# Presentation: 649876

- Jonathan Efron
**The Ohio State University**

-  Search for the Standard Model Higgs in the ZH → *l*+*l**-* *bb *Channel

## Outline

- Why the Higgs?
- Tevatron and CDF II
- Event selection
- Improving signal-background separation
- Neural network jet corrections
- Neural network classification
- Results
- Prospects for future

- Jonathan Efron

## Standard Model

- Excellent description of particle physics
- All observable parameters
-   	within 3 of expectations
- Combines weak, EM, and strong forces.
- Electrodynamics and weak forces combined into SU(2) x U(1) symmetry
- But . . .

- W and Z bosons
- required to be
- massless

- Jonathan Efron

## Enter the Higgs Mechanism

- The Higgs field breaks electroweak symmetry and consequently adds masses to the gauge bosons
- Mass is directly proportional to interaction with Higgs field

- Gauge
- Bosons

- H

- Fermions similarly can attain mass with interaction with Higgs

- As a consequence of Higgs field, a new spin 0 boson is predicted

- Jonathan Efron

## Constraints on the Higgs Boson

- Higgs searches ongoing for last 30 years
**Direct search at LEP:**
- 	MHiggs > 114.4 GeV/c2 @ 95%
**Indirect searches:** including new Tevatron precision measurements
**M****top****= 170.9 ± 1.8 GeV/c****2**
**M****W****=80.398 ± 0.025 GeV/c****2**

- Jonathan Efron

## Higgs Boson at the Tevatron

- Higgs Boson interact with other particles proportionally to their mass
- At Higgs masses less than
- 	135 GeV/c2, *bb *is the primary Higgs decay
- Associated production (WH/ZH) of Higgs has much lower backgrounds than direct production

- Jonathan Efron

## Higgs at the Tevatron

**Higgs at the Tevatron**

**WH ******** l********bb**

**ZH ******** ********bb**

**ZH ******** llbb**

**gg ******** H ******** WW ******** l********l******

- BR(Z *l**+**l**-* ~ 7%

- (ZH) ~ .1 pb

- Total

- Jonathan Efron

## Tevatron Run II 2000-2009 (est.)

- Tevatron Run II 2000-2009 (est.)

- Proton-Antiproton collider
- Center of mass energy  1.96 TeV
- Current Peak Instantaneous Luminosity:
- 		286*1030 cm-2/s
- Integrated Luminosity:
- 3.0 fb-1 Delivered
- 2.5 fb-1 on tape at CDF
- Projected total luminosity for Run II: 	6-8 fb-1
- This analysis 1 fb-1

- Jonathan Efron

## CDF II Detector

- Forward-background
- 	and radially symmetrical
- Multipurpose:
- 	Run II achievements
- Observation of *B**s** *mixing - 17.77 ± 0.10 (stat.) ± 0.07 (syst.) ps-1
- World’s most precise single measurement of the *W* boson mass (80413 ± 34  ± 34 MeV/c2)
- World’s most precise single measurements of top quark mass (170.9 ± 1.6 ± 2.0 GeV/c2)
- Many other work in electroweak, QCD, b hadrons, top quark, and exotic physics

- Jonathan Efron

## Features of ZHl+l-bb

- The signal is small
- For MH = 120 GeV/c2
- (ZH) ~ .1 pb
- ~100 events/1 fb-1
- BR(Z  *l**+**l**-*) = 6.8%
- ~7 events/fb-1 remaining
- BR(H * bb*) ~ 70%
- ~5 events/1fb-1 remaining
- But so are the backgrounds
- And all final products are measurable
- Find two leptons that form a Z boson
- Find two or more jets from b-hadrons

- ZH MC simulation

- Jonathan Efron

## Guiding Goals of this Analysis

- Try to push the limits of basic event selection
- Increase lepton acceptance
- Use innovative techniques to separate signal from backgrounds

- Jonathan Efron

## Steps of the Analysis	

- Find events with a Z and b-tagged jets
- Separate events into two loose and exclusive single tight b-tags
- Correct jet energy using a neural network
- 	(NN jet corrections)
- Use event kinematics in an optimized 8 input 2 output artificial neural network
- 	(NN classification)
- Fit neural network output shape to find expected and measured upper limit

