#### A new tagger for hadronically decaying heavy particles at the LHC

Eur. Phys. J. C
A new tagger for hadronically decaying heavy particles at the LHC
T. Lapsien 0
R. Kogler 0
J. Haller 0
0 Institut für Experimentalphysik, Universität Hamburg , Hamburg , Germany
A new algorithm for the identification of boosted, hadronically decaying, heavy particles at the LHC is presented. The algorithm is based on the known procedure of jet clustering with variable distance parameter R and adapts the jet size to its transverse momentum pT. Subjets are found using a mass jump condition. The resulting algorithm - called Heavy Object Tagger with Variable R (HOTVR) - features little algorithmic complexity and combines jet clustering, subjet finding and rejection of soft clusters in one sequence. While the HOTVR algorithm can be used for the identification of any heavy object decaying hadronically, e.g. W, Z, H, t, or possible new heavy resonances, this paper targets specifically the tagging of boosted top quarks. The studies presented here demonstrate a stable performance of the HOTVR algorithm in a wide range of top quark pT, from low pT, where the decay products can be resolved, to the region of boosted decays at high pT.
1 Introduction
The identification of hadronically decaying heavy standard
model (SM) particles (W, Z, H, t) is an important
ingredient in an increasing number of SM analyses and searches
for new physics at the LHC. For a particle with high energy,
the large Lorentz factor leads to decay products which are
collimated in the laboratory rest frame and result in a
single jet. The task of separating these decays from the vast
amount of background from QCD multijet production has
been approached with a variety of jet substructure
developments in recent years [1–7]. The techniques face the
challenge of a stable performance in significantly different
kinematic regimes: from the region of low transverse momentum
pT, where the decay products can be resolved, to the boosted
regime of high pT.
The existing algorithms can be classified into two
approaches. The bottom-up approach extrapolates from the resolved
into the boosted regime by successively combining small
radius jets, similar to an exclusive jet clustering (e.g.
the JADE algorithm [8,9]). Modern algorithms have been
devised for the task of heavy object identification; examples
are the collection of jets in buckets [10,11] or the recently
proposed XCone algorithm [12,13]. These methods
combine manageable complexity with promising performance,
but have not been studied in experimental analyses so far. The
larger number of algorithms follows the top–down approach
which starts from large radius jets followed by subsequent
declustering steps. These algorithms are based on jet
clustering with a fixed distance parameter R, where jet grooming
methods like filtering [1], pruning [14], trimming [15] or soft
drop [16] are used to remove soft radiation and contributions
from the underlying event such that substructure observables
like the jet mass reflect the hard underlying process.
Alternatively the variable R jet algorithm [17] can be used to
dynamically reduce the jet distance parameter with increasing pT of
the decaying particle. The algorithm was used in studies of
new heavy resonances decaying to final states with two and
four gluons [15], and also top quark, W and Higgs decays
at LHC energies [18], and a similar algorithm at energies of
a future hadron collider [19]. Additional substructure
information like the kt splitting scales [20], N-subjettiness [21–
23], energy correlation functions [24] or Qjets [25] are often
used to further improve the performance of substructure
algorithms. Combinations of these methods are used for the
tagging of top quarks [26–30], where also more theoretically
motivated taggers have been proposed [31,32]. The ATLAS
and CMS collaborations have commissioned a number of the
techniques mentioned above and studied their behaviour [33–
40]. Several top-tagging algorithms have been employed
successfully in various searches for new physics [41–69] and in
SM top quark measurements [70,71] in LHC analyses with
Run 1 data.
At LHC’s Run 2 the production rates of particles with high
pT have increased and the importance of boosted analyses is
further enhanced. Modifications of existing taggers have been
proposed and studied in simulation [72–75]. In most cases, a
modest performance improvement is contrasted with a
significantly increased algorithmic complexity. A simple but robust
algorithm [76] proposed by the ATLAS collaboration has a
slightly reduced performance. In addition, recent
developments of top–down taggers aim at closing the gap between
the resolved and the boosted regime with rather complex
algorithmic procedures using several clustering,
declustering, mass drop and filtering steps. Examples are the scale
invariant tagger [77] and the HEPTopTagger in OptimalR
mode [30].
