Conveners
Session5: Session5
- Maurice Garcia-Sciveres (Lawrence Berkeley National Lab. (US))
The data input rates foreseen in High-Luminosity LHC (circa 2026) and High-Energy LHC (2030s) High Energy Physics (HEP) experiments impose new challenging requirements on data processing. Polynomial algorithmic complexity and other limitations of classical approaches to many central HEP problems induce searches for alternative solutions featuring better scalability, higher performance and...
A 100 TeV proton collider represents a core aspect of the Future Circular Collider (FCC) study.
An integral part of this project is the conceptual design of individual detector systems that can be
operated under luminosities up to 3×10^35 cm^−2 s^−1. One of the key limitations in the design arises from an increased number of pile-up events O(1000), making both particle tracking and...
Conformal tracking is the novel and comprehensive tracking strategy adopted by the CLICdp Collaboration. It merges the two concepts of conformal mapping and cellular automaton, providing an efficient pattern recognition for prompt and displaced tracks, even in busy environments with 3 TeV CLIC beam-induced backgrounds. In this talk, the effectiveness of the algorithm will be shown by...
The design of next-generation particle accelerators evolves to higher and higher luminosities, as seen in the HL-LHC upgrade and the plans for the Future Circular Collider (FCC). Writing track reconstruction software that can cope in these high-pileup scenarios is a big challenge, due to the inherent complexity of current algorithmic approaches. In this contribution we present TrickTrack, a...
Reconstructing charged particles trajectories is a central task in the reconstruction of most particle physics experiments. With increasing intensities and ever increasing track densities this combinatorial problem becomes increasingly challenging. Preserving physics performance in these difficult experimental conditions while at the same keeping the computational cost at a reasonable level,...
For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data. While these approaches have shown some promise, image-based methods face challenges in scaling up...
