Within high transverse momentum jet cores, the separation between charged-
particles is reduced to the order of the sensor granularity in the ATLAS tracking detectors, resulting in overlapping charged-particle measurements in the detector. This can degrade the efficiency of reconstructing charged-particle trajectories. This presentation identifies the issues within the current reconstruction...
Particle tracking plays a pivotal role in almost all physics analyses at the Large Hadron Collider. Yet, it is also one of the most time-consuming parts of the particle reconstruction chain. In recent years, the Exa.TrkX group has developed a promising machine learning-based pipeline that performs the most computationally expensive part of particle tracking, the track finding. As the pipeline...
For the ATLAS experiment at the High-Luminosity LHC, a hardware-based track-trigger was originally envisioned, which performs pattern recognition via AM ASICs and track fitting on an FPGA.
A linearized track fitting algorithm is implemented in the Track-Fitter that receives track candidates as well as corresponding fit-constants from a database and performs the $\chi^2$-test of the track as...
This poster summarizes the main changes to the ATLAS experiment’s Inner Detector track reconstruction software chain in preparation for LHC Run 3 (2022-2024). The work was carried out to ensure that the expected high-activity collisions (with on average 50 simultaneous proton-proton interactions per bunch crossing, pile-up) can be reconstructed promptly using the available computing resources...
We introduce a new algorithmic deep architecture which combines graph neural networks, set transformers and Monte Carlo tree search like random sampling. The algorithm targets large scale combinatorial inverse problems, such as clustering of hypergraphs, encountered in high energy physics and beyond. We demonstrate the method on the tracking problem of high energy collisions.
