Kavli Affiliate: Mihoko M. Nojiri
| First 5 Authors: A. Hammad, Mihoko M. Nojiri, , ,
| Summary:
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.
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