# Presentation: 947658

## DØ Results on Top Quark Mass

- Thomas G. Trippe
- Lawrence Berkeley National Laboratory
- For the DØ Collaboration
- 12th  Intl. Conf. on Hadron Collider Physics
- Stony Brook
- June 5-11, 1997
- Contents
- Introduction
- Lepton **+** jets analysis
- Dilepton analysis
- Combined results
- Summary

## Introduction

- Top mass* m**t** *is large
*t**®**Wb* before hadronizing
- Can measure *m**t** *to greater fractional precision than for light quarks
- Important because
- Controls quark loop corrections to electroweak parameters
- Constrains Higgs mass
- Compared to earlier DØ** ***m**t** *
- Improved methods:  errors halved
- Doubled ò**L***dt *to @ 125 pb-1

## The DØ Detector

## Lepton + jets mass analysis

*W**® *l*n*  signature
*E*T* **>*20 GeV
- isolated l, *E*T* **>*20 GeV, |h|<2(1.7) for *e*(m)
*W**®*`*qq*  and`*t*(*t*)*®*`*b*(*b*) signature
*N*jets 3 4 with *E*T* **>*15 GeV and |*h *| < 2
- Further cuts
- Either *E*T* *+ *E*T*  **> *60 GeV, |*h**W *| < 2
- Or *b*-tag:  *m* with *p*T > 4 GeV/c in jet cone *R* < 0.5
- Yields 91 events, 7 with *b*-tags

*p*

*p*

*b*

*b*

*q*

- l*+ *(*e**+**,* m+)

- n

*q*

-  ̄

-  ̄

-  ̄

*t*

*t*

-  ̄

*W**+*

*W*-

- l

- l

*X*

## l + jets kinematic fit

- 2C fit
- Constrain *m**W**+* = *m**W**- *o* *pole mass
- Constrain *m **t* = *m*`*t**  *o* m** *fit* *
- Fit gives *m*fit *, **c*2
- Jet assignments
- If *N*jets > 4, use 4 highest *E**T* jets
- Assign jets to`*bbjj *
- 12 ways (6 if a jet has b-tag)
- Require *c*2 < 10
- Use *m*fit from lowest *c*2 fit
- Fit yields 77 events, 5 with b-tags

## Monte Carlo Tests

- For *m**t* = 170 GeV/c2 Herwig
- Shaded = correct permutation

## Background (unshaded) {

- Background (unshaded) {
- mT = 175* *GeV/c2 Herwig top (shaded)

*H*T2 o å *E*Tjet-i - *E*T jet-1
- |*p*z*l *| + |*p*z*n *| + å |*p**z*jet-i|

- D*R*jjmin* **E*Tmin* *
*  **E*T*l** *+ *E*T

*W *+ jets: VECBOS
- Multijets: Data

- measures centrality            measures jet clustering

## Multivariate Discriminants

- Combine *x*1-* x*4 into discriminant *D*
-                  0 < *D* < 1
- Two approaches
- Likelihood or Low Bias (LB)
- Parameterize *L**i*(*x**i*) = *s**i *(*x**i*) / *b**i *(*x**i*)
- For each event *lnL ****= ***å*i** w**i** lnL**i*
- Choose *w**i* to minimize mfit bias
*D*LB = *L */ (1 + *L* )
- Neural Network (NN)
- Three layer feed-forward NN
- Four input nodes fed by *x*1 - *x*4
- Sensitive to correlations among the *x**i*
- Train on MC top and background samples
*D*NN (NN output) » signal probability
- Agreement shows robustness of results

##  The Discriminants

-              Signal is 175 GeV/c2 Herwig top
-            Comparison with data (histogram)
- ·  top + background      D background only

## Likelihood fit in (D, mfit) plane

- Bin data, signal, and background
- 20 *m*fit bins, 80 - 280 GeV/c2
- 0 < *D* < 1, bins differ in LB and NN
- LB analysis
- 2 bins in* D*LB, cut at:
- Either: 	*D*LB > 0.43 and *H*T2 > 90 GeV
- Or:	Event has b-tag
- NN analysis
- 10 bins in *D*NN
- Fit data with* n*s* * ́* *sig +* n*b* * ́ bkgnd
- Fit at each MC top mass *m*t
- Use binned Poisson likelihood
- Fit gives *L*(*m*t)
- Quadratic fit to -ln*L*(*m*t) gives *m*t±s*m*

## DNN vs. mfit.

-    Expected 172 GeV/c2 top      Expected background

- Data

## Fit Results.

- Fit Results.

