Knowledge Vault 6 /92 - ICML 2024
Machine Learning Opportunities for the Next Generation of Particle Physics
Javier Duarte
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Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:

Machine Learning Opportunities
for the Next
Generation of Particle
Physics
Physics Fundamentals
Hardware & Detection
Machine Learning Integration
Data Processing
Future Developments
Universes basic building blocks 1
Particle Physics Model shows
symmetries 2
Higgs gives particles mass 3
Large Collider designs functions 4
CMS layers detect particles 5
Real-time detection needs hardware 8
ML algorithms match limits 9
Compress models through reduction 10
HLS4ML converts to hardware 11
Hardware improves processing speed 18
ML transforms physics research 6
Networks track particle moves 16
Transformers track efficiently 19
Networks respect Lorentz rules 20
ML discovers physical laws 21
Models simulate collisions 22
Fast processing collision events 7
Autoencoders find anomalies 12
Multiple detectors share insights 14
Hash functions reduce calculations 17
GANs create accurate particles 24
Framework extends beyond particles 13
Future colliders planned ahead 26
Program optimizes detector design 27
Foundation models aid research 28
Self-learning enhances data 29
JetNet tests generation methods 23
Evaluate generation quality 25
Physics guides data enhancement 30
Reconstruct from detector signs 15

Resume:

1.- Fundamental questions about universe's composition and building blocks

2.- Standard Model of Particle Physics structure and symmetries

3.- Higgs boson as mechanism for particle mass

4.- Large Hadron Collider design and operation principles

5.- CMS detector's multi-layered structure for particle detection

6.- Machine learning transforming traditional particle physics approaches

7.- Ultra-fast ML processing for 40 million collisions/second

8.- FPGA implementation challenges for real-time particle detection

9.- Co-design principles combining ML algorithms with hardware constraints

10.- Quantization and pruning techniques for model compression

11.- HLS4ML framework translating ML models to hardware

12.- Anomaly detection using variational autoencoders for new physics

13.- Applications of HLS4ML beyond particle physics

14.- Multimodal ML combining different detector data types

15.- Particle flow reconstruction from detector signatures

16.- Graph neural networks for particle track reconstruction

17.- Locality-sensitive hashing reducing computational complexity

18.- Performance improvements using GPU and specialized hardware

19.- Point transformer architecture for efficient particle tracking

20.- Lorentz symmetry incorporation into neural networks

21.- Discovering physical symmetries through machine learning

22.- Generative models for particle collision simulation

23.- JetNet dataset for benchmarking generative models

24.- Message-passing GANs for physics-accurate particle generation

25.- Metrics for evaluating physics-based generative models

26.- Future collider proposals post-LHC era

27.- Differentiable programming for detector optimization

28.- Foundation models potential in particle physics

29.- Self-supervised learning approaches for physics data

30.- Physics-driven data augmentation techniques

Knowledge Vault built byDavid Vivancos 2024