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Dispersive analysis of the pion vector form factor without zeros
No full textInternational audienceWe perform an updated analysis of cross-section data using a dispersive representation of the pion vector form factor. We show that the available data are compatible with the assumption that the form factor is free of complex zeros and that under this assumption the largest systematic uncertainty in a previous analysis can be eliminated. We investigate both a constrained Omnès representation as well as a hybrid phase-modulus representation and we quantify the discrepancies in the hadronic vacuum polarization contribution to the anomalous magnetic moment of the muon based on different data sets. We find that the dispersive constraints exacerbate these discrepancies. Together with the assumption of the absence of zeros, the pion charge radius becomes a useful observable to discriminate between the different data sets. This provides an opportunity for future improved lattice-QCD determinations to probe the discrepancies independently of full computations of hadronic vacuum polarization. We also reevaluate the two-pion contribution to Euclidean windows and we observe that systematic discrepancies between the data sets persist even at very long distances
Derivation of a 4-moment model for electron transport in Hall thrusters from a gyrokinetic model
No full textWe model the motion of a population of electrons in a strong electromagnetic field undergoing elastic electron/electron collisions. This regime is derived from a dimensional analysis of the electron confinement in Hall-effect thrusters. The electrons exhibit a very high cyclotron frequency and a E × B-drift, modelled by stiff PDEs at the mesoscopic scale. We obtain a gyrokinetic model in which the fastest oscillations of the system are filtered out by averaging the rotation of the electrons around the magnetic field lines. The model is derived in the strong electromagnetic field limit. Based on this gyrokinetic model, we then develop a 10-moment model. The averaging operation performed at the kinetic scale leads to symmetry properties that allow to reduce the 10-moment model to a 4-moment model.</div
Complete definition of transition generalized parton distributions
No full textInternational audienceWe revisit the definition of the leading-twist chiral-even generalized parton distributions (GPDs) for baryon transitions. We identify and address deficiencies in previous definitions of the transition GPDs inspired by the transition form factors of the vector and axial-vector currents. Through systematic analysis of all possible covariant structures, respecting discrete symmetries and the baryon spinor equations of motion, we derive complete sets of independent structures for the transition matrix elements of the vector and axial-vector partonic operators. They contain additional structures proportional to the light-cone vector, corresponding to transition GPDs of vanishing first moment, which were not included in previous parametrizations. Their presence is confirmed independently by the light-front multipole expansion and the cross-channel SO(3) partial-wave analysis of the transition matrix elements. Our analysis provides a complete definition of the transition GPDs for use in theoretical and phenomenological studies
Limitation strategies for high-order discontinuous Galerkin schemes applied to an Eulerian model of polydisperse sprays
No full textInternational audienceIn this paper, we tackle the modeling and numerical simulation of polydisperse sprays. Starting from a kinetic description for point particles, we focus on an Eulerian high-order geometric method of moment (GeoMOM) in size and consider a system of partial differential equations on a vector of successive fractional size moments of order 0 to N/2, N > 2, over a compact size interval. These moments correspond to physical quantities, which can be interpreted in terms of the geometry of the interface at small scale. There exists a stumbling block for the usual approaches using high-order moment methods resolved with high-order numerical methods: the transport algorithm does not naturally preserve the moment space. Indeed, reconstruction of moments by polynomials inside computational cells can create N-dimensional vectors which can fail to be moment vectors. We thus propose a new approach, as well as an algorithm, which is arbitrarily high-order in space and time with limited numerical diffusion, including at the boundaries of the state space, where a specific study is proposed. It allows to accurately describe the advection process and naturally preserves the moment space, at a reasonable computational cost. We show that such an approach is competitive compared to second order finite volume schemes, where limiters generate numerical diffusion and clipping at extrema. An accuracy study assesses the order of the method as well as the low level of numerical diffusion on structured meshes. We focus in this paper on cartesian meshes and 2D test cases are presented where the accuracy and efficiency of the approach are assessed
Turning qubit noise into an advantage: Automatic state preparation and long-time dynamics for impurity models on quantum computers
No full text17 pages, 6 figuresInternational audienceNoise is often regarded as a limitation of quantum computers. In this work, we show that in the dynamical mean-field theory (DMFT) approach to strongly correlated systems, it can actually be harnessed to our advantage. Indeed, DMFT maps a lattice model onto an impurity model, namely, a finite system coupled to a dissipative bath. While standard approaches require a large number of high-quality qubits in a unitary context, we propose a circuit that harvests amplitude damping to reproduce the dynamics of this model with a blend of noisy and noiseless qubits. We find compelling advantages with this approach: a substantial reduction in the number of qubits, the ability to reach longer time dynamics, and no need for ground-state search and preparation. This method would naturally fit in a partial quantum error correction framework
Learning interactions between Rydberg atoms