In this work we introduce a new tagger useful in the
resolved, the transition and the boosted regime, achieved with
only little algorithmic complexity. The tagger is based on the
variable R jet algorithm [17], which adapts the jet distance
parameter dynamically to the pT of the boosted object. A
mass jump condition [78,79] is included in the clustering
process, which forms subjets reflecting the dynamics of the
underlying hard decay, and enables efficient background
suppression. The resulting Heavy Object Tagger with Variable
R (HOTVR) accommodates jet clustering, subjet finding and
the rejection of soft radiation in one sequence, without the
need of declustering and following grooming steps. In this
paper we demonstrate the algorithm’s properties and
characteristics in hadronic top decays and leave studies of the
decays of W, Z, H and possible new resonances to future
work.
The paper is organised as follows. In Sect. 2 the HOTVR
algorithm is described. Its characteristics, free parameters
and their influence on the jet and subjet clustering, the
collinear and infrared safety and timing performance are
discussed in Sect. 3. The algorithm’s performance for hadronic
top quark decays and a comparison with other commonly
used taggers is presented in Sect. 4. A conclusion is given in
Sect. 5.
2 The algorithm
The HOTVR algorithm is based on the variable R (VR) jet
algorithm [17]. Like all sequential recombination algorithms,
it starts with an input list of pseudojets1 and continues the
processing until the input list is empty. The algorithm uses
the distance measures di j and diB, defined as
di j = min pT2n,i , pT2n,j
The value of di j can be interpreted as distance between two
pseudojets i and j , where pT,i is the transverse momentum
of pseudojet i and Ri j = (yi − y j )2 + (φi − φ j )2 is
the angular distance in rapidity y and azimuth φ between
the pseudojets i and j . The value of diB denotes the
distance between pseudojet i and the beam. For a fixed distance
parameter of Reff = R in Eq. (3), the anti-kt [80],
Cambridge/Aachen (CA) [81,82] and kt [83,84] algorithms are
obtained for the choices n = −1, 0, 1, respectively. For the
HOTVR algorithm n = 0 is used, corresponding to CA
clustering. However, in the VR algorithm Reff is an effective
distance parameter, which scales with 1/ pT (cf. Eq. (3)) leading
to broader jets at low pT and narrower jets at high pT. The
scale ρ determines the slope of Reff . For robustness of the
algorithm with respect to experimental effects a minimum
and a maximum cut-off for Reff is introduced,
Reff = ⎨ Rmax
A known shortcoming of the VR algorithm is the
clustering of additional radiation into jets in QCD multijet
production, resulting in a higher jet pT on average and an increased
rate once a pT selection is applied [17]. The HOTVR
algorithm approaches this issue by modifying the jet clustering
procedure with a veto based on the invariant mass of the
pseudojet pair, inspired by the recently proposed mass jump
algorithm [78]. The mass jump veto prevents the
recombination of two pseudojets i and j if the combined invariant mass
mi j is not large enough,
The parameter θ determines the strength of the mass jump
veto and can be chosen from the interval [0, 1]. The mass
jump criterion (5) is only applied if the mass mi j is larger
than a mass threshold μ
In case a mass jump is found and the pT of the pseudojets i
and j fulfil
1 We use the notation pseudojet to denote entities entering the jet
clustering. These can be partons, stable particles, reconstructed detector
objects or combined objects from a previous clustering iteration.
the pseudojets are combined. The resulting pseudojet enters
the next clustering step and the initial pseudojets are stored
as separate subjets. In case the mass jump criterion is not
fulfilled or the pseudojets are softer than pT,sub, the lighter
pseudojet or the one too soft is removed from the list. This
step reduces the effect of additional activity (soft radiation,
underlying event, pile-up) and effectively stabilises the jet
mass over a large range of pT.
The full HOTVR algorithm can be summarised as follows.
