The flavor dependence of jet quenching is a powerful handle to discriminate
between models of parton energy loss in heavy-ion collisions. We demonstrate
the capacity of CMS to identify jets initiated by bottom quarks using displaced
vertices reconstructed in the silicon tracking system. The b-jet to inclusive
jet ratio is measured in PbPb collisions and compared to pp collisions and
simulations at the same center-of-mass energy

Nguyen, Matthew

English

arXiv

The flavor dependence of jet quenching is a powerful handle to discriminate between models of parton energy loss in heavy-ion collisions. We demonstrate the capacity of CMS to identify jets initiated by bottom quarks using displaced vertices reconstructed in the silicon tracking system. The b-jet to inclusive jet ratio is measured in PbPb collisions and compared to pp collisions and simulations at the same center-of-mass energy.The flavor dependence of jet quenching is a powerful handle to discriminate between models of parton energy loss in heavy-ion collisions. We demonstrate the capacity of CMS to identify jets initiated by bottom quarks using displaced vertices reconstructed in the silicon tracking system. The b-jet to inclusive jet ratio is measured in PbPb collisions and compared to pp collisions and simulations at the same center-of-mass energy.The flavor dependence of jet quenching is a powerful handle to discriminate between models of parton energy loss in heavy-ion collisions. We demonstrate the capacity of CMS to identify jets initiated by bottom quarks using displaced vertices reconstructed in the silicon tracking system. The b-jet to inclusive jet ratio is measured in PbPb collisions and compared to pp collisions and simulations at the same center-of-mass energy

CERN Document Server

b-jet Identification in PbPb Collisions with CMS

Matthew Nguyen

Crossref

Nuclear Physics A

AbstractThe flavor dependence of jet quenching is a powerful handle to discriminate between models of parton energy loss in heavy-ion collisions. We demonstrate the capacity of CMS to identify jets initiated by bottom quarks using displaced vertices reconstructed in the silicon tracking system. The b-jet to inclusive jet ratio is measured in PbPb collisions and compared to pp collisions and simulations at the same center-of-mass energy

