CMSPASSMP16005  
Measurement of the differential cross section for the production of a W ($\rightarrow \mu\nu$) boson in association with jets at $\sqrt{s} = $ 13 TeV  
CMS Collaboration  
August 2016  
Abstract: A measurement of the differential cross sections for a W ($\rightarrow \mu\nu$) boson produced in association with jets is presented. The measurement is based on the 13 TeV protonproton collisions data corresponding to an integrated luminosity of 2.5 fb$^{1}$ recorded by the CMS detector at the CERN LHC. The cross sections are reported as a function of jet multiplicity, the jet transverse momenta, the jet rapidity, and the scalar sum of the jet transverse momenta for different jet multiplicities. The measured cross sections are compared with the predictions that include multileg leading order and nexttoleading order matrix element calculations interfaced with parton showers and a nexttonexttoleading order calculation for W+1 jet.  
Links:
CDS record (PDF) ;
CADI line (restricted) ;
These preliminary results are superseded in this paper, PRD 96 (2017) 072005. The superseded preliminary plots can be found here. 
Figures  
png pdf 
Figure 1a:
Data to simulation comparison of exclusive jet multiplicity before (a) and after (b) the application of the b tag veto. The diboson samples (WW, WZ, and ZZ) are represented by VV. The error bars on the ratio panel represent the statistical uncertainty of the data and simulated signal sample. 
png pdf 
Figure 1b:
Data to simulation comparison of exclusive jet multiplicity before (a) and after (b) the application of the b tag veto. The diboson samples (WW, WZ, and ZZ) are represented by VV. The error bars on the ratio panel represent the statistical uncertainty of the data and simulated signal sample. 
png pdf 
Figure 2a:
Data to simulation comparison of exclusive (a) and inclusive (b) jet multiplicity. QCD background is estimated using a datadriven method. The diboson samples (WW, WZ, and ZZ) are represented by VV. The error bars on the ratio panel represent the statistical uncertainty of the data and simulated signal sample. 
png pdf 
Figure 2b:
Data to simulation comparison of exclusive (a) and inclusive (b) jet multiplicity. QCD background is estimated using a datadriven method. The diboson samples (WW, WZ, and ZZ) are represented by VV. The error bars on the ratio panel represent the statistical uncertainty of the data and simulated signal sample. 
png pdf 
Figure 3a:
Data to simulation comparison of $1^{st}$ (a) and $2^{nd}$ (b) jet $ {p_{\mathrm {T}}} $. QCD background is estimated using a datadriven method. The diboson samples (WW, WZ, and ZZ) are represented by VV. The error bars on the ratio panel represent the statistical uncertainty of the data and simulated signal sample. 
png pdf 
Figure 3b:
Data to simulation comparison of $1^{st}$ (a) and $2^{nd}$ (b) jet $ {p_{\mathrm {T}}} $. QCD background is estimated using a datadriven method. The diboson samples (WW, WZ, and ZZ) are represented by VV. The error bars on the ratio panel represent the statistical uncertainty of the data and simulated signal sample. 
png pdf 
Figure 4:
Data to simulation comparison of $3^{rd}$ jet $ {p_{\mathrm {T}}} $. QCD background is estimated using a datadriven method. The diboson samples (WW, WZ, and ZZ) are represented by VV. The error bars on the ratio panel represent the statistical uncertainty of the data and simulated signal sample. 
png pdf 
Figure 5a:
Data to simulation comparison of exclusive (a) and inclusive (b) jet multiplicity in the $ {\mathrm {t}\overline {\mathrm {t}}} $enriched control sample. The diboson samples (WW, WZ, and ZZ) are represented by VV. The error bars on the ratio panel represent the statistical uncertainty of the data and simulated signal sample. 
png pdf 
Figure 5b:
Data to simulation comparison of exclusive (a) and inclusive (b) jet multiplicity in the $ {\mathrm {t}\overline {\mathrm {t}}} $enriched control sample. The diboson samples (WW, WZ, and ZZ) are represented by VV. The error bars on the ratio panel represent the statistical uncertainty of the data and simulated signal sample. 
png pdf 
Figure 6a:
The differential cross section measurement for the exclusive and inclusive jet multiplicities, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. Black circular markers with the grey hatched band represent the unfolded data measurement and its total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled band around MGaMC FxFx prediction represents its theoretical uncertainty including both statistical and systematical uncertainties. The lower panels show the ratios of the prediction to the unfolded data. 
