Abstract: We present the measurement of the intrinsic hadronic contamination at the CERN SPS H4 beamline configured to transport electrons and positrons at 100 GeV/c. The analysis, performed using data collected by the NA64-e experiment in 2022, is based on calorimetric measurements, exploiting the different interaction mechanisms of electrons and hadrons in the NA64 detector. We determined the contamination by comparing the results obtained using the nominal electron/positron beamline configuration with those from a dedicated setup, in which only hadrons impinged on the detector. We also obtained an estimate of the relative protons, antiprotons and pions yield by exploiting the different absorption probabilities of these particles in matter. We cross-checked our results with a dedicated Monte Carlo simulation for the hadron production at the primary T2 target, finding a good agreement with the experimental measurements.

Abstract: The need to achieve high energies in particle accelerators has led to the development of new accelerator technologies, resulting in higher beam intensities and more compact devices with stronger accelerating fields. In such scenarios, beam-loading effects occur, and intensity-dependent gradient reduction affects the accelerated beam as a consequence of its interaction with the surrounding cavity. In this study, a power-diffusive partial differential equation is derived to account for this effect. Its numerical resolution has been implemented in the tracking code RF-Track, allowing the simulation of apparatuses where transient beam loading plays an important role. Finally, measurements of this effect have been carried out in the CERN Linear Electron Accelerator for Research (CLEAR) facility at CERN, finding good agreement with the RF-Track simulations.

Abstract: We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative l(2) error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference ''ground truth'', and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions.