Outline I. Introduction II. Experimental requirements III. Analysis strategies and results IV. Conclusions 1 SM H→  discovery potential with ATLAS Yaquan Fang University of Wisconsin, Madison On Behalf of the ATLAS Collaboration Physics at LHC /29/2008

Introduction 2 Motivation for SM H →  Sensitive at low Higgs mass region: 114<M H <150GeV. Robust : side band. What is new ? Simulation/reconstruction : More realistic simulation. Massive production of MC samples for the Computing System Commissioning (CSC) since QCD higher order corrections in MC. Signal and background processes’ cross-sections known to NLO. Contributions of reducible backgrounds’ fragmentation from hard partons to photons are taken into account. Updated analysis strategies: Inclusive vs Combined analysis (H+0jet, 1jet and 2jets). One variable (M  ) vs additional variables (P T  and cos Θ  *). Significance: Event counting vs maximum likelihood fit based. 9/29/2008

Main Experimental Issues 3 Need good energy and angular resolution to achieve ~1% resolution in Higgs mass reconstruction Photon Calibration energy scale and resolution Separation of converted and unconverted photons Photon angle correction Photon angle from calorimeter pointing and tracking-based vertices Need good photon identification to reject the large QCD background Rejection larger than10 3 per single jet with photon efficiency larger than 80%. 9/29/2008

Irreducible:  (real)  +(Jets) Reducible (fake)  :  j(jets)   (jets), jj(jets)   (jets) 4 Signal and Backgrounds for H →  + … signal Born Box fragmentation + … gg Fusion: Dominant Vector Boson Fusion (VBF): Second largest Associated: small cross-section 9/29/2008 fragmentation High order correction

Photon Conversion Around 50% selected H→  events have at least one true conversion with a radius smaller than 80 cm. An algorithm tagging early converted photons based on reconstructed single/double tracks has a high tagging efficiency for those photons (left plot). P T /E T (right plot) provides additional discriminating power between selected converted photons and those from  0. 9/29/ preliminary Arbitrary units

Calibration and vertex correction for Photons 9/29/ Calibration Longitudinal weights calibration: 3x5 cluster for unconverted photon 3x7 cluster for converted photon Refined energy correction Lateral leakage and  /  modulation Refined position correction: S-shape (  correction) and Phi-offset Vertex correction Precise measurement of Z vertex is very important to improve the Higgs mass resolution. Method : a linear fit of multi-layer centers of the EM shower + event vertex The best Higgs boson position accuracy is achieved, with a Gaussian width 0.07 mm (see plot). A likelihood method is used to distinguish the hard scattering vertices from pile-up vertices. preliminary

7 Results of calibration and vertex correction  The reconstructed mass peaks for geometries with nominal and additional dead material are shown.   /M H is close to 1.2%, degrading by a few percent with s -1 cm -2 pileup. Nominal geometry Additional dead material mHmH 120 GeV130 GeV140 GeV No pileuppileupNo pile uppileupNo pileuppileup Mass fitted (GeV)  m (GeV) /29/2008 preliminary ATLAS preliminary  /M H =1.19%  /M H =1.22%

Photon ID and jet rejection 9/29/ Three photon id methods: Cut based (current analysis) Likelihood ratio algorithm H-Matrix method  Rejection of gluon-initiated jets is much higher than that of quark-initiated jets.  After photon identification, the fake photons are dominated by  0. preliminary ATLAS preliminary

Cuts for Analyses 9/29/2008 1P T  1 >45 GeV, P T  2 >25 GeV 2 P TJ1 >20 GeV,  j1 <5.0 3M  j >350 GeV 4 Mass Window(  1.4σ) 1 P T  1 >40 GeV, P T  2 >25 GeV 2 Mass Window(  1.4  ) 1P T  1 >50 GeV, P T  2 >25 GeV 2  J1   J2 40 GeV, P TJ2 >20 GeV,  j1j2 >3.6 3Photons in between tagging jets 4M J1J2 >500 GeV 5 Central jet veto (P TJ >20 GeV |  |<3.2) 6 Mass window (  1.4σ) H+2jets (VBF) H+1jet Inclusive/H+0jet 9 Trigger and photon reconstruction identification, calibration (vertex correction) are applied beforehand

Discriminating variables 9/29/  M  jet (left plot) is used in H+1jet analysis.  ∆  J1J2 (middle plot) and M J1J2 (right plot) are for H+2jets analysis.  The cuts of those variables can be optimized with data.

Results of Inclusive, H+1jet, H+2jet (VBF) 11 Inclusive H+1jet H+2jets  The analyses have various sensitivities.  Inclusive analysis has well defined side-band.  Robust in extrapolating background shapes. Cross-section (unit fb) with ±1.4  after analyses cuts 9/29/2008 preliminary M H =120GeV ATLAS preliminary InclusiveH+1jetH+2jet Signal Background

In addition: H+E T miss and H+ 1 lepton from associated production The signal for Higgs+missing E T and Higgs+1 lepton is mostly from ttH( →  ) and W/ZH. Background mostly from W+  (  ), tt(bar)+  (  ), Z+  and  Reducible backgrounds (  -jets, jets) are negligible. 9/29/ H+ E T miss preliminary H+ 1 lepton preliminary M H =120GeV

Combined analysis for H+0jet, 1jet and 2jets 13  Events passed inclusive cuts are divided into ( H+0jet,1jet,2jets) sub-channels:  All events from the inclusive analysis are used, and each is used only once  Take advantage of the different sensitivities from three individual sub-channels. 9/29/2008 event H+2jets cut H+1jet cut H+0jet cut reject event H+2jets analysis H+1jet analysis H+0jet analysis pass

