often struggle to describe strong correlation efficiently. Geminal-based theories, by contrast, naturally capture static correlation in bond-breaking and multireference problems, though at the expense of implementation complexity and limited treatment of dynamic effects. In this work, we examine the interplay between orbital and geminal frameworks, focusing on perfect-pairing (PP) wavefunctions and their relation to pair coupled-cluster doubles (pCCD) and Richardson-Gaudin (RG) states. We show that PP arises as an eigenvector of a simplified reduced Bardeen-Cooper-Schrieffer (BCS)
Hamiltonian expressed in bonding/antibonding orbital pairs, with the complementary eigenvectors enabling a systematic treatment of weak correlation. Second-order Epstein-Nesbet perturbation theory on top of PP is found to yield energies nearly equivalent to pCCD. These results clarify the role of pair-based ansätze and open avenues for hybrid approaches that combine the strengths of orbital- and geminal-based methods.
Read the full abstract ▾
All authors Paul A. Johnson, Charles-Émile Fecteau, Samuel Nadeau, Mauricio Rodríguez-Mayorga, Pierre-François Loos
Atmospheric and Oceanic Physics
Zeeman: A Deep Learning Regional Atmospheric Chemistry Transport Model
Mijie Pang, Jianbing Jin, Arjo Segers et al. (+4 more)
Atmospheric chemistry encapsulates the emission of various pollutants, the complex chemistry reactions, and the meteorology dominant transport, which form a dynamic system that governs air quality. While deep learning (DL) models have shown promise in capturing intricate patterns for
forecasting individual atmospheric component - such as PM2.5 and ozone - the critical interactions among multiple pollutants and the combined influence of emissions and meteorology are often overlook. This study introduces an advanced DL-based atmospheric chemistry transport model Zeeman for multi-component atmospheric chemistry simulation. Leveraging an
attention mechanism, our model effectively captures the nuanced relationships among these constituents. Performance metrics demonstrate that our approach rivals numerical models, offering an efficient solution for atmospheric chemistry. In the future, this model could be further integrated with data assimilation techniques to facilitate efficient and accurate atmospheric emission estimation and concentration forecast.
Read the full abstract ▾
All authors Mijie Pang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Guoqiang Wang, Hong Liao, Wei Han
Atmospheric and Oceanic Physics
Deep Learning Reconstruction of Tropical Cyclogenesis in the Western North Pacific from Climate Reanalysis Dataset
Duc-Trong Le, Tran-Binh Dang, Anh-Duc Hoang Gia et al. (+8 more)
This study presents a deep learning (DL) architecture based on residual convolutional neural networks (ResNet) to reconstruct the climatology of tropical cyclogenesis (TCG) in the Western North Pacific (WNP) basin from
climate reanalysis
datasets. Using different TCG data labeling strategies and data enrichment windows for the NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2)
dataset during the 1980-2020 period, we demonstrate that ResNet can reasonably reproduce the overall TCG climatology in the WNP, capturing both its seasonality and spatial distribution. Our sensitivity analyses and optimizations show that this TCG reconstruction depends on both the type of TCG climatology that one wishes to reconstruct and the strategies used to label TCG data. Of interest, analyses of different input features reveal that DL-based reconstruction of TCG climatology needs only a subset of channels rather than all available data, which is consistent with previous modeling and observational studies of TCG. These results not only enhance our understanding of the TCG process but also provide a promising pathway for predicting or downscaling TCG climatology based on large-scale environments from global model forecasts or
climate output. Overall, our study demonstrates that DL can offer an effective approach for studying TC climatology beyond the traditional physical-based simulations and
vortex-tracking
algorithms used in current
climate model analyses.
Read the full abstract ▾
All authors Duc-Trong Le, Tran-Binh Dang, Anh-Duc Hoang Gia, Duc-Hai Nguyen, Minh-Hoa Tien, Xuan-Truong Ngo, Quang-Trung Luu, Quang-Lap Luu, Tai-Hung Nguyen, Thanh T.N. Nguyen, Chanh Kieu
Applied Physics
The Feasibility of Acoustophoresis Multimodal Control
Guilherme Perticarari, Dongjun Wu and Thierry Baasch
Actuating the acoustic resonance modes of a microfluidic device containing suspended particles (e.g.,
cells) allows for the manipulation of their individual positions. In this work, we investigate how the number of resonance modes $M$ chosen for actuation and the number of particles $P$ affect the probability of success $S$ of manipulation tasks, denoted Acoustophoretic Control Problems (ACPs). Using simulations, we show that the ratio of locally controllable volume to the state-space volume correlates strongly with $S$. This ratio can be efficiently computed from the pressure field geometry as it does not involve solving a control problem, thus opening possibilities for experimental and numerical device optimization routines. Further, we show numerically that in noise-free 1D systems $S \approx 1 - P/M$, and that in noisy 1D and 2D systems $S$ is accurately predicted by Wendel's
Theorem. We also show that the relationship between $M$ and $P$ for a given $S$ is approximately linear, suggesting that as long as $P/M$ is constant, $S$ will remain unchanged. We validate this finding by successfully simulating the control of systems with up to $60$ particles with up to $600$ modes.
