A new generation of electron scattering experiments is underway at the world-leading QCD facilities such as Brookhaven National Lab (BNL) and Jefferson Lab (JLab). New projects include the Electron Ion Collider (EIC) at BNL, SOLID and Moller at JLab, and upgrades of the existing detectors in the two labs, sPHENIX and CLAS12, respectively. Experiments at the high-intensity frontier are expected to produce an enormous amount of data that needs to be collected and stored for offline analysis. Thanks to the continuous progress in computing and networking technology, it is now possible to replace the standard ‘triggered’ data acquisition systems with a new, simplified, and outperforming scheme. ‘Streaming readout’ (SRO) DAQ aims to replace the hardware-based trigger with a much more powerful and flexible software-based one, that considers the whole detector information for efficient real-time data tagging and selection. AI-supported algorithms for real-time data analysis and reconstruction are implemented within the SRO framework providing a novel and powerful tool to select useful information from the background. SRO on-beam tests of different detectors (PbWO-based electromagnetic calorimeters and plastic scintillator hodoscope) with AI-supported algorithms, are planned for spring 2023 at Jefferson Lab. In this thesis, the candidate will contribute to JLab hardware and software SRO setup for on-beam tests comparing traditional to AI-supported real-time analysis.

Questa tesi può essere supportata da una borsa di studio INFN (Bando n .249999, scadenza 17/12/22) per un importo trimestrale di 8000 euro per la copertura delle spese di permanenza presso il Jefferson Lab (Newport News VA, US).