Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance

Tsan, Steven; Kansal, Raghav; Aportela, Anthony; Díaz, D.; Duarte, J.; Krishna, Sukanya; Mokhtar, F.; Vlimant, J. R.; Pierini, M.

doi:10.48550/arxiv.2111.12849

Cited by 7 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a Lund string model the quarks and gluons are thought of being connected by QCD color flux tubes, or strings, that carry significant amounts of energy, and shed it in the process of hadron creation. While there were already attempts to use ML to improve parton shower simulations [27,[65][66][67][68][69][70][71], this manuscript represents the first attempt to use ML for hadronization. In both cases the physics is described by a Markov chain, however, for different reasons.…”

Section: The Simplified Lund String Hadronization Modelmentioning

confidence: 99%

“…Such ML models could be directly built from data and provide insights into the current phenomenological models. While ML techniques have recently entered into the development of event generators, through adaptive integration [15][16][17][18][19][20], parton showers [21][22][23][24][25][26][27][28][29], ML based fast detector or event simulations , and model parameter tuning [56,57], the application of ML to the problem of hadronization as the final step in the Monte Carlo pipeline is entirely new, to the best of our knowledge. The present manuscript represents the first step toward building a full-fledged ML based hadronization framework.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling hadronization using machine learning

et al. 2023

View full text Add to dashboard Cite

We present the first steps in the development of a new class of hadronization models utilizing machine learning techniques. We successfully implement, validate, and train a conditional sliced-Wasserstein autoencoder to replicate the Pythia generated kinematic distributions of first-hadron emissions, when the Lund string model of hadronization implemented in Pythia is restricted to the emissions of pions only. The trained models are then used to generate the full hadronization chains, with an IR cutoff energy imposed externally. The hadron multiplicities and cumulative kinematic distributions are shown to match the Pythia generated ones. We also discuss possible future generalizations of our results.

show abstract

Section: The Simplified Lund String Hadronization Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling hadronization using machine learning

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Recent paradigms emerging in the HEP community, for example anomaly detection methods for model-agnostic physics searches [164], present an exciting opportunity to develop novel graph-based algorithms. To date, several studies have demonstrated the applicability of autoencoders applied to particle graphs for anomaly detection [165,166].…”

Section: New Task Typesmentioning

confidence: 99%

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Thais¹,

Calafiura²,

Chachamis³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs. This allows a wide variety of high-and low-level physical features to be attached to measurements and, by the same token, a wide variety of HEP tasks to be accomplished by the same GNN architectures. GNNs have found powerful use-cases in reconstruction, tagging, generation and end-to-end analysis. With the wide-spread adoption of GNNs in industry, the HEP community is well-placed to benefit from rapid improvements in GNN latency and memory usage. However, industry use-cases are not perfectly aligned with HEP and much work needs to be done to best match unique GNN capabilities to unique HEP obstacles. We present here a range of these capabilities,

show abstract

“…5. Additionally, network architectures based on sets or graphs explicitly encoding permutation symmetry of the final state particles have been investigated [39][40][41][42][43][44].…”

Section: Parton Showermentioning

confidence: 99%

Machine Learning and LHC Event Generation

Butter¹,

Plehn²,

Schumann³

et al. 2022

Preprint

View full text Add to dashboard Cite

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

show abstract

Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance

Cited by 7 publications

References 36 publications

Modeling hadronization using machine learning

Modeling hadronization using machine learning

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Machine Learning and LHC Event Generation

Contact Info

Product

Resources

About