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.
@article{arxiv.1808.00711,
title = {Using holistic event information in the trigger},
author = {Dylan Bourgeois and Conor Fitzpatrick and Sascha Stahl},
journal= {arXiv preprint arXiv:1808.00711},
year = {2018}
}