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Reinforcement learning optimizes Large Hadron Collider triggers in real-time

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a reinforcement learning agent capable of optimizing trigger thresholds in real-time at the Large Hadron Collider. This system, adapted from Group-Filtered Policy Optimization (GFPO), aims to maximize signal efficiency while adhering to background rate constraints. When tested on simulated data, the agent improved in-tolerance time intervals by up to 48% and demonstrated a 2% cumulative gain in signal efficiency. Crucially, the same agent, without further tuning, achieved significant improvements on real CMS collision data, marking the first successful application of RL for trigger control in this environment. AI

IMPACT Demonstrates a new method for optimizing complex scientific instruments with AI, potentially improving data collection efficiency in high-energy physics.

RANK_REASON This is a research paper detailing a novel application of reinforcement learning to a scientific instrument.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Reinforcement learning optimizes Large Hadron Collider triggers in real-time

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zixin Ding, Shaghayegh Emam, Giovanna Salvi, Cecilia Tosciri, Abhijith Gandrakota, Jennifer Ngadiuba, Nhan Tran, Christian Herwig, David W. Miller, Yuxin Chen ·

    Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

    arXiv:2606.23993v1 Announce Type: cross Abstract: High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely s…

  2. arXiv cs.LG TIER_1 English(EN) · Yuxin Chen ·

    Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

    High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as …

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