png pdf 
Figure 6b:
The differential cross section measurement for the exclusive and inclusive jet multiplicities, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. Black circular markers with the grey hatched band represent the unfolded data measurement and its total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled band around MGaMC FxFx prediction represents its theoretical uncertainty including both statistical and systematical uncertainties. The lower panels show the ratios of the prediction to the unfolded data. 
png pdf 
Figure 7a:
The differential cross section measurement for the leading three jets' transverse momenta, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. The NNLO prediction for W+1jet is included in the first leading jet transverse momentum. Black circular markers with the grey hatched band represent the unfolded data measurement and its total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled bands around MGaMC FxFx and NNLO predictions represent their theoretical uncertainties including both statistical and systematical uncertainties. The lower panels show the ratios of the prediction to the unfolded data. 
png pdf 
Figure 7b:
The differential cross section measurement for the leading three jets' transverse momenta, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. The NNLO prediction for W+1jet is included in the first leading jet transverse momentum. Black circular markers with the grey hatched band represent the unfolded data measurement and its total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled bands around MGaMC FxFx and NNLO predictions represent their theoretical uncertainties including both statistical and systematical uncertainties. The lower panels show the ratios of the prediction to the unfolded data. 
png pdf 
Figure 7c:
The differential cross section measurement for the leading three jets' transverse momenta, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. The NNLO prediction for W+1jet is included in the first leading jet transverse momentum. Black circular markers with the grey hatched band represent the unfolded data measurement and its total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled bands around MGaMC FxFx and NNLO predictions represent their theoretical uncertainties including both statistical and systematical uncertainties. The lower panels show the ratios of the prediction to the unfolded data. 
png pdf 
Figure 8a:
The differential cross section measurement for the leading three jets' absolute rapidities, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. The NNLO prediction for W+1jet is included in the first leading jet absolute rapidity. Black circular markers with the grey hatched band represent the unfolded data measurement and total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled bands around MGaMC FxFx and NNLO predictions represent their theoretical uncertainties including both statistical and systematical uncertainties. The lower panels show the ratios of the prediction to the unfolded data. 
png pdf 
Figure 8b:
The differential cross section measurement for the leading three jets' absolute rapidities, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. The NNLO prediction for W+1jet is included in the first leading jet absolute rapidity. Black circular markers with the grey hatched band represent the unfolded data measurement and total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled bands around MGaMC FxFx and NNLO predictions represent their theoretical uncertainties including both statistical and systematical uncertainties. The lower panels show the ratios of the prediction to the unfolded data. 
png pdf 
Figure 8c:
The differential cross section measurement for the leading three jets' absolute rapidities, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. The NNLO prediction for W+1jet is included in the first leading jet absolute rapidity. Black circular markers with the grey hatched band represent the unfolded data measurement and total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled bands around MGaMC FxFx and NNLO predictions represent their theoretical uncertainties including both statistical and systematical uncertainties. The lower panels show the ratios of the prediction to the unfolded data. 
png pdf 
Figure 9a:
The differential cross section measurement for $ {H_{\mathrm {T}}} $ for inclusive jet multiplicities 13, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. The NNLO prediction for W+1jet is included in the jets $ {H_{\mathrm {T}}} $ for one inclusive jet. Black circular markers with the grey hatched band represent the unfolded data measurement and total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled bands around MGaMC FxFx and NNLO predictions represent their theoretical uncertainties incuding both statistical and systematical uncertainties. The lower panels show the ratio of the prediction to the unfolded data. 
png pdf 
Figure 9b:
The differential cross section measurement for $ {H_{\mathrm {T}}} $ for inclusive jet multiplicities 13, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. The NNLO prediction for W+1jet is included in the jets $ {H_{\mathrm {T}}} $ for one inclusive jet. Black circular markers with the grey hatched band represent the unfolded data measurement and total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled bands around MGaMC FxFx and NNLO predictions represent their theoretical uncertainties incuding both statistical and systematical uncertainties. The lower panels show the ratio of the prediction to the unfolded data. 
png pdf 
Figure 9c:
The differential cross section measurement for $ {H_{\mathrm {T}}} $ for inclusive jet multiplicities 13, compared to the predictions of MGaMC FxFx and MGaMC, where latter denoted as MG in the legends. The NNLO prediction for W+1jet is included in the jets $ {H_{\mathrm {T}}} $ for one inclusive jet. Black circular markers with the grey hatched band represent the unfolded data measurement and total experimental uncertainty. MGaMC is given only with its statistical uncertainty. Color filled bands around MGaMC FxFx and NNLO predictions represent their theoretical uncertainties incuding both statistical and systematical uncertainties. The lower panels show the ratio of the prediction to the unfolded data. 