(1) If the smallest distance parameter is diB, store the
pseudojet i as jet and remove it from the input list of pseudojets.
(2) If the smallest distance parameter is di j and mi j ≤ μ,
combine i and j .
(3) If the smallest distance parameter is di j and mi j > μ,
check the mass jump criterion θ · mi j > max[mi , m j ].
(a) If the mass jump criterion is not fulfilled, compare
the masses of the two pseudojets and remove the one
with the lower mass from the input list.
(b) If the mass jump criterion is fulfilled, check the
transverse momenta of the subjets i and j .
(i) If pT,i < pT,sub or pT, j < pT,sub, remove the
respective pseudojet from the input list.
(ii) Else, combine pseudojets i and j . Store the
pseudojets i and j as subjets of the combined
pseudojet. In case i or j have already subjets, associate
their subjets with the combined pseudojet.
(4) Continue with (1) until the input list of pseudojets is
empty.
The algorithm results in jets with an effective size depending
on pT and associated subjets. It incorporates jet finding,
subjet finding and the rejection of soft radiation in one clustering
sequence.
The algorithm is available as plugin to FastJet [85, 86]
and can be obtained through the FastJet Contribs
package [87]. Its implementation is based on the
implementations of the mass jump and VR algorithms in the
FastJet Contribs packages ClusteringVetoPlugin 1.0.0 and
VariableR 1.1.1, respectively. These implementations have
been adapted and modified to make the HOTVR software an
independent FastJet plugin.
3 Characteristics and properties
Parameters, jet and subjet finding
In total, the algorithm has six parameters, which are listed in
Table 1. While the first three parameters steer the VR part of
the algorithm, the last three define the mass jump condition.
The default values given in the table have been optimised for
top quark tagging in pp collisions at √s = 13 TeV.
The original VR algorithm is recovered for μ → ∞. In
this case, for ρ → 0 the algorithm is identical to the CA
Table 1 Parameters of the HOTVR algorithm. The default values are
given for the top-tagging mode
Rmin
Rmax
ρ
μ
θ
algorithm with a distance parameter of R = Rmin. Similarly,
for ρ → ∞ the CA algorithm is obtained with R = Rmax. For
values of ρ corresponding to the typical scale of an event (m
or pT in the range of O(100 GeV)) jets are clustered with an
adaptive distance parameter between Rmin and Rmax. Higher
values of ρ result in larger jet sizes.
The number of subjets found is modified by the mass jump
parameters μ, θ and pT,sub. Once the pseudojets become
sufficiently heavy due to clustering, the mass jump threshold
μ results in a rejection of soft and light pseudojets. For a
fixed value of μ, the strength of this jet grooming depends
on the parameters θ and pT,sub. For θ = 1 the condition (5)
is always fulfilled and no pseudojets are rejected (equivalent
to the case μ → ∞). Conversely, the case of θ = 0 results
in a VR jet clustering which stops as soon as a jet mass of μ
is reached. The algorithm results in subjets with a maximum
mass of μ. Additional jet grooming is obtained by setting
pT,sub > 0. This results in subjets with a minimum pT of
pT,sub, effectively removing soft radiation and improving the
tagging performance at small pT of the heavy object.
The algorithm’s behaviour is visualised in Fig. 1 where
two example tt events, generated with Pythia 8 [88–90] at
low pT (top row, Event 1) and at high pT (bottom row, Event
2), are clustered with the CA algorithm (left column) and with
the HOTVR algorithm (right column). The active catchment
areas of the hard jets are obtained using ghost particles [91]
and are illustrated by the coloured (orange/blue) areas.2 The
impact of the VR part of the algorithm is nicely illustrated by
the largely different jet sizes of the two events clustered with
the HOTVR algorithm (right column). The grey regions in the
right panels were rejected by the mass jump criterion and are
not part of the HOTVR jets. This criterion has largest impact
in events at low pT as exemplified in Event 1 (top, right).
The HOTVR jets together with their subjets reproduce the
kinematics of the top decay adequately, both at low and high
pT, demonstrating a better adaptation to the decay topology
than CA jets. A similar picture is obtained when comparing
HOTVR jets to anti-kt jets.
