Towards a Self-Driving Trigger at the LHC: Adaptive Response in Real Time

2026-01-13

AI Summary

This work proposes the first data-driven autonomous trigger framework for high-energy physics experiments, addressing the limitations of conventional trigger systems that rely on static, hand-crafted rules and struggle to adapt to dynamic detector conditions and environments. By jointly optimizing signal efficiency and rate stability under constraints of bandwidth, latency, and computational cost, the framework dynamically adjusts trigger thresholds and resource allocation in real time without human intervention. It integrates traditional energy-sum triggers with machine learning–based anomaly detection and establishes a reproducible real-time benchmark environment using both simulated data and publicly available CMS collision data. The approach significantly enhances system flexibility, uniformly supports both standard and non-standard event topologies, and thereby strengthens the discovery potential of future high-energy physics analyses.

Technology Category

• Machine Learning: Hardware-aware ML
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Real-time data filtering and selection -- or trigger -- systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider (LHC) must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid (CMS) experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Problem

• trigger system
• real-time data filtering
• Large Hadron Collider
• adaptive response
• high-energy physics

Innovation

• self-driving trigger
• real-time adaptive optimization
• anomaly detection
• machine learning
• trigger system