# Presentation: 695971

## TeV4LHC Workshop

- Single Top Searches at the Tevatron

- CERN April 29, 2005

- Electroweak top quark production
- Signal and background modeling, b-tagging
- CDF analysis
- s-channel, t-channel, combined results
- DØ analysis
- Cut-based, Decision Trees, Neural Networks
- Cross section limits
- Outlook

- Arán García-Bellido
- on behalf of CDF and DØ

## Top quark electroweak production

- Observe single top quark production in Run II ← Final Goal!
- Direct access to Vtb CKM matrix element
- Look for new physics: FCNC, 4th generation, W', SUSY,...
- Probe W+jets understanding and help SM higgs searches

## Signal topology

- We are looking for:
- One high pT isolated lepton (from W)
- MET (ν from W)
- One b-quark jet (from top)
- A light flavor jet and/or another b-jet

## Signal modeling

- Have to get the t-channel right:
- Avoid double counting when different diagrams produce same final states in different kinematic regions
- Use ZTOP as NLO benchmark http://home.fnal.gov/~zack/ZTOP

- CDF: Reweight MADEVENT to fit ZTOP
- Generate: b+q→t+q' and g+q→t+b+q' separately and merge them to reproduce the b pT spectrum from NLO
- DØ: “Effective” NLO CompHEP (also used in CMS)
- Match 2→2 and 2→3 processes using b pT for cross over, normalize to NLO σ
- Resulting distributions reproduce ZTOP & MCFM

## Background modeling

- W+jets
- Distributions from Alpgen
- Normalization from data
- Flavor fractions from Alpgen
- Top pairs
- Use Pythia (CDF) and Alpgen (DØ)
- Multijet events (misidentified lepton)
- From data
- Diboson (WZ, WW)
- Estimated from Pythia (CDF) and Alpgen (DØ)

## Tagging b-jets

- Both CDF and DØ use a secondary vertex reconstruction algorithm
- ~40% b-jet efficiency
- ~  1% mistag efficiency (light jets)
- Charm tagging rate ~30% of b
- DØ performance is very similar
- CDF has recently released an improved version (15% better)

## CDF search strategy

- Basic Selection Cuts

- s-channel: use two tagged events
- t-channel: use 1 tag Q×η distribution
- Combined (s+t): use HT distribution

- Bayesian Likelihood

- s+t

- s

- t

- 

- 

- 

- 

- 

- Lepton(e,μ): pT>20 GeV, |η|<1.0
- Jets: Njets=2, ET>15GeV,|η|<2.8
- MET: MET>20GeV
- Other clean-up cuts

** ****Phys.Rev.D71, 012005 (2005)**

- s, t and combined searches
- Joint likelihood for separate searches
- Likelihood with HT distribution for s+t

- 1 tag

- 2 tags

- 

- 

- b-tagging: At least one SVT jet
- mt window: 140<M(lνb)<210GeV

## CDF analysis after selection

- Acceptance:
- 	1.06±0.08% (s-channel)
- 	0.89±0.07% (t-channel)
- Main systematics:
- 	B-ID            7%
- 	Luminosity  6%
- 	mt               4%
- 	JES              4%

** ****Systematics**

## CDF distributions

- jet1: ET>30 GeV
- 1-tag events only

** ****Combined search**

** ****t-channel**

## CDF limits

- Build a likelihood as a function of βi=σi/σiSM
- Background is allowed to float but is constrained to expectation
- Find maximum of the likelihood:
- 	L(separate)=L(2tag events)⊗L(1tag Q×η distribution)
- 	L(comb)=L(HT distribution)
- Shape of systematic uncertainties included in the likelihood

- L=162pb-1

- Cross section upper limits (pb) at 95% CL:

## DØ search strategy

- Basic Selection Cuts

- 1-tag

- 2-tags

- Binned Likelihood

- s

- t

- 

- 

- Lepton(e,μ): pT>15 GeV, |ηe(μ)|<1.1 (2.0)
- Jets: 2≤Njets≤4, ET>15 GeV,|η|<3.4
-         Jet1: ET>25 GeV
- MET: MET>15 GeV
- Other clean-up cuts

- 

- 

- 

- 

- s

- t

- Cuts

- DT

- NN

- s limit

- t limit

- 

- 

- 1-tag

- 2-tags

- 

- 

- 1 tag

- 2 tags

- 

- 

- Require =1 and ≥2 SVT tags

- t-channel: at least one non-b-tagged jet

- Multivariate analysis: Simple cuts, Decision Trees, Neural Networks

- Cut analysis: count events
- DT, NN: use 2D output in a likelihood

## DØ analysis after selection

- DØ optimizes selection to maximize acceptance
- Allows a lot of background at this stage!
- Then use multiple distributions to separate signal-background

- CDF has a more restrictive selection: optimize S/B to maximize separation and use one distribution

- Acceptance: 2.7±0.2% (s-channel) and 1.9±0.2% (t-channel)

