Using holistic event information in the trigger

Abstract

In order to achieve the data rates proposed for the future Run 3 upgrade of the LHCb detector, new processing models must be developed to deal with the increased throughput. For this reason, we aim to investigate the feasibility of purely data-driven holistic methods, with the constraint of introducing minimal computational overhead, hence using only raw detector information. These filters should be unbiased - having a neutral effect with respect to the studied physics channels. In particular, the use of machine learning based methods seems particularly suitable, potentially providing a natural formulation for heuristic-free, unbiased filters whose objective would be to optimize between throughput and bandwidth.

References in corpus (8)

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• On the difficulty of training Recurrent Neural Networks
• Deep Learning and its Application to LHC Physics
• Mining gold from implicit models to improve likelihood-free inference
• Stable Recurrent Models
• SparseMAP: Differentiable Sparse Structured Inference
• Scalable Learning of Non-Decomposable Objectives
• Machine Learning in High Energy Physics Community White Paper