Distinguishing quark and gluon jets at the LHC

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1 Distinguishing quark and jets at the LHC Giorgia Rauco (on behalf of the ALAS and CMS Collaborations) Universität Zürich, Zürich, Switzerland Abstract: Studies focused on discriminating between jets originating from s and quarks at the LHC are presented. he results here discussed are obtained with proton collisions collected by the ALAS experiment [] at s = 8 ev and by the CMS experiment [2] at s = 3 ev. Introduction Partons emitted from hard scattering process at the LHC form, due to QCD confinement and hadronization process, hadronic jets, which can be revealed with tracking and calorimeter systems. As known from theoretical principles and from experimental measurements, reconstructed jets show different properties depending on flavor of original parton. In general, due to the large color factor of s, -initiated jets have higher particle multiplicity, a softer fragmentation function, and are less collimated than quark-initiated jets. hese differences can be exploited to tag jets, and such a capability plays a fundamental role in several physics analyses. It results in an increased ability to discriminate full-hadronic final searches - composed mainly by quark-originated jets, from QCD background - where the component is predominant. ALAS studies he ALAS Collaboration, within a method based on data-driven template extraction of lightquark and jet properties, tested a variety of discriminants and derived the systematic uncertainties on their performances [3]. Additionally, a precision measurement of the jet constituents multiplicity has been carried on [4]. Both approaches are presented in the following. Discriminant variables and data-driven templates emplates of light-quark and jet properties are derived from data, exploiting Z/γ + jets (quark-enriched) and dijets (-enriched) events. Assuming the samples to be independent, the shape of an inclusive jet distribution is computed as the a linear combination of the pure light quark shape, weighted by the amount of light-flavor quarks, and the pure shape, weighted by the amount of s, plus contamination from heavy flavor partons, weighted by the amount of contamination. As an example, Fig. shows the extracted templates for the calorimetric and tracker based jet width (w calo and w trk ), comparing the two parton flavors and the data with two parton shower generators. A good agreement between the data and the hadronizers is found for light-flavor quarks, while data are falling between the two parton showers in the case of -like jets. In addition to the samples used for the template extraction, other events have been selected to validate the extrapolated distributions. By selecting particular regions of the phase space, γ+jets and trijets events have been purified, becoming highly quark-like in the former sample and -like in the latter sample. 73

2 /Events /Events ALAS s = 8 ev 2.3 fb 9 GeV < P < 2 GeV, η <.8 Herwig++ Pythia /Events /Events ALAS s = 8 ev 2.3 fb 9 GeV < P < 2 GeV, η <.8 Herwig++ Pythia w calo w trk Figure : templates for w calo (left) and w trk (right) comparing data (points), PYHIA (solid line) and HERWIG (dashed line) [3]. he comparison for the mean values of w calo (left) and n trk (right) along the jet p spectrum between the extracted and validated templates are shown in Fig. 2, resulting in a good agreement for quarks and in a 5% disagreement in distributions. <w calo > ALAS s = 8 ev 2.3 fb η <.8 <n trk > ALAS s = 8 ev 2.3 fb η < Jet P Jet P Jet P Jet P Figure 2: Comparison between the mean values of w calo and n trk as a function of p [3]. Measurement of the charged constituents of the jet An additional study to enhance the discrimination between quark-like and -like jets is the precision measurement of the jet constituents multiplicity, performed by applying unfolding techniques to remove distortions due to detector effects and by comparing several particle-level models. Figure 3 shows the jet p dependence of the average charged-particle multiplicity for quark- and -initiated jets, extracted with the fractions from PYHIA, along with the N3LO pqcd prediction. As expected, for both the quark-initiated jets and -initiated jets the average multiplicity increases with jet p, but the increase is faster for -initiated jets, for which the multiplicity is also higher. 74

3 charged n ALAS s = 8 ev = 2.3 L int track p >.5 GeV 2 Quark Jets (Data) Gluon Jets (Data) Quark Jets (Pythia 8 AU2) Gluon Jets (Pythia 8 AU2) 3 Quark Jets N LO pqcd 3 Gluon Jets N LO pqcd 5 5 Jet p [GeV] Figure 3: he average charged-particle multiplicity for quark- and -initiated jets depending on the jet p spectrum [4]. CMS studies he CMS Collaboration optimized observables based on performances established with Monte Carlo simulated QCD events. he CMS Collaboration optimized a likelihood-based discriminator based on performances established with Monte Carlo simulated QCD events and validated it using the data. A data-driven method is employed to derive corrections to account for observed differences with the data. Effect of using different parton shower models on the performance of the discriminator is also compared[5][6]. Construction of a quark- likelihood discriminant and its validation on data Following the theoretical differences between quark- and -like jets as explained in the Introduction, three observables are investigated: (i) the jet constituents multiplicity; (ii) the jet minor axis of the ellipse cone projected on the η φ plane; (iii) the jet fragmentation distribution p D, defined as i p2,i / i p,i. he probability density functions of these variables, extracted in QCD events (showered with PYHIA) where the jets have been tagged as or quark, are used to build a likelihood product, which will have an output value in the [,] range, expressing the probability for a jet to be a quark-like jet. he performances of the quark- likelihood (QGL) discriminator are checked in QCD simulated events by using the so-called ROC curves and are shown in Fig. 4. he training observables and the taggers have been validated using 3 ev collisions data in two control regions: events, which are quark enriched and dijets events, -rich. For both control regions, the full 25 dataset has been used, corresponding to an integrated luminosity of 2.6 fb for the events and of 23 nb for the dijets ones. Distributions of these variables observed in data are reasonably well described by the simulation, as shown in Fig. 5 [6]. 75