## Systematic Uncertainites 

- Result is statistics limited

- Some systematic uncertainties also affect shape:
- 	JES, b-tag and trigger modeling
- Total uncertainty:

## Discriminating variables

- This is where our phenomenology friends come so handy!
- Three broad categories:
- Object kinematics
- Global event kinematics
- Angular correlations
- Reconstruct W: from l and ν
- To reconstruct the top quark:
- s-channel: “best” jet algorithm
- Chose the jet that gives mt closest to 175GeV
- t-channel: lead b-tagged jet + W
- Reconstruct q': lead untagged jet

## Object kinematics

## Event kinematics & angular variable

## Separating signal from backgrounds

- DØ has implemented three analysis methods:
-     Cut-based           Decision Trees       Neural Networks

- Use same pool of discriminating variables for all 3 analyses
- Optimize separately for s-channel and t-channel
-     Input variables are independent of l flavor, but optimize separately for e  and μ datasets, given the different coverage and resolutions
- Focus on two dominant backgrounds: Wbb and tt→l+jets
- A total of 8 sets of cuts/trees/networks:
-    tb-Wbb, tb-tt→l+jets, tqb-Wbb & tqb-tt→l+jets (for e and μ)

## Cut-based analysis

- 1) Rate each discriminant variable according to its optimal cut
- Optimal cut values are obtained minimizing the expected limit
- with respect to cut values derived from the signal sample
- 2) Try several sets of ANDed variables and re-optimize
- 3) Select the set that yields the lowest expected limit

- before cuts  S/B =     1/52                   1/32
- after cuts     S/B =     1/34                   1/21

## Cut-based analysis: cuts

## Decision Trees

- Send each event down the tree
- Each node        corresponds to a cut
- 	Pass cut (P): right branch
- 	Fail cut (F): left branch
- A leaf      corresponds to a node without branches
- Define purity= NS/(NS+NB)
- Training: optimize Gini improvement
- 	Gini=2 NS NB /(NS + NB)
- Output:                                      purity for each event

- Multivariate technique widely used in social sciences
- Recently applied to HEP: MiniBooNE (object ID)
- Gives probability for an event to be signal

## Decision Tree outputs

## Neural Networks

**Output Node:** linear combination of hidden nodes

**Hidden Nodes:**  Each is a sigmoid dependent on the input variables

**Input Nodes:**  One for each variable xi

## Neural Networks separation

- Focus on two main backgrounds:
- Wbb takes care well of Wjj (j=g,u,d,s,c)
- tt→dilepton is small compared to tt→l+jets

## Neural Networks output

## Limits from binned likelihood

- No evidence for single top signal
- Set 95% CL upper cross section limit with Bayesian approach
- Use 2D histograms as input to build binned likelihood
- Including bin-by-bin systematics and correlations

- Used for DT and NN analyses
- Cut-based analysis uses likelihood from event count

## DØ results

- L=230pb-1

- Use Bayesian approach to combine channels (e, μ and 1 tag, 2 tags)
- Take systematics and correlations into account
- Decision Trees and Neural Networks have similar sensitivity
- Multivariate analysis + shape information from output:
**	****	****→ ****factor 2 better than simple cuts**

## Beyond the SM

- electron data only

- muon data only

- DØ exclusion contours on the (σs ,σt) plane, built from a joint likelihood with independent data samples

## Summary and next steps

- Upgrade to improved b-tagging
- Explore multivariate methods:
- Kinematic fitter, Neural Network, Matrix Element, optimized likelihoods

- Optimizing current analyses
- Increasing  acceptance
- Improving object ID
- New methods under study

- Increased dataset!

- Current analyses need a few fb-1 for observation

## Conclusions

- Single top search at the Tevatron is more challenging than anticipated (see Matt Bowen's talk next)
- Signal generators are well established
- W+jets & heavy flavor fractions estimation are the big issues
- Need advanced analysis techniques to extract signal, but these require to understand well the systematics
- CDF has published the first Run II limits
- DØ is about to submit the results presented here
- Reaching sensitivity to new Physics
- Plenty of room for improvements:
- New analysis methods
- Better object ID, better b-tagging
- More data helps reduce the systematics

- 	Our detectors are being understood better every day
-  	Data is pouring in (expect ×3 dataset by the end of the year)
- 	This is an exciting time for single top

## Tevatron luminosity prospects

## Extra1: CDF candidate event

** ****Run 153389 Event 361345**
** ****CEM electron E****T****=50.9GeV**
** ****MET=27.4 GeV**
** ****Jet1 E****T****=175.2 GeV, ****η****=0.45**
** ****Jet2 E****T****=147.2 GeV , ****η****=-0.13**

## Extra2: quadr. b-tagged event in DØ

** ****h****→****bb search**

## Extra 3: More info

- CDF: _[http://www-cdf.fnal.gov/physics/new/top/top.html](http://www-cdf.fnal.gov/physics/new/top/top.html)_
- DØ: _[http://www-d0.fnal.gov/Run2Physics/WWW/results/top.htm](http://www-d0.fnal.gov/Run2Physics/WWW/results/top.htm)_