ATLAS-CONF-2012-138 24September2012
ATLAS NOTE
ATLAS-CONF-2012-138
September 24, 2012
Light-quark and gluon jets: calorimeter response, jet energy scale systematics and jet properties
The ATLAS Collaboration
Abstract
The calorimeter response to jets depends on how the jet fragments. Differences in the fragmentation of jets initiated by light quarks and gluons give rise to differences in their jet energy scale. Monte Carlo simulations are used to establish a sample-dependent jet energy scale systematic uncertainty. The difference in response is largely caused by differences in observable properties of light-quark-initiated and gluon-initiated jets, including the jet transverse width and the number of charged hadrons associated to the jet. These properties can also be exploited to discriminate between light-quark and gluon jets. Jets produced in proton-proton collisions at a center-of-mass-energy of √ s = 7 TeV and measured with the ATLAS detector are used to establish the accuracy of the description of such properties. The 2011 dataset, with an integrated luminosity of 4.7 fb
−1is used. The studies are performed on inclusive and photon-jet samples. They show agreement between data and Monte Carlo simulation for the jet width but not for the number of charged hadrons in gluon jets. These results are verified using enriched samples of gluon and light-quark jets. The differences between data and Monte Carlo simulation suggest that the charged hadron multiplicity is less powerful in discriminating between light-quark and gluon jets than expected.
c Copyright 2012 CERN for the benefit of the ATLAS Collaboration.
Reproduction of this article or parts of it is allowed as specified in the CC-BY-3.0 license.
1 Introduction
The determination of the jet energy scale and its uncertainty and the achievement of the optimal jet energy resolution are two major tasks of the ATLAS jet calibration program. The jet energy scale, resolution, and uncertainty should be universally applicable to all event topologies. All jet calibration schemes developed in ATLAS achieve an average response of the calorimeter to jets near unity for jets in the inclusive jet sample [1]. However, the response also exhibits variations that can be correlated to the flavor of the partons produced in the sample under study. This dependence is to a large extent due to differences in fragmentation and showering properties of jets loosely labeled as originating from a light quark or a gluon [1].
In this note, the dependence of the jet energy scale on whether a jet originates from a light quark or a gluon is studied. Also, a systematic uncertainty that accounts for the sample dependence of the jet energy scale is established using different Monte Carlo simulations. In addition, jet properties that have been shown to have discriminating power between light-quark and gluon-induced jets are studied [2, 3]. The understanding of these properties and their use for light-quark/gluon discrimination in physics analyses is not devoid of ambiguity and analysis dependence, given that the definition of a light-quark or gluon jet is at best a leading-order concept. However, for the purpose of the performance studies, a leading- order definition is used and is sufficient. The properties are measured for light-quark and gluon jets by exploiting differences between an inclusive jet and a photon-jet sample and are further validated through the aid of samples made of predominantly light-quark or gluon-induced jets enriched through kinematic selections.
2 Object reconstruction, selection and calibration
Calorimeter jets with transverse momentum p
T> 20 GeV and pseudorapidity
1| η | < 4.5 are reconstructed using the anti-k
tjet algorithm [4–6] with a four-momentum recombination scheme and a distance param- eter of R = 0.4. All results were also obtained using jets built with a distance parameter of R = 0.6 and were found to be consistent. Topological clusters of energy in the calorimeter are used as input to the jet algorithm [7].
Three calibration schemes are used in this note. The “EM+JES” scheme calibrates the average jet energy to that of a “true” jet constructed from stable particles in the Monte Carlo (MC) simulation
2. The
“LCW+JES” calibration scheme first calibrates the topological clusters based on their properties and applies an additional final correction to the jet energy. The “GS” calibration scheme applies a sequence of corrections to the jet energy based on the jet shower properties after the EM+JES calibration has been applied. All three calibration schemes include an “offset” correction for the multiple pp collisions in each bunch crossing [8]. All three calibration schemes are discussed in detail in Ref. [1]. The jet transverse momentum response, R , defined as R = p
jetT/ p
truthT(henceforth simply “jet response”), is calculated by matching calorimeter jets to true jets with a matching radius ∆R = p
∆η
2+ ∆φ
2= 0.3. Only jets with
| η | < 2.8 are included when counting the number of jets in the samples used to study the jet properties.
