ML4Jets2022

Session

Generative Models -- Particle Level

1 Nov 2022, 16:10

Multipurpose Room (aka Livingston Hall) (Rutgers University)

Conveners

Generative Models -- Particle Level

• Tilman Plehn
• Marat Freytsis (Rutgers University)

72. Particle Cloud Generation

Raghav Kansal (Univ. of California San Diego (US))

01/11/2022, 16:10

Zoom

Particle Cloud Generation

There has been significant development recently in generative models for accelerating LHC simulations. Work on simulating jets has primarily used image-based representations, which tend to be sparse and of limited resolution. We advocate for the more natural ‘particle cloud’ representation of jets, i.e. as a set of particles in momentum space, and discuss...

53. Point Cloud Generation using Transformer Encoders and Normalising Flows

Benno Kach (Deutsches Elektronen-Synchrotron (DE))

01/11/2022, 16:30

Machine-learning-based data generation has become a major topic in particle physics, as the current Monte Carlo simulation approach is computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of particles poses difficult problems similar as is the case for point clouds. We propose that a transformer setup is well fitted to this task....

45. Conditional generative networks for pure quark and gluon jets

Ayodele Ore

01/11/2022, 16:50

The separation of quarks and gluons is of key interest at hadron colliders. While it is only possible to obtain mixed samples of quark and gluon jets from experimental data, some recent works have proposed methods for disentangling the underlying distributions in an unsupervised manner. However, these approaches typically lack a generative model for the separated distributions. In this work we...

4. Modeling Hadronization with Machine Learning

Manuel Szewc

01/11/2022, 17:10

A fundamental part of event generation, hadronization is currently
simulated with the help of fine-tuned empirical models. In this talk,
I'll present MLHAD, a proposed alternative for hadronization where the
empirical model is replaced by a surrogate Machine Learning-based
model to be ultimately data-trainable. I'll detail the current stage
of development and discuss possible ways forward.

17. MadNIS: Neural networks for multi-channel integration

Ramon Winterhalder (UC Louvain)

01/11/2022, 17:30

High-precision theory predictions require the numerical integration of high-dimensional phase-space integrals and the simultaneous generation of unweighted events to feed the full simulation chain and subsequent analyses. While current methods are based on first principles and are mathematically guaranteed to converge to the correct answer, the computational cost to decrease the numerical...