- Jonathan Efron

## Event Criteria

- “Tight” lepton is required for event to be triggered
- *** **| <* 1
- ET (PT) > 18 GeV(/c) for electrons (muons)
- Other fiducial requirements
- Isolation
- Had/Em of electrons
- Calorimeter energy deposition of muons
- “Loose” lepton Selection
- Lower ET (PT) requirement
- Looser requirements
- Reconstruction as Z
- Same flavor opposite sign leptons
- 76 < M*ll**<*106 GeV/c2
- Looser lepton criteria yields ~70% signal events

-  2 or more jets
- 1st Jet ET >25 GeV
- Other jets ET>15 GeV
-  |***|< *2
-  B-tagging requirement

- Jonathan Efron

## B-tagging

- Secondary Vertex Algorithm at CDF
- 40 - 50 % efficient (tight, loose)
- 0.5 - 1.5 %  fake rate from light jets
- Can take advantage of the Higgs decaying to bb
- from ~1:1000
- to ~1:100
- A separate channel for events with 2 b-tags
- Most fake b-jets removed

- Jonathan Efron

##  Background Contributions

- Z →*l**+**l**-* + jets
- Z →*l**+**l**-* + *bb*  (MC)
- Z →*l**+**l**-* + *cc* (MC)
- Z →*l**+**l**-* + other jets (“mistags”-data simulated)
*tt *→ *WbWb *→*l****b l****b*
- Dilepton events within Z mass window (MC)
- Diboson - (2 leptons + 2 jets - MC)
- ZZ, ZW, WW+2p
- QCD background - “fake” leptons
- Muons modeled by same-sign data
- Electrons – estimated using jet data

- Jonathan Efron

## Mistag Modeling

- Basic Idea

*L**xy* is the distance between vertex and primary in *x*-*y* plane
- Positive Tags: *L**xy* > 0
- Light flavor jets: slightly more likely to have positive than negative *L**xy*

*L**xy*

*L**xy*

- CDF has made a function based on jet properties to estimate likelihood of jets being “mistagged”
- Higher energy Jets are **more** likely to be mistagged

- +Lxy

- -Lxy

- Tracks from primary vertex

- Good tag with positive Lxy

- Potentially fake tag with negative Lxy

- Jonathan Efron

## Fake Electron Estimations

- Jet  Electron fake rate function for loose electrons
- Use jet only data
- Calculated as a function of Jet ET
- Separate functions for Central and Plug electrons
- Steps to eliminate possible real electron contributions
- Real electron in jet data would greatly inflate our electron fake rate
- Do not use trigger jet to calculate error function
- Eliminates biggest source of electrons from W  e
- Missing ET cut < 15 GeV

- Jonathan Efron

**Notes:**
underscore et

## Events Expected in 1fb-1

| Background | 2 Tags | 1 Tags |
| --- | --- | --- |
| tt | 2.8 | 5.2 |
| Diboson | 1.5 | 5.2 |
| fakes | 0.2 | 2 |
| Z + bb | 6.3 | 35 |
| Z + cc | 1.0 | 22 |
| Mistags | 1.0 | 32 |
| Total bkg. | 12.8 | 101 |
| Data | 11 | 100 |

| Higgs
Mass
(GeV/c2) | (ZH)*BR
(H  bb) SM | 2 Tags | exclusive
1 Tag |
| --- | --- | --- | --- |
| 100 | 0.14 pb | 0.41 | 0.87 |
| 110 | 0.092 pb | 0.32 | 0.65 |
| 115 | 0.080 pb | 0.28 | 0.56 |
| 120 | 0.061 pb | 0.23 | 0.45 |
| 130 | 0.037 pb | 0.15 | 0.27 |
| 140 | 0.017 pb | 0.09 | 0.15 |
| 150 | 0.007 pb | 0.03 | 0.06 |

- Backgrounds

- _Signal_

- Jonathan Efron

## Introduction to Artificial Neural Networks

- Designed to mimic a brain
- Interconnected nodes
- Connections “learned” based on experience
- Each nodes “fires” based on incoming information
- Has been shown to be able to reproduce any non-linear function

- Train neural networks toward target outputs.
- Look to reduce error
- E = 1/NP T – O)2
- Repeat based on previous performance
- Adjustable parameters
- Weight of each nodes inputs
- Threshold for “firing” of each node