The jets are required to pass several selection criteria, which remove specific non-collision back- grounds [9]. Any event containing jets with p
T> 20 GeV that fail these quality criteria is discarded.
For a fraction of the data-taking period, a portion of the barrel electromagnetic calorimeter had readout problems. Any event during this period containing jets with p
T> 20 GeV that fall in the region of the
1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and they-axis points upward. Cylindrical coordinates (r, φ) are used in the transverse plane,φbeing the azimuthal angle around the beam pipe. The pseudorapidity is defined in terms of the polar angleθasη=−ln tan(θ/2).
2Stable particles include all those with lifetimes longer than 10 ps, excluding muons and neutrinos.
calorimeter with readout problems is discarded. In a corresponding fraction of the Monte Carlo simula- tion events, events with jets in the same fiducial region are removed. Jets are considered isolated if there is no other jet with p
T> 10 GeV within a radius of 2.5 times the distance parameter of the jet (1.0 for R = 0.4 and 1.5 for R = 0.6). Only isolated jets are considered in this study.
Additional variables to describe the properties of jets, described in Section 6, are defined using tracks with p
T> 1 GeV that are associated to the jets if they are within a distance R, equal to the distance parameter used to build the jet, of the jet axis. The tracks are additionally required to have at least one pixel hit, at least six SCT hits, and χ
2per degree of freedom below three. In order to reduce the effects of pile-up, the tracks are required to be close to the primary vertex (impact parameters z
0× sin(θ) < 1 mm and d
0< 1 mm). The studies have also been performed using a 500 MeV cut and no qualitative difference in the results has been observed.
Jets are labeled by partonic flavor if they have p
T> 40 GeV and | η | < 2.1. They are matched to the highest energy parton produced directly off the hard scatter or as radiation that lies inside the cone of the jet. This definition of partonic jet flavor is not theoretically sound, and that may have implications when attempting to apply this labeling to physics analyses. However, several studies with M ad G raph [10]
have demonstrated that this definition is not changed by the parton shower and is equivalent to a matrix- element-based labeling for 90–95 % of jets. Since the partonic flavor of a jet can only be a leading- order concept, and this note requires only a labeling that results in differences in the jet properties, this definition is adopted as sufficient.
To construct a photon+jet sample, photons with p
T> 25 GeV and | η | < 1.37 are selected if they pass “tight” identification criteria [11]. Photons are additionally required to be unaffected by problematic calorimeter regions and isolated in the calorimeter, with no more than 5 GeV of energy in the calorimeter within a cone of 0.4 around the photon, excluding the photon itself.
3 Data and event selection
The data used in this analysis were collected from March to October 2011 with a bunch spacing of 50 ns.
The average number of interactions per bunch crossing was 6 in the initial, low-luminosity, part of the run, and 11 in the final, high-luminosity, part of the run, ranging overall from 3 to 17. Two main event samples are used. The first selects inclusive jet events (dijet sample). The second selects jets with a high-transverse momentum (high-p
T) photon back-to-back with a jet (γ-jet sample) [11]. Both samples are defined using standard data quality criteria and the requirement of a primary vertex with at least three associated tracks. A total integrated luminosity of 4.67 ± 0.08 fb
−1[12, 13] is selected based on these criteria.
Central jet triggers are used for the dijet sample selection. These triggers provide a fully efficient jet selection above 40 GeV of transverse momentum. In order to limit the data rate, below p
T= 500 GeV a pre-scale was applied during data taking, so that only a fraction of the events are recorded.