Tables  
png pdf 
Table 1:
Number of events in data and simulation as a function of the exclusive jet multiplicity after the implementation of b tag veto. QCD background is estimated using a datadriven method. The diboson samples (WW, WZ, and ZZ) are represented by VV. 
Summary 
The first measurement of the differential cross sections for a W boson produced in association with jets in protonproton collisions at a centreofmass energy of 13 TeV is presented. The collisions data used correspond to an integrated luminosity of 2.5 fb$^{1}$ with 25 ns bunch crossing and were collected with the CMS detector during 2015 at the LHC. The differential cross sections are measured using the muon decay mode of the W boson as a function of the exclusive and the inclusive jet multiplicities up to a multiplicity of five, the jet $p_{\mathrm{T}}$ and rapidity $y$ for the three leading jets, and the jet $H_{\mathrm{T}}$ for a multiplicity up to at least three jets. The data distributions are corrected for all detector effects by means of regularised unfolding and compared with the particle level predictions by MGaMC FxFx at NLO accuracy and by MGaMC tree level at LO accuracy. The measured data is compared with a calculation at NNLO accuracy for W+1jet production. The predictions are able to describe data well on the exclusive and inclusive jet multiplicities within the uncertainties. The predictions are in good agreement with data on the jet $p_{\mathrm{T}}$ spectra. The jet $y$ and $H_{\mathrm{T}}$ spectra are well modeled by both MGaMC FxFx merged NLO prediction for all inclusive jet multiplicities and NNLO calculation for one inclusive jet. Overall, MGaMC tree level slightly underestimates data on the observables. 
References  
1  CMS Collaboration  Differential cross section measurements for the production of a W boson in association with jets in protonâ€“proton collisions at $ \sqrt s=7 $ TeV  PLB741 (2015) 1237  CMSSMP12023 1406.7533 
2  CMS Collaboration  Differential cross section measurements of W bosons produced in association with jets in protonproton collisions at $ \sqrt{s}=8 $ TeV  
3  CMS Collaboration  The CMS experiment at the CERN LHC  JINST 3 (2008) S08004  CMS00001 
4  GEANT4 Collaboration  GEANT4: A Simulation toolkit  NIMA506 (2003) 250303  
5  J. Alwall et al.  The automated computation of treelevel and nexttoleading order differential cross sections, and their matching to parton shower simulations  JHEP 07 (2014) 079  1405.0301 
6  R. Frederix and S. Frixione  Merging meets matching in MC@NLO  JHEP 12 (2012) 061  1209.6215 
7  P. Nason  A New method for combining NLO QCD with shower Monte Carlo algorithms  JHEP 11 (2004) 040  hepph/0409146 
8  S. Frixione, P. Nason, and C. Oleari  Matching NLO QCD computations with Parton Shower simulations: the POWHEG method  JHEP 11 (2007) 070  0709.2092 
9  S. Alioli, P. Nason, C. Oleari, and E. Re  A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX  JHEP 06 (2010) 043  1002.2581 
10  S. Alioli, P. Nason, C. Oleari, and E. Re  NLO singletop production matched with shower in POWHEG: s and tchannel contributions  JHEP 09 (2009) 111, , [Erratum: JHEP02,011(2010)]  0907.4076 
11  T. Melia, P. Nason, R. Rontsch, and G. Zanderighi  W+W, WZ and ZZ production in the POWHEG BOX  JHEP 11 (2011) 078  1107.5051 
12  T. Sjostrand, S. Mrenna, and P. Z. Skands  A Brief Introduction to PYTHIA 8.1  CPC 178 (2008) 852867  0710.3820 
13  CMS Collaboration  Event generator tunes obtained from underlying event and multiparton scattering measurements  EPJC76 (2016), no. 3, 155  CMSGEN14001 1512.00815 
