2.2 General Considerations
25
The MPV of the R max distribution is about twice as large as the corresponding
MPV of the R distribution for W boson decays, over the full p T range. The same
is true for the 70th, 80th and 90th percentiles, showing the need for larger jets in
analyses targeting top quark decays, compared to vector boson or H decays.
2.3 Jet Algorithms
The colour charges of quarks and gluons lead to collimated sprays of particles as
their footprint in high energy collisions. The first detailed proposal of measuring
these jets in e
+ e
− collisions has been made more than 40 years ago [47], and since
then jets have become an indispensable tool in particle physics. However, there is
no unique way of defining a jet. Instead, many jet definitions have been invented,
tested, modified, re-invented or discarded. Nowadays, with the advent of the LHC,
only a small set of jet algorithms is in use. These are the three sequential clustering
algorithms k T [53, 54], anti-k T [57] and CA [55, 56], which have proven to be
practical in numerous theoretical and experimental works. They all have in common
that they fulfil the criteria of the Snowmass accord [158]. It was a long struggle to
devise jet definitions fulfilling all the Snowmass criteria simultaneously, being IRC
safe [159], while computationally feasible [160]. The reader is referred to [59, 161–
163] for a comprehensive overview of the developments that led to modern clustering
algorithms. With the development of the FastJet [164] package, jet algorithms got a
common framework which resulted in a new standard for jet physics at the LHC with
numerous applications [165–174]. With these developments the situation became
satisfactory for jet algorithms themselves, fulfilling all experimental and theoretical
requirements.
The much younger field of jet substructure is adolescent with developments in
progress and new ideas still shaping the field. Some of the algorithms in use in jet
substructure analyses have been developed by maximizing the sensitivity of a search
for a given signal model, or by optimising the efficiency versus the misidentification
rate for the hadronic decay of a heavy particle. While in some cases the first developments lacked simplicity, experimental feasibility, or calculability in all orders in
perturbation theory, these already showed the huge potential of jet substructure for
data analysis at the LHC. The ongoing developments have guided us and helped to
gain an understanding of the possibilities that jet substructure offers. These may be
a gain in performance, a deeper understanding of the underlying partonic dynamics and hadronisation processes, or resilience against experimental effects. The use
of new developments in substructure techniques have been shown to improve the
sensitivity and physics potential of LHC analyses. These developments have been
facilitated largely by the availability of extensions to the FastJet package, which is
now the standard for new developments in this area. The common framework allows
for an easier comparison of different methods and a faster integration time of new
developments in analyses.
25
The MPV of the R max distribution is about twice as large as the corresponding
MPV of the R distribution for W boson decays, over the full p T range. The same
is true for the 70th, 80th and 90th percentiles, showing the need for larger jets in
analyses targeting top quark decays, compared to vector boson or H decays.
2.3 Jet Algorithms
The colour charges of quarks and gluons lead to collimated sprays of particles as
their footprint in high energy collisions. The first detailed proposal of measuring
these jets in e
+ e
− collisions has been made more than 40 years ago [47], and since
then jets have become an indispensable tool in particle physics. However, there is
no unique way of defining a jet. Instead, many jet definitions have been invented,
tested, modified, re-invented or discarded. Nowadays, with the advent of the LHC,
only a small set of jet algorithms is in use. These are the three sequential clustering
algorithms k T [53, 54], anti-k T [57] and CA [55, 56], which have proven to be
practical in numerous theoretical and experimental works. They all have in common
that they fulfil the criteria of the Snowmass accord [158]. It was a long struggle to
devise jet definitions fulfilling all the Snowmass criteria simultaneously, being IRC
safe [159], while computationally feasible [160]. The reader is referred to [59, 161–
163] for a comprehensive overview of the developments that led to modern clustering
algorithms. With the development of the FastJet [164] package, jet algorithms got a
common framework which resulted in a new standard for jet physics at the LHC with
numerous applications [165–174]. With these developments the situation became
satisfactory for jet algorithms themselves, fulfilling all experimental and theoretical
requirements.
The much younger field of jet substructure is adolescent with developments in
progress and new ideas still shaping the field. Some of the algorithms in use in jet
substructure analyses have been developed by maximizing the sensitivity of a search
for a given signal model, or by optimising the efficiency versus the misidentification
rate for the hadronic decay of a heavy particle. While in some cases the first developments lacked simplicity, experimental feasibility, or calculability in all orders in
perturbation theory, these already showed the huge potential of jet substructure for
data analysis at the LHC. The ongoing developments have guided us and helped to
gain an understanding of the possibilities that jet substructure offers. These may be
a gain in performance, a deeper understanding of the underlying partonic dynamics and hadronisation processes, or resilience against experimental effects. The use
of new developments in substructure techniques have been shown to improve the
sensitivity and physics potential of LHC analyses. These developments have been
facilitated largely by the availability of extensions to the FastJet package, which is
now the standard for new developments in this area. The common framework allows
for an easier comparison of different methods and a faster integration time of new
developments in analyses.