The γ-jet sample is selected using single photon triggers [11]. These triggers are fully efficient for photons with p
T> 25 GeV. However, the photon triggers with p
Tthresholds lower than 80 GeV were pre-scaled in 2011. The high-p
Tphoton in the event is required to be back-to-back (∆φ > 2.8 rad) to the leading jet, and the second-leading jet is required to have no more than 10 % of the photon p
T. These requirements are relaxed in the study of γ+two-jet events.
4 Monte Carlo simulations
The data are compared to Monte Carlo simulation events produced with the P ythia 6 event generator [14].
The proton parton distribution function (PDF) set used is the modified leading-order PDF set MRST
LO* [15]. The ATLAS MC11 AUET2B MRST LO** tune of the soft model parameters is used [16].
Samples to evaluate systematic uncertainties are based on P ythia 6 with the Perugia2011 [17] tune and the H erwig++ [18] 2.5.2 event generator. The underlying event and soft inclusive interactions in H erwig++ are described using a hard and soft multiple partonic interactions model [19] and its default parameters are used.
For understanding ambiguities in the flavor tagging procedure used here, samples are generated with M ad G raph [10], using the CTEQ6L1 PDF set [20]. Up to three outgoing partons are included in the matrix element, with MLM matching applied in the parton shower [21]. The events are showered using P ythia .
Additional minimum bias events (pile-up events) are generated with P ythia 6 with the ATLAS MC11 AUET2B CTEQ6L1 tune [16] using the CTEQ6L PDF [20] and are super-imposed on hard-scattering events. The Monte Carlo simulation events are re-weighted in order to closely follow the average number of interactions per bunch crossing in the data. The same trigger, event, jet and track selection criteria are used in the Monte Carlo simulation as in the data. Some additional studies are performed with the M ad G raph event generator.
5 Calorimeter response to light-quark and gluon jets
Jets labeled as originating from light quarks have significantly different response from those labeled as originating from gluons in the MC simulation. This difference is a result of a difference in fragmentation that can be correlated to differences in observable properties of the two types of jets. The jets labeled as gluon jets tend to have more particles, and as a result, those particles tend to have lower p
Tthan in the case of light-quark jets. Additionally, the gluon jets tend to have a wider angular energy profile before interacting with the detector. The harder particles in light-quark jets also have a higher probability of penetrating further into the calorimeter, more often reaching the hadronic calorimeter layers. The lower response of the calorimeter for low-p
Tparticles combined with threshold and response effects correlated to the energy density inside the jet suggest that gluon jets should have a lower response than light-quark jets. The difference in calorimeter response between isolated light-quark and gluon jets is shown in Figure 1, for anti-k
tjets built with R = 0.4 in the barrel calorimeter ( | η | < 0.8).
Several calibration schemes are shown. In all cases, the flavor-dependent response difference is largest at low p
T(up to 8 %) and decreases to a few percent at high p
T. The more sophisticated cali- bration schemes achieve a smaller difference, because they benefit from additional information about the showering in the calorimeter. The GS calibration shows the best performance due to its use of a vari- able that is correlated with the transverse structure of the jet and is thus sensitive to differences between light-quark and gluon initiated jets. The response difference between light-quark and gluon initiated jets is reduced by roughly 1 % for anti-k
tjets built with R = 0.6 (not shown), because the larger jet area diminishes the effect of the energy loss of the broader jet.
The differences in response between light-quark and gluon initiated jets can impact analyses in which the flavor composition of the sample is not well known, as detailed in Ref. [2]. Such uncertainties can be reduced if the flavor composition of the analysis sample is known and the accuracy of the MC’s description of the data can be established. This uncertainty can be extracted directly from Figure 1 and amounts to about 2% at low p
Tand 0.5% at high p
Tif the flavor composition of the sample is known within 25%, when using the EM+JES calibration. This uncertainty can be reduced by a factor of two at low p
Tand even more at high p
Tthrough the use of one of the more sophisticated calibration schemes.