4 quark-jet efficiency < η <.3, 8 < p < GeV qg-likelihood training variables: ptd only axis2 only multiplicity only All variables (3 ev) CMS Simulation jet rejection quark-jet efficiency < η <.3, 8 < p < η <.3, 4 < p 2.7 < η < 3, 4 < p < GeV < 5 GeV jet rejection CMS Simulation < 5 GeV (3 ev) Figure 4: Quark jet tagging efficiency as a function of the jet rejection: (left) individual variable discrimination compared to the full QGL (right) QGL performance in different kinematic regions [6]. Events/. 4 CMS fb (3 ev) Events/ CMS 2.6 fb Data (3 ev) quark Events/.4 4 CMS fb (3 ev) GeV < p < GeV η < GeV < p < GeV η < GeV < p < GeV η < DAA Multiplicity DAA p D DAA log(σ 2 ) Figure 5: Comparisons of the training observables in events in data and simulation [6]. Systematic uncertainties and tagging efficiencies o estimate shape uncertainties on the QGL, a based method, taking into account the discriminator shape variations observed in the validation of the simulated samples, is pursued. he chosen approach is the reshaping of the Monte Carlo distribution of both parton flavors components, with weights distributions binned in the likelihood output, by constraining them using the yields observed in the data. Both control regions used in the validation, and dijets, have been simultaneously exploited and the method resulted in a relevant improvement in the data/ agreement on the QGL distribution, as shown in Fig.6. on data and reshaping have been performed on HERWIG generated samples too and a comparison between the two hadronizers in the efficiencies to select - and quark-jets with using a fixed cut on the likelihood output are shown in Fig. 7, before and after the application 76

5 Events/.4 2 CMS fb (3 ev) 8 GeV < p < GeV η < 2. Events/.4 35 CMS dijets nb (3 ev) 8 GeV < p < GeV η < DAA Quark-Gluon Likelihood DAA Quark-Gluon Likelihood Figure 6: Data- comparisons on the tagger on events (left) and dijets events (right) after the reshaping of the distributions [6]. of the data-driven procedure. he efficiencies obtained with the data-driven corrected performances are very similar (within the percent level) for both parton flavors, comparing the two parton showers. Efficiency (LD >.5) CMS Quark (Herwig++) Quark (Pythia8) 2.6 fb (3 ev) Gluon (Herwig++) Gluon (Pythia8) before after Efficiency (LD >.5) CMS Quark (Herwig++) Quark (Pythia8) 2.6 fb (3 ev) Gluon (Herwig++) Gluon (Pythia8) before after , η < 2.. dijets, η < Jet p [GeV] Jet p [GeV] Figure 7: HERWIG and PYHIA selection efficiencies by applying a fixed cut on the likelihood output LD>.5. Efficiencies are evaluated in dijet events (left) or Z+jet events (right), as a function of the jet p with or without the data-driven shape of the outputs [6]. 77

6 Conclusions he efforts made by the ALAS and CMS Collaboration to experimentally distinguish between quark-like and -like jets have been presented. he former extracted data-driven templates for several discriminating variables and performed also an independent precision measurement of the jet constituents multiplicity. he latter built a likelihood-based discriminant, validated it on data and compared its performances on two hadronizers. References [] ALAS Collaboration, he ALAS Experiment at the CERN Large Hadron Collider, JINS 3 (28) S83. [2] CMS Collaboration, he CMS experiment at the CERN LHC, JINS 3 (28) S84. [3] ALAS Collaboration, Discrimination of Light Quark and Gluon Jets in pp collisions at s = 8 ev with the ALAS Detector, ALAS-CONF [4] ALAS Collaboration, Measurement of the charged-particle multiplicity inside jets from s = 8 ev pp collisions with the ALAS detector, Eur. Phys. J. C 76 (26) no.6, 322 arxiv: [hep-ex]]. [5] CMS Collaboration, Performance of quark/ discrimination in 8 ev pp data, CMS- PAS-JME3-2. [6] CMS Collaboration, Performance of quark/ discrimination in 3 ev data, CMS-DP

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