Measurement of dijet production with a jet veto at ATLAS

1. Measurement of dijet production with a jet veto at ATLAS Alessandro Tricoli - CERN on behalf of the ATLAS collaboration LOW-X MEETING SANTIAGO DE COMPOSTELA 3rd-7th June 2011

2. Dijet Production with Jet Veto High pTdijets are a key probe for understanding activity in the rest of the event, such as radiation between two leading or two most forward jets • Test pQCD calculations and constrain phenomenological models used in event generators in HEP • Beneficial for Higgs searches Jet 1 Jet 2 • Selection of a sample of dijet events and study of radiation in rapidity range bounded by dijet system: • in Dy range bounded by dijet system • estimatejet activity by measuring average jet multiplicity • estimateabsence of jet activity by measuring the fraction of dijet events with no additional jet with pT> Q0 (veto scale) - “gap fraction” Dy A. Tricoli

3. Purpose of analysis • Test pQCD calculations and constrain phenomenological models used in event generators in HEP • test of BFKL-like dynamics – important for large jet rapidity separation • study effect of wide-angle soft-gluon radiation when avrg. jet pT >> veto scale (Q0) • study of colour singlet exchange when events have high pTand large Δy • beneficial for Higgs searches • jet veto used in Higgs searches in Vector-Boson-Fusion channel (H+2 jets) to reject background A. Tricoli

4. ATLAS-CONF-2011-038 Boundary Conditions Selection A boundaries set by highest pTjets Selection B boundaries set by most forward jets in rapidity (y) |Dy| |Dy| • increased sensitivity to wide-angle • soft-gluon radiation • increased sensitivity to BFKL dynamics Observables (in <pT> and Dy of boundary jets): 1) Mean Jet Multiplicity: between boundary jets 2) Gap Fraction: fraction of events without jet in gap A. Tricoli

5. ATLAS Detector Design Goal: Precision measurements of the Standard Model and New Physics discovery • Focus on sub-systems relevant • to this analysis: • Inner Detector for Tracking (|h|<2.5) • EM and HAD Calorimeters (|h|<4.9) • Different technologies: • LAr/Pb in EM calorimeter(|h|<3.2) • Three layers and high granularity • HAD calorimeters(|h|<4.9) • Tile scintillator/steel in barrel and extended barrel • LAr/Cu in endcaps • LAr/Cu, LAr/Tu in forward region A. Tricoli

6. ATLAS-CONF-2011-038 Event and Jet Selection Events collected in year 2010 corresponding to 38 pb-1 • Event Triggering: • specific single jet triggers used in slices of <pT> of boundary jets, such that trigger efficiency greater than 99% in each <pT> slice • Event Selection: • Anti-kT jets with R = 0.6 (Infrared safe, collinear safe) • Select inclusive dijet events with jet pT> 20 GeV and |y| < 4.5 • <pT> of boundary jets > 50 GeV • Veto Jet pT > 20 GeV (Q0) – for ‘gap fraction’ measurement • Single interaction-vertex events [91% (19%) events retained in early (late) data-taking periods] • Jets reconstructed at the EM scale and calibrated to the Jet Energy Scale (JES) using h-pT dependent corrections derived from MC simulation • Jet Energy Scale uncertainty is evaluated from combination of measurements and MC • For jet pT=20 GeV JES uncertainty between ~5%(barrel) - 13% (forward) [ATLAS-CONF-2011-032] → Cancellation of JES uncertainty in ratios (gap fraction) A. Tricoli

7. ATLAS-CONF-2011-038 Experimental Uncertainties Experimental data points dominated by systematic uncertainties at low <pT> and Dy • Systematic uncertainties dominated by • Uncertainties on Jet Energy Scale and Unfolding of detector effects • few % (up to ~9% at very large Dy) in Gap-Fraction • 5-8% in mean number of jet multiplicity gap-fraction Experimental uncertainties smaller than theoretical uncertainties mean number of jets • Unfolding of detector effects • bin-by-bin unfolding in each observable with PYTHIA • including jet reconstruction efficiency and jet energy resolution and their uncertainties A. Tricoli