- Inputs

- Hidden

- Outputs

- S

- B

- Sample 1-D NN

- Jonathan Efron

## NN Jet Corrections

- Training to match corrected jet energies to generator level parton energies
- Input Variables from 2 b-quark jets
**1** Corrected Jet Et
**2** Detector η
**3** Jet φ
**4** MET projected on axis of individual jet
- Total event MET and MET φ
- Two output nodes -  corrected jet energies
- Create single NN for all masses
- (Hmass from 60-180 GeV used in training)
- ttbar used in training as well

- Jet System
- Lep Sys.
- Met

- Jonathan Efron

## NN Jet EnergyCorrection Comparison

- NN Jet EnergyCorrection Comparison

- Jet energy resolution is better using the NN
- More accurate and precise measurement of Higgs Boson mass
- Rule of thumb for fitting MJJ
- 	1% improvement mass resolution
- 	~ 10% luminosity

| Mjj 
Ave Std. | 2-tag | 1-tag |
| --- | --- | --- |
| NN JetCorr | 11% | 14% |
| Standard | 18% | 22% |

- Jonathan Efron

## Neural Network Event Classification

- A separate neural network from NN jet corrections
- Dijet mass still has large backgrounds
- A NN takes advantage of correlations between different variables

- With a 1-D NN that separated Z+jets from ZH,  *tt* remains prominent in most Higgs like region
- Therefore a 2-D network is used
- NN estimated to improve search by a factor of 2.5x more luminosity versus dijet mass alone

- Jonathan Efron

## Classification Artificial Neural Network with 2-D output 

- Develop NN classification after NN jet corrections
- Training
- Neural net output determined by exposure to training data Iteratively adjust parameters to minimize error
- Training accomplished through JETNET program (Peterson et al. CERN-TH/7135-94)
- Will fit resulting shape from data against signal background shapes to measure upper limit of ZH cross-section

- (1,0)

- (1,1)

- (0,0)

- ZH

- TT

- Z+jets

- Training on :  TT,ZH,Z+jets

- Allow other shapes to fall in place:
- 	Fakes, ZZ, ZW

- ZZ, ZW

- 2D Neural Net Discriminant

- Fakes

- Jonathan Efron

## Kinematic Input Variables in Neural Network Classification

- Ht (Sum of 2 leptons, Missing Et, and top two jets)
- Missing Et
- Mass of top two jets
- R of Jet 1 and Z candidate
- (R***2*+**2)
- R of Jet 2 and Z candidate
- R of Jet 1 and Jet 2
- Sphericity
- Jet 2 **

- Jonathan Efron

## Input Kinematic Variables

- Single Tag Histograms

- Jonathan Efron

## Neural Network Shapes

- Normalized to single tag expectations

- Jonathan Efron

## “Pretag” Data Output

- “Pretag” Data Output

- With no b-tag
- requirements
- ~95% Z+jets
- Use as NN
- validation

- Jonathan Efron

## Projections and Slices

- Looking at projections
- Z+jets vs ZH axis
- 0  1 increasing energy
- Most energetic corner (1) has *tt* and ZH
- Slice
- Separates Z+jets v ZH
- Excludes *tt*
- ZH vs *tt*
- 0  1 increasing Met
-  0 contains Z+jets and ZH, most events
- Slice
- Separates: ZH v *tt*
- Most Z+jets excluded

- (1,0)

- (1,1)

- (0,0)

- ZH

- TT

- Z+jets

- ZZ, ZW

- 2D Neural Net Discriminant

- Fakes

- Jonathan Efron

## Pretag Output Projections

- Jonathan Efron

## Pretag Output Projections

- Jonathan Efron

## Single Tag Output

- Jonathan Efron

## Single Tag  Output Projections

- Jonathan Efron

## Double Tag Output

- Higgs Like
- Double Tagged Event

- Jonathan Efron

## Double Tag Output Projections

- Jonathan Efron

## Run 196170 Event 6577

- 2 SecVtx tagged Jets
- Jet 1 Et(NN corrected) = 126.6 GeV
- Jet 2 Et(NN corrected) = 37.1 GeV

- NN output = 0.958, 0.009
- MJJ = 120 GeV/c2

**S/B = 1/4.2**

- April 5, 2005
- ~8:09 am
- Inst.Lumi
- ~28*1030

| Zbb | 0.11 |
| --- | --- |
| tt | 0.019 |
| Zcc | 0.017 |
| ZZ | 0.016 |
| Mistag | 0.010 |
| Total bkg | 0.18 |
| ZH(120) | 0.042 |

