Les marges, zones de récréation

Cet ouvrage n’est pas destiné qu’à des spécialistes: il intéressera également un public plus large, attentif aux enjeux européens et au rôle de l’Union européenne dans l’organisation des espaces mondiaux. Un Atlas qui contribue au débat sur l’ordre européen et mondial et laisse néanmoins toute liberté au lecteur, sans lui imposer de point de vue critique ou appréciateur sur le rôle de l’Europe dans la mondialisation

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data