Foundations of automatic feature extraction at LHC--point clouds and graphs

Abstract

Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as "replacing the expert" in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.

Matches published in The European Physical Journal Special Topics (2024)

References in corpus (77)

• The anti-k_t jet clustering algorithm
• Geometric deep learning: going beyond Euclidean data
• Machine learning and the physical sciences
• Searching for Exotic Particles in High-Energy Physics with Deep Learning
• Towards an understanding of jet substructure
• Fast inference of deep neural networks in FPGAs for particle physics
• Towards Jetography
• ParticleNet: Jet Tagging via Particle Clouds
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Classification without labels: Learning from mixed samples in high energy physics
• Principles of general final-state resummation and automated implementation
• Jet-Images -- Deep Learning Edition
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Searching for New Physics with Deep Autoencoders
• Energy Flow Networks: Deep Sets for Particle Jets
• Equivariant flow-based sampling for lattice gauge theory
• Sudakov Safety in Perturbative QCD
• Graph Neural Networks in Particle Physics
• QCD or What?
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Learning representations of irregular particle-detector geometry with distance-weighted graph networks
• W+3 jet production at the Tevatron
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• The Lund Jet Plane
• JEDI-net: a jet identification algorithm based on interaction networks
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
• Calculating Track-Based Observables for the LHC
• ABCNet: An attention-based method for particle tagging
• Anomaly detection with Convolutional Graph Neural Networks
• Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
• Weakly Supervised Classification in High Energy Physics
• MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
• Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
• Probing stop pair production at the LHC with graph neural networks
• Machine Learning for Anomaly Detection in Particle Physics
• Uncertainty Aware Learning for High Energy Physics
• Casting a graph net to catch dark showers
• Point Cloud Transformers applied to Collider Physics
• Resonant anomaly detection without background sculpting
• Mapping Machine-Learned Physics into a Human-Readable Space
• The Hidden Geometry of Particle Collisions
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• Equivariant Energy Flow Networks for Jet Tagging
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• Graph Neural Networks for Particle Tracking and Reconstruction
• Quarks and gluons in the Lund plane
• Energy-weighted Message Passing: an infra-red and collinear safe graph neural network algorithm
• Infrared Safety of a Neural-Net Top Tagging Algorithm
• Towards Machine Learning Analytics for Jet Substructure
• Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at $\sqrt{s}=13$ TeV with the ATLAS detector
• Reports of My Demise Are Greatly Exaggerated: $N$-subjettiness Taggers Take On Jet Images
• Learning the language of QCD jets with transformers
• Explainable Equivariant Neural Networks for Particle Physics: PELICAN
• OmniJet-$\alpha$: The first cross-task foundation model for particle physics
• Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• Lorentz group equivariant autoencoders
• Topology classification with deep learning to improve real-time event selection at the LHC
• Lund and Cambridge multiplicities for precision physics
• Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks
• Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
• Constraints on the trilinear and quartic Higgs couplings from triple Higgs production at the LHC and beyond
• Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
• Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC
• Jet tagging algorithm of graph network with HaarPooling message passing
• Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
• Lund multiplicity in QCD jets
• On the BSM reach of four top production at the LHC
• Does Lorentz-symmetric design boost network performance in jet physics?
• Equivariant, Safe and Sensitive -- Graph Networks for New Physics
• Comparing Point Cloud Strategies for Collider Event Classification
• Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction
• Interpretable Uncertainty Quantification in AI for HEP
• Interpretable deep learning models for the inference and classification of LHC data
• A rotation-equivariant graph neural network for learning hadronic SMEFT effects
• A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics