3 papers
physics.ins-det2025
Graph Neural Network-Based Pipeline for Track Finding in the Velo at LHCb
Anthony Correia, Fotis I. Giasemis, Nabil Garroum +2
Over the next decade, increases in instantaneous luminosity and detector granularity will amplify the amount of data that has to be analysed by high-energy physics experiments, whe…
hep-ex2025
Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb
Fotis I. Giasemis, Vladimir LonÄar, Bertrand Granado +1
In high-energy physics, the increasing luminosity and detector granularity at the Large Hadron Collider are driving the need for more efficient data processing solutions. Machine L…
physics.ins-det2024
Graph Neural Network-Based Track Finding in the LHCb Vertex Detector
Anthony Correia, Fotis I. Giasemis, Nabil Garroum +2
The next decade will see an order of magnitude increase in data collected by high-energy physics experiments, driven by the High-Luminosity LHC (HL-LHC). The reconstruction of char…