Low latency event-selection (trigger) algorithms are essential components of Large Hadron Collider (LHC) operation. Modern machine learning (ML) models have shown great offline performance as classifiers and could improve trigger performance, thereby improving downstream physics analyses. However, inference on such large models does not satisfy the $40\text{MHz}$ online latency constraint at the LHC. In this work, we propose \texttt{PHAZE}, a novel framework built on cryptographic techniques like hashing and zero-knowledge machine learning (zkML) to achieve low latency inference, via a certifiable, early-exit mechanism from an arbitrarily large baseline model. We lay the foundations for such a framework to achieve nanosecond-order latency and discuss its inherent advantages, such as built-in anomaly detection, within the scope of LHC triggers, as well as its potential to enable a dynamic low-level trigger in the future.

翻译：低延迟事件选择（触发）算法是大型强子对撞机（LHC）运行的关键组成部分。现代机器学习（ML）模型作为分类器已展现出卓越的离线性能，有望提升触发系统效能，从而改进下游物理分析。然而，此类大规模模型的推断过程无法满足LHC在线运行中$40\text{MHz}$的延迟约束。本研究提出\texttt{PHAZE}——一种基于哈希与零知识机器学习（zkML）等密码学技术的新型框架，通过可验证的提前退出机制，在任意大规模基线模型上实现低延迟推断。我们为该框架奠定了实现纳秒级延迟的理论基础，探讨了其在LHC触发系统内的固有优势（如内置异常检测），并展望了其未来实现动态低级触发的潜力。