a study of t t +jets

A Study of tt+jets Higgs/Top Workshop, July 25th, 2005 Slide 1

A Study of tt+jets

Anthony Affolder, Joe Incandela, Jim LambUniversity of California-Santa Barbara


Motivation

• 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


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


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 ||- Initial state radiation might be more forward Minimum Mjj (4 jets)- Final state radiation cause peaking at low

mass Maximum Rjj-Initial state radiation might have large R 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 2 (4 jets)- Events with additional jets have higher 2 Fit pT

tt (4 jets)- Initial state radiation causes higher pT

Indicates variable has some discriminating power


NJets


Discriminating Variables


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


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


PYTHIA vs. Data Discriminating Variables


PYTHIA vs Data Non-Discriminating Variables


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

KS Probabilities

0

1

2

3

KS Probability

Non-discriminatingVariables

DiscriminatingVariables


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.


FSR

Similarly, only has shown any discriminating power for FSR.


Conclusions

• Using 318 pb-1 data set, developing/tuning tools for measurement with 1 fb-1 of dataWill 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 globallyAnd 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


More than just jet counting

• Loss of jets (feed-down) Jet merging Detector acceptance Reconstruction efficiency

• Gain of jets (feed-up) Jet splitting Radiation

– Single hard

– Multiple soft

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 ||<2 originating from initial state particles, top quarks, W decay quarks, or top decay bottom quarks

a study of t t +jets

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