Read the full abstract ▾
Optics · Cross-listed from quant-ph
Universal computational super-resolution framework for imaging of quantum dots
Dominik Vašinka, Jaewon Lee, Charlie Stalker et al. (+9 more)
We present a universal deep-learning method that reconstructs super-resolved images of
quantum emitters from a single camera frame measurement. Trained on physics-based synthetic data spanning diverse point-spread functions, aberrations, and noise, the network generalizes across experimental conditions without system-specific retraining. We validate the approach on low- and high-density In(Ga)As
quantum dots and strain-induced dots in 2D monolayer WSe$_2$, resolving overlapping emitters even under low signal-to-noise and inhomogeneous backgrounds. By eliminating calibration and iterative acquisitions, this single-shot strategy enables rapid, robust computational super-resolution for nanoscale characterization and
quantum photonic device fabrication.
Read the full abstract ▾
All authors Dominik Vašinka, Jaewon Lee, Charlie Stalker, Victor Mitryakhin, Ivan Solovev, Sven Stephan, Sven Höfling, Falk Eilenberger, Seth Ariel Tongay, Christian Schneider, Miroslav Ježek, Ana Predojević
Optics · Cross-listed from cond-mat.mtrl-sci
Colorimetry and Tribology of Ultrapure Copper Surface Micromodification
Aleksandra Szczupak, Grzegorz Cios and Benedykt R. Jany
Controlling optical and tribological properties of metal surfaces, like color and wear rate, without altering their chemical composition is a highly desirable process across numerous fields of science and industry. It represents a cost-effective alternative to traditional chemical methods, particularly for copper, one of the most important metals widely used where high electrical and thermal conductivity, alongside resistance to corrosion, are required. We investigated the control of copper surface texture through a controlled micromodification process, utilizing constant force and velocity with abrasive silicon carbide sandpaper on ultrapure copper pellets exhibiting elongated crystallographic grains, and its impact on optical properties. Systematically varying grit size and rubbing direction, both along and across the grains, resulted in tunable microgroove morphology, demonstrating a marked difference in wear rate between single-grain and multi-grain abrasion. Furthermore, modification along copper grain boundaries yielded a change in the wear rate by a factor of two, related to single-grain and multi-grain abrasion regime changes, enabling precise control over material performance via tuned abrasion conditions. Colorimetric analysis via C-Microscopy revealed a strong, statistically significant relationship between abrasive parameters, microgroove geometry (inclination angle, depth, and size), and optical spectral signatures, which were then parametrized to achieve targeted control. This research demonstrates a simple yet effective approach to color and reflectance modification via microgroove engineering, offering a pathway to customized material properties by uniquely coupling contact mechanics, surface morphology, and colorimetry at the microscale level.
Read the full abstract ▾
Fluid Dynamics
Effect of viscoelasticity on electrohydrodynamic drop deformation
Santanu Kumar Das, Sarika Shivaji Bangar, Amaresh Dalal et al. (+1 more)
The impact of viscoelasticity on drop deformation in the presence of an electric field is investigated using both analytical and numerical methods. The study focuses on two configurations: a viscoelastic drop suspended in a Newtonian fluid and a Newtonian drop suspended in a viscoelastic medium. Oldroyd-B constitutive equation is employed to model constant viscosity viscoelasticity. Effect of Deborah number (ratio of
polymer relaxation time to convective time scale) on drop deformation is studied and explained by examining the electric, elastic and viscous stresses at the interface. For small deformations, we apply the method of domain perturbations, and show that the viscoelastic properties of the drop significantly influence its deformation more than when the surrounding fluid is viscoelastic. Numerical computations are performed using a finite volume framework for larger drop deformations. The transient dynamics of the drops show distinct oscillatory patterns before eventually stabilizing at a steady deformation value. We observe a trend of decreased deformation in both configurations as the Deborah number increases. Relative magnitude of normal and tangential stresses plays a crucial role in drop deformation.
