arXiv Physics @arxiv_physics@qoto.org

The QCLAM electron spectrometer at the S-DALINAC electron accelerator at Technische Universität Darmstadt has been extended by the DAGOBERT $γ$-detector array consisting of fast timing and high efficiency LaBr$_3$:Ce detectors to perform electron-gamma coincidence measurements. The functionality of the setup and data acquisition system was demonstrated in a commissioning measurement on $^{12}\textrm{C}$ observing the $4.44\,$MeV and $15.11\,$MeV states. A medium-heavy nucleus, $^{96}\textrm{Ru}$, has been studied for the first time up to excitation energies of $15\,$MeV using the $(e,e'γ)$ reaction. In particular, the angular distribution of the $2_1^+$ state and the $γ$-decay branching ratios of the mixed-symmetric $2_3^+$ state were observed. DAGOBERT@QCLAM is a new and worldwide unique setup for nuclear structure studies of excitation and decay using purely electromagnetic probes, with a significantly improved sensitivity compared to previous experiments.

For computational spectrometers, we propose an inverse-design approach in which the scattering media are topology-optimized to achieve better performance in inference, without the need of a training set of spectra and a distribution of detector noise. Our approach also allows the selection of the inference algorithm to be decoupled from that of the scatterer. For smooth spectra, we additionally devise a regularized reconstruction algorithm based on Chebyshev interpolation, which yields higher accuracy compared with conventional methods in which the spectra are sampled at equally spaced frequencies/wavelengths with equal weights. Our approaches are numerically demonstrated via inverse design of integrated computational spectrometers and reconstruction of example spectra. The inverse-designed spectrometers exhibit significantly better performance in the presence of noise than their counterparts with random scatterers.

With the increasing brightness of Light sources, including the Diffraction-Limited brightness upgrade of APS and the high-repetition-rate upgrade of LCLS, the proposed experiments therein are becoming increasingly complex. For instance, experiments at LCLS-II-HE will require the X-ray beam to be within a fraction of a micron in diameter, with pointing stability of a few nanoradians, at the end of a kilometer-long electron accelerator, a hundred-meter-long undulator section, and tens of meters long X-ray optics. This enhancement of brightness will increase the data production rate to rival the largest data generators in the world. Without real-time active feedback control and an optimized pipeline to transform measurements to scientific information and insights, researchers will drown in a deluge of mostly useless data, and fail to extract the highly sophisticated insights that the recent brightness upgrades promise. In this article, we outline the strategy we are developing at SLAC to implement Machine Learning driven optimization, automation and real-time knowledge extraction from the electron-injector at the start of the electron accelerator, to the multidimensional X-ray optical systems, and till the experimental endstations and the high readout rate, multi-megapixel detectors at LCLS to deliver the design performance to the users. This is illustrated via examples from Accelerator, Optics and End User applications.

Bragg diffraction is a fundamental technique used to enhance the sensitivity of atom interferometers through large momentum transfer, making these devices among the most precise quantum sensors available today. To further improve their accuracy, it is necessary to achieve control over multiple interferometer paths and increase robustness against velocity spread. Optimal control theory has recently led to advancements in sensitivity and robustness under specific conditions, such as vibrations, accelerations, and other experimental challenges. In this work, we employ this tool to focus on improving the accuracy of the interferometer by minimizing the diffraction phase. We consider the finite temperature of the incoming wavepacket and the multi-path nature of high-order Bragg diffraction as showcased in a Mach-Zehnder(MZ) geometry. Our approach can achieve diffraction phases on the order of microradians or even below a microradian for a momentum width of the incoming wavepacket $σ_p = 0.01\hbar k$, below a milliradian for $σ_p= 0.1 \hbar k$ and milliradians for $σ_p = 0.3 \hbar k$.