2 The exact borders of the jet areas depend slightly on the specific
configuration of the ghost particles.
Fig. 1 Two simulated tt events clustered with the CA algorithm with
distance parameter R = 0.8 (left column) and with the HOTVR
algorithm (right column). The top quarks have either low pT (top row,
Event 1) or high pT (bottom row, Event 2). The two leading jets in
the events are shown as coloured areas (orange/blue). The stable
particles, input for the jet finders, are drawn as grey dots. The quarks from
the top quark decay are depicted by red circles and are shown for
illustration purposes only. In case of the HOTVR algorithm the subjets are
shaded from light to dark, corresponding to increasing pT. The grey
areas correspond to regions rejected by the mass jump criterion
Collinear and infrared safety
The HOTVR algorithm is infrared and collinear (IRC) safe,
except for the unnatural parameter choice of μ = 0. For
parameter choices corresponding to the original VR
clustering, the HOTVR algorithm is trivially infrared and collinear
(IRC) safe [17]. Similarly, for choices of μ > 0 the
algorithm is IRC safe, as soft and collinear splittings do not
generate mass. This has also been verified in a numerical test,
where the stability of the jets as well as subjets found with the
HOTVR algorithm was studied with respect to soft radiation
and collinear splittings. The algorithm proved to be IRC safe
with no events out of 106 failing the test [92].
For timing tests, and throughout this work, the FastJet
3.2.1 [85, 86] framework is used, together with FastJet
Contribs version 1.024. Starting from FastJet version 3.2,
advanced clustering strategies became available which led
to substantial speed improvements, especially at high
particle multiplicities. For this reason the run time of the
algorithm has been studied for different particle multiplicity
scenarios, low O(50), medium O(300) and high O(3000).
In Table 2 the CPU time of the HOTVR algorithm with
default parameters (cf. Table 1) is compared to those of the
CA jet algorithm [81, 82], the CMS top tagger [26, 27], the
HEPTopTagger [28, 29], the HEPTopTagger in OptimalR
mode [30], the VR algorithm [17] as well as the mass jump
algorithm [78].
For the various top taggers the CPU time listed includes the
time for the underlying jet finding as well as for the top
tagger specific processing steps. The developments in FastJet
3.2 result in a much faster runtime of the VR and HOTVR
clustering, compared to previous versions (not shown). At
low and medium multiplicities, the runtime of the HOTVR
algorithm is comparable to that of the other top-tagging
algorithms tested. At high multiplicities, it is about a factor four
slower than the HEPTopTagger algorithms, but it is still fast
Table 2 CPU time comparison
of various algorithms for low,
medium and high particle
multiplicities. The values are
normalised to the CPU time of
the CA algorithm with R = 0.8
enough for practical uses.3 The original mass jump algorithm
has not been updated to employ the new clustering strategies,
which leads to a much worse performance at medium and
high multiplicities.
4 Physics performance
Studies of the physics performance are carried out using the
event generator Pythia 8 [88–90]. A pp → tt sample is used
as signal process, background events are obtained by
simulating QCD dijet production in pp collisions. For both samples
a centre-of-mass energy of √s = 13 TeV is used, the
multiple parton interaction tune Monash 2013 [93] and the LO
NNPDF2.3 QCD+QED [94] PDFs with αs (MZ ) = 0.130
are employed. At this stage no additional pp interactions
during a single bunch crossing (pile-up) are simulated.4
Throughout this work, jets are clustered using all stable
particles from the Pythia 8 output. In some studies,
additional jets (labelled parton jets) are obtained using a list of
all final state partons5 as input to the anti-kt algorithm with
distance parameter R = 0.4 with a minimum pT of 100 GeV.
In case of tt production, the top quark is effectively treated
as stable for the purposes of defining the parton jet: after
showering the top quarks are added to the parton list, and
all partons from the top quark decay are removed. In case a
matching between particle and parton jets is employed, the
geometrical matching condition R < Reff is used.