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1A list of members of the CMS Collaboration and acknowledgements can be found at the end of this issue.
Abstract
Identiﬁcation of reconstructed jets from fragmentation of b quarks has so far not been per-
formed in heavy-ion collisions. However, recent CMS data demonstrate that non-prompt J/ψ
mesons, i.e., those coming from decay of B hadrons, are indeed suppressed in PbPb collisions
with respect to the pp expectation [1]. This indirect measurement, which samples typical J/ψ pT
of values on the order of 10 GeV/c, provides a strong motivation to perform a more direct mea-
surement using fully reconstructed jets. Such a measurement would enable a direct comparison
of b quark energy loss to that of inclusive jets at much larger values of jet pT, where the ﬂavor
dependence of parton energy loss can be probed in detail.
CMS is described in detail in [2]. Jets formed from heavy ﬂavor quark fragmentation can be
tagged by the presence of displaced vertices, either by direct reconstruction of these vertices or
by the impact parameter (i.e., the distance of closest approach to the primary vertex) of tracks
originating from these vertices [3]. Information from these tracks and vertices are typically
combined into a quantity which optimizes their discrimination between heavy and light ﬂavor
jets. In this analysis, we use a discriminator to tag b-jets which is based on the ﬂight distance
of the reconstructed secondary vertex (SV) with respect to the primary vertex of the interaction.
The eﬃciency of this SV tagging is then evaluated both directly from simulation, and with a
data-driven method using a weakly correlated tagger based on the impact parameter of tracks
associated to the jets.
The performance of lifetime-based tagging relies on the high eﬃciency and low fake rate of
reconstruction of charge particle tracks from displaced vertices. The standard heavy-ion tracking
algorithm [4] in CMS is largely restricted to the reconstruction of charged particles from the pri-
mary vertex. To enhance the eﬃciency of tracks from secondary vertices, additional track recon-
struction is performed. Secondary track reconstruction in central heavy-ion events is extremely
resource intensive, due to the large number of possible hit combinations when incorporating hits
over a suﬃciently large region to ﬁnd such displaced tracks. To mitigate the number of possible
track candidates, the additional track reconstruction uses reconstructed jets as seeds and limits
the search window for tracker hits to a region deﬁned around the jet axis.
The ﬂavor dependence of jet quenching is a powerful handle to discriminate between models of
parton energy loss in heavy-ion collisions. We demonstrate the capacity of CMS to identify jets
initiated by bottom quarks using displaced vertices reconstructed in the silicon tracking system.
The b-jet to inclusive jet ratio is measured in PbPb collisions and compared to pp collisions and
simulations at the same center-of-mass energy.
b-jet Identiﬁcation in PbPb Collisions with CMS
Matthew Nguyen (for the CMS Collaboration)1
Laboratoire Leprince-Ringuet, E´cole Polytechnique, Palaiseau, France
Available online at www.sciencedirect.com
Nuclear Physics A 904–905 (2013) 705c–708c
0375-9474 © 2013 Elsevier B.V. 
www.elsevier.com/locate/nuclphysa
http://dx.doi.org/10.1016/j.nuclphysa.2013.02.112
Open access under CC BY-NC-ND license. 
Identiﬁcation of b-jets is achieved using a discriminating variable, denoted Simple Secondary
Vertex High Eﬃciency (SSVHE) [3], which is based on the ﬂight distance of reconstructed SVs.
Detailed studies of quantities related to SV reconstruction show that it is well modeled by PbPb
simulations, lending conﬁdence to the use of this discriminator to identify b-jets. Another dis-
criminating variable, called Jet Probability (JP), is also used in this analysis. The JP algorithm
orders jet-associated tracks based on their impact parameter signiﬁcance (impact parameter di-
vided by its uncertainty) and calculates the likelihood that they come from the same primary
vertex [3]. The JP algorithm provides a measure of discrimination for nearly all b-jets, even in
the case when no SV is reconstructed. This property of the JP tagger is exploited to obtain a
data-driven estimate of the SSVHE tagging eﬃciency [3]. This is done by evaluating the fraction
of jets from bottom fragmentation with and without the SSVHE tagging requirement, ﬁtting the
JP discriminator distribution with the same template ﬁtting method described below.
The performance of the discriminators is typically benchmarked by plotting the b-jet tagging
eﬃciency vs. the light (udsg) jet mis-tag eﬃciency, such that the resulting curves are indepen-
dent of the underlying b-jet fraction. Fig. 1 shows the b-jet tagging eﬃciency vs. the light jet
mis-tag eﬃciency from Pythia and Pythia+Hydjet. No centrality selection is applied to the
Pythia+Hydjet sample, but its centrality distribution is weighted to match the distribution in
data after the jet selection. The two taggers used in this analysis, SSVHE and JP, are shown.
The performance of the b-tagging degrades somewhat with the increased multiplicity of PbPb
collisions, giving roughly a factor of 3 poorer rejection of light jets for a b-jet eﬃciency of 50%,
relative to Pythia alone. Despite the reduced performance, one is still able to achieve roughly
a factor of 100 rejection of light jets for a b-jet eﬃciency around 50%. The charm rejection for
this b-jet eﬃciency is about a factor of 10 (not shown).
The ratio of b-jet to inclusive jets (the “b-jet fraction”) is extracted from the data using the
SV mass. The SV mass, which is simply the invariant mass of the charged tracks used in the SV
reconstruction, provides good separation between the charm and bottom contributions. Unbinned
maximum likelihood ﬁts are then performed where the b-jet and non-b-jet shapes are ﬁxed by the
simulation, but their normalizations are allowed to ﬂoat. Figure 2 shows SV mass distributions in
b-jet efficiency
