Speaker
Description
In response to the rising computational and storage demands of the High-Luminosity Large Hadron Collider (HL-LHC), efforts are underway to boost the processing efficiency of ATLAS Inner Detector (ID) event reconstruction. Our strategy to reduce the computational demands employs a Track-Overlay approach, which uses pre-reconstructed pile-up tracks (from separate minimum-bias simulations) and then runs the ID reconstruction procedure exclusively on hits corresponding to the simulated hard-scatter event. This method concentrates computing resources on events of interest, achieving a reduction in the required CPU time of up to 40% compared to standard reconstruction techniques.
A crucial component of this workflow is the incorporation of Machine Learning (ML), which guides the selection of events suitable for the Track-Overlay approach. High-density track environments, like those associated with high-pT jets, are intentionally directed toward the standard reconstruction method. This ensures a balanced approach that maximises resource efficiency while successfully maintaining high precision across all relevant physics processes.
This contribution details the construction of the ML model and the validation of the full workflow under the anticipated data-taking conditions of the HL-LHC's first run. Our validation studies include detailed comparisons of physics modeling for a diverse selection of processes, alongside robust CPU benchmark studies comparing the performance of standard reconstruction against our Track-Overlay approach. A core element of this effort is the rigorous evaluation of the ML model’s performance under the extreme conditions of the HL-LHC, which must contend with up to 200 pile-up vertices, far exceeding the 50–60 vertices encountered in current Run 3 data taking.