The difference in the response of light-quark and gluon initiated jets results in a sample dependence
of the energy scale and suggests that the jet energy scale determined from in situ techniques might not
be directly applicable to different jet samples. With the techniques commissioned up to date, the 2011
dataset only allows for a coarse validation of the differences in the jet energy scale between light-quark
and gluon initiated jets. MC simulations are instead used to understand the impact of systematic effects
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HERWIG++
PYTHIA Perugia2011 Simulation
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Figure 1: Difference in jet response ( R ) of isolated light-quark and gluon-induced jets as a function of the
true jet p
Tfor anti-k
tjets with R = 0.4 in the barrel calorimeter. Three different calibration schemes are
shown: the EM+JES calibration (top left), the LCW+JES calibration (top right), and the GS calibration
(bottom). Three different MC simulation samples are also shown: P ythia MC11 (solid red circles),
Herwig++ (open blue circles) and P ythia Perugia2011 (open black squares).
in the response differences between light-quark and gluon jets.
Figure 1 also shows the jet response difference between light-quark and gluon initiated jets in the low | η | region for P ythia MC11, P ythia Perugia2011 and Herwig++. Comparisons between the first two simulations display the impact of the underlying event tune on the response differences. Comparisons between P ythia MC11 and Herwig++, provide an estimate of the impact of differences in the modeling of the parton shower and the decay and hadronization processes for generators that have been shown to model the jet fragmentation within the constraints provided by data [16, 22]. The effect of the underlying event tune is small, while differences between P ythia MC11 and Herwig++, like their different particle compositions, cause large differences in response.
The differences between Herwig++ and P ythia MC11 are caused almost exclusively by differences in the response to gluon jets. Thus, they result in sizable differences in the response of the inclusive jet sample and small differences in the response of the samples used to calibrate the absolute jet response, such as the γ-jet and Z-jet samples.
The systematic effect illustrated by the difference between the two MC simulations can be included as an additional systematic uncertainty. For this purpose, one can start with Equation 2 from Ref. [2]:
∆ R
s= ∆ f
g× ( R
g− 1) + ∆ f
q× ( R
q− 1) + f
g× ∆ R
g+ f
q× ∆ R
q+ f
b× ∆ R
b+ f
c× ∆ R
c, (1) where the subscripts refer to the sample of interest (s) or to the different jet flavors, R is the response, and f
xis the fraction of jets with partonic flavor x. Under the simplifying assumption that the jet energy scale uncertainty is established in situ for light-quark jets and it is the same for b and c jets, this equation can be simplified to:
∆ R
s= ∆ f
q× ( R
q− R
g) + ∆ R
j+ f
g× ∆ R
exg, (2) where ∆ R
j≡ ∆ R
q= ∆ R
b= ∆ R
cand ∆ R
gexis an additional variational term that represents the un- certainty on the response of gluon jets that arises from the systematic effects captured by the different MC simulations. This is the same result as that reached in Equation 3 of Ref. [2] with an additional uncertainty term for gluon jets. Note that the first term of this equation was used in the first paragraph of this section to estimate the effect of the results shown in Figure 1 on the systematic uncertainty of the jet energy scale in a sample of imprecisely known flavor composition.
The additional term was not added to the 2010 ATLAS jet energy scale uncertainty for simplicity, since it was much smaller than the dominant contributing effects. The improvements in the jet energy measurement achieved with the 2011 dataset require this more careful treatment. Based on Figure 1, where one can read off the values that are used for the uncertainty on R
exgfrom this effect, that amounts to about 3% in a sample with a 75% gluon content (close to the inclusive jet sample) and is reduced to about 1% in a sample with 25% gluon content (close to what is expected in a t¯t sample with radiation) at low p
Twhen using the EM+JES calibration scheme. The uncertainty at high p
Tis smaller than 1%. This term in the uncertainty can also be reduced by half or more when using the more sophisticated calibration schemes.
