Combining Triggers in HEP Data Analysis

TL;DR

The paper addresses how to correct offline for trigger inefficiencies and downscaling, especially when merging data from multiple independent triggers. It introduces methods to calculate offline correction factors and evaluates their statistical performance. The results inform how to implement corrections in analyses to recover original statistics and shed light on implications for designing and operating trigger systems under bandwidth constraints. Overall, the work provides a framework for accurate cross-trigger data combination and improves reliability of high-energy physics measurements.

Abstract

Modern high-energy physics experiments collect data using dedicated complex multi-level trigger systems which perform an online selection of potentially interesting events. In general, this selection suffers from inefficiencies. A further loss of statistics occurs when the rate of accepted events is artificially scaled down in order to meet bandwidth constraints. An offline analysis of the recorded data must correct for the resulting losses in order to determine the original statistics of the analysed data sample. This is particularly challenging when data samples recorded by several triggers are combined. In this paper we present methods for the calculation of the offline corrections and study their statistical performance. Implications on building and operating trigger systems are discussed.