Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities

TL;DR

This paper presents a new interpretable, data-driven trigger system for high-throughput physics facilities like the LHC, aiming to improve data filtering efficiency without sacrificing physics coverage.

Contribution

It introduces an interpretable, cost-sensitive learning approach to optimize trigger systems, reducing runtime costs while maintaining or enhancing physics data quality.

Findings

Achieves minimal runtime cost in data filtering

Maintains or improves physics event selection accuracy

Provides a transparent, interpretable model for trigger decisions

Abstract

Data-intensive science is increasingly reliant on real-time processing capabilities and machine learning workflows, in order to filter and analyze the extreme volumes of data being collected. This is especially true at the energy and intensity frontiers of particle physics where bandwidths of raw data can exceed 100 Tb/s of heterogeneous, high-dimensional data sourced from hundreds of millions of individual sensors. In this paper, we introduce a new data-driven approach for designing and optimizing high-throughput data filtering and trigger systems such as those in use at physics facilities like the Large Hadron Collider (LHC). Concretely, our goal is to design a data-driven filtering system with a minimal run-time cost for determining which data event to keep, while preserving (and potentially improving upon) the distribution of the output as generated by the hand-designed trigger system. We introduce key insights from interpretable predictive modeling and cost-sensitive learning in order to account for non-local inefficiencies in the current paradigm and construct a cost-effective data filtering and trigger model that does not compromise physics coverage.