"Enhancing the LHCb experiment at CERN with sustainable technology"

The LHCb collaboration at CERN is currently using a pioneering data filtering system in its trigger system that is based on real-time particle reconstruction using graphics processing units (GPUs). This involves processing a data rate of 5 TB/s and has required significant hardware and software development. The Allen project, in particular, consists of over 365 algorithms that execute real-time vertex finding and reconstruction, fast-track particle reconstruction, calorimeter clustering, and muon identification with very high efficiency and throughput.

Several of Allen's algorithms use a fast and powerful neural network (NN) to suppress reconstructed objects from random hits in the detectors. This enables false trigger selection decisions, which contribute to the background, to be suppressed. An important feature to consider is the power consumption and sustainability of this framework. This requires the development of tools to optimise energy usage through efficient computing architectures and algorithms. This involves exploring hybrid computing platforms to take advantage of the strengths of various hardware systems in terms of versatility, parallelisation and customisability, as well as implementing efficient software solutions to increase physics output and reduce energy consumption.

Contract duration: 3 years

Aimed at: MSc students in physics, computing science, or similar subjects.

Location: Instituto de Física Corpuscular, IFIC (CSIC-UV), València, Spain.

Contact: Arantza.Oyanguren@ific.uv.es or Álvaro.Fernandez@ific.uv.es.

Keywords: Artificial Intelligence (AI), technologies for processing large amounts of data and information, high-performance computing and green algorithms.