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Dielectric capillaries are widely used to generate plasmas for plasma wakefield acceleration. When a relativistic drive bunch travels through a capillary with misaligned trajectory with respect to the capillary axis, it is deflected by the effect of the dielectric transverse wakefields it drives. We experimentally show that the deflection effect increases along the bunch and with larger misalignment, and we investigate the decay of dielectric wakefields by measuring the effect on the front of a trailing bunch. We discuss the implications for the design of a plasma wakefield accelerator based on dielectric capillaries., Comment: Proceedings of the 2024 Advanced Accelerator Concepts Workshop

The space-charge field of a relativistic charged bunch propagating in plasma is screened due to the presence of mobile charge carriers. We experimentally investigate such screening by measuring the effect of dielectric wakefields driven by the bunch in a uncoated dielectric capillary where the plasma is confined. We show that the plasma screens the space-charge field and therefore suppresses the dielectric wakefields when the distance between the bunch and the dielectric surface is much larger than the plasma skin depth. Before full screening is reached, the effects of dielectric and plasma wakefields are present simultaneously.

We report tunneling spectroscopy and transport measurements in superconducting Al and ferromagnetic-insulator EuS bilayers. The samples display remanent spin-splitting, roughly half the superconducting gap, and supercurrent transport above the average paramagnetic limit. We interpret this behavior as arising from the interplay between two characteristic length scales: the superconducting coherence length, $\xi$, and the magnetic domain size, $d$. By comparing experimental results to a theoretical model, we find $\xi/d \approx 10$. In this regime, spin-averaging across the micromagnetic configuration can locally suppress superconductivity, resulting in percolative supercurrent flow., Comment: 15 pages, 4+7 figures

Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The recent addition of machine learning methods to the toolbox for solving such problems is pushing the boundary of the quality vs. speed tradeoff of such numerical simulations. In this work, we lead the way to Lagrangian fluid simulators compatible with deep learning frameworks, and propose JAX-SPH - a Smoothed Particle Hydrodynamics (SPH) framework implemented in JAX. JAX-SPH builds on the code for dataset generation from the LagrangeBench project (Toshev et al., 2023) and extends this code in multiple ways: (a) integration of further key SPH algorithms, (b) restructuring the code toward a Python package, (c) verification of the gradients through the solver, and (d) demonstration of the utility of the gradients for solving inverse problems as well as a Solver-in-the-Loop application. Our code is available at https://github.com/tumaer/jax-sph., Comment: Accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science

Safe and reliable disclosure of information from confidential data is a challenging statistical problem. A common approach considers the generation of synthetic data, to be disclosed instead of the original data. Efficient approaches ought to deal with the trade-off between reliability and confidentiality of the released data. Ultimately, the aim is to be able to reproduce as accurately as possible statistical analysis of the original data using the synthetic one. Bayesian networks is a model-based approach that can be used to parsimoniously estimate the underlying distribution of the original data and generate synthetic datasets. These ought to not only approximate the results of analyses with the original data but also robustly quantify the uncertainty involved in the approximation. This paper proposes a fully Bayesian approach to generate and analyze synthetic data based on the posterior predictive distribution of statistics of the synthetic data, allowing for efficient uncertainty quantification. The methodology makes use of probability properties of the model to devise a computationally efficient algorithm to obtain the target predictive distributions via Monte Carlo. Model parsimony is handled by proposing a general class of penalizing priors for Bayesian network models. Finally, the efficiency and applicability of the proposed methodology is empirically investigated through simulated and real examples.

