Abstract
Jet substructure techniques are devised to analyse the internal radiation pattern of jets, thereby identifying their origin or revealing the dynamics of the strong force. These techniques are intricately connected with jet finding algorithms, either by modifying the clustering process, reversing it, or by storing information during the clustering. In this chapter, an overview is given of the currently known methods, algorithms and observables. The kinematics of heavy particle decays is discussed in detail to gain an understanding how individual jets can capture the full information from these decays. Theoretical methods used to calculate jet substructure observables are introduced, as well as models to simulate the rich substructure of jets.
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Notes
- 1.
The text in this subsection has been taken from [26] and has been written by the author. It has been adjusted to fit this book.
- 2.
The ALICE and LHCb detectors are also well-equipped to perform jet substructure studies. While these experiments do not have access to boosted massive particles due to their data rate (ALICE) or acceptance (LHCb), they are performing many interesting QCD studies with jet substructure. This review will be focused on ATLAS and CMS, but the future of jet substructure will involve key contributions from all four LHC experiments.
- 3.
The only exceptions are the \(H \rightarrow W W ^{*}\) and \(H \rightarrow Z Z ^{*}\) processes, with subsequent decays of the EW gauge bosons, which are four body decays. These decays are not discussed in detail here.
- 4.
Except for invisible decays in the SM, namely \(H \rightarrow Z Z ^{*} \rightarrow \nu \bar{\nu }\nu \bar{\nu }\) with a branching fraction of \(0.11\%\).
- 5.
The name is derived from exclusive cone algorithm.
- 6.
Following the FastJet  [164] terminology, a pseudojet denotes an entity entering the jet clustering. This can be coloured partons, stable particles, reconstructed detector objects or combined objects from a previous clustering iteration.
- 7.
The jet axis will change between the different clustering steps because of combining particles i and j, resulting in a slight deviation from the exact value of \(\pi R^2\).
- 8.
This is in contrast to a multi-pass minimisation, where 100 or more minimisations are done, with different initial axes in each attempt. While this procedure is more likely to find the global minimum, the one-pass minimisation gives reasonable results even though it only finds a local minimum.
- 9.
In its original version, trimming was formulated using a hard scale \(\Lambda _\text {hard}\), instead of the original jet’s transverse momentum \( {p_{\mathrm {T,{\mathrm {jet}}}}} \).
- 10.
When using soft drop in tagging mode, meaning that jets failing the soft drop condition get rejected, the groomed \(p_\mathrm {T}\) is IRC safe.
- 11.
The text in this paragraph has been taken from [245] and has been written by the author. It has been adjusted to fit this book.
- 12.
The exact borders of the jet areas depend slightly on the specific configuration of the ghost particles.
- 13.
These would result in \(1\rightarrow 3\) splittings and corrections to the \(1\rightarrow 2\) splitting functions.
- 14.
In parton showers, the ‘± prescriptions’ and \(\delta (1-z)\) terms are absent. These ensure flavour and energy conservation in analytical calculations, which are encoded at each step of the parton shower where each step is traced in detail.
- 15.
Herwig  7 also offers the possibility to use external matrix element calculations as input.
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Kogler, R. (2021). Phenomenology of Jet Substructure. In: Advances in Jet Substructure at the LHC. Springer Tracts in Modern Physics, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-72858-8_2
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DOI: https://doi.org/10.1007/978-3-030-72858-8_2
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