Read the full abstract ▾
All authors Santanu Kumar Das, Sarika Shivaji Bangar, Amaresh Dalal, Gaurav Tomar
Fluid Dynamics
Turbulence Closure in RANS and Flow Inference around a Cylinder using PINNs and Sparse Experimental Data
Z. Zhang, K. Shukla, Z. Wang et al. (+8 more)
Traditional Reynolds-averaged Navier-Stokes (RANS) closures, based on the Boussinesq eddy viscosity hypothesis and calibrated on canonical flows, often yield inaccurate predictions of both mean flow and
turbulence statistics. Here, we consider flow past a circular cylinder over a range of Reynolds numbers (3,900-100,000) and Mach numbers (0-0.3), encompassing incompressible and weakly compressible regimes, with the goal of improving predictions of mean velocity and Reynolds forces. To this end, we assemble a cross-validated
dataset comprising hydrodynamic particle image velocimetry (PIV) in a towing tank, aerodynamic PIV in a wind tunnel, and high-fidelity spectral element DNS and LES. Analysis of these data reveals a universal distribution of Reynolds stresses across the parameter space, which provides the foundation for a data-driven closure. We employ physics-informed neural networks (PINNs), trained with the unclosed RANS equations, to infer the velocity field and Reynolds-stress forcing from boundary information alone. The resulting closure, embedded in a forward PINN solver and the numerical solver OpenFOAM, significantly improves RANS predictions of both mean flow and
turbulence statistics relative to conventional models.
Read the full abstract ▾
All authors Z. Zhang, K. Shukla, Z. Wang, A. Morales, T. Käufer, S. Salauddin, N. Walters, D. Barrett, K. Ahmed, M. S. Triantafyllou, G. E. Karniadakis
Chemical Physics
Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches
Rohit Goswami and Hannes Jónsson
Gaussian process (GP) regression provides a strategy for accelerating saddle point searches on high-dimensional energy surfaces by reducing the number of times the energy and its derivatives with respect to atomic coordinates need to be evaluated. The computational overhead in the hyperparameter optimization can, however, be large and make the approach inefficient. Failures can also occur if the search ventures too far into regions that are not represented well enough by the GP model. Here, these challenges are resolved by using geometry-aware optimal transport measures and an active
pruning strategy using a summation over Wasserstein-1 distances for each atom-type in farthest-point sampling, selecting a fixed-size subset of geometrically diverse configurations to avoid rapidly increasing cost of GP updates as more observations are made. Stability is enhanced by permutation-invariant metric that provides a reliable trust radius for early-stopping and a logarithmic barrier penalty for the growth of the signal variance. These physically motivated algorithmic changes prove their efficacy by reducing to less than a half the mean computational time on a set of 238 challenging configurations from a previously published data set of chemical reactions. With these improvements, the GP approach is established as, a robust and scalable
algorithm for accelerating saddle point searches when the evaluation of the energy and atomic forces requires significant computational effort.
Read the full abstract ▾
Medical Physics
MC BTS: simultaneously resolving magnetization transfer effect and relaxation for multiple components
Albert Jang, Hyungseok Jang, Nian Wang et al. (+2 more)
We propose a signal acquisition and modeling framework for multi-component tissue quantification that encompasses transmit field inhomogeneity, multi-component relaxation and magnetization transfer (MT) effects. By applying off-resonance irradiation between excitation and acquisition within an RF-spoiled gradient-echo scheme, in combination with multiple echo-time acquisitions, both Bloch-Siegert shift and magnetization transfer effects are simultaneously induced while relaxation and
spin exchange processes occur concurrently. Simulation results showed excellent agreement with the derived analytical signal equation across a wide range of flip angles and echo times. Monte Carlo analyses further validated that the three-pool parameter estimation pipeline performed robustly over various signal-to-noise ratio conditions. Multi-parameter fitting results from in vivo
brain and knee studies yielded values consistent with previously reported literature. Collectively, these findings confirm that the proposed method can reliably characterize multi-component tissue parameters in macromolecule-rich environments while effectively compensating for $B_1^+$ inhomogeneity.
