Abstract
We construct a procedure to separate boosted Higgs bosons decaying into hadrons, from the background due to strong interactions. We employ the Lund jet plane to obtain a theoretically well-motivated representation of the jets of interest and we use the resulting images as the input to a convolutional neural network classifier. In particular, we consider two different decay modes of the Higgs boson, namely into a pair of bottom quarks or into light jets, against the respective backgrounds. For each case, we consider both a moderate- and high- boost scenario. The performance of the tagger is compared to what is achieved using a traditional single-variable analysis which exploits a QCD inspired color-singlet tagger, namely the jet color ring observable.
Highlights
High-energy collision events at the CERN Large Hadron Collider (LHC) are characterized by copious hadronic activity
We have studied the primary Lund jet plane in the context of tagging hadronically decaying Higgs bosons, in the boosted regime, where the Higgs’ decay products are reconstructed into a single large-radius jet
Inspired by previous work on W and top tagging using the Lund jet plane [22,26], we have built images that are used as inputs to a convolutional neural network for classification
Summary
High-energy collision events at the CERN Large Hadron Collider (LHC) are characterized by copious hadronic activity. This has sparked an interesting debate on whether one should take a rather agnostic approach and favor raw data, such as particles’ kinematics, as inputs to NN, or whether one should make good use of the expert-knowledge developed thanks to our theoretical understanding of the underlying physical processes, and exploit higher-level, theory-inspired, objects as inputs to the NN.1 In this context, particle physics in general, and jet physics in particular, find themselves in a rather unique position to address these types of questions, because, thanks to the Standard Model, we have a deep understanding of the physical processes we are studying. We believe that this study is an interesting addition to the rather extensive literature on ML-based Higgs taggers Some of these methods exploit low-level inputs to construct jet images and use CNN [37,38,39,40] or interaction networks [41].
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