- Jonathan Efron

## Setting a Limit

- Fit data using background (b) only template and signal + background (s+b) template
- Vary signal cross-section
- Systematics are included in a possible model of the fit
- Takes advantage of sidebands to determine background rates
- Run many pseudo-experiments based on nominal expectations
- Randomly “create” data from templates
- Fit “pseudo-data” just like real data
- Find likelihood ratio (Q) of the s+b model versus b only
- Exclude values of ZH  where Q is less than 5% likely from s+b hypothesis

- Jonathan Efron

## Systematics

- All Systematics errors increase the expected limit by 14%
- Main culprits (>5% affect on expected limit)
- 8% B hadron b-tagging efficiency (per tag): – worsens limit 12 %
- 6% Luminosity uncertainty: - worsens limit 7%
- 40% error on Z+heavy flavor production : - worsens limit 6%
- Other systematics
- Jet Energy : +/- 1 sigma fluctuations (shape and counting)
- Z+l.f. mistag error (and shapes): 17%* *
- Alpgen vs Pythia for Z+jets NN Output Shapes
- C hadron b-tagging efficiency: 16%  per tag
- Lepton scale factors : 1% each
- PDF :  2%
- Diboson cross-section normalization: 20%
*tt *cross-section: 20%
- QCD fake rate error: 50%
- Initial State Radiation/Final State Radiation :  for signal ZH signal

- Jonathan Efron

## B-tagging Efficiency

- Measured in dijet sample
- One jet with soft lepton
-  Second jet with a SecVtx tag
- Very pure sample of heavy flavor jets
- Efficiency measured using soft lepton-tagged jet
- Parameterized for Jet ET, eta, etc. . .
- Source of uncertanity
- Statistical
- Heavy flavor fraction
- Mistag uncertainty

- SecVtx

- Soft Lepton

- Is there a 2nd
- SecVtx Tag?

- Jonathan Efron

## Z + Heavy Flavor Cross-section

- 40% systematic motivated by error on error from Z + b-jet measurement
- Measurement fit mass of SecVtx tags to templates of Z+b, Z+c and Z+lf
- Z+b-jet= 0.93 ± 0.32 (stat.) ± 0.14 (syst.) pb
- Phys. Rev. D 74, 032008 (2006)

- Jonathan Efron

## Jet Energy Scale

- Determine parton energy from measurements in calorimeter
- Correct for
- Detector effects
- Fragmentation/Hadronization
- Underlying event
- Energy scale determined from data and MC
- Uncertainties in jet energy scale directly affect kinematic uncertainties
- Allow uncertainties to change acceptance
- Allow uncertainties to change kinematic shape of NN output
- Correlated errors between MC modeling

- Jonathan Efron

## Mistag Shape Errors

- Adjust all bins by their bin error to get alternative shapes
- Contributions to shape errors
- Pretag statistical errors
- Ratio of negative tags to mistags
- Errors of mistag matrix

- Single Tag Mistag shape
**BLACK** nominal shape
**BLUE** shifted up and down
- mistagged shape

- Jonathan Efron

## ZH → l+l- bb  with 1 fb-1 Upper Limit at 95% CL

| Mass (GeV/c2) | 1-tag
ZH /SM | 2-tags
ZH /SM | Combined
ZH /SM |
| --- | --- | --- | --- |
| 100 | 23    (23) | 15   (16) | 12   (13) |
| 110 | 25    (24) | 20   (19) | 14   (14) |
| 115 | 28    (27) | 23   (22) | 16   (16) |
| 120 | 30    (31) | 28   (26) | 18   (18) |
| 130 | 48    (49) | 43   (39) | 29   (28) |
| 140 | 110  (96) | 86   (75) | 65   (54) |
| 150 | 260  (250) | 220 (200) | 160 (140) |

- Observed Limit (Expected Limit)

- 115 GeV/c2 limit
- corresponds to 1.3 pb
- for ZH * BR(H * bb*)

- Jonathan Efron

## Higgs at CDF II with 1fb-1

- Expected limits similar to other SM low-mass Higgs searches
- Despite lowest cross-section
- Despite lowest branching ratio

- CDF collectively, expected limit is 8.0 times SM at MH = 115 GeV/c2
- Observed = 11 x SM

