# LHCb trigger streams optimization

## TL;DR

This paper introduces an optimization method for organizing event preselection algorithms into data streams in the LHCb experiment, significantly improving analysis efficiency and reducing processing time.

## Contribution

The paper presents a novel method for optimizing stream composition in LHCb data, enhancing analysis speed by 15% through improved grouping and balancing of event selection lines.

## Key findings

- 15% reduction in analysis job processing time
- Effective stream grouping improves data access efficiency
- Method applied to real LHCb Turbo stream data

## Abstract

The LHCb experiment stores around $10^{11}$ collision events per year. A typical physics analysis deals with a final sample of up to $10^7$ events. Event preselection algorithms (lines) are used for data reduction. Since the data are stored in a format that requires sequential access, the lines are grouped into several output file streams, in order to increase the efficiency of user analysis jobs that read these data. The scheme efficiency heavily depends on the stream composition. By putting similar lines together and balancing the stream sizes it is possible to reduce the overhead. We present a method for finding an optimal stream composition. The method is applied to a part of the LHCb data (Turbo stream) on the stage where it is prepared for user physics analysis. This results in an expected improvement of 15% in the speed of user analysis jobs, and will be applied on data to be recorded in 2017.