A Study of t t +jets

1. A Study of tt+jets Anthony Affolder, Joe Incandela, Jim Lamb University of California-Santa Barbara

2. The study of tt events with additional jets is of interest at both the Tevatron and at the LHC At the Tevatron: Lack of understanding of rate/momenta of additional jets is a potential systematic to the Mt measurement Background to ttH and top color Test of QCD at high q2 For the LHC, tt+jets is the dominant background to: Di-Higgs Mh>140 VBF ttH Multi-lepton SUSY events Top color Motivation

3. MC Study of tt+jets • Obviously the number of reconstructed jets has the strongest correlation with extra radiation, but within any given jet multiplicity bin, is it possible to extract additional information about the true number of hard radiated partons? • At parton level - we divided tt PYTHIA MC in +0 jet, +1 jet, ≥ 2 jet samples • Also separate data into sub-samples according to reconstructed jet multiplicity • We then studied many variables to determine which have the power to discriminate between events with/without additional jets • We will repeat the same studies with Madevent ME+ PYTHIA MC samples using Mrenna matching method, Alpgen+HERWIG with MLM matching, NLO@MC with CKKW matching as data sets becomes available

4. Variables Studied • We studied the additional following variables in each jet multiplicity bin: • HT- Might have higher values with additional jets • Minimum Jet ET–Additional jets might have a softer ET • Maximum Jet |h|- Initial state radiation might be more forward • Minimum Mjj (4 jets)- Final state radiation cause peaking at low mass • Maximum DRjj-Initialstate radiation might have large DR • Aplanarity-Initial state radiation might distort shape • Sphericity-Initial state radiation might make event less spherical • Fit Mt (4 jets)- Events with additional jets do not peak at the top mass • Top Fit c2 (4 jets)- Events with additional jets have higher c2 • Fit pTtt (4 jets)- Initial state radiation causes higher pT Indicates variable has some discriminating power

5. NJets

6. Discriminating Variables

7. Plan of Attack • Using the 318 pb-1 data sample, we will see if PYTHIA describes the data well. • Using templates from PYTHIA, calculate the KS probabilities of the discriminating variables, combine the probabilities to see if simulation describes data • Study ME+PS MC, MC@NLO to see if there exists additional discriminating variables • Then, we will make a comparison using Madevent, ALPGEN, MC@NLO • With samples, we will vary the kT scale at which the hard (ME) and soft (PS) processes are matched • Calculate the KS probabilities of the discriminating variables • With ~1 fb-1 of data and using the kT scale determined by the 318 pb-1 data sample, we will make the same comparisons between data and PYTHIA, Madgraph, ALPGEN, MC@NLO • Combine KS probabilities into global likelihood of data and simulation agreeing • Try to make some statement about ISR/FSR

8. PYTHIA vs. Data • To get a first look at the data, we compared the default output of PYTHIA (ttopel) to the data • Standard top cross section selection including HT cut • The backgrounds shapes were determined using same methodology as the lepton+jets top cross section measurement • The relative rates of signal and background were taken from the cross section measurement and normalized to the total data sample. • PYTHIA predicts 75%, 18%, and 7% for 0, 1, ≥2 additional jets

9. PYTHIA vs. Data Discriminating Variables

10. PYTHIA vs Data Non-Discriminating Variables

11. KS Probabilities • The KS probabilities of the 9 variables looked at is consistent with a flat distribution, i.e. PYTHIA describes the data well • …but there is a trend for the variables that are sensitive to extra jets to pile-up at low probabilities • Combining KS probabilities assuming NO correlations, we find that: • 5% agreement between data and PYTHIA in discriminating variables • 89% agreement between data and PYTHIA in non-discriminating variables • Will combine KS probabilities taking into account correlations in the near future • ME+PS and MC@NLO may give better agreement • But it is likely that 1 fb-1 data needed to make any significant differentiation

12. ISR So far, only has shown any discriminating power for ISR. Unless another more powerful variable found, the small expected difference requires much more data to say anything quantitative.

13. FSR Similarly, only has shown any discriminating power for FSR.

14. Conclusions • Using 318 pb-1 data set, developing/tuning tools for measurement with 1 fb-1 of data • Will get first look of how well PYTHIA describes data relative to ME+PS MC and MC@NLO • With 1 fb-1 of data, hope to have enough discriminating power to determine which Monte Carlos best describes data globally • And see if current systematic uncertainties cover the differences seen between data and MC • Separating between sources (ISR/FSR) will be extremely difficult and will likely require more than 1 fb-1 of data to say anything definitive

15. Loss of jets (feed-down) Jet merging Detector acceptance Reconstruction efficiency Gain of jets (feed-up) Jet splitting Radiation Single hard Multiple soft More than just jet counting Different processes can cause the gain/loss of reconstructed jets We are interested in hard radiated jets An additional jet is defined as a gluon with ET>15 GeV and |h|<2 originating from initial state particles, top quarks, W decay quarks, or top decay bottom quarks