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Christopher Rogan

Christopher Rogan. The Path to Discovery. Christopher Rogan. 1. Outline. Pre-requisites: Candles and Handles. Standard Model candles and handles towards discovery - Z/ W+jets : separation Detector/reconstruction commissioning NP search bkg estimations

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Christopher Rogan

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  1. Christopher Rogan The Path to Discovery Christopher Rogan 1

  2. Outline Pre-requisites: Candles and Handles • Standard Model candles and handles towards discovery - Z/W+jets: • separation • Detector/reconstruction commissioning • NP search bkg estimations • Example: Search for SUSY • All hadronic analysis - “Jets+MET+X” • Re-engineered approach to maximize discovery potential • “tail search” to “bump hunt” • Example: H → ZZ → 4l • Branch of LHC inverse problem: Measuring new resonances’ JPC Search: Innovative analyses BKG BKG BKG Log(N) Log(N) SIG SIG SIG x x’ Characterize: What did we discover? 2

  3. Candles and Handles - Z/W+jets W Z CDF aka “Berends-Giele” (BG) scaling CONSTANT SLOPE ☐ FULLY DATA-DRIVEN METHODS FOR STUDYING W/Z+jets PRODUCTION, USING candles/handles TO: study detector and reconstruction performance - estimate bkgs to BSM searches - probe pQCD - … 3

  4. W/Z+jets Analysis Strategy Event Reconstruction and Cut-Based W+jets, Z+jets selection Background control samples Fit PDF validation Maximum Likelihood Fits sPlots Background estimation High Eff. Selection MF Fits MET characterization/corrections Z boson “Candle” Tests of the Fit Candle Apps Probe of NP Scaling/Ratios Modular strategy with staged and varied physics applications 4

  5. W/Z+jets control samples Inverting lepton isolation and vertexing requirements →QCD control sample →background shapes for ML Fit Excellent agreement btw Data/MC QCD MT shape taken from control sample and used in signal extraction ML fit Analysis strategy works “out-of-the-box” 5

  6. Candles and Handles With the ML fit, statistical background subtraction (sPlots) yields a “pure” Z(ll)+jets “candle” sample Z()+jets bkg normalization from Z()+jets →Reproduces Z()+jets MET distribution MET correction for W()+jets derived from Z()+jets →Recovers sharp Jacobian peak emerging discrepancy Low jet multiplicities can be used to predict high mult. yields - deviations from BG scaling can indicate new physics contributions more SUSY 6

  7. W(l)/Z(ll) Data V+jets program 2 years in the making is yielding first results LINT = 0.0111pb-1 Nsig = 43.4 +/- 7.3 NQCD = 44.6 +/- 7.4 Yield, with  from MC, agrees with NLO x-section (28 nb) within 10%

  8. The Search for SUSY • All hadronic SUSY search has a number of challenges: • Controlling W/Z and top backgrounds → W/Z+jets methodology essential component • Controlling QCD backgrounds→ especially important in the context of jet/MET reconstruction • We repeat the ‘canonical’ jets+MET SUSY search • Looking for excess of events in MET or HT tails • Studying this approach allows us to identify its strengths/weaknesses Re-engineered SUSY search: new variables, higher signal efficiency, more control over background distributions, distinctive signature: tail search becomes bump hunt BKG Log(N) BKG BKG Log(N) SIG SIG SIG x x’

  9. Kinematic Variables for SUSY arXiv:1006.2727v1 [hep-ph] Presented at PLANCK2010 Two variables I designed to be used together for discovery and characterization of SUSY • Doesn’t involve MET • Uses both transverse and longitudinal information • Invariant under long. boosts • Peaks for signal: 9

  10. Kinematic Variables for SUSY arXiv:1006.2727v1 [hep-ph] Presented at PLANCK2010 Two variables I designed to be used together for discovery and characterization of SUSY • Dimension-less variable used for S/B discrimination • Not only suppresses backgrounds, but also shapes their distributions in the variable in a predictable and well-understood way - the Razor 10