Read the full abstract ▾
All authors Albert Jang, Hyungseok Jang, Nian Wang, Alexey Samsonov, Fang Liu
Physics and Society · Cross-listed from cs.SI
Emergent Directedness in Social Contagion
Fabian Tschofenig and Douglas Guilbeault
An enduring challenge in contagion theory is that the pathways contagions follow through social networks exhibit emergent complexities that are difficult to predict using network structure. Here, we address this challenge by developing a causal modeling framework that (i) simulates the possible network pathways that emerge as contagions spread and (ii) identifies which edges and nodes are most impactful on
diffusion across these possible pathways. This yields a surprising discovery. If people require exposure to multiple peers to adopt a contagion (a.k.a., 'complex contagions'), the pathways that emerge often only work in one direction. In fact, the more complex a contagion is, the more asymmetric its paths become. This emergent directedness problematizes canonical theories of how networks mediate contagion. Weak ties spanning network regions - widely thought to facilitate mutual influence and integration - prove to privilege the spread contagions from one community to the other. Emergent directedness also disproportionately channels complex contagions from the network periphery to the core, inverting standard centrality models. We demonstrate two practical applications. We show that emergent directedness accounts for unexplained nonlinearity in the effects of tie strength in a recent study of job
diffusion over LinkedIn. Lastly, we show that network
evolution is biased toward growing directed paths, but that cultural factors (e.g., triadic closure) can curtail this bias, with strategic implications for network building and behavioral interventions.
Read the full abstract ▾
Physics and Society
Peoples Water Data: A Scalable Student-Led Methodology for Global Water Security
Suzan Kagan, Sankar Sudhir, Ben Hamilton et al. (+10 more)
Peoples Water Data proves that a people-powered, open-data model can deliver reliable household water evidence at scale, driving equitable decisions, accountability, and safer water where it is most needed.
Read the full abstract ▾
All authors Suzan Kagan, Sankar Sudhir, Ben Hamilton, Ramya Dwivedi, P. Amaravathy, Alona Maslennikov, Timor Lichtman, Sonali Seth, Tanmayaa Nayak, Ganesan Velmurugan, Hadas Mamane, Ram Fishman, Thalappil Pradeep
Optics
The Fourier modal method for gratings with bi-anisotropic materials
Ilia Smagin, Sergey Dyakov and Nikolay Gippius
We report an advanced formulation of the
Fourier modal method developed for two-dimensionally periodic multilayered structures containing materials with non-zero macroscopic magneto-electric coefficients (also known as coefficients of chirality and bi-anisotropy) represented as arbitrary 3 by 3 tensors. We consider two numerical schemes for this formulation: with and without generalized
Fourier factorization rules. For both schemes, we provide explicit expressions for the
Fourier tensors of macroscopic material parameters and demonstrate that, in the absence of magneto-electric coupling, they reduce to the conventional factorization rules. We show that the scheme employing factorization rules facilitates improved convergence, even when the macroscopic chirality coefficient is large. The described formulation represents a fast and rigorous technique for theoretical studies of periodic structures with
chiral, bi-anisotropic, or non-reciprocal materials in the widely used framework of the
Fourier modal method.
Read the full abstract ▾
Fluid Dynamics
Quantum Lattice Boltzmann Method for Multiple Time Steps Without Reinitialization for Linear Advection-Diffusion Problems
Aaron Nagel and Johannes Löwe
To simulate highly-resolved flow fields, we extend the
Quantum Lattice Boltzmann Method (QLBM) to be able to simulate multiple time steps without state extraction or reinitialization. We adjust and extend given QLBM approaches from the literature to completely remove the need to measure or reinitialize the flow field in between the simulation time steps. Therefore, our
algorithm does not require to sample the entire flow field at any time. We solve the linear advection-
diffusion problem and derive all necessary equations and build the corresponding
quantum circuit diagrams, including details on the QLBM blocks and explicitly drawing the
circuit gates. We discuss the general decay of a QLBM step and how that effects our
algorithm. The new
algorithm is verified on 1D and 2D test cases using the shot method of IBMs Qiskit package. We show excellent agreement and convergence between our QLBM and classical LBM methods. The conclusion section includes a discussion on the advantages of our
algorithm as well as limitations and to what extent it is more efficient.
Read the full abstract ▾
Atmospheric and Oceanic Physics
Representing Subgrid-Scale Cloud Effects in a Radiation Parameterization using Machine Learning: MLe-radiation v1.0
Katharina Hafner, Sara Shamekh, Guillaume Bertoli et al. (+4 more)
Improvements of Machine Learning (ML)-based radiation emulators remain constrained by the underlying assumptions to represent horizontal and vertical subgrid-scale cloud distributions, which continue to introduce substantial uncertainties. In this study, we introduce a method to represent the impact of subgrid-scale clouds by applying ML to learn processes from high-resolution model output with a horizontal grid spacing of 5km. In global storm resolving models, clouds begin to be explicitly resolved. Coarse-graining these high-resolution simulations to the resolution of coarser Earth System Models yields radiative heating rates that implicitly include subgrid-scale cloud effects, without assumptions about their horizontal or vertical distributions. We define the cloud radiative impact as the difference between all-sky and clear-sky radiative fluxes, and train the ML component solely on this cloud-induced contribution to heating rates. The clear-sky tendencies remain being computed with a conventional physics-based radiation scheme. This hybrid design enhances generalization, since the machine-learned part addresses only subgrid-scale cloud effects, while the clear-sky component remains responsive to changes in greenhouse gas or aerosol concentrations. Applied to coarse-grained data offline, the ML-enhanced radiation scheme reduces errors by a factor of 4-10 compared with a conventional coarse-scale radiation scheme. This shows the potential of representing subgrid-scale cloud effects in radiation schemes with ML for the next generation of Earth System Models.