The fusion of non-Abelian anyons or topological defects is a fundamental operation in measurement-only topological quantum computation. In topological superconductors, this operation amounts to a determination of the shared fermion parity of Majorana zero modes. As a step towards this, we implement a single-shot interferometric measurement of fermion parity in indium arsenide-aluminum heterostructures with a gate-defined nanowire. The interferometer is formed by tunnel-coupling the proximitized nanowire to quantum dots. The nanowire causes a state-dependent shift of these quantum dots' quantum capacitance of up to 1 fF. Our quantum capacitance measurements show flux h/2e-periodic bimodality with a signal-to-noise ratio of 1 in 3.7 $\mu$s at optimal flux values. From the time traces of the quantum capacitance measurements, we extract a dwell time in the two associated states that is longer than 1 ms at in-plane magnetic fields of approximately 2 T. These results are consistent with a measurement of the fermion parity encoded in a pair of Majorana zero modes that are separated by approximately 3 $\mu$m and subjected to a low rate of poisoning by non-equilibrium quasiparticles. The large capacitance shift and long poisoning time enable a parity measurement error probability of 1%., Comment: Added data on a second measurement of device A and a measurement of device B, expanded discussion of a trivial scenario. Refs added, author list updated

Predicting flashback represents a pivotal challenge in the development of innovative perforated burners for household appliances, especially for substituting natural gas with hydrogen as fuel. Most existing numerical studies have utilized two-dimensional (2D) simulations to investigate flashback in these burners, primarily to reduce computational costs. However, the inherent complexity of flashback phenomena suggests that 2D models may inadequately capture the flame dynamics, potentially leading to inaccurate estimations of flashback limits. In this study, three-dimensional (3D) simulations are employed to examine the impact of the actual slit shapes on the flashback velocities of hydrogen-premixed flames. Steady-state simulations are conducted to compute flashback velocities for three equivalence ratios ($\phi=0.6$, $0.8$, and $1.0$), investigating slits with fixed width and varying lengths. Additionally, transient simulations are performed to investigate the flashback dynamics. The results are compared with those from 2D configurations to assess the reliability of the infinite slit approximation. For stable flames, 2D simulations underpredict the burner plate temperature compared to slits with lengths typical of practical devices but match the 3D results as $L\to\infty$. Conversely, flashback velocities are consistently underpredicted in 2D simulations compared to 3D simulations, even as $L\to\infty$. This is due to the critical role of the slit ends in flashback dynamics, where preferential diffusion, the Soret effect, and higher preheating due to a higher surface-to-volume ratio trigger the initiation of flashback in those regions. These findings underscore the necessity of employing 3D simulations to accurately estimate the flashback velocities in domestic perforated burners.

Hydrogen has emerged as a promising option for promoting decarbonization in various sectors by serving as a replacement for natural gas while retaining the combustion-based conversion system. However, its higher reactivity compared to natural gas introduces a significant risk of flashback. This study investigates the impact of operating and geometry parameters on flashback phenomena in multi-slit burners fed with hydrogen-methane-air mixtures. For this purpose, transient numerical simulations, which take into account conjugate heat transfer between the fluid and the solid walls, are coupled with stochastic sensitivity analysis based on Generalized Polynomial Chaos. This allows deriving comprehensive maps of flashback velocities and burner temperatures within the parameter space of hydrogen content, equivalence ratio, and slit width, using a limited number of numerical simulations. Moreover, we assess the influence of different parameters and their interactions on flashback propensity. The ranges we investigate encompass highly H2-enriched lean mixtures, ranging from 80% to 100% H2 by volume, with equivalence ratios ranging from 0.5 to 1.0. We also consider slit widths that are typically encountered in burners for end-user devices, ranging from 0.5 mm to 1.2 mm. The study highlights the dominant role of preferential diffusion in affecting flashback physics and propensity as parameters vary, including significant enrichment close to the burner plate due to the Soret effect. These findings hold promise for driving the design and optimization of perforated burners, enabling their safe and efficient operation in practical end-user applications.

