Using holistic event information in the trigger

TL;DR

This paper explores data-driven, machine learning-based holistic filtering methods using raw detector data to enhance processing efficiency in future high-rate LHCb experiments, aiming for unbiased and minimal-overhead solutions.

Contribution

It investigates the feasibility of unbiased, purely data-driven holistic filtering methods with minimal computational overhead using raw detector information.

Findings

Potential for unbiased, data-driven filters

Use of machine learning for heuristic-free filtering

Optimization of throughput and bandwidth

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.