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The importance of calorimetry for highly-boosted jet substructure

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arxiv 1709.08705 v3 pith:TB6I3GS3 submitted 2017-09-25 hep-ph hep-ex

classification hep-phhep-ex
keywords substructureinformationperformancecalorimetrycasediscriminationgainsparticles
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Jet substructure techniques are playing an essential role in exploring the TeV scale at the Large Hadron Collider (LHC), since they facilitate the efficient reconstruction and identification of highly-boosted objects. Both for the LHC and for future colliders, there is a growing interest in using jet substructure methods based only on charged-particle information. The reason is that silicon-based tracking detectors offer excellent granularity and precise vertexing, which can improve the angular resolution on highly-collimated jets and mitigate the impact of pileup. In this paper, we assess how much jet substructure performance degrades by using track-only information, and we demonstrate physics contexts in which calorimetry is most beneficial. Specifically, we consider five different hadronic final states - W bosons, Z bosons, top quarks, light quarks, gluons - and test the pairwise discrimination power with a multi-variate combination of substructure observables. In the idealized case of perfect reconstruction, we quantify the loss in discrimination performance when using just charged particles compared to using all detected particles. We also consider the intermediate case of using charged particles plus photons, which provides valuable information about neutral pions. In the more realistic case of a segmented calorimeter, we assess the potential performance gains from improving calorimeter granularity and resolution, comparing a CMS-like detector to more ambitious future detector concepts. Broadly speaking, we find large performance gains from neutral-particle information and from improved calorimetry in cases where jet mass resolution drives the discrimination power, whereas the gains are more modest if an absolute mass scale calibration is not required.

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  1. JEDI-net: a jet identification algorithm based on interaction networks

    hep-ex 2019-08 conditional novelty 5.0 of 10

    JEDI-net, an interaction-network jet tagger, outperforms DNN, CNN, and GRU taggers on a five-class simulated LHC jet dataset.

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