Playing tag with ANN: boosted top identification with pattern recognition

By Leandro Giordano Almeida, Mihailo Backovic, Mathieu Cliche, Seung J. Lee, M. Perelstein. 2015

170 citations recorded by the source corpus.

Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image” of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with pT in the 1100-1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

Subfields: Nuclear and High Energy Physics

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Citations in the corpus

Energy flow networks: deep sets for particle jets (2018, background): “For instance, hadronic jet classification has been thoroughly studied using low-level [4–23] and high-level [24–29] input observables.”
Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks (2016, background): “Signals produced by the LHC detectors may be processed into pixelated jet images [20], and machine learning algorithms can be adapted to discriminate between a signal (such as h→ bb̄ decays or boosted hadronic top decays [21]) and background.”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, background): “The first applications of DNNs using all of the available information [533, 534, 535] represented jets as images(3) [537].”
Jet-images — deep learning edition (2015, background): “[5], whereby jets are represented as images with the energy depositions of the particles within the jet serving as the pixel intensities.”