ML4Jets2024

Session

Theorie

5 Nov 2024, 09:00

LPNHE, Paris, France

Conveners

Theorie

• Tilman Plehn

52. Learning the Simplicity of Scattering Amplitudes

Aurelien Dersy (Harvard University)

05/11/2024, 09:00

The simplification and reorganization of complex expressions lies at the core of scientific progress, particularly in theoretical high-energy physics. This work explores the application of machine learning to a particular facet of this challenge: the task of simplifying scattering amplitudes expressed in terms of spinor-helicity variables. We demonstrate that an encoder-decoder transformer...

74. Taming perturbation theory in QCD with Normalizing Flows

Rikab Gambhir (MIT)

05/11/2024, 09:20

When predicting the distribution of an observable, $p(x)$, in QCD, fixed-order (FO) perturbation theory can suffer from many undesirable artifacts, including large logarithms spoiling the expansion, unphysical divergences or negative bins, non-smooth kinks, and non-normalizability on physical $x$’s. However, one expects the "true" $p(x)$, as accessed by experiment, to be finite, positive,...

76. Efficient SMEFT fits with neural importance sampling

Nikita Schmal

05/11/2024, 09:40

Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. We show how normalizing flows can be used to accelerate sampling from the SMEFT likelihood. The networks are trained without a pre-generated dataset by combining neural importance sampling with Markov chain methods....

34. Machine learning the likelihoods

Rafal Maselek

05/11/2024, 10:00

In recent years, the ATLAS collaboration has provided full statistical models for some of their analyses, enabling highly precise reinterpretation of experimental limits. These models account for multiple nuisance parameters and correlations between signal bins, but their complexity often leads to lengthy computation times. This project aims to develop a method for efficient yet accurate...