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Reconstruction

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

Reconstruction turns detector signals into physics objects: tracks from hits, showers into energies, particles into jets, and jets into flavor labels. It is where machine learning entered collider physics earliest and where it is now most thoroughly deployed — almost every major experiment runs ML taggers in production. The problems share a structure that has shaped architectures across the field: inputs are variable-length, unordered, and geometric, so sets, graphs, and attention were adopted here earlier than in most other areas, replacing image- and sequence-based approaches. Tracking is the hardest instance, being a combinatorial problem at a scale where even good heuristics struggle.

  • Exploring jets: substructure and flavour tagging in CMS and ATLAS, Malara (2024) (arXiv:2410.14330) — jet substructure and flavour tagging as practised in CMS and ATLAS
  • The Machine Learning Landscape of Top Taggers, Butter et al. (2019) (arXiv:1902.09914) — the top-tagging comparison: many architectures measured on one task, still the cleanest empirical read on what structure buys
  • Graph Neural Networks for Particle Tracking and Reconstruction, Duarte et al. (2020) (arXiv:2012.01249) — graph neural networks for tracking and reconstruction

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.

  • Energy Flow Networks: Deep Sets for Particle Jets, Komiske et al. (2019) (arXiv:1810.05165) — Energy Flow Networks: permutation invariance in a physics-motivated form, with a representation guarantee
  • ParticleNet: Jet Tagging via Particle Clouds, Qu et al. (2020) (arXiv:1902.08570) — ParticleNet: dynamic graph convolutions on particle clouds, long the standard strong baseline
  • Particle Transformer for Jet Tagging, Qu et al. (2022) (arXiv:2202.03772) — Particle Transformer, with the JetClass dataset that made large-scale comparison possible

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