Speaker
Description
Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive
and require substantial data for training. In this paper, we introduce a new jet classification network
using an MLP mixer, where two subsequent MLP operations serve to transform particle and feature
tokens over the jet constituents. The transformed particles are combined with subjet information
using multi-head cross-attention so that the network is invariant under the permutation of the jet
constituents. We utilize two clustering algorithms to identify subjets: the standard sequential recombination algorithms with fixed radius parameters and a new IRC-safe, density-based algorithm of
dynamic radii based on HDBSCAN. The proposed network demonstrates comparable classification
performance to state-of-the-art models while boosting computational efficiency drastically. Finally,
we evaluate the network performance using various interpretable methods, including centred kernel
alignment and attention maps, to highlight network efficacy in collider analysis tasks.
| Track | Tagging (Classification) |
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