We use a three-dimensional formulation of the cell vertex model to describe the mechanical properties of a confluent planar monolayer of prismatic cells. Treating cell height as a degree of freedom, we reduce the model to a two-dimensional form. We show how bulk effects, associated with cell volume and total surface area, lead to coupling between energy variations arising from changes in cell apical area and apical perimeter, a feature missing from standard implementations of the two-dimensional vertex model. The model identifies four independent mechanisms by which cells can lose in-plane rigidity, relating to variations in total cell surface area, the strength of lateral adhesion, and constrictive forces at the apical cortex. The model distinguishes bulk from in-plane stresses, and identifies two primary measures of cell shear stress. In the rigid regime, the model shows how lateral crowding in a disordered isolated monolayer can lead to cell elongation towards the monolayer centre. We examine loss of in-plane rigidity in a disordered monolayer and connect isolated patches of stiffness that persist during the rigidity transition to the spectrum of a Laplacian matrix. This approach enables bulk mechanical effects in an epithelium to be captured within a two-dimensional framework.

Turbulent flows and fluid-structure interactions (FSI) are ubiquitous in scientific and engineering applications, but their accurate and efficient simulation remains a major challenge due to strong nonlinearities, multiscale interactions, and high computational demands. Traditional CFD solvers, though effective, struggle with scalability and adaptability for tasks such as inverse modeling, optimization, and data assimilation. Recent advances in machine learning (ML) have inspired hybrid modeling approaches that integrate neural networks with physics-based solvers to enhance generalization and capture unresolved dynamics. However, realizing this integration requires solvers that are not only physically accurate but also differentiable and GPU-efficient. In this work, we introduce Diff-FlowFSI, a GPU-accelerated, fully differentiable CFD platform designed for high-fidelity turbulence and FSI simulations. Implemented in JAX, Diff-FlowFSI features a vectorized finite volume solver combined with the immersed boundary method to handle complex geometries and fluid-structure coupling. The platform enables GPU-enabled fast forward simulations, supports automatic differentiation for gradient-based inverse problems, and integrates seamlessly with deep learning components for hybrid neural-CFD modeling. We validate Diff-FlowFSI across a series of benchmark turbulence and FSI problems, demonstrating its capability to accelerate scientific computing at the intersection of physics and machine learning.

In this work, we propose a prototype set-up exploiting terahertz time-domain spectroscopy (THz-TDS) to investigate gaseous compounds. The system is portable and allows to perform remote measurements. We used the prototype to characterise for the first time in literature over a broad THz range, pure dichloromethane and chloroform, two pollutants known as very short-lived substances (VSLS) that strongly contribute to ozone depletion. The THz range allows selectively detecting their absorption lines related to the rotational molecular motion for which we also present the theoretical confirmation. Then, we investigate the optical response of a multi-component mixture achieved with the two aforementioned chlorine-based compounds mixed with two widely distributed volatile pollutants (acetone and methanol). For these first measurements, we developed the set-up specifically for laboratory condition in which the substances are directly injected into the gas-cell circuit. Finally, we modified the prototype to ensure that the ambient atmosphere is drawn directly into the gas cell via a long pipe and a suction system opportunely developed. The analysis of the mixtures in both laboratory and in-field conditions demonstrates that the prototype together with the approach employed in this work can simultaneously identify and quantify single components in the atmosphere. The results obtained open up new possibilities for the development and applications of an efficient portable THz-based sensor for the remote detection of multi-component environmental contaminants.

Understanding condensed-phase polariton experiments requires accurately accounting for both realistic cavity geometries and the interplay between polaritons and material dark modes arising from microscopic molecular interactions. The finite-difference time-domain (FDTD) approach numerically propagates classical Maxwell's equations in the time domain, offering a versatile scheme for modeling polaritons in realistic cavities. However, the simple dielectric functions routinely used in FDTD often fail to describe molecular details. Consequently, standard FDTD calculations, to date, cannot accurately describe processes involving the complex coupling between polaritons and dark modes, such as polariton relaxation, transport, and condensation. For more faithful simulations of the energy flow between the polaritons and dark modes, herein, local bath degrees of freedom coupled to the material polarization are explicitly included in FDTD to describe the dark-mode dynamics. This method -- FDTD with auxiliary bath fields (FDTD-Bath) -- is implemented in the open-source MEEP package by adding a Lorentz-Bath material susceptibility, where explicit bath modes are coupled to conventional Lorentz oscillators. With this Lorentz-Bath susceptibility, linear polariton spectra and Rabi-splitting-dependent polariton relaxation rates in planar Fabry--Pérot cavities are reproduced more accurately than those with the conventional Lorentz susceptibility. Supported by a user-friendly Python interface and efficient MPI parallelism, the FDTD-Bath approach implemented in MEEP is ready to model a wide range of polariton phenomena involving realistic cavity geometries.