Reconstruction of masses and transverse momenta
The key to the tagger’s effectiveness is the accurate
reconstruction of subjets originating from the top quark decay,
3 For example, on a MacBook Pro with a 2.5 GHz Intel Core i5 proces
sor and 16 GB 1600 MHz DDR3 Memory the runtime is about 25 ms
per event for multiplicities of O(3000).
4 While pile-up effects will worsen the overall performance of the
algorithm, the change is not expected to be significant for moderate
pile-up scenarios (up to 20–30 additional pile-up interactions).
5 Final state partons are defined as partons which enter the hadronisa
tion step.
achieved by the VR condition and the mass jump criterion.
This leads to a stable peak position for the mass of top jets
over a large range of jet pT, as shown in Fig. 2. The jet mass
mjet distribution for jets with two different subjet multiplicity
Nsub selections is shown for two ranges in the pT of the
parton jet matched to the particle jet. For tt events with Nsub ≥ 2
the distributions feature a dominant peak, stable around the
top quark mass6 for fully merged decays, and two smaller
peaks at lower masses corresponding to partially merged top
quark decays. The requirement of Nsub ≥ 3 leads to a
depletion of the two secondary peaks, while the peak around the
mass of the top quark is hardly affected. At low pT (left)
the top quark peak is wider with a larger tail and is situated
on a larger plateau than at high pT (right) because of
contributions from additional radiation which aggregate in the jet
due to its large size. While this leads to a larger
misidentification rate at low pT, it results in a non-vanishing efficiency
already at top quark transverse momenta as low as 100 GeV.
For typical QCD jets a falling distribution is observed. The
wide peak at mass values around 140 GeV observed at low
pT (left) is a result of the subjet kinematics, where an
angular separation of R = 1.0–1.5 leads to jet masses around
this value. When changing the kinematics by relaxing the
pT,sub requirement, the peak vanishes and a falling
distribution is obtained. The width of this peak is reduced for
intermediate (400 < pT < 600 GeV) transverse momenta
(not shown) and a monotonically falling background
distribution is obtained for values of 600 < pT < 800 GeV
(right). Very similar distributions are obtained for values of
pT > 800 GeV.
The distributions of the leading subjet’s fractional
transverse momentum f pT = pT,1/ pT is shown in Fig. 2
(middle). Signal jets contain subjets with more evenly distributed
transverse momenta, while for background jets the leading
subjet carries a larger fractional pT on average. The variable
f pT shows good separation power between signal and
background jets before a subjet multiplicity selection. After the
6 The VR algorithm alone affects the jet mass distribution similarly to
a trimming [15] procedure for anti-kt jets at high top quark pT [18].
Nsub ≥ 2
tt
QCD
Nsub ≥ 3
tt
QCD
Nsub ≥ 2
tt
QCD
Nsub ≥ 3
tt
QCD
Nsub ≥ 3
tt
QCD
ts 0.1
e
jf
o
n0.08
o
it
c
a
fr 0.06
Nsub ≥ 2
tt
QCD
Nsub ≥ 2
tt
QCD
Nsub ≥ 3
tt
QCD
Fig. 2 Distribution of the jet mass (top), fractional leading subjet
transverse momentum (middle) and minimum pairwise mass (bottom) for
signal (black) and background (red) events as obtained with the HOTVR
algorithm for two different ranges in parton jet pT. The distributions are
shown for subjet multiplicities Nsub ≥ 2 (dashed lines) and Nsub ≥ 3
(solid lines). Note that the minimum pairwise mass is only defined
for Nsub ≥ 3. The distributions have been normalised to unit area for
Nsub ≥ 2
requirement of Nsub ≥ 3, the separation power is reduced,
but the variable is still useful, especially at high pT.
