Dijet Resonances (Update) Kazım Z. Gümüş and Nural Akchurin Texas Tech University

1. Dijet Resonances (Update) Kazım Z. Gümüş and Nural Akchurin Texas Tech University Selda Esen and Robert M. Harris Fermilab SUSY/BSM Meeting September 06, 2005

2. Introduction • Search: • We search for narrow resonances decaying to dijets: • pp g X g Jet Jet (inclusive) • We use Zprime as a model for dijet resonances. • Motivation: • There are lots of new resonances which decay to dijets. (See next slide) • LHC is a parton parton collider. Resonances made from partons will decay to partons, giving jets. • Update: • This update includes estimates of CMS capability with 1 fb-1 to • Exclude dijet resonances at 95% CL • Discover dijet resonances with 5 sigma significance Kazim Gumus, Texas Tech University

3. Review: Cross Section for Dijet Resonances Resonances produced with strong force, or from valence quarks in proton, have highest cross sections.

4. Review: Analysis • Jet Reconstruction & Correction • Iterative cone jet algorithm with R=0.5 and E scheme. • Generated Jets: particles in the jet cone, excluding pileup. • Reconstructed Jets: EcalPlusHcalTowers with ET>500 MeV. • Corrected Jets: Reconstructed jets with jetCalibV1 applied. • Correction back to particles in jet cone before pileup. • Event Selection • Find the two jets in the event with highest PT: leading jets. • Require each leading jet have |eta|<1. Enhances sensitivity to new physics which is produced at low |eta|. Also, Ecal end caps may not be there, or understood, on day 1. • Dijet mass: M = sqrt( (E1+E2)2 - (px1+px2)2 – (py1+py2)2 – (pz1+pz2)2 ). • Cross Section • Bin the dijet mass in ~10% wide bins: bin size increases with mass. • Divide rate by the luminosity and bin width: differential cross section (from “New Physics with Dijet” talk given by R.Harris at HCAL/JetMet Workshop, 11/12/2004) Kazim Gumus, Texas Tech University

5. Review: Cross section for QCD and Z’ Signals (|eta|<1 ) (Lum=1fb-1) Kazim Gumus, Texas Tech University

6. Beginning of update: Binned Likelihood Calculation We are constructing the likelihood for a data sample resulting from a QCD background and a resonance signal as a function of the resonance signal cross section. Briefly; ni: Observed events (We consider first the case where observed events are QCD only and next the case where observed events are QCD plus a mass resonance. ) Nsignali: Predicted signal events in mass bin i Nqcdi: Predicted qcd background events in mass bin i Let the signal to be multiplied by an unknown parameter alpha and add in the background to obtain the predicted number of events in the mass bin i: mi = alpha * Nsignali + Nqcdi Probability Pi of observing ni events when miare predicted is given by Poisson statistics Pi = mini * exp( - mi ) / factorial( ni ) The product of Pi over all bins in the mass spectrum is the likelihood function L for seeing the observation given the prediction L = P1 * P2 * .....Pn Kazim Gumus, Texas Tech University

7. QCD Cross Section and Fit We fit the QCD background to a smooth parameterization to remove fluctuations that would distort our likelihood. We are smoothing the background. Kazim Gumus, Texas Tech University

8. Likelihood Distributions (No Signal) When there is no signal, the likelihood peaks at zero cross section for the signal. The 95% area point gives us the limit. Kazim Gumus, Texas Tech University

9. 5 sigma discovery cross section Method: To produce 5 sigma discovery cross section, we formed a new distribution of "signal + background“. We found the likelihood for this distribution. We increased the signal cross section until we had a gaussian likelihood that was above zero cross section by 5 sigma (conservative approach). The result was very close to a 5 sigma exclusion from the likelihood distribution without signal, as it should be for large statistics." Kazim Gumus, Texas Tech University

10. Likelihood Distributions (Signal) The likelihoods are gaussian and the most likely cross section is 5 sigma above 0. Kazim Gumus, Texas Tech University

11. Resonance Bumps at 95 %CL and 5 Sigma Discovery The size of the resonances for a 95% CL exclusion and a 5 sigma discovery is compared with the size of the statistical error bars from QCD. Cross sections appear to be conservative estimates of what we can exclude or discover (but no systematics!). Kazim Gumus, Texas Tech University

12. 95%C.L. Cross Section Exclusion and 5 Sigma Discovery Cross section compared to cross section predictions With 1 fb-1 we can exclude at 95% CL all models above the dotted line. With 1 fb-1 we can discover at 5 sigma significance all models above the solid line We need to do estimates for more resonance points, and make the line into a realistic curve. We also need systematics. Kazim Gumus, Texas Tech University

13. Mass Limits (95% CL) and Discovery Range (5s) We will not be sensitive to Zprime, Wprime and RS Graviton with only 1 fb-1. Kazim Gumus, Texas Tech University

14. Conclusions and Future Plans • We have made a first estimate of CMS ability with 1 fb-1 to exclude (at 95% CL) or discover (at 5s) resonances in the dijet mass distribution. • Although the 5 sigma discovery plot has three points for the moment, It gives us an idea about the cross section limits we can bring in search of new resonances. • We are working on interpolating more points. • Selda is preparing a new qcd dataset which we will have binning corresponding to the dijet mass resolution. • Plan to use the new jet trigger table prepared by Robert and Selda. • Plan to include the first estimates of systematic uncertainties. Kazim Gumus, Texas Tech University

15. BACKUP SLIDEResidual Plot We test the goodness of our fit to the qcd data. Kazim Gumus, Texas Tech University