Speaker
Description
The High Luminosity Large Hadron Collider (HL-LHC) is scheduled to begin operation in 2030 and will increase the number of proton-proton collisions per bunch-crossing from around 60 to 200. The upgraded trigger system of the ATLAS experiment will record around 10kHz of the collisions to disk for physics analysis and this reduction is achieved with an L0 trigger that will feed the Event Filter (EF) at a rate of up to 1MHz. An important signature in the EF is high transverse momentum muons. The HL-LHC conditions require significant improvements to the existing muon reconstruction algorithms used in the EF. In this talk we will present developments for the ATLAS EF muon reconstruction, including the use of Machine Learning algorithms. The performance of the updated software is tested in simulated samples and on ATLAS run-3 data. Significant improvements in the speed of the algorithms are seen, while maintaining high efficiency to select muons.