For jets with Nsub ≥ 3, the distribution of the
minimum pairwise mass mmin [26, 27], defined as the
minimum invariant mass of pairs of the three highest pT subjets
mmin = min[m12, m13, m23], is shown in Fig. 2 (bottom) for
two regions of pT of the parton jet. The distributions show a
clear cut-off at the chosen value of the mass jump threshold
(μ = 30 GeV). Above this value the distribution is steeply
falling for background jets, while tt signal jets exhibit a
pronounced peak around the value of the W boson mass, as
expected for top quark jets. The tail below the mass jump
threshold is a result of light subjets combined with a heavier
pseudojet, fulfilling the mass jump criterion in step (3) of the
algorithm.
Besides an adequate reconstruction of masses, algorithms
should also be able to reconstruct the kinematics of the initial
heavy particle. In particular the size of the catchment area,
which is responsible for the amount of additional radiation
clustered into the jets, and the intensity of the grooming
procedure are critical components for the performance in this
area. For an evaluation of the kinematic object
reconstruction by the HOTVR algorithm, we calculate the pT ratio of
the HOTVR jets and the matched parton jets containing a
top quark. We find a mean value of the pT ratio of 1.0 within
small deviations of the order of 1%, independent of the
parton jet pT. The widths of the pT ratio distributions are about
5%. This shows that the HOTVR algorithm is able to
accurately reconstruct the kinematics of the heavy object with the
parameter choice given above.
Selection cuts in top-tagging mode
For the discrimination of hadronically decaying top quarks
from QCD multijets a selection based on simple cuts using
commonly employed substructure variables has been
implemented. The variables mjet and mmin calculated from the
HOTVR subjets are in principle sufficient for building a
robust top tagger over a large region of pT. However, cuts
on additional variables have been added to obtain a
selection that allows a fair comparison with other top-tagging
algorithms using similar selections. To ensure only a limited
impact of not-included experimental effects (e.g.
broadening of distributions) the cut values have not been optimised
rigorously. Nevertheless, they result in an improved
discrimination between signal and background.7 The following
selection defines the standard working point of the HOTVR
algorithm in top-tagging mode.
1. The leading subjet is required to have a fractional
transverse momentum with respect to the jet, f pT =
pT,1/ pT < 0.8, which ensures that the jet’s momentum
is distributed among its subjets and not carried by only
the leading subjet.
2. The number of subjets Nsub is required to be Nsub ≥ 3,
which increases the probability of reconstructing fully
merged top jets and rejects a fair amount of QCD jets.
3. The jet mass is required to fulfil 140 < mjet < 220 GeV.
4. The minimum pairwise mass has to fulfil mmin >
50 GeV.
These selection criteria lead to similar subjet kinematics as
obtained by the CMS and HEPTopTagger algorithms with
default parameters. This provides the basis for the
comparison made in the following.
Performance comparison with ROC curves
The signal efficiency and misidentification rate are studied
using single variable receiver operating characteristic (ROC)
curves. The signal efficiency εS is defined as the fraction of
tagged jets matched to parton jets containing the top quark,
with respect to all top quarks decaying hadronically. The
background efficiency (or misidentification rate) εB is
calculated as the fraction of tagged jets matched to parton jets
in a QCD multijet sample, with respect to the total number
of parton jets. Both, εS and εB therefore combine
identification and matching efficiencies. These definitions allow for
a comparison of different tagging algorithms, in particular
using different choices of the jet distance parameter R, since
the reference pT is defined by the parton jet matched to the
tagged jet and does not depend on the specifics of the tagging
algorithm under study.
In the following the performance of the HOTVR
algorithm in top-tagging mode is compared with the performance
of three top-tagging algorithms especially designed for
dedicated regions of pT: the CMS top-tagger targets the region
of high pT, the HEPTopTagger is designed for low pT and
its improved version with OptimalR has been developed to
extend its usability to higher pT. The free parameters of these
taggers are listed in Table 3 together with a choice of working
points [29, 73, 95]. The ROC curves are obtained by
keeping the free parameters fixed at the values given and
scanning only the N-subjettiness [21–23] ratio τ3/2 = τ3/τ2 with
β = 1. The choice of τ3/2 as scanning variable8 ensures an
unprejudiced comparison of the algorithms, which all rely
on different reconstruction techniques and substructure
variables, since this variable is not used in the definition of any of
the taggers under study. Furthermore, τ3/2 has been shown to
improve the performance of existing taggers (see for example
Refs. [30, 73]).