Machine learning has been successfully applied to grid-based PDE modeling in various scientific applications. However, learned PDE solvers based on Lagrangian particle discretizations, which are the preferred approach to problems with free surfaces or complex physics, remain largely unexplored. We present LagrangeBench, the first benchmarking suite for Lagrangian particle problems, focusing on temporal coarse-graining. In particular, our contribution is: (a) seven new fluid mechanics datasets (four in 2D and three in 3D) generated with the Smoothed Particle Hydrodynamics (SPH) method including the Taylor-Green vortex, lid-driven cavity, reverse Poiseuille flow, and dam break, each of which includes different physics like solid wall interactions or free surface, (b) efficient JAX-based API with various recent training strategies and three neighbor search routines, and (c) JAX implementation of established Graph Neural Networks (GNNs) like GNS and SEGNN with baseline results. Finally, to measure the performance of learned surrogates we go beyond established position errors and introduce physical metrics like kinetic energy MSE and Sinkhorn distance for the particle distribution. Our codebase is available at https://github.com/tumaer/lagrangebench ., Comment: Accepted at 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks

Missing data arises when certain values are not recorded or observed for variables of interest. However, most of the statistical theory assume complete data availability. To address incomplete databases, one approach is to fill the gaps corresponding to the missing information based on specific criteria, known as imputation. In this study, we propose a novel imputation methodology for databases with non-response units by leveraging additional information from fully observed auxiliary variables. We assume that the variables included in the database are continuous and that the auxiliary variables, which are fully observed, help to improve the imputation capacity of the model. Within a fully Bayesian framework, our method utilizes a flexible mixture of multivariate normal distributions to jointly model the response and auxiliary variables. By employing the principles of Gaussian Cluster-Weighted modeling, we construct a predictive model to impute the missing values by leveraging information from the covariates. We present simulation studies and a real data illustration to demonstrate the imputation capacity of our method across various scenarios, comparing it to other methods in the literature, Comment: 23 pages, 9 figures

This paper proposes a methodology for exploring how linguistic behaviour on social media can be used to explore societal reactions to important events such as those that transpired during the SARS CoV2 pandemic. In particular, where spatial and temporal aspects of events are important features. Our methodology consists of grounding spatial-temporal categories in tweet usage trends using time-series analysis and clustering. Salient terms in each category were then identified through qualitative comparative analysis based on scaled f-scores aggregated into hand-coded categories. To exemplify this approach, we conducted a case study on the first wave of the coronavirus in Italy. We used our proposed methodology to explore existing psychological observations which claimed that physical distance from events affects what is communicated about them. We confirmed these findings by showing that the epicentre of the disease and peripheral regions correspond to clear time-series clusters and that those living in the epicentre of the SARS CoV2 outbreak were more focused on solidarity and policy than those from more peripheral regions. Furthermore, we also found that temporal categories corresponded closely to policy changes during the handling of the pandemic.

We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accurate dynamic-interaction models than their non-equivariant counterparts. We benchmark two well-studied fluid-flow systems, namely 3D decaying Taylor-Green vortex and 3D reverse Poiseuille flow, and evaluate the models based on different performance measures, such as kinetic energy or Sinkhorn distance. In addition, we investigate different embedding methods of physical-information histories for equivariant models. We find that while currently being rather slow to train and evaluate, equivariant models with our proposed history embeddings learn more accurate physical interactions., Comment: GSI'23 6th International Conference on Geometric Science of Information; 10 pages; oral. arXiv admin note: substantial text overlap with arXiv:2304.00150

We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accurate dynamic-interaction models than their non-equivariant counterparts. We benchmark two well-studied fluid flow systems, namely the 3D decaying Taylor-Green vortex and the 3D reverse Poiseuille flow, and compare equivariant graph neural networks to their non-equivariant counterparts on different performance measures, such as kinetic energy or Sinkhorn distance. Such measures are typically used in engineering to validate numerical solvers. Our main findings are that while being rather slow to train and evaluate, equivariant models learn more physically accurate interactions. This indicates opportunities for future work towards coarse-grained models for turbulent flows, and generalization across system dynamics and parameters., Comment: ICLR 2023 Workshop on Physics for Machine Learning

In this article, we share our experience related to the new photo-injector commissioning at the SPARC\_LAB test facility. The new photo-injector was installed into an existing machine and our goal was not only to improve the final beam parameters themselves but to improve the machine handling in day-to-day operations as well. Thus, besides the pure beam characterization, this article contains information about the improvements, that were introduced into the new photo-injector design from the machine maintenance point of view, and the benefits, that we gained by using the new technique to assemble the gun itself.