The project is focused on the development of novel analysis tools based on Artificial Intelligence to enable new insights from both experimental and computational data while revolutionising the way high-level data analysis is currently done in nuclear and particle physics. The Electron-Ion collider (EIC) -- a new $3B high-luminosity collider facility expected to go online in 2030 -- will allow a topographical picture of the nucleon and nuclei in unprecedented detail while providing key insights into the nature of the strong interaction.
This project will focus on development of state-of-the-art AI methods that will allow the extraction of physics information from data with unprecedented precision solving complex optimization problems in the most efficient way possible. Precise cross-section measurements are of fundamental importance for a successful physics programme at the EIC. This in turn constrains the required precision of both the absolute (better than 1%) and relative (∆L/L less than 10-4) luminosity determination. Unfortunately, techniques previously used in collider experiments that only utilise reactions with well known cross sections can not be applied due to event-pile up from the much higher luminosities planned at EIC, as well as due to other challenges related to the order of magnitude higher synchrotron radiation. Complementary measurements of the luminosity, utilising a pair spectrometer is critical to reduce systematics effects and better constraint detector characteristics that are crucial in the precise determination of the luminosity (acceptance, pair conversion factor, gain stabilities, beam size effects and beam divergence …). This complex, multidimensional problem is well suited for an AI approach, which will utilise data from two detector systems, as well as extensive Monte Carlo to establish the luminosity on a bunch-by-bunch basis with high precision.
The developed AI framework for luminosity determination will be integrated with the plethora of planned EIC analyses and will thus be a catalyst to the physics output of the EIC. Tools developed during the studentship will be also employed to data already collected at Thomas Jefferson Laboratory using a UK-led equipment (the Forward Tagger, FT) and allow the reconstruction of charged particles in very forward angles for the first time. In this case, information from two subsystems (FT hodoscope and FT calorimeter) will be used in conjunction with events in the CLAS12 detector to identify charged tracks other than electrons within the FT. This will significantly extend the allowed kinematics and is expected to have a direct impact in the physics programme of CLAS12.
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