8. ATLAS-CONF-2011-038 Theoretical Predictions Measurement (unfolded to hadron level) compared to various theoretical predictions • L.O. Event Generators commonly used for predictive purposes • PYTHIA 6(MRST LO* PDF with AMBT1 tune) • HERWIG++ (MRST LO* PDF with internal tune for LO* PDF) • ALPGEN+HERWIG/JIMMY(CTEQ6L1 PDF with AUET1 tune) • Theoretical Predictions beyond L.O.: POWHEG and HEJ (MSTW2008 NLO PDF) • HEJ:parton level calculation, based on BFKL resummation • uncertainties include renormalisation/factorisationscale and PDF uncertainties • POWHEG: NLO dijet calculations interfaced to PYHTIA or HERWIG • for parton showering, hadronisation and underlying event • Scale variation in POWHEG (fixed order) leads to very small uncertainty with respect to the uncertainty predicted by HEJ (BFKL-resummation) • Difference between POWHEG+PYTHIA and POWHEG+HERWIG larger than uncertainty error on POWHEG (Matrix-Element) prediction A. Tricoli

9. ATLAS-CONF-2011-038 Jet Multiplicity in Gap Jet activity in gap increases as function of <pT> and Dy Selection A Similar results for Selection B (see backup slides) • PYTHIA slightly overestimates jet activity at low <pT> and low Dy, • but gives best description of data • HERWIG++ underestimate (overestimate) jet activity at low (high) Dy • ALPGEN shows largest deviation from data: too much jet activity A. Tricoli

10. ATLAS-CONF-2011-038 Gap Fraction Gap fraction decreases as function of <pT> and Dy Selection A Similar results for Selection B (see backup slides) • Similar features as in previous slide: • PYTHIA slightly underestimates gap fraction at low <pT> and low Dy, • but gives best description of data • HERWIG++ tends to overestimate (underestimate) gap fraction at low (high) Dy • ALPGEN shows largest deviation from data, underestimating gap fraction A. Tricoli

11. ATLAS-CONF-2011-038 Jet Multiplicity and Gap Fractionas function of <pT> Gap Fraction Jet Multiplicity Selection A • Deviations between HEJ • and data at large <pT> • includes all order resummation in Dy • but not all important terms as <pT>/Q0 increases • POWHEG+PYTHIA • describes data well • POWHEG+HERWIGoverestimates jet activity (underestimates gap fraction) A. Tricoli

12. ATLAS-CONF-2011-038 Jet Multiplicity and Gap Fractionas function of <pT> Gap Fraction Jet Multiplicity Selection B • Smaller deviation between HEJ and data • at large <pT> • expected as includes all order resummation in Dy • POWHEG+PYTHIA describes data well • POWHEG+HERWIGoverestimates jet activity (underestimates gap fraction) A. Tricoli

13. ATLAS-CONF-2011-038 Jet Multiplicity and Gap Fraction as function of Dy Selection B Selection A Jet Multiplicity Gap Fraction • HEJ • describes well jet activity in low, medium Dy • some discrepancies at high Dy • POWHEG underestimates Gap Fraction at large Dy • with Selections A and B • partonshower not recovering resummation terms important as Dyincreases A. Tricoli

14. ATLAS-CONF-2011-038 Jet Multiplicity and Gap Fractionas function of Dy with Veto Scale Q0=<pT> Jet Multiplicity Gap Fraction Selection B • HEJagreement degrades at high Dy • POWHEG+PYTHIA • &POWHEG+HERWIG • describe data well • reduced dependence on modeling of parton shower, hadronisation and underlying event A. Tricoli

15. ATLAS-CONF-2011-038 Summary (I) • ATLAS measurement of jet activity in the rapidity interval between boundary jets • Average Jet Multiplicity • Gap Fraction(fraction of events without jet in gap) These results test important aspects of pQCD and will benefit global efforts to produce phenomenological tunes for the event generators. • Two different selections adopted to probe both soft and hard emissions between widely separated jets • Probing different approximations implemented in Event Generetors • L.O. or N.L.O. + Parton Shower (e.g. PYTHIA, POWHEG) • - all-order QCD for soft and/or collinear higher order emissions • BFKL-like dynamics (HEJ) • - all-order QCD for hard and well-separated higher order emissions A. Tricoli

