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Triggering

Section status

Contributor: Unclaimed — this section has no named author yet. See Contribute.

Last reviewed: —

Status: Stub — a starting point only. The curated list is not yet written.

Overview

The LHC produces collisions far faster than any system can record, so a trigger decides within microseconds what to keep. Everything discarded is gone permanently, which makes this the most consequential selection in the experiment and an unusually demanding place to deploy machine learning: latency is fixed by hardware, models run on FPGAs or ASICs, and the resource budget is measured in look-up tables. The resulting literature is concerned less with accuracy than with accuracy achievable under hard constraints — quantisation, pruning, and architectures chosen for what synthesises well.

  • Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb, Boggia et al. (2025) (arXiv:2506.14578) — a review of machine learning for real-time analysis at the LHC
  • Fast convolutional neural networks on FPGAs with hls4ml, Aarrestad et al. (2021) (arXiv:2101.05108) — fast convolutional networks on FPGAs with hls4ml — the practical starting point

Curated paper list

This list is a seed, not a curated selection

A few landmark papers are listed to give the section a starting shape. A proper curated list — thematically grouped, with an annotation on every entry — is what this section still needs. See Contribute.

  • Fast inference of deep neural networks in FPGAs for particle physics, Duarte et al. (2018) (arXiv:1804.06913) — made sub-microsecond neural network inference on FPGAs practical, and introduced the toolchain the field now uses
  • Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML, Ngadiuba et al. (2020) (arXiv:2003.06308) — binary and ternary precision networks, pushing the compression limit
  • LHC physics dataset for unsupervised New Physics detection at 40 MHz, Govorkova et al. (2021) (arXiv:2107.02157) — trigger-level anomaly detection posed as a concrete dataset problem

Benchmarks, datasets & software

Not yet compiled for this section.

Open questions

What is settled here, what is contested, and what remains unsolved? This is the part a bibliography structurally cannot provide, and often the most useful paragraph on the page.

Further reading

Relevant work that is not an entry point — too specialized, too recent, or simply not where a newcomer should start. Suggestions that do not fit the curated list above belong here rather than being turned away.

Nothing listed yet.

Cross-references