Report on scipost_202108_00046v1
Year: 2021
Type: peer-review
Abstract: Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic.We introduce JetCLR to solve the mapping from low-level data to optimized observables though self-supervised contrastive learning.As an example, we construct a data representation for top and QCD jets using a permutation-invariant transformer-encoder network and visualize its symmetry properties.We compare the JetCLR representation with alternative representations using linear classifier tests and find it to work quite well.
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4293112976