Speaker
Description
The upcoming upgrades to the Large Hadron Collider for the HL-LHC era will progressively increase the nominal luminosity, aiming to a reach peak value of $5×10^{34} $ cm$^{-2}$ s$^{-1}$ for the ATLAS and CMS experiments. Higher luminosity will naturally lead to a larger number of proton–proton interactions occurring in the same bunch crossing, with pileup levels that may reach up to 200, creating a significantly more complex environment for track reconstruction.
To cope with these conditions, several experiments have begun redesigning parts of their track reconstruction software so that it can run efficiently on heterogeneous computing architectures. Although these initiatives have yielded promising results, they have generally remained internal to each individual experiment.
In this work, we present the capabilities of a standalone framework designed to operate across multiple backends including CPUs, NVIDIA GPUs and AMD GPUs, and to reconstruct tracks in cylindrical tracker detectors used by different high-energy physics experiments. We evaluate its physics performance as well as its computational performance for a variety of detectors.
This effort constitutes a first step toward a unified and experiment-independent reconstruction tool for HL-LHC–like detectors defined only in terms of its fundamental components: a silicon tracking system, at least one calorimeter, and a muon subsystem; which is capable of taking advantage of heterogeneous computing resources.