Read the full abstract ▾
All authors Katharina Hafner, Sara Shamekh, Guillaume Bertoli, Axel Lauer, Robert Pincus, Julien Savre, Veronika Eyring
Computational Physics
msmJAX: Fast and Differentiable Electrostatics on the GPU in Python
Florian Buchner, Johannes Schörghuber, Nico Unglert et al. (+2 more)
We present msmJAX, a Python package implementing the multilevel summation method with B-spline interpolation, a linear-scaling
algorithm for efficiently evaluating electrostatic and other long-range interactions in particle-based simulations. Built on the JAX framework, msmJAX integrates naturally with the machine-learning methods that are transforming chemistry and materials science, while also serving as a powerful tool in its own right. It combines high performance with Python's accessibility, offers easy deployment on GPUs, and supports automatic differentiation. We outline the modular design of msmJAX, enabling users to adapt or extend the code, and present
benchmarks and examples, including a verification of linear scaling, and demonstrations of its stability in molecular-dynamics simulations.
Read the full abstract ▾
All authors Florian Buchner, Johannes Schörghuber, Nico Unglert, Jesús Carrete, Georg K. H. Madsen
Plasma Physics · Cross-listed from astro-ph.SR
Kinetic collisionless model of the solar transition region and corona with spatially intermittent heating
Luca Barbieri and Pascal Démoulin
We develop a three-dimensional kinetic model of the
solar transition region and corona in which the
plasma above the chromosphere is collisionless and embedded in a uniform
magnetic field. Heating occurs intermittently at discrete locations on the chromospheric surface, modeled through a surface coarse-graining procedure that produces non-thermal boundary conditions for the Vlasov equation. The resulting stationary distribution functions generate suprathermal particle populations and naturally lead to a temperature inversion via gravitational filtering, without any local coronal heating. The model reproduces realistic temperature and density profiles with a thin transition region and a hot corona, consistent with
solar observations. These results demonstrate that the spatial intermittency of heating at the chromospheric interface is sufficient to account for the formation of the transition region and the high-temperature corona.
Read the full abstract ▾
Optics
Unveiling the Bright Spot with Mie Scattering
Vinícius dos Passos de Souza and Antonio Alvaro Ranha Neves
This work applies Mie scattering theory to provide a new perspective on the propagation of light through a spherical obstacle, offering a novel explanation for the formation of the Poisson spot (also known as the Arago or Fresnel spot). We demonstrate that the diffraction patterns generated by a sphere and by a circular disk can be understood as complementary outcomes of the same underlying scattering process. Our analysis highlights the constructive interference responsible for the bright central spot, and extends the classical wave optics framework by connecting it directly with the scattering coefficients of spherical harmonics. This approach not only deepens the theoretical understanding of diffraction phenomena, but also provides a practical framework that may be applied in modern optical experiments and photonic device design.
Read the full abstract ▾
Chemical Physics
Large circular dichroism in the total photoemission yield of free chiral nanoparticles created by a pure electric dipole effect
Sebastian Hartweg, Dusan k. Bozanic, Gustavo A. Garcia-Macias et al. (+1 more)
Spectroscopic techniques that are sensitive to molecular chirality are important analytical tools to quantitatively determine enantiomeric excess and purity of
chiral molecular samples. Many chiroptical processes however produce weak enantio-specific asymmetries due to their origin relying on weak
magnetic dipole or electric quadrupole effects. Photoelectron circular dichroism (PECD) in contrast, is an intense effect, that is fully contained in the electric dipole description of light matter interaction and creates a
chiral asymmetry in the photoelectron angular distribution. Here, we demonstrate that this
chiral signature in the angular distribution of emitted electrons can be translated into the total photoionization yield for submicron-sized condensed samples. The resulting
chiral asymmetry of the photoionization yield (CAPY), mediated by the attenuation of light within the particles, can be detected experimentally without requiring high
vacuum systems and electron spectrometers. This effect can be exploited as an analytical tool with high sensitivity to chirality and enantiopurity for studies of
chiral organic and hybrid submicron particles in environmental, biomedical or catalytic applications.