No full text19 pages, 11 figuresInternational audienceQuantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To fulfill their prospects, quantum simulators must be fully controllable, allowing for precise tuning of the microscopic physical parameters that define their implementation. We consider Rydberg-atom arrays, a promising platform for quantum simulations. Experimental control of such arrays is limited by the imprecision on the optical tweezers positions when assembling the array, hence introducing uncertainties in the simulated Hamiltonian. In this work, we introduce a scalable approach to Hamiltonian learning using graph neural networks (GNNs). We employ the Density Matrix Renormalization Group (DMRG) to generate ground-state snapshots of the transverse field Ising model realized by the array, for many realizations of the Hamiltonian parameters. Correlation functions reconstructed from these snapshots serve as input data to carry out the training. We demonstrate that our GNN model has a remarkable capacity to extrapolate beyond its training domain, both regarding the size and the shape of the system, yielding an accurate determination of the Hamiltonian parameters with a minimal set of measurements. We prove a theorem establishing a bijective correspondence between the correlation functions and the interaction parameters in the Hamiltonian, which provides a theoretical foundation to our learning algorithm. Our work could open the road to feedback control of the positions of the optical tweezers, hence providing a decisive improvement of analog quantum simulators
Finite element neural network interpolation: Part II—hybridisation with the proper generalised decomposition for non-linear surrogate modelling
No full textInternational audienceThis work introduces a hybrid approach that combines the Proper Generalised Decomposition (PGD) with deep learning techniques to provide real-time solutions for parametrised mechanics problems. By relying on a tensor decomposition, the proposed method addresses the curse of dimensionality in parametric computations, enabling efficient handling of high-dimensional problems across multiple physics and configurations. Each mode in the tensor decomposition is generated by a sparse neural network within the HiDeNN framework, with an element-based approach presented in Part I, where network parameters are constrained to replicate the classical shape functions used in the Finite Element Method. This constraint enhances the interpretability of the model, facilitating transfer learning, which improves significantly the robustness and cost of the training process. As shown in Part I, the HiDeNN framework can be leveraged to find the optimal spatial and parametric discretisation dynamically during training, which accounts to optimising the network’s architecture on the fly. This hybrid framework offers a flexible and interpretable solution for real-time surrogate modelling. We highlight the efficiency of the proposed neural Network-PGD (NN-PGD) approach through 1D, 2D and 3D benchmark problems, validating its performance against analytical and numerical reference solutions. The framework is illustrated through linear and non-linear elasticity problems, showing the flexibility of the method in terms of changes in physics
On the use of pulsed DC bias for etching high aspect ratio features
No full textInternational audienceInductively coupled plasmas (ICPs) containing Cl2 are widely used for plasma etching in the semiconductor industry. One common issue during plasma etching is aspect ratio dependent etching (ARDE), which is generally attributed to variation in the flux of etchant species to the bottom of features with different dimensions. Insufficient fluxes of neutral etchants to the bottom of high aspect ratio features can also result in sputtering, which tends to distort the feature profile. This article addresses two issues relevant to Cl2 ICP and plasma etching in these plasmas. First, a comprehensive set of diagnostics is used to validate a model for Cl2 ICP for gas pressure between 3 and 90 mTorr. The plasma diagnostics include microwave resonant hairpin probe-based measurements of electron density, photolysis-calibrated two-photon laser induced fluorescence measurement of Cl density, photo-detachment-based measurement of Cl− density, and laser diode absorption spectroscopy of argon metastable species to measure the gas temperature. Consistent with the experiments, the model shows that the electron density peaks near the center of the chamber at low gas pressure due to rapid diffusion. The electron density peak moves under the coils at higher pressures. Using the validated Cl2 model, we investigate ICPs with rectangular pulsed DC voltage for bias. It is shown that the Cl flux at the bottom of a trench decreases significantly with increasing aspect ratio of the trench. Neutral to ion flux ratio is therefore low at the bottom of higher aspect ratio trenches. The duty cycle of the pulsed bias waveform is found to be an effective means of increasing the neutral to energetic ion flux ratio, which should help with ARDE and sputter reduction
Measurement of meson production in fixed-target Ne collisions at = 68.5 GeV
No full textInternational audienceThe first measurement of meson production in fixed-target Ne collisions at GeV is presented. The mesons are reconstructed in their decay in a data sample consisting of proton collisions on neon nuclei at rest, corresponding to an integrated luminosity of nb, collected by the LHCb detector at CERN. The production cross-section in the centre-of-mass rapidity range of and transverse momentum range of MeV/c is found to be b/nucleon. A double-differential measurement of the cross-section is also provided in four regions of rapidity and six regions of transverse momentum of the meson and compared with the predictions from Pythia and EPOS4, which are found to underestimate the experimental values
Mitigating Farmland Biodiversity Loss A Bio-Economic Model of Land Consolidation and Pesticide Use
No full textInternational audienceBiodiversity loss driven by agricultural intensification is a pressing global issue, with significant implications for ecosystem stability and human well-being. We design an integrated bio-economic agent-based model, informed by historical data from the French agricultural sector, to project future biodiversity trends and evaluate policy interventions. Our model predicts further biodiversity decline under a business-as-usual scenario, primarily due to intensified land consolidation. We evaluate two policy options: reducing pesticide use and subsidizing small farmers. While pesticide reduction rapidly benefits biodiversity in the beginning, it eventually leads to increased land consolidation and further biodiversity loss. In contrast, subsidizing small farmers by reallocating a small fraction of existing subsidies, stabilizes farm sizes and enhances biodiversity in the long run. The most effective strategy results from combining both policies, leveraging pesticide reduction alongside targeted subsidies to balance economic pressures and consistently improve biodiversity