  11. MR and R in CMS Using just trigger requirements and a cut on R gives excellent S/B discrimination with ~40% signal efficiency w.r.t. inclusive SUSY x-section Razor cut shapes surviving background events, falling ~exponentially

  12. Discovery Potential 3 Observation 5 Discovery Analysis approach using MR and R is potentially a very powerful discovery tool Allows for characterization of SUSY mass scale Variables designed to reduce/remove sensitivity to anomalous signals/noise which is problematic for ‘canonical’ search 12

  13. MR and R in Data QCD MC DATA Behavior of MR and R is confirmed with data both qualitatively and quantitatively Loose R cut 13

  14. Looking towards the Higgs W/Z+jets SUSY Higgs These variables have many applications in the program of NP discovery and characterization Int. Lumi • MR and R can be used to study any final state featuring pair-production of heavy particles decaying to visible SM and weakly interacting particles: • MSSM Higgs sector • UED • Little Higgs • Leptoquarks • … MR peaks at 1/2 the Higgs mass. R cut efficiency is stable as a function of Higgs mass →variables can be used in a mass independent analysis for Higgs discovery and characterization in the leptons + MET final state 14

  15. Higgs  ZZ  4l Discovery and Characterization • In order to improve Higgs discovery and allow for direct measurement of its quantum numbers JPC we consider the Z decay and Higgs production angles • Calculate the PDF’s for these angles as a function of the most general spin 0, 1 and 2 couplings • Running 10’s of trillions of toy experiments with a detector simulation, we build multi-dimensional numerical PDF’s Decay angles Production angles Neyman-Pearson hypothesis tests are used, with ML fits, to distinguish between different Higgs Look-a-likes (HLL’s) arXiv:1001.5300 To be published in PRD 15

  16. Higgs and its Imposters Table shows number of signal events required for the mean expected significance for rejecting in favor of (assuming is true) to exceed SM Higgs vs. pseudoscalar In some cases, JPC of HLL can be measured near the moment of discovery Correlations between observables are very important and must be taken into account

  17. Higgs and its Imposters: 0+ vs. J = 1 Can I exclude all J=1 possibilities when I have an SM Higgs? Can I exclude an SM Higgs when I have the J = 1 case? Can distinguish between SM 0+ and general J=1 case at 5 with for Most general spin 1 Lagrangian

  18. Outlook Pre-requisites: Candles and Handles Search: Innovative analyses Characterize: What did we discover? A comprehensive discovery/characterization program that is already underway - some aspects already being adopted by the LHC experiments 18 • Standard Model candles and handles towards discovery - W/Z+jets: • separation • Detector/reconstruction commissioning • NP search bkg estimations • Example: Search for SUSY • All hadronic analysis - “Jets+MET+X” • Re-engineered approach to maximize discovery potential • “tail search” to “bump hunt” • Example: H →ZZ →4l • Branch of LHC inverse problem: Measuring new resonances’ JPC 18

  19. EXTRA SLIDES 19

  20. W+jets and top separation • ML fit based on the transverse mass is split into two categories • Categories based on b-tag variables • Heavy flavor (h.f.) enriched category is predominantly top • h.f. depleted category is predominantly W+jets • sPlots statistical background subtraction allows one to study derived pure “W+jets” or “top” samples 20

  21. h.f. variable control samples 21

  22. Higgs and its imposters Median expected significance for differentiating between SM 0+ and general J=1 case should exceed 5 with J=0 composite Higgs Lagrangian for We find that at low masses, discrimination between SM 0+ and a composite Higgs is possible - discrimination is degraded at higher masses - need to appeal to ratios of Most general spin 1 Lagrangian 22

  23. H(ZZ  4l) discovery • Although there is a fairly large irreducible background from ZZ production, this can be statistically “subtracted” using a fit+weighting scheme called sPlots • Hence, for discriminating between different JPC hypotheses we use a signal-only sample and quantify the expected power of discrimination as a function of the number of these observed signal events Likelihood fits using the Higgs mass and decay angles (with correlations) reduce the mean int. luminosity needed for discovery by 30-50% 23

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