In Fig. 3 the ROC curves of the four top-tagging
algorithms are shown for four different pT regions, where pT
is defined by the parton jet matched to the tagged jet. The
events were reweighted to obtain a flat pT spectrum such
that all events in the interval have the same weight. At low
pT (200 < pT < 400 GeV, top left) the CMS top-tagging
algorithm has very small efficiency due to the choice of
R = 0.8 which results in jets not large enough to cluster
all particles from the top quark decay chain. The HOTVR
7 A more sophisticated selection based on multivariate analysis tech
niques or more complex observables might provide further performance
improvement [73] over this simple approach. However, the aim of
the studies presented here is a comparison of the performance of the
HOTVR algorithm with existing algorithms.
8 The usual procedure for obtaining the ROC curves by scanning the
free parameters of each algorithm could provide misleading results in
this case, as it cannot be ensured that the usage of additional or different
scanning variables for a given tagger would not improve its performance
considerably.
B 10−1
ε
B 10−1
ε
10−2
10−3
10−4 0
10−2
10−3
10−4 0
Rfimlatx = 0.3
B 10−1
ε
10−2
10−3
10−4 0
10−2
10−3
10−4 0
B 10−1
ε
HOTVR
HOTVR
Table 3 Settings of the top-tagging algorithms used. The parameter R is the distance parameter of the jet clustering. The definition of the parameters
follows Ref. [95] for the CMS top tagger, Ref. [29] for the HEPTopTagger and Ref. [30] for the HEPTopTagger in OptimalR mode
algorithm is able to provide a comparable performance as
the two HEPTopTagger algorithms which were optimised
for this pT region. For increasing values of pT the CMS
tagger becomes more efficient, with a similar performance as the
OptimalR HEPTopTagger starting from pT > 600 GeV. In
the pT regions with 400 < pT < 600 GeV (top right) and
600 < pT < 1000 GeV (bottom left) the HOTVR
algorithm shows a similar relation between εS and εB as the
CMS and OptimalR HEPTopTagger, and is especially
useful for high efficiencies. In the highest pT region considered
(1000 < pT < 2000 GeV, bottom right) the HOTVR
algorithm features overall the best performance over all εS values,
outperforming the CMS tagger, which was designed for the
high pT region.
In summary, the HOTVR algorithm shows a remarkably
stable performance over a large range in pT with similar or
even better performance than algorithms especially designed
for certain pT regions. Detector reconstruction and resolution
effects, which are not included in these studies, are expected
to improve the performance of the HOTVR algorithm relative
to the other algorithms studied [92].
5 Conclusion
A new algorithm for the reconstruction and identification of
hadronically decaying heavy particles at the LHC has been
introduced in this paper. The algorithm combines variable
R jet clustering with a veto based on a mass jump criterion.
It performs jet and subjet finding, and the rejection of soft
radiation in one sequence. This combination results in a stable
determination of jet substructure variables like the jet mass
over a large range in pT of the heavy object. In top-tagging
mode the HOTVR algorithm provides an excellent ratio of
signal to background efficiency at low top quark pT as well
as at high pT, making the HOTVR algorithm useful in the
regions of resolved and boosted decays at the same time.
While we focussed on top tagging in this work, the
algorithm is also applicable for the tagging of W, Z, H or
possible BSM resonances, where studies are ongoing. Because
of its algorithmic simplicity combined with remarkable
performance, this tagger could become a helpful ingredient for
future boosted analyses at the LHC.
Acknowledgements We thank Michael Spannowsky for fruitful
discussions during the development of the algorithm. We also thank Jesse
Thaler for helpful suggestions on improvements of the document and
for advise on speed improvements. This work is supported by the
German Research Foundation (DFG) in the Collaborative Research Centre
(SFB) 676 “Particles, Strings and the Early Universe” located in
Hamburg.
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