14  J. M. Campbell and R. K. Ellis  MCFM for the Tevatron and the LHC  NPPS 205206 (2010) 1015  1007.3492 
15  R. Gavin, Y. Li, F. Petriello, and S. Quackenbush  W Physics at the LHC with FEWZ 2.1  CPC 184 (2013) 208214  1201.5896 
16  J. Alwall et al.  MadGraph 5 : Going Beyond  JHEP 06 (2011) 128  1106.0522 
17  J. Pumplin et al.  New generation of parton distributions with uncertainties from global QCD analysis  JHEP 07 (2002) 012  hepph/0201195 
18  J. Alwall et al.  Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions  EPJC53 (2008) 473500  0706.2569 
19  J. Alwall, S. de Visscher, and F. Maltoni  QCD radiation in the production of heavy colored particles at the LHC  JHEP 02 (2009) 017  0810.5350 
20  NNPDF Collaboration  Parton distributions for the LHC Run II  JHEP 04 (2015) 040  1410.8849 
21  R. D. Ball et al.  A first unbiased global NLO determination of parton distributions and their uncertainties  Nucl. Phys. B838 (2010) 136206  1002.4407 
22  R. D. Ball et al.  Impact of Heavy Quark Masses on Parton Distributions and LHC Phenomenology  Nucl. Phys. B849 (2011) 296363  1101.1300 
23  R. Boughezal, C. Focke, X. Liu, and F. Petriello  $ W $boson production in association with a jet at nexttonexttoleading order in perturbative QCD  PRL 115 (2015), no. 6, 062002  1504.02131 
24  R. Boughezal, X. Liu, and F. Petriello  Wboson plus jet differential distributions at NNLO in QCD  1602.06965  
25  S. Dulat et al.  New parton distribution functions from a global analysis of quantum chromodynamics  PRD93 (2016), no. 3, 033006  1506.07443 
26  CMS Collaboration  ParticleFlow Event Reconstruction in CMS and Performance for Jets, Taus, and MET  CDS  
27  CMS Collaboration  Commissioning of the Particleflow Event Reconstruction with the first LHC collisions recorded in the CMS detector  CDS  
28  CMS Collaboration  Performance of CMS muon reconstruction in $ pp $ collision events at $ \sqrt{s}=7 $ TeV  JINST 7 (2012) P10002  CMSMUO10004 1206.4071 
29  CMS Collaboration  Performance of muon identification in pp collisions at $ \sqrt{s}=7 $ TeV  CDS  
30  CMS Collaboration  Commissioning of the ParticleFlow reconstruction in MinimumBias and Jet Events from pp Collisions at 7 TeV  CDS  
31  M. Cacciari, G. P. Salam, and G. Soyez  The Antik(t) jet clustering algorithm  JHEP 04 (2008) 063  0802.1189 
32  CMS Collaboration  Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV  CMSJME13004 1607.03663 

33  CMS Collaboration  Pileup Jet Identification  CMSPASJME13005  CMSPASJME13005 
34  CMS Collaboration  Identification of b quark jets at the CMS Experiment in the LHC Run 2  CMSPASBTV15001  CMSPASBTV15001 
35  G. D'Agostini  A Multidimensional unfolding method based on Bayes' theorem  NIMA362 (1995) 487498  
36  W. H. Richardson  BayesianBased Iterative Method of Image Restoration  J. Opt. Soc. Am. 62 (1972), no. 1, 5559  
37  L. B. Lucy  An iterative technique for the rectification of observed distributions  Astron. J. 79 (1974), no. 6, 745754  
38  T. Adye  Unfolding algorithms and tests using RooUnfold  in Proceedings of the PHYSTAT 2011 Workshop, CERN, Geneva, Switzerland, January 2011, CERN2011006, pp 313318, pp. 313318 2011  1105.1160 
39  CMS Collaboration  CMS Luminosity Based on Pixel Cluster Counting  Summer 2013 Update  CMSPASLUM13001  CMSPASLUM13001 
40  S. Gangal and F. J. Tackmann  Nexttoleadingorder uncertainties in Higgs+2 jets from gluon fusion  PRD87 (2013), no. 9, 093008  1302.5437 
41  I. W. Stewart and F. J. Tackmann  Theory Uncertainties for Higgs and Other Searches Using Jet Bins  PRD85 (2012) 034011  1107.2117 
Compact Muon Solenoid LHC, CERN 