Read the full abstract ▾
All authors Sebastian Hartweg, Dusan k. Bozanic, Gustavo A. Garcia-Macias, Laurent Nahon
Instrumentation and Detectors · Cross-listed from nucl-ex
Advances in Radiative Capture Studies at LUNA with a Segmented BGO Detector
Jakub Skowronski, Riccardo Maria Gesuè, Axel Boeltzig et al. (+43 more)
Studies of charged-particle reactions for low-energy
nuclear astrophysics require high sensitivity, which can be achieved by means of detection setups with high efficiency and low backgrounds, to obtain precise measurements in the energy region of interest for
stellar scenarios. High-efficiency total absorption spectroscopy is an established and powerful tool for studying radiative capture reactions, particularly if combined with the
cosmic background reduction by several orders of magnitude obtained at the Laboratory for Underground
Nuclear Astrophysics (LUNA). We present recent improvements in the detection setup with the Bismuth Germanium
Oxide (BGO) detector at LUNA, aiming to reduce high-energy backgrounds and to increase the summing detection efficiency. The new design results in enhanced sensitivity of the BGO setup, as we demonstrate and discuss in the context of the first direct measurement of the 65 keV resonance ($E_{x} = 5672$ keV) of the $^{17}$O($p,\gamma$)$^{18}$F reaction. Moreover, we show two applications of the BGO detector, which exploit its
segmentation. In case of complex $\gamma$-ray cascades, e.g. the de-excitation of $E_{x} = 5672$ keV in $^{18}$F, the BGO
segmentation allows to identify and suppress the beam-induced background signals that mimic the sum peak of interest. We demonstrate another new application for such a detector in form of in-situ activation measurements of a reaction with $\beta ^{+}$ unstable product
nuclei, e.g., the $^{14}$N($p,\gamma$)$^{15}$O reaction.
Read the full abstract ▾
All authors Jakub Skowronski, Riccardo Maria Gesuè, Axel Boeltzig, Giovanni Francesco Ciani, Denise Piatti, David Rapagnani, Marialuisa Aliotta, Chemsedinne Ananna, Francesco Barile, Daniel Bemmerer, Andreas Best, Carlo Broggini, Carlo Giovanni Bruno, Antonio Caciolli, Matteo Campostrini, Francesca Cavanna, Paolo Colombetti, Alessandro Compagnucci, Piero Corvisiero, Laszlo Csedreki, Tom Davinson, Rosanna Depalo, Antonino Di Leva, Zoltan Elekes, Federico Ferraro, Alba Formicola, Zsolt Fülöp, Gianpiero Gervino, Alessandra Guglielmetti, Carlo Gustavino, György Gyürky, Gianluca Imbriani, Matthias Junker, Maria Lugaro, Paola Marigo, Eliana Masha, Roberto Menegazzo, Vincenzo Paticchio, Roberto Perrino, Paolo Prati, Valentino Rigato, Luigi Schiavulli, Ragandeep Singh Sidhu, Oscar Straniero, Tamás Szűcs, Sandra Zavatarelli
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Retardance of lab grown diamond substrates as a function of thickness: momentum-drift random walk model
Thanh Tran, Phuong Vo, Thomas Sheppard et al. (+2 more)
This work studies the correlation between mean retardance and thickness of diamond substrates grown homoepitaxially via microwave
plasma-enhanced chemical vapor deposition (MPCVD). We measure the retardance of a diamond substrate in two orientations: perpendicular and parallel to the growth direction. Our experimental results demonstrate that the correlation between mean retardance and thickness differs for these orientations. When measured perpendicular to the growth direction, the mean retardance is approximately proportional to the square root of the substrate thickness. In contrast, when measured parallel to the growth direction, we observe a generally higher mean retardance and an approximately linear correlation with thickness. This anisotropy arises not from differences in stress magnitude but from differences in the interlayer correlation of the principal stress axes, as evidenced by correlation coefficients between the azimuth angles of consecutive layers in the diamond
crystal. To simulate the integrated retardance of diamond wafers, we propose a two-dimensional random walk model with momentum drift, which captures the diamond
crystal tendency to preserve the azimuth angle across the samples. By optimizing the momentum factor, we show that the model can closely match experimental data. The momentum factor is found higher along the growth direction, which is consistent with the calculated correlation coefficients. Furthermore, both the model and experiments indicate that retardance-to-thickness ratios of thin samples converge toward similar base retardances in both orientations. These findings establish a quantitative framework for interpreting birefringence in diamond substrates, with implications for material selection and development in thermal management,
quantum sensing, high-power electronics, and optical applications.
Read the full abstract ▾
All authors Thanh Tran, Phuong Vo, Thomas Sheppard, Timothy Grotjohn, Paul Quayle
Applied Physics · Cross-listed from cond-mat.mtrl-sci
High- and medium-entropy nitride coatings from the Cr-Hf-Mo-Ta-W-N system: properties and high-temperature stability
Pavel Souček, Stanislava Debnárová, Šárka Zuzjaková et al. (+9 more)
High- and medium-
entropy nitride coatings from the Cr-Hf-Mo-Ta-W-N system were studied using ab initio calculations and experiments to clarify the role of
entropy and individual elements in phase stability, microstructure, and high-temperature behaviour. Formation energy calculations indicated that nitrogen vacancies stabilise the cubic (fcc) phase, with hafnium and tantalum acting as strong stabilisers, while tungsten destabilises the
lattice. Coatings were deposited by reactive magnetron sputtering at approx. 50C (AT) and approx. 580C (HT). All exhibited columnar fcc structures; high-temperature deposition produced denser coatings, lower nitrogen content, and larger crystallites, resulting in higher hardness and elastic modulus. Thermal stability was tested up to 1200C on Si and oxidation at 1400C on sapphire. AT coatings failed early, while most HT coatings endured. Nitrogen loss less than 10 at.% at 1000C was critical for survival. TEM revealed tungsten segregation and HfO2 formation, while fcc nitride remained dominant. Ta enrichment proved essential for superior thermal and oxidation stability.
Read the full abstract ▾
All authors Pavel Souček, Stanislava Debnárová, Šárka Zuzjaková, Shuyao Lin, Matej Fekete, Zsolt Czigány, Katalin Balázsi, Lukáš Vrána, Tatiana Pitoňáková, Ondřej Jašek, Petr Zeman, Nikola Koutná
Instrumentation and Detectors
Low-energy Cross Section Measurements of ${}^{\mathsf{12}}\mathsf{C}(\mathsf{p},\gamma)$ Deep Underground at LUNA
Jakub Skowronski and Axel Boeltzig
The ${}^{\mathsf{12}}\mathsf{C}(\mathsf{p},\gamma)$ reaction cross section is currently under investigation in the low-background environment of the Laboratory for Underground
Nuclear Astrophysics (LUNA). It is being studied using different types of solid targets, and employing two complementary detection techniques: HPGe spectroscopy and activation counting. To reduce systematic uncertainties, targets have been accurately characterized and their degradation under the intense beam of the LUNA-400 accelerator monitored. We present the experimental techniques and the corresponding analyses used to extract the reaction cross section.
Read the full abstract ▾
Plasma Physics
Investigating the Lower Hybrid Drift Instability in Reconnecting Current Sheets Using a Hybrid Kinetic Model (ssV Code)
S.Thatikonda, F. N. De Oliveira-Lopes, A. Mustonen et al. (+3 more)
We investigate the nonlinear
evolution of the lower hybrid drift instability (LHDI) in reconnecting current sheets using a hybrid kinetic simulation model implemented in the Super Simple Vlasov (ssV) code. The model treats ions kinetically and electrons with a drift-kinetic approximation, solving self-consistent coupled electrostatic and electromagnetic fields. A parametric study explores the effects of mass ratio, temperature ratio,
plasma beta, and sheet thickness. In electrostatic cases, LHDI remains localized at the sheet edges, flattening density gradients. In electromagnetic regimes,
turbulence induced by LHDI generates
magnetic perturbations that kink the current sheet and enhance anomalous resistivity. These dynamics may facilitate fast
magnetic reconnection under certain conditions. Our results bridge prior theoretical predictions and simulations, emphasizing the importance of kinetic instabilities in reconnection physics.
Read the full abstract ▾
All authors S.Thatikonda, F. N. De Oliveira-Lopes, A. Mustonen, K. Pommois, D.Told, F.Jenko
Instrumentation and Detectors · Cross-listed from cond-mat.supr-con
Superconducting Nanowire Single Photon Detectors based on NbRe nitride ultrafilms
F. Avitabile, F. Colangelo, M. Yu. Mikhailov et al. (+5 more)
The influence of the reactive DC sputtering parameters on the superconducting properties of NbReN ultrathin films was investigated. A detailed study of the current-voltage characteristics of the
plasma was performed to optimize the superconducting critical temperature, Tc. The thickness dependence of Tc for the films deposited under different conditions was analyzed down to the ultrathin limit. Optimized films were used to fabricate superconducting nanowire single
photon detectors which, at T=3.5 K, show saturated internal detection efficiency (IDE) up to a wavelength of 1301 nm and 95% IDE at 1548 nm with recovery times and timing jitter of about 8 ns and 28 ps, respectively.