Approximately seventy-one percent of the Earth is covered in water. Of that area, ninety-five percent of the ocean has never been explored or mapped. There are several engineering challenges that have prevented the exploration of the deep ocean through human or autonomous means. These challenges include but are not limited to high pressure, cold temperatures, little natural light, corrosion of materials, and communication. Ongoing research has been focused on trying to find optimal and low-cost solutions to effective communication between autonomous underwater vehicles (AUVs), and the surface or air. In this paper, an architecture is introduced that utilizes an edge computing approach to establish computation nearer to the source of data, allowing further exploration of the deep ocean. Taking the most common interpolation techniques used today in the field of bathymetry, the data are tested and analyzed to find the feasibility of switching from CPU to GPU computation. Specifically, the focus is on writing efficient interpolation algorithms that can be run on low-level GPUs, which can be carried onboard AUVs as payload.

Aortic valve replacement is a key surgical procedure for treating aortic valve pathologies, such as stenosis and regurgitation. The precise placement of the prosthetic valve relative to the native aortic annulus plays a critical role in the post-operative hemodynamics. This study investigates how the positioning of a biological prosthetic valve-either intra-annular (within the native annulus) or supra-annular (slightly downstream, in the widened portion of the aortic root)-affects cardiac fluid dynamics. Using high-fidelity numerical simulations on a patient-specific left heart model derived from CT imaging, we simulate physiological flow conditions to isolate the impact of valve placement. Unlike previous clinical studies that compare different patients and valve models, our approach evaluates the same valve in both positions within a single virtual patient, ensuring a controlled comparison. Key hemodynamic parameters are assessed, including transvalvular pressure drop, effective orifice area, wall shear stress, and hemolysis. Results reveal that supra-annular implantation offers significant advantages: lower pressure gradients, larger orifice area, and reduced shear-induced stress. Furthermore, hemolysis analysis using advanced red blood cell stress models indicates a decreased risk of blood damage in the supra-annular configuration. These findings offer valuable insights to guide valve selection and implantation strategies, ultimately supporting improved patient outcomes.

Turbulence, left unforced, decays and invades the surrounding quiescent fluid. Though ubiquitous, this simple phenomenon has proven hard to capture within a simple and general framework. Experiments in conventional turbulent flow chambers are inevitably complicated by proximity to boundaries and mean flow, obscuring the fundamental aspects of the relaxation to the quiescent fluid state. Here, we circumvent these issues by creating a spatially-localized blob of turbulent fluid using eight converging vortex generators focused towards the center of a tank of water, and observe its decay and spread over decades in time, using particle image velocimetry with a logarithmic sampling rate. The blob initially expands and decays until it reaches the walls of the tank and eventually transitions to a second regime of approximately spatially uniform decay. We interpret these dynamics within the framework of a nonlinear diffusion equation, which predicts that the ideal quiescent-turbulent fluid boundary is sharp and propagates non-diffusively, driven by turbulent eddies while decaying with characteristic scaling laws. We find direct evidence for this model within the expansion phase of our turbulent blob and use it to account for the detailed behavior we observe, in contrast to earlier studies. Our work provides a detailed spatially-resolved narrative for the behavior of turbulence once the forcing is removed, and demonstrates unexpectedly that the turbulent cascade leaves an indelible footprint far into the decay process.