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
ud
sg
-je
t e
ffi
ci
en
cy
-310
-210
-110
1
CMS Simulation  = 2.76 TeVs
Pythia (pp)
JP
SSVHE
b-jet efficiency
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
ud
sg
-je
t e
ffi
ci
en
cy
-310
-210
-110
1
CMS Simulation  = 2.76 TeVNNs
Pythia+Hydjet (PbPb), 0-100%
JP
SSVHE
Figure 1: The b-jet tagging eﬃciency vs. the light jet mis-tag eﬃciency for simulated pp events from Pythia (left) and
simulated PbPb events from Pythia embedded in Hydjet (right) for the SSVHE and JP discriminators. The red cross
marks the working point of the SSVHE discriminator used in this analysis.
M. Nguyen / Nuclear Physics A 904–905 (2013) 705c–708c706c
)2Secondary vertex mass (GeV/c
0 1 2 3 4 5 6
N
um
be
r 
of
 je
ts
10
210
310
410
510
N
um
be
r 
of
 je
ts
PbPb data
b-jet template
c-jet template
usdg-jet template
 < 100 GeV/c
T
80 < p
/NDF = 10.1 / 112χ
CMS Preliminary  = 2.76 TeVNNs
b-tagged sample
)2Secondary vertex mass (GeV/c
0 1 2 3 4 5 6
N
um
be
r 
of
 je
ts
10
210
310
410
N
um
be
r 
of
 je
ts
PbPb data
b-jet template
c-jet template
usdg-jet template
 < 150 GeV/c
T
120 < p
/NDF = 13.9 / 112χ
CMS Preliminary  = 2.76 TeVNNs
b-tagged sample
Figure 2: Template ﬁts to the SV mass distributions in PbPb collisions, after tagging with the SSVHE discriminator for
jets of 80 < pT < GeV/c (left) and 120 < pT < 150 GeV/c (right).
data and corresponding simulation templates for two of the jet pT bins used in this analysis. The
ratio of the charm to light normalizations are ﬁxed in the template ﬁts according to their relative
contributions in the input simulation.
The SSVHE tagging eﬃciency is shown in the left panel of Fig. 3. The result obtained using
JP as a reference tagger is compared to the result obtained directly from simulation. The b-jet
purity is the fraction of jets from bottom quarks in the SSVHE-tagged sample. The right panel
of Fig. 3 shows the b-jet purity of the tagged sample as a function of the jet pT extracted from
the template ﬁts in Fig. 2. These purity values are close to those from the input simulation.
The b-jet to inclusive jet ratio is calculated from (N taggedjets /Njets)(P/), where N
tagged
jets and Njets
are the number of tagged jets and the total number of jets counted from the data, respectively,
 (GeV/c)
T
Jet p
80 100 120 140 160 180 200
b-
je
t e
ffi
ci
en
cy
0
0.2
0.4
0.6
0.8
1
-1bμ L dt = 150 ∫PbPb,
Pythia+Hydjet
Reference tagger (JP)
Centrality 0-100%
SSVHE-tagged
CMS Preliminary  = 2.76 TeVNNs
 (GeV/c)
T
Jet p
80 100 120 140 160 180 200
b-
je
t p
ur
ity
0
0.2
0.4
0.6
0.8
1
-1bμ L dt = 150 ∫PbPb,
Pythia+Hydjet
Template fit to data
Centrality 0-100%
SSVHE-tagged
CMS Preliminary  = 2.76 TeVNNs
Figure 3: Left: The tagging eﬃciency of the SSVHE discriminator from simulation and from the reference tagger method.
Right: The b-jet purity extracted from template ﬁts to the SV mass distributions, compared to the input simulation.
M. Nguyen / Nuclear Physics A 904–905 (2013) 705c–708c 707c
 (GeV/c)
T
Jet p
80 100 120 140 160 180 200
b-
je
t f
ra
ct
io
n
0
0.01
0.02
0.03
0.04
0.05
0.06
CMS Preliminary  = 2.76 TeVs
-1 L dt = 231 nb∫
pp Data
Pythia
Syst. uncertainty
b-
je
t f
ra
ct
io
n
 (GeV/c)
T
Jet p
80 100 120 140 160 180 200
b-
je
t f
ra
ct
io
n
0
0.01
0.02
0.03
0.04
0.05
0.06
CMS preliminary  = 2.76 TeVNNs
Centrality 0-100%
-1bμ L dt = 150 ∫
PbPb Data
Pythia+Hydjet
Syst. uncertainty
b-
je
t f
ra
ct
io
n
Figure 4: The b-jet to inclusive jet ratio in 0-100% PbPb collisions (left) and pp collisions (right) as a function of jet pT
compared to Pythia embedded in Hydjet (PbPb) and Pythia (pp).
and P and  correspond to the purity and eﬃciency plotted in the left and right panels of Fig. 3,
respectively. The left panel of Fig. 4 shows this ratio as function of jet pT in 0-100% PbPb cen-
trality collisions. The b-jet fraction is around 2.9–3.5% with no signiﬁcant pT dependence, with
an absolute uncertainty in the range of 0.6–1.1%, increasing with jet pT. The values predicted by
Pythia+Hydjet, which decrease from about 3% to 2.5% as a function of pT, are consistent with
the data. The b-jet fraction in pp collisions as a function of jet pT is shown in the right panel of
Fig. 4. The values predicted by Pythia are consistent with the data.
There are several sources of systematic uncertainty which aﬀect the b-jet fraction. The un-
certainty on the tagging eﬃciency is evaluated from the diﬀerence between the simulation and
data-driven eﬃciencies. A second category of uncertainty is composed of eﬀects which inﬂu-
ence the b-tagging purity. One such uncertainty is the relative contribution from charm and light
jets, which changes the shape of the non-b-jet template. The relative normalization is ﬁxed in
our template ﬁts, and then alternately allowed to ﬂoat to obtain a systematic uncertainty. To de-
termine the uncertainty on the modeling of the non-b-jet template shapes by the simulation, we
compared to results obtained with a data-driven template formed by requiring an anti-b-tag on the
JP discriminator. An uncertainty on the stability of the template shapes was estimated by varying
the working point of the SSVHE tagger. Other sources of systematic uncertainty considered are
the b-jet and inclusive jet energy scales and the modeling of b-jet production in Pythia.
To within the fairly sizable uncertainties, the data disfavor an extreme scenario in which b-jets
suﬀer no energy loss in PbPb collisions. A more detailed understanding of the jet pT dependence
of the b-jet to inclusive jet ratio and, more generally, the parton mass and ﬂavor dependence of
energy loss, would require a reduction of the statistical and systematic uncertainties.
References
[1] CMS Collaboration, JHEP 05 (2012) 063.
[2] CMS Collaboration, JINST 3 (2008) S08004.
[3] CMS Collaboration, CMS Physics Analysis Summary CMS-PAS-BTV-11-004 (2012).
[4] CMS Collaboration, Eur. Phys. J. C 72 (2012) 1945.
M. Nguyen / Nuclear Physics A 904–905 (2013) 705c–708c708c


b-jet Identification in PbPb Collisions with CMS

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