Read the full abstract ▾
All authors F. Avitabile, F. Colangelo, M. Yu. Mikhailov, Z. Makhdoumi Kakhaki, A. Kumar, I. Esmaeil Zadeh, C. Attanasio, C. Cirillo
Fluid Dynamics
Radially weighted Backus- and Childress-type bounds for spherical dynamos
Ralf Kaiser and Andreas Tilgner
In MHD dynamo theory well-known necessary criteria for dynamo action are formulated in terms of lower bounds either on the maximum modulus of the velocity field (Childress-type) or the maximum strain of the velocity field (Backus-type). We generalize these criteria for spherical dynamos by introducing a radially varying weight $f(r)$. The corresponding lower bound Reynolds numbers $R_{lb}^C [f]$ (based on velocity) and $R_{lb}^B [f]$ (based on strain) are determined for two types of such weights: a power law profile $f(r) = r^\alpha$, $0\leq \alpha \leq 2$ and an optimal radial profile $f_v$ depending on the velocity field $\bf{v}$ in question. To assess the quality of these lower bounds we compare them with weighted critical Reynolds numbers $R_c^C$ (Childress-type) and $R_c^B$ (Backus-type), respectively, for the onset of dynamo action of the well known efficient $s_2t_2$ velocity field (Dudley & James 1989) and a recently determined “most efficient” velocity field (Chen et al.\ 2018). For the latter field we find a Backus-type ratio $R^B_c /R^B_{lb}$ of about $6.4$ with the optimal profile compared to a ratio of about $16.3$ without weight.
Read the full abstract ▾
Instrumentation and Detectors
Low-energy threshold demonstration for dark matter searches in TREX-DM with an $^{37}$Ar source produced at CNA HiSPANoS
J. Castel, S. Cebrián, T. Dafni et al. (+16 more)
We report on the successful implementation of an $^{37}$Ar calibration source in the TREX-DM detector, a high-pressure time projection chamber designed for low-mass dark matter searches. The $^{37}$Ar source was produced through fast
neutron activation of CaO powder at the HiSPANoS facility of Centro Nacional de Aceleradores (CNA) in Spain, yielding $O(1)$ kBq of activity. Using a novel combined GEM-Micromegas readout system, we successfully detected both characteristic emissions from $^{37}$Ar decay (2.82 keV and 270 eV) and achieved unprecedented energy threshold performance in TREX-DM, approaching the single-electron ionization energy of argon.
Read the full abstract ▾
All authors J. Castel, S. Cebrián, T. Dafni, D. Díez-Ibáñez, A. Ezquerro, B. Fernández, J. Galán, J.A. García, C. Guerrero, I.G. Irastorza, G. Luzón, C. Margalejo, H. Mirallas, L. Obis, A. Ortiz de Solórzano, O. Pérez, J. Porrón, M. J. Puyuelo, A. Quintana
Plasma Physics · Cross-listed from astro-ph.SR
Exploring Complexity Measures for Analysis of Solar Wind Structures and Streams
Venla Koikkalainen, Emilia Kilpua, Simon Good et al. (+1 more)
In this paper we use statistical complexity and information theory metrics to study structure within
solar wind time series. We explore this using
entropy-complexity and information planes, where the measure for
entropy is formed using either permutation
entropy or the degree distribution of a horizontal visibility graph (HVG). The
entropy is then compared to the Jensen complexity (Jensen-Shannon complexity plane) and Fisher information measure (Fisher-Shannon information plane), formed both from permutations and the HVG approach. Additionally we characterise the
solar wind time series by studying the properties of the HVG degree distribution. Four types of
solar wind intervals have been analysed, namely fast streams, slow streams,
magnetic clouds and sheath regions, all of which have distinct origins and interplanetary characteristics. Our results show that, overall, different metrics give similar results but Fisher-Shannon, which gives a more local measure of complexity, leads to a larger spread of values in the
entropy-complexity plane.
Magnetic cloud intervals stood out in all approaches, in particular when analysing the
magnetic field magnitude. Differences between
solar wind types (except for
magnetic clouds) were typically more distinct for larger time lags, suggesting universality in fluctuations for small scales. The fluctuations within the
solar wind time series were generally found to be
stochastic, in agreement with previous studies. The use of information theory tools in the analysis of
solar wind time series can help to identify structures and provide insight into their origin and formation.
Read the full abstract ▾
All authors Venla Koikkalainen, Emilia Kilpua, Simon Good, Adnane Osmane