Abstract: The production of strip sensors for the ATLAS Inner Tracker (ITk) started in 2021. Since then, a Quality Assurance (QA) program has been carried out continuously, by using specific test structures, in parallel to the Quality Control (QC) inspection of the sensors. The QA program consists of monitoring sensor-specific characteristics and the technological process variability, before and after the irradiation with gammas, neutrons, and protons. After two years, half of the full production volume has been reached and we present an analysis of the parameters measured as part of the QA process. The main devices used for QA purposes are miniature strip sensors, monitor diodes, and the ATLAS test chip, which contains several test structures. Such devices are tested by several sites across the collaboration depending on the type of samples (non-irradiated components or irradiated with protons, neutrons, or gammas). The parameters extracted from the tests are then uploaded to a database and analyzed by Python scripts. These parameters are mainly examined through histograms and timeevolution plots to obtain parameter distributions, production trends, and meaningful parameter-to-parameter correlations. The purpose of this analysis is to identify possible deviations in the fabrication or the sensor quality, changes in the behavior of the test equipment at different test sites, or possible variability in the irradiation processes. The conclusions extracted from the QA program have allowed test optimization, establishment of control limits for the parameters, and a better understanding of device properties and fabrication trends. In addition, any abnormal results prompt immediate feedback to a vendor.

Abstract: Purpose: The aim of this work is to investigate the feasibility of the Jagiellonian Positron Emission Tomography (J -PET) scanner for intra-treatment proton beam range monitoring. Methods: The Monte Carlo simulation studies with GATE and PET image reconstruction with CASToR were performed in order to compare six J -PET scanner geometries. We simulated proton irradiation of a PMMA phantom with a Single Pencil Beam (SPB) and Spread -Out Bragg Peak (SOBP) of various ranges. The sensitivity and precision of each scanner were calculated, and considering the setup's cost-effectiveness, we indicated potentially optimal geometries for the J -PET scanner prototype dedicated to the proton beam range assessment. Results: The investigations indicate that the double -layer cylindrical and triple -layer double -head configurations are the most promising for clinical application. We found that the scanner sensitivity is of the order of 10-5 coincidences per primary proton, while the precision of the range assessment for both SPB and SOBP irradiation plans was found below 1 mm. Among the scanners with the same number of detector modules, the best results are found for the triple -layer dual -head geometry. The results indicate that the double -layer cylindrical and triple -layer double -head configurations are the most promising for the clinical application, Conclusions: We performed simulation studies demonstrating that the feasibility of the J -PET detector for PET -based proton beam therapy range monitoring is possible with reasonable sensitivity and precision enabling its pre -clinical tests in the clinical proton therapy environment. Considering the sensitivity, precision and cost-effectiveness, the double -layer cylindrical and triple -layer dual -head J -PET geometry configurations seem promising for future clinical application.

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.