- Expected Limits CDF II Preliminary

- Jonathan Efron

## Step One to Discovery

- Scaling current analysis with 8 fb-1 of data, expected upper limit is 5.2 times SM at
-  	MH = 120 GeV/c2
- Improvements in the works
- Greater event acceptance
- Improve b-taggers
- Higher efficiency
- Wider eta range
- Lower systematics
- Mix in other advanced selection techniques

- Current Analysis Extrapolated with Data

- Jonathan Efron

## Conclusions

- Standard Model adds a Higgs sector to explain electroweak symmetry breaking, resulting in Higgs Boson
- Designed an analysis using the CDF detector to search for ZH→ *l*+*l**-* *bb*
- Use different artificial neural networks to signal to background
- Set an upper limit on production of ZH to 16 times the SM expectation and a Higgs mass of 115 GeV/c2
- Opportunity for discovery at the Tevatron exists
- Combination within CDF and with D0 necessary

- Jonathan Efron

## Locate the event primary vertex to within ~17 m (on average)
Organizes tracks into jets using a cone of R = 2+2 = 0.4
Searches each jet for one or more displaced secondary vertices in two passes
Pass 1: require 3+ track vertices
Pass 2: 2 track vertices, tighter cuts
Additional requirements 
Displaced from primary by at least 7.5 (6.0) 
Other vertices have a resolution ~190 m 

- Organizes tracks into jets using a cone of *R* = **2+**2 = 0.4
- Searches each jet for one or more displaced secondary vertices in two passes
- Pass 1: require 3+ track vertices
- Pass 2: 2 track vertices, tighter cuts
- Additional requirements
- Displaced from primary by at least 7.5 (6.0) **
- Other vertices have a resolution ~190 m

- Jet cone

- Tracks in jet

- Tracks outside jet

- Displaced vertex

- Secondary Vertex Tagging

- _[return](slide14.xml)_

- Jonathan Efron

## Tag Rates

- _[return](slide16.xml)_

- Jonathan Efron

## Electron Acceptance Table

- _[Return](slide18.xml)_

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## Muon Acceptance Table

- _[Return](slide18.xml)_

- Jonathan Efron

## Pretag Kinematic Shapes

- _[Return](slide25.xml)_

- Jonathan Efron

## Pretag Kinematic Shapes

- _[Return](slide25.xml)_

- Jonathan Efron

## Correlations Profiles

- At least one tight SecVtx tag
**BLACK** ZH(120 GeV/c2)
**RED **Z+BB Alpgen

- A quick investigation into the
- correlations SANN takes advantage

- _[Return](slide25.xml)_

- Jonathan Efron

## Ht

- Rj1j2

- Rj1j2

- Rj1Z

- Met

**BLACK** ZH
**BLUE** ttbar
**RED Z+BB**

- Comparison
- of Profiles in the
- Training MC Samples

- Rj1j2

- Mjj

- Return

- Jonathan Efron

## Test of Correlations

- x,y = ( [(xi-x)(yi-y)]/N ) / (x * y)
- Correlation coefficient is the
- average of the individual correlations.
-  The distribution of these correlations
-  has more info that the single number

- Return

- Jonathan Efron

## Jet 2 Eta

- Return

- Jonathan Efron

## More NN Templates

- Return

- Jonathan Efron

## Double Tagged Input Shapes

- Jonathan Efron

## Double Tagged Input Shapes

- Return

- Jonathan Efron

## What about the slide from W+C

- Around 80 GeV, the population of secondary jets diminish and leading jet energies dominate

- Return

- Jonathan Efron

## How does NN JetCorr Work

- Return

- Jonathan Efron

## Z PT Balancing Introduction

- Consider Z+2 jet events
- Z should recoil against jets
- Assume Z Pt is absolute
- leptons measured better than jets
- Perpendicular component arises from underlying event, unclustered energy, extra radiation, boost of Z+jets system
- Parallel component of J-J system can be measured by Z Pt
- Dijet resolution can be measured relative to Z Pt as “true” value

- dphi = Z->dphi(JJ)
- proj|| :	(ZPt + JJ Pt * cos(dphi)) / Z Pt
- proj⊥: JJ Pt*sin(dphi) / Z Pt

- Jonathan Efron

## Validation NN Jet Correction

- Return

- Jonathan Efron