The in-situ jet energy scale uncertainty is validated using γ-jet and Z-jet samples, which at low
p
Tare dominated by light quark jets. The expression for the total uncertainty presented here could be
generalized to account for the fact that there is, in fact, some gluon-initiated jet contamination, and that
the uncertainty on the light-quark jet response, ∆ R
j, cannot be made using these samples alone. However,
the approximation that the γ-jet and Z-jet sample are pure light-quark jet samples is most accurate at low-
p
T, where the gluon jet response uncertainty is largest. Thus, this approximation leads to inaccuracies
that are significantly smaller than other systematic uncertainties in the average jet response.
6 Study of variables for light-quark and gluon jet discrimination
The differences between light quarks and gluons lead to differences in observable final state jet properties on average. Jets initiated by gluons are expected to be broader, with more low-p
Tparticles than those ini- tiated by light quarks. The jet width and number of tracks have already been used to measure the average flavor fractions in different data samples [2], and they have been identified as powerful discriminators for the purpose of understanding partonic flavor in previous studies [3].
The significant pile-up at the LHC in 2011 means that any measurement of jet properties may be affected by particles from other interactions. Calorimetric properties are particularly sensitive to the effects of pile-up. However, since charged particle tracks can be associated to a specific proton-proton collision via vertex association, jet properties calculated from tracks associated to one primary vertex are inherently less sensitive to pile-up. Thus, for this study, the properties used to distinguish different classes of jets are the number of charged tracks associated to the jet and the jet width, W, defined as
W =
P p
T,i× ∆R
iP p
T,i, (3)
where the sum is over the tracks associated to the jet, p
T,iis the p
Tof the track, and ∆R
iis the opening angle in η–φ between the jet axis and the track.
Properties of jets based on tracks depend upon a good description of hadronization and fragmentation.
Although the phenomenological models used in various generators have been tuned to match measure- ments of correlated properties (such as the fragmentation function and differential jet shapes) [16, 22], the charged particle spectra within a jet remain difficult to describe. This is illustrated in Figure 2, where the mean value of each property is shown as a function of p
Tfor P ythia MC11, P ythia Perugia2011 and Herwig++.
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Solid Markers: Light-quark Jets Empty Markers: Gluon Jets
[GeV]
jet
pT
50 100 150 200 250 300 350
MC/Pyt. MC11
0.9 1 1.1
(b)<W>
Figure 2: Average n
trkand track width for light-quark-induced (closed markers) and gluon-induced (empty markers) jets as a function of the reconstructed jet p
Tfor isolated jets with | η | < 0.8. Results are shown for P ythia MC11 (black circles), P ythia Perugia2011 (red triangles) and Herwig++ (blue squares). The error bars represent only statistical uncertainties.
Differences are most significant for the charged particle multiplicity of gluon jets, for which P ythia
predicts higher values than Herwig++. Significant differences are also found in the description of the track width, but these are mostly present at low p
T. Because of these differences, it is critical to separately derive binned distributions of jet properties for light-quark and gluon induced jets in data and in Monte Carlo simulation.
