Feasibility studies for the measurement of time-like proton electromagnetic form factors from p¯ p→ μ+μ- at P ¯ ANDA at FAIR

This paper reports on Monte Carlo simulation results for future measurements of the moduli of time-like proton electromagnetic form factors, | GE| and | GM| , using the p¯ p→ μ+μ- reaction at P ¯ ANDA (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at P ¯ ANDA , using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is p¯ p→ π+π-, due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distributions of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented

Deep learning and data assimilation approaches to sensor reduction in estimation of disturbed separated flows

Unsteady loads created by environmental perturbations - namely gusts - can strongly affect small and light-weighted aerial vehicles. To control vehicle's behavior in this perturbed environment, a robust, cheap and accurate estimator of the surrounding flow field and aerodynamic load is essential. Low-order inviscid vortex models constitute an attractive solution to this problem. For aerodynamic applications, the role of viscosity is primarily to inject vorticity into the flow. Intrinsically, this mechanism can't be captured by an inviscid model and need to be modeled. In modern inviscid models, the vorticity shedding criterion is set by the critical leading edge suction parameter (LESP) (Ramesh et al., Theor. Comput. Fluid Dyn., 2013). Without satisfying closure model, the critical LESP has been estimated from data assimilation (Darakananda et al., Phys. Rev. Fluids, 2018) and deep learning (Hou et al., AIAA J., 2019). Refining these works, we explore the influence of the spatial distribution of sensors through these two questions: what is the optimal placement of the pressure sensors? How many sensors are required to accurately estimate the LESP? For the deep learning model, a weight vector is determined which measures the influence of each pressure sensor on the final estimate. This weight vector is regularized by the L1 norm to promote sparsity. The number of sensors used is skrunk from 126 to 3 without significant loss of accuracy. Our deep learning framework is interpreted as the learning of a Koopman invariant subspace for the LESP and angle of attack. In the ensemble Kalman filter framework, an iterative algorithm based on the representers identifies the most impactful sensors. We reduce the number of sensors from 50 to 30