Accurate depth estimation of magnetic sources plays a crucial role in various geophysical applications, including mineral exploration, resource assessments, regional hydrocarbon exploration, and geological mapping. Thus, this abstract presents a fast and simple method of estimating the depth of a magnetic body using the TDX derivative of the total magnetic field. TDX is a first-order derivative of the magnetic field that, in addition to edge detection, is less affected by noise, allowing for better depth resolution. The reduced sensitivity to noise enables a clearer estimation of depth and enhances the accuracy of the depth determination process. The TDX, as a variant of the phase derivative, is independent of magnetization and can be used to identify the edge of a magnetic body. In addition to excelling at edge detection, they can also estimate the depth of the magnetic source producing the anomalies. In this study, we explore the utilization of contour of the TDX derivative for estimating depth, assuming a vertical contact source. We demonstrate the effectiveness of the method using a two-prism block model and a simple bishop model with a uniform susceptibility of 0.001 cgs. The results agree with the known depth, providing evidence of the reliability of the method despite the restrictive nature of the assumption, especially for the Bishop model, where there are numerous fault structures.

The evolution of rotor wakes is an important problem for a wide range of wind-energy and aerodynamic applications, and is of particular relevance to dynamic wake control strategies for wind farms. This study aims to clarify the influence of turbine thrust and tip-speed ratio on tip-vortex breakdown and the evolution of the near wake. Scaling arguments show that these parameters contribute to the wake dynamics in distinct ways, and that neither thrust nor tip-speed ratio are alone sufficient to describe near and intermediate wake development. These considerations are especially critical for time-varying flows. To demonstrate these principles, a wind turbine at a near utility-scale Reynolds number ($Re_D=4\times10^6$) is forced in periodic rotation-rate oscillations at low Strouhal numbers ($St=0.04$). The slow time-varying forcing protocol decouples thrust and tip-speed ratio effects without introducing nonlinear dynamics into the wake that would appear at higher forcing frequencies. Flow measurements in the wake of the turbine show disturbances propagate through the wake as traveling waves, with thrust and tip-speed ratio variations displaying synergistic or competing effects on wake dynamics depending on the relative phase and amplitude of such disturbances. The results provide key insights into the dynamics of time-varying wakes, limitations in existing models of rotor wake dynamics, and to future novel wake-control schemes.

A slender two-dimensional (2D) channel bounded by a rigid bottom surface and a slender elastic layer above deforms when a fluid flows through it. Hydrodynamic forces cause deformation at the fluid-solid interface, which in turn changes the cross-sectional area of the fluidic channel. The nonlinear coupling between flow and deformation, along with the attendant asymmetry in geometry caused by flow-induced deformation, produces a streaming effect (a non-zero cycle-average despite time-periodic forcing). Surprisingly, fluid inertia provides another nonlinear coupling, tightly connected to deformation, that enhances streaming, termed ``elastoinertial rectification'' by Zhang and Rallabandi [J. Fluid Mech. 996, A16 (2024)]. We adapt the latter theory of how two-way coupled fluid--structure interaction (FSI) produces streaming to a 2D rectangular configuration, specifically taking care to capture the deformations of the nearly incompressible slender elastic layer via the combined foundation model of Chandler and Vella [Proc. R. Soc. A 476, 20200551 (2020)]. We supplement the elastoinertial rectification theory with direct numerical simulations performed using a conforming arbitrary Lagrangian-Eulerian (ALE) FSI formulation with streamline upwind Petrov-Galerkin stabilization, implemented via the open-source computing platform FEniCS. We examine the axial variation of the cycle-averaged pressure as a function of key dimensionless groups of the problem: the Womersley number, the elastoviscous number, and the compliance number. Assuming a small compliance number, we find excellent agreement between theory and simulations for the leading-order pressure and deformation across a range of conditions. At the next order, the cycle-averaged pressures agree well; however, the cycle-averaged deformation is found to exhibit significant axial and vertical displacements, unlike the combined foundation model.

I show that when there is a symmetry in a process defined on the nodes of a network, this can be captured by a new structure, the ``group graph'', in which group elements label the links of a network. I show that group graphs are a generalisation of signed networks which are an example of a $Z_2$ group graph. I also show that the concept of balance in signed networks can be generalised to group graphs. Finally, I show how the dynamics of processes on a consistent group graph are completely controlled by the topology of the underlying network, not by the symmetry group. This generalises recent results on signed networks (Tian and Lambiotte, 2024a) and complex networks (Tian and Lambiotte, 2024b).