single top quark production

11/2/2009 Ulrich Heintz - seminar - Stony Brook 1

Ulrich HeintzBrown University

Ulrich Heintz - seminar - Stony Brook 2

outlinetop quark introductionTevatron and DØ experimentevent selectionmatrix elementsboosted decision treesbayesian neural networkscross section and |Vtb|other measurementssummary

11/2/2009

top-antitop quark pair productionobserved first in 1995 by CDF and DØ

“easy” to see

top-antitop quark pair productionobserved first in 1995 by CDF and DØ

measure strong coupling of top quarkpp = 7.6 0.9 pb mtop = 173.11.3 GeV q = +2/3 (tW+b) preferred over -4/3 (tW-b)

top quark decayweak interaction

tWb’b’ = Vtd d + Vts s + Vtb b

tt production B(tWb) > 0.79 @ 95% CL |Vtb| >0.89 @ 96% CLtop << experimental resolution

B decays |Vub|2 + |Vcb|2 + |Vtb|2 = 1|Vub|=0.00393, |Vts| = 0.0412 |Vtb| = 0.9991

assume unitarity of 33 CKM matrix

VVVVVVVVV

tbtstd

cbcscd

ubusud

single top quark productionweak interaction

|Vtb|2

no assumptions on number of generations or unitarity of CKM matrix

NLO = 1.120.05 pb = 2.340.13 pb Kidonakis and Vogt, PRD 68, 114014 (2003) for mt =170 GeV

11/2/2009 7Ulrich Heintz - seminar - Stony Brook

s-channel t-channel

11/2/2009 Ulrich Heintz - seminar - Stony Brook

single top quark productionsensitive to new physics 4th quark generation anomalous Wtb vertex new particles (H+, W’) FCNC

important benchmark in understanding the backgrounds to Higgs search in WH channel

single top quark production

s-channel

single top quark production2006: D0 announces evidence for single top production

11/2/2009

DØ Evidence paperPRL “Editor’s Suggestion”110 SPIRES citations

11/2/2009

the Tevatron

counter rotating beams of protons and antiprotonsradius = 1 kmbeam energy = 980 GeV21011 protons in 36 bunches 21010 antiprotons in 36 bunchesenergy stored in beams = 35 kJtime for one revolution = 21 stime between collisions = 396 nspeak luminosity = 2.81032cm-2s-1

the Tevatron… still the only place to find top quarks

090.0878.128/1)( 2

CDF DØ

the Tevatron

0.9 fb-1 evidence

2.3 fb-1 observation

the DØ detector

beam pipe

calorimeter

muon toroid

➔ 19 countries ➔ 80 institutions➔ 700 physicists

D0 Collaboration

11/2/2009

a needle in a hay stacksingle top

dominant background: Wbb, W+jets

event selection

11/2/2009

muon pT > 15 GeV, || < 2.0 electron pT > 15 GeV, || < 1.1

20 < missing pT < 200 GeV

2-4 jetsleading jet pT > 25 GeV, || < 3.4other jets pT > 15 GeV, || < 3.4

1 b-tagged jetleading b-jet pT > 25 GeV, || < 3.4

24 channels: 2 running periods 2 lepton flavors 3 jet multiplicities 2 b-tag multiplicities

event selectionb-jet taggingb lifetime 1.6 ps

travels a few mm before decaying

secondary

vertex

large impact parameter

primary vertex

event selectionseparate b-jets from light-quark and gluon jets to

reject most W+jets backgroundneural network algorithm

based on impact parameter and reconstructed vertexleading b-jet pT > 20 GeVdefine two mutually exclusive samplesone tight tag (eb = 40%, ec = 9%, el = 0.4%)two loose tags (eb = 50%, ec = 14%, el = 1.5%)

11/2/2009

signal and background modelssingle top quark production

modeled using SINGLETOPbased on COMPHEP reproduces NLO kinematic distributions

PYTHIA for hadronization

top-antitop pair productionmodeled using ALPGEN

parton-jet matching to avoid double-counting final statesPYTHIA for hadronizationnormalized to σ = 7.91pb

Kidonakis and Vogt, PRD 68, 114014 (2003) uncertainty +7.7% −12.7% (theory, pdf, mtop)

11/2/2009

signal and background modelsW