Work

Learning the language of QCD jets with transformers

Year: 2023

Type: article

Abstract: Transformers have become the primary architecture for natural language processing. In this study, we explore their use for auto-regressive density estimation in high-energy jet physics, which involves working with a high-dimensional space. We draw an analogy between sentences and words in natural language and jets and their constituents in high-energy physics. Specifically, we investigate density estimation for light QCD jets and hadronically decaying boosted top jets. Since transformers allow easy sampling from learned densities, we exploit their generative capability to assess the quality of the density estimate. Our results indicate that the generated data samples closely resemble the original data, as evidenced by the excellent agreement of distributions such as particle multiplicity or jet mass. Furthermore, the generated samples are difficult to distinguish from the original data, even by a powerful supervised classifier. Given their exceptional data processing capabilities, transformers could potentially be trained directly on the massive LHC data sets to learn the probability densities in high-energy jet physics.

Source: RWTH Publications (RWTH Aachen)

DOI: 10.48550/arxiv.2303.07364

Authors: Finke, Thorben, Krämer, Michael, Mück, Alexander, Tönshoff, Jan

Institution: RWTH Aachen University

Language: English

Topic: Particle physics theoretical and experimental studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: Deutsche Forschungsgemeinschaft, RWTH Aachen University

Awards: TRR 257: Phänomenologische Elementarteilchenphysik nach der HiggsEntdeckung, G2645866196, G2731709755, G3486459688, GRK 2497: Physik der schwersten Teilchen am Large Hadron Collider