- Failed
- LB cuts

- Passed
- LB cuts

-    LB *      *174.0 ± 5.6 	23.8		53.2

- +8.3		+10.7
- -7.8		- 9.3

-    NN *      *171.3 ± 6.0 	28.8		48.2

- +8.4		+11.4
- -9.1		- 8.7

- Method 	   *m*t (GeV/c2)	        *n*s		       *n*b

- Combined *m*t = 173.3± 5.6 GeV/c2 (88% correlation)

## Lepton + Jets Result

- Systematic errors in *m**t*
- Lepton + Jet result
- 	***m******t***** = 173.3 ± 5.6(stat) ± 6.2(sys) GeV/c****2**

## Dilepton Mass Analysis

- Selection » like cross section anal.
- 2 l with *E*T > 15 (20)  GeV for *e**m**, **mm**  *(*ee*)
*E*T > 25 (20) GeV for ee (em);  reject *Z**®mm* fits
- 3 2 jets with *E*T > 20 GeV and |*h *| < 2.5
*H**T  *(*H**T*) > 120 (100) GeV for *ee, e**m  *(*mm*)
- Selection results, background estimates

*p*

*p*

*b*

*b*

- l+* *(*e**+**,* *m**+*)

*n*

*n*

-  ̄

-  ̄

*t*

*t*

-  ̄

*W**+*

*W**-*

*X*

- l-* *(*e**-**,* *m**-*)

*e*

-  ̄

## Dilepton Analysis Method

- Underconstrained (-1C) fit for *m*t
- 18 variables, 14 meas., 3 constraints
- If *m*t assumed: 0C fit.  Can solve (up to four-fold ambiguity in n momenta)
- Not all solutions are equally likely
- Estimate likelihood of event at each *m**t*
- Weight solutions by comparing  final state kinematics to expectations from *tt* production
- Sum weights for different solutions
- Two methods (shows robustness of results)
- Matrix-Element Weighting (MWT)
- Neutrino Weighting (nWT)
- Gives weight vs *m*t for each event
- Maximum likelihood fit for mass
- Compare weight distributions for data with signal and background expectations

## Matrix-Element Weighting (MWT)

- Extension of Kondo; Dalitz+Goldstein ideas
- Use the weight
- 	where
- Include detector resolution
- Smear event many times
- Sum resulting likelihoods
- Also sum over pairings of b-jets with leptons
- Example of *W*(*m**t*) for first *e**m** *event

## Neutrino Weighting (nWT)

- Solve event without using measured *E*T
- Up to possible 4-fold ambiguity
- Calculate
- Weight compares *    * with
- Integrate over neutrino rapidities:
- Approximate prior probabilities by Gaussians with widths from     MC.
- Smearing, jet combinatorics as in MWT

- Neutrino Weighting (nWT)

## Dilepton Jet Combinatorics

- Gluon radiation may make additional jets
- 1*e**m*, 1*ee*, and 1*mm* event have 3 jets
- For a three jet event we could
- Ignore one jet (initial state radiation, ISR)
- Merge two jets
- Six ways to do this:
- Gives six likelihood curves; weight each
- Sum the six weighted likelihoods to give a single likelihood curve for each event

## W(mt) for Dilepton Candidates.

- nWT method
- MWT method

## Maximum Likelihood Fit for mt

- Want to find* m**t* most consistent with data* *
- Bin normalized *W*(*m**t*) in 5 bins.
- Typical      event:
- Each event described by 4 independent quantities,
- At each* m**t* do* *ML fit for *n*s, *n*b using
- Estimate prob. densities *f*s and *f*b using MC
- Quadratic fit to -ln*L*(*m*t) gives *m*t±s*m*

## Dilepton Results.

- signal + background
- background only

- data

## Summary

- sDØ(172 GeV/c2) = 5.6 ± 1.8 pb
- (Reference: this conference)

## Higgs mass constraint

## Higgs mass constraint