6.1 In situ extraction of light-quark and gluon distributions
Distributions of properties of light-quark jets and gluon-jets can be extracted using the inclusive jet and
γ-jet event samples and the fraction of light-quark and gluon jets predicted by P ythia with the MC11
tune. These fractions have been verified to be stable within about 5% to changes in the proton structure functions and changes in the matrix element calculation, using the 2 → n matrix element calculation implemented in M ad G raph . The fractions in the γ-jet samples are corrected for the purity of the photon reconstruction, estimated in Ref. [11], assuming the flavor composition of the background sample is that of the dijet sample. This assumption has been verified to hold with non-negligible statistical uncertain- ties using a dijet sample defined by using the γ-jet selection criteria. The assignment of a systematic uncertainty due to these assumptions is left for future studies, but is negligible beyond p
T= 100 GeV, where the photon purity is high. For each jet η, jet p
T, and jet property (track width or number of tracks in this note) bin i, a simple set of linear equations is solved bin-by-bin:
P
i= f
q,i× P
q,i+ f
g,i× P
g,i+ f
c,i× P
c,i+ f
b,i× P
b,i, (4) where P
iis the number of jets in a bin of width or number of tracks i, f
q,iand f
g,iare the light-quark and gluon fractions predicted by P ythia in that bin, and P
q,iand P
g,iare the distributions of the track width or number of tracks for light-quark and gluon-induced jets in the bin. The fractions, f
c,iand f
b,i, and distributions, P
c,iand P
b,i, for b-jets and c-jets are incorporated into this simplified equation and taken from the Monte Carlo simulation. By using the different fractions of light quarks and gluons in dijet and γ-jet events in each p
Tand η bin, the expected “pure” jet sample properties can be estimated. Systematic effects arising from uncertainties in the fraction of c- and b-jets have been verified to be below 10 % using a conservative factor of two variation of the fractions [23]. Systematic effects arising from uncertainties in their shapes are also expected to be small as suggested by cross checks performed in a t¯t sample [24].
Figure 3 shows the mean number of tracks and the mean track width as a function of the jet p
T, separated either using the flavor labels or using the extraction procedure in the same Monte Carlo sim- ulation events. Differences are observed between the average of the distributions in the dijet and γ-jet samples, which lead to biases in the extracted distributions that favor the dijet (γ-jet) sample for gluon (light-quark) induced jets. The differences are larger at low p
Tand for the track width distributions.
The bias demonstrates a sample dependence, which needs to be considered as a systematic uncertainty in discrimination methods attempting to use these variables. These differences are, however, small if compared to the differences between light-quark and gluon-induced jets, demonstrating the sensitivity of the extraction method.
Figure 4 shows the same results as Figure 3, but this time data is used in the extraction. Agreement is found between data and P ythia MC11 for both light-quark and gluon-induced jets for the track width.
However, the mean number of associated tracks is significantly smaller for gluon-induced jets in the data than what is observed in the P ythia Monte Carlo simulation, being closer to the prediction from Her- wig++. This brings gluon-induced jets much closer to light-quark-induced jets in this variable, making it less powerful in discriminating between the two.
6.2 Validation of distributions using kinematically purified samples
The results shown in the previous section suggest that differences exist between data and Monte Carlo
simulation in the number of tracks, considered as one of the most powerful single variables in discrim-
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Open markers Gluons
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pT
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(b)<W>
Figure 3: Average n
trkand track width for light-quark (solid marker) and gluon-induced (open marker) jets as a function of reconstructed jet p
Tfor isolated jets with | η | < 0.8. Results are shown for distributions obtained using the in situ extraction method in MC simulation (black circles), labeled jets in the dijet sample (blue trianges) and labeled jets in the γ-jet sample (red squares). The error bars represent only statistical uncertainties.
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γ+jet Pythia Closed markers Quarks
Open markers Gluons
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jet
pT
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(b)<W>
Figure 4: Average n
trkand track width for light-quark (solid marker) and gluon-induced (open marker)
jets as a function of reconstructed jet p
Tfor isolated jets with | η | < 0.8. Results are shown for distributions
obtained using the in-situ extraction method in data (black circles), labeled jets in the dijet sample (blue
trianges) and labeled jets in the γ-jet sample (red squares). The error bars represent only statistical
uncertainties.
inating light-quark and gluon-induced jets [3]. It is, thus, important to verify this result with as many independent methods as possible.