16. ATLAS-CONF-2011-038 Summary (II) • Accuracy of Experimental Results sensitive to theoretical modeling • None of the theory calculations describe the data in all kinematic regions • PYTHIA and POWHEG+PYTHIA describe well gap fraction and mean jet multiplicity • HEJgenerally well describes Dy dependence, but predicts too little jet activity at large values of <pT>/Q0 , i.e. when soft and collinear emissions need to be accounted for (not interfaced with Parton Shower) • POWHEG+HERWIG, HERWIG++ and ALPGEN+HERWIG predict too much activity between jets The constraining the event generator modeling of QCD radiation between widely separated jets leads to an improved understanding in the application of jet vetoes in Higgs-plus-2 jet analyses A. Tricoli

17. Backup A. Tricoli

18. The ATLAS Calorimeters A. Tricoli

19. ATLAS-CONF-2011-038 Jet Multiplicity in Gap Selection B • PYTHIAslightly overestimates jet activity, but gives best description of data • HERWIG++ underestimate jet activity at low Dy • ALPGEN shows largest deviation from data • to much jet activity, especially at large Dy and <pT> A. Tricoli

20. ATLAS-CONF-2011-038 Gap Fraction Selection B • PYTHIA slightly underestimates gap fraction at low <pT> and low Dy, • but gives best description of data • HERWIG++ tends to overestimate (underestimate) gap fraction at low (high) Dy • ALPGEN shows largest deviation from data, underestimating gap fraction A. Tricoli

21. ATLAS-CONF-2011-038 Jet Multiplicity in Gapas function of Dy Selection B Selection A • HEJ • well describes jet activity in low, medium Dy • underestimates it at high Dy with Selection A • POWHEG • describes data well • POWHEG+PYTHIA in better agreement with data A. Tricoli

22. ATLAS-CONF-2011-038 Gap Fractionas function of Dy Selection A Selection B • HEJ • well describes Gap Fract. in low, medium Dy • slightly underestimates at high Dy with Selection B • POWHEG slightly underestimates data at large Dy • parton shower not recovering resummation terms important as Dy increases A. Tricoli

23. Jet Reconstruction Starting point: ➢ calorimeter cells calibrated to electromagnetic (EM) scale Input to jet reconstruction ➢ 3D Topological clusters ➔ uses nearest neighbor energy significance to localize showers in the calorimeter ➔ efficient noise suppression Jet reconstruction ➢ Jets are reconstructed using the anti-Kt algorithm with size parameter R set at 0.6 • Jet calibration • ➢ Energy and momentum of a jet measured in the calorimeter are corrected using kinematics of a Monte Carlo truth jet as reference • ➔ for non-compensation, energy losses in dead material, shower leakage • ➔ using PYTHIA inclusive QCD events • EM+JES schema simple default Monte Carlo based calibration • ➔ (h, pT) dependent correction factor Etruth/ Ecalo A. Tricoli

24. ATLAS-CONF-2011-032 Jet Energy Scale Uncertainty • JES uncertainty evaluated from combination of measurements and MC • Uncertainty of single hadrons measured in data and propagated to jets using MC • Uncertainty for single isolated hadrons is measured by E/p from isolated tracks (p<20 GeV), or from test-beam • Correlate single particle uncertainty with jet uncertainty using jet composition • Uncertainty is assessed up to |h|=4.5 using dijet balance measurements • Finally combined with additional uncertainties evaluated using systematic variations of MC • ➔ dead material, noise, hadronic shower models, soft physics effects, generators • Monte Carlo based jet energy calibration has tested insitu • (good agreement with JES uncertainty • from single hadron response) • ➔ multijet balance • ➔ calorimeter jet – track jet balance • ➔ direct gamma-jet balance • ➔ photon balance using missing transverse momentum projection 0.3< |η| < 0.8 Summary on fractional systematic JES uncertainty as a function of jet pT A. Tricoli

25. Jet Energy Resolution Jet momentum resolution measured insitu with dijets using bisector technique Advanced calibrations improve resolution by 10-30% ➔ Monte Carlo agrees with data within 10% A. Tricoli

26. ATLAS Jets • Anti-kT Algorithm • Infrared safe, collinear safe • Regular, cone-like jets in calorimeter [Cacciari, Salam - JHEP 0804:063,2008] A. Tricoli