p17 ZH -> ll bb

Data / MC / Cuts

QCD background studies

Results

NN event selection

Data samples / MC / Cuts

2MUhighpt or 2EMhighpt skims

jets pT > 15 GeV (after JES – no muon corr.) and |eta|<2.5

leptons pT>15 and |eta|<2 (mu), 2.5 (e, except crack)

mix of triggers is ~100% efficient (?)

p17 NN b-tagging v1.1, L4 operating point

MC:

“p14” JSSR

Taggability parameterized from e+jets data

TRFs for b-tagging

We still have some work to do for MC:

apply proper p17 ALPGEN matching weights for light jet samples

are still using “p14” k-factor for Z + heavy flavor (1.35)

Need to include tt->l+jets

Z peak

Normalize MC to Z peak in data, derived from fit in data (mu channel)

Or

Normalize to inclusive cross-section x luminosity measured (e channel)

Z peak is too narrow in p17.09.01 MC (smearing needs to be redone)

More Z's would fall inside the inv. mass window (70-110) in MC, than in data...

Need to be more careful about Drell-Yan vs. background contribution...

ZH MC has the right smearing

Z+jets MC has too little smearing,

overshoots peak – even without QCD

background!

QCD Background

Looking at double b-tagged events, I was disturbed to see many very non-isolated muons forming the Z

Comes from 2/4 isolation cuts

As long as one muon is very isolated, the other can be in a jet!

Enhanced in b-tagged events, due to muons in jets from b-jets

Require that both muons are isolated
(allow 0/4 isolation cuts to fail)

was not very efficient for signal

New method: require that the product of the scaled isolation is <0.01

scaled isolation = (track + cal halo) / muon pT

gives better signal efficiency, and less background fraction in 2b channel...

isolated

non-isolated

scaled isolation < 0.01

Z signal = 27.2, qcd background (+DY) = 9.6

QCD Background, 2 b-tags

M(j0,j1) – 0 b-tags

Good agreement at low mjj now. Not well-modeled at high mjj.

0b total = 2899.29 predicted, 3022 observed

Data/MC ratio in 0 tag sample

Will divide by this histogram to get “corrected background”

1 b-tag

1b total = 348.44 predicted, 371.19 predicted corrected, 393 observed

2 b-tags

Data

Total background

Corrected background

Z->mumu+Xlp

Z->mumu+2b+Xlp

QCD

ttbar->2l+bb

Z->mumu+2c+Xlp

ZZ

WZ

ZH->mumubb (115)

QCD background added as fraction (30+-5%) of total background.

2b total = 32.34 predicted, 36.04 predicted corrected, 35 observed

2 b-tags

Data

Total background

Corrected background

Z->mumu+Xlp

Z->mumu+2b+Xlp

QCD

ttbar->2l+bb

Z->mumu+2c+Xlp

ZZ

WZ

ZH->mumubb (115)

Limits (mu-channel only)

scalediso<0.01

(QCD
background

included)

NN

Including QCD background from data as a background sample (M(mumu) outside Z mass 70-110 window)

- Can train the NN against QCD kinematics

Find an optimal cut on the NN that maximizes S/sqrt(B), then measure the ratio of S/sqrt(B) for this cut, vs. no NN but with an Mjj cut of 1.5 sigma

Optimized many parameters...

- # training epochs

- # hidden neurons

- training method

- NN weighting method of backgrounds

- Initial Mjj cut

Separate NN for each m_H (105, 115, ... 155)

pt1

d_eta (j0,j1)

m_(j0,j1)

dR(j0,j1)

pt2

zmQCD

is hacked

to QCD bkgd.

shape for NN

Final NN

We finally settled on

“full p2b weighting” where each event is weighted by its probability to be in the double b-tagged data (can take advantage of paculiarities of kinematics of actual tagged events)

(
the alternatives are:
“average p2b weighting” where each event is weighted by the average probability for an event of this type to be in the double b-tagged data
and
“p2b and p1b as variables” where each event is weighted by its probability to be in the untagged data, but p2b and p1b are variables in the NN itself
)

BFGS training

175 training epochs

8 hidden neurons

Initial mjj cut of 2 sigma

Significance increases such that only 55 – 65 % (mH=155->105) of the integrated luminosity is needed for the same expected limits compared to just a simple window cut at +-1.5 sigma on the Mjj

Agrees with the preliminary expectations for Higgs sensitivity

Will be even better once CLs is used directly on the NN output for limit setting...

Conclusions (and a look to the future...)

QCD background can be measured in the double b-tagged data with reasonable accuracy

also needs to be done for e channel

Scaled isolation does a good job for muons (what about e?)

Background agrees pretty well with data

after accounting for QCD

need correct matching weights for Z+ light jets

should recalculate Z+ heavy jet
k-factor

An optimized NN has been trained using 8 input variables in MC and using QCD background from data, for each m_H

Next (deadline is DPF!):

Apply the NN in both analyses, and extract limits (using CLs?)

Odds and ends

Systematics

Analysis notes

Mean too low

Sigma at peak too wide

Radiative tail too long

Tail at high mass

Need to improve inv. mass reconstruction if we are to

find the Higgs.

Jet energy resolution is not the only problem!

Shaohua Fu

06/22/2006

Andy Haas

2004-07-07T15:59:13