Recently, it has been demonstrated that with a few kinematic selection criteria it is possible to signifi- cantly enhance the purity of light-quark or gluon-induced jet samples [25]. One set of criteria selects jets close to the photon in γ+two-jet events, purifying the quark-jet sample by exploiting the fact that gluons do not have an electric charge. The other set of criteria attempts to purify a gluon-jet sample by isolating soft third-jet radiation in events with two jets at high p
T. Both of these selections give reasonably high purities in M ad G raph (> 90 %) across a broad p
Trange, but the selection criteria lead to low numbers of events. Nevertheless, this method can be used to verify the conclusions of the in-situ extraction method.
The discriminants are defined as in Ref. [25]:
L
q= η
γη
j1+ ∆R
γj2, (5)
for purifying light-quark jets in the γ+two-jet sample and
L
g= | η
j3| − | η
j1− η
j2| , (6) for purifying gluon jets. Here, η
xrefers to the pseudorapidity of object x (photon, or leading, subleading or third leading jet in p
T) and ∆R
γj2is the distance in η × φ between the photon and the subleading jet.
A purification cut of L
q< 2 (L
g< 0) is applied on the γ+two-jet (trijet) sample, corresponding to typical purities of 80-90 %. The purities are expected to be lower in the selected γ+two-jet sample due to the contamination from three-jet events, which has not been studied for this note.
Figure 5 compares the properties of light-quark jets and gluon jets extracted via the method described in Section 6.1 to those measured in the purified samples. Agreement is found between the results obtained in the purified gluon samples in the data and those obtained with the distribution extraction method for both variables. The agreement is less good for the light-quark-induced jets, particularly in the average number of charged particles at high p
T. Whether this difference arises from a sample dependence, such as that shown in Figure 4, background contamination or some other effect has not been established and is beyond the scope of this note. The reasonable agreement found in most observables, however, lends further credence to the results presented in the previous section, while raising important considerations in the use of purified samples, such as the impact of photon purity and the availability of sufficient events to build a discriminant devoid of statistical fluctuations.
7 Conclusions
The dependence of the jet energy scale on the flavor of the originating parton of the jet was evaluated in both Monte Carlo simulations and data. This difference, which enters the jet energy scale systematic uncertainty, was shown to be sensitive to certain details of the modeling of the decay and fragmentation of jets in the Monte Carlo simulations. An additional term has been derived that needs to be added to the jet energy scale uncertainty to account for this dependence on the decay and fragmentation modeling.
The uncertainty from this effect amounts to about 3% in a sample with a 75% gluon content (close to the inclusive jet sample) and is reduced to about 1% in a sample with 25% gluon content at low p
Twhen using the EM+JES calibration scheme. The uncertainty at high p
Tis smaller than 1%. This term in the uncertainty can also be reduced by half or more when using the more sophisticated calibration schemes and is included as a part of the combined ATLAS jet energy scale uncertainty [26].
The flavor dependence of the jet energy scale arises to a great extent from differences in the jets’
observable properties, such as the number of tracks and the jet width measured with tracks. These
properties can be exploited to reduce this dependence, as well as to discriminate between light-quark and
gluon jets. They were studied individually for light-quark and gluon-induced jets in data and Monte Carlo
[GeV]
jet
pT
〉 trkn〈
5 10 15 20 25 30
ATLAS Preliminary
| < 0.8 η R=0.4, | anti-kt
Extracted from 2011 Data = 7 TeV s
-1, L dt = 4.7 fb
∫
Extracted Extracted + 2j, L < 1 γ Trijet, L < 0
[GeV]
jet
pT
50 100 150 200 250 300 350
Extracted/Pure Data
0.8 1.0 1.2
(a)<ntrk>
[GeV]
jet
pT
〉Track Width〈
0.05 0.1 0.15 0.2 0.25 0.3
ATLAS Preliminary
| < 0.8 η R=0.4, | anti-kt
Extracted from 2011 Data = 7 TeV s
-1, L dt = 4.7 fb
∫
Extracted Extracted + 2j, L < 1 γ Trijet, L < 0
[GeV]
jet
pT
50 100 150 200 250 300 350
Extracted/Pure Data
0.8 1.0 1.2
(b)<W>