Signal significances (from event counting) for an integrated luminosity of 10 fb -1 The mass window is ± 1.4  . Combined significance obtained as the sum in quadrature of H+0jet (not inclusive), H+1jet, H+2jets. The combined significance is ~25% higher than the significance of the inclusive analysis. 14 9/29/2008 ATLAS preliminary

Signal significance computation with maximum likelihood fit 15 Fit variables: M , P T , cos(Θ  *) (relatively low correlations). Classification 1: three  categories for photons in order to group photons with similar M  resolutions together. Classification 2: classify three jet categories (H+0jet, H+1jet, H+2jets). Based on RooFit 9/29/2008 Combination of different classifications and discriminating variables is obtained by doing a single simultaneous fit signal background preliminary

Signal significances with mass scan 16  The gain in significance obtained by doing a combined fit (including three fit variables and different classifications) is ~40% with respect to the inclusive analysis (for 10 fb -1 ).  For a fixed Higgs mass fit, the 5σ discovery can be achieved with ~20 fb -1.  For the fit with Higgs mass floating, the 5σ discovery can be expected with ~30 fb -1.  The unknown location of the resonance reduces the expected sensitivity. 9/29/2008 preliminary

Conclusions 17 The impact of detector performance on H →  channel has been studied: Our current knowledge of the detector allows us to achieve the performance required for this analysis. Extensive work is needed to understand the detector performance with early data. The inclusive study has been readdressed, The H+1jet, H+2jets have been studied, H+1lepton and H+E T miss are investigated. The combined analysis for H+0jet,1jet and 2jets has been proposed and the improvement of significance is about 25% wrt the inclusive one. Significance studies have been done using also maximum likelihood fit with various event classifications and discriminating variables. Enhance the significance ~40% wrt the inclusive one. It is possible to have 5  discovery with integrated luminosity fb -1. 9/29/2008

Backup Slides 18

19 Trigger for H →  Level 1:calorimeter which pass Region of Interest (ROI) data to Level 2. Level 2: refine the analysis of LVL1 across different detectors. Level 3: analysis data in the full detector and do more complicated physics analysis. The efficiency on H→  normalized wrt kinematic cuts. ATLAS preliminary

Summary of MC’s and initial cross-sections for different signals and backgrounds 20 9/29/2008  Signal : the cross-section unit is pb.  the branching ratio is corrected from HDecay (2  for M H =120GeV).  All signal cross-sections are normalized to the NLO cross-sections taking into account only QCD corrections.  ResBos and DIPHOX agreement better than 10%.  For  j:  JETPHOX /  PYTHIA ~ 2.1  For jj:  NLOJET /  PYTHIA ~ 1.3 ATLAS preliminary signal background

21 Photon identification variables Shower shape variables and track isolations are used. Hadronic leakage Track isolation 1 st layer of EMC: W tot1 1 st layer of EMC : (E 7strips -E 3strips )/E 7strips (Fracs1) 1 st layer of EMC: W  1 2 nd layer of EMC: E 237 /E nd layer of EMC: W  2 1 st layer of EMC: E max2 -E min 1 st layer of EMC: E max2 /E t

Calibration with longitudinal weights 3x5 Linearities and resolutions after applying the weights. 9/29/2008 Sb W1W1 W3W3 22

VBF H →  sub-channel 9/29/ Good S/B. High statistical fluctuation expected at low luminosity One pseduo-experiment with30fb -1

Jetphox results for high order and fragmentation 9/29/

Reweighing ALPGEN/PYTHIA w.r.t RESBOS for Born/Box processes 25  MC’s ALPGEN (Born) (upper left plot) and PYTHIA (box) (upper right plot) has LO cross-section.  RESBOS has NLO and re-summation calculation, however in parton level.  Solution: Reweighing alpgen/pythia P T  them w.r.t RESBOS.  M  distributions are reasonable consistent after reweighing (bottom plots). 9/29/2008

Correlation study between kinematic variables and M  for background 9/29/

maximum likelihood fit for signal significance computation 27 Likelihood: with: Where: μ corresponds to the hypothesis (μ=1 standard model) N s c presents the signal events in classification c. is the signal PDF in classification c. is the background PDF in classification c. N b c presents the background events in classification c. N c =N s c + N b c Technically, NLL(μ,p) = -log(L(μ,p)) is used in the fit. Test some hypothesis : ∆NLL(μ) = NLL(μ)-NLL( ) ( e.g. hypothesis μ=0 for discovery, μ=1 for exclusion) 9/29/2008

P T  for signal and background 9/29/ preliminary

Compute NLL distributions from Toy Monte Carlo: 1. Randomly generate a pseudo-experimental outcome. 2. Perform fits (RooFit) with +B hypothesis with μ free NLL( ) and BG (Background-only) hypothesis, compute ∆NLL( μ=0) 3. Repeat many times to get a probability distribution Compute two probability distributions: 1. for outcomes with signal (“S+B toy MC”, μ=1 ) and 2. For outcomes without signal (“BG-only toy MC”) Compute CL B from plots Δ NLL(BG-only toy MC) and Δ NLL(S+B toy MC) and convert it into significance ( integrating from the median of S+B toy MC whiling computing CL B ) as right plot shows. Procedure to compute the significance with maximum likelihood fit BG hyp. fit S+BGBG hyp. fit BG only +BG hyp. fit BG only +BG hyp. fit S+BG 29 9/29/2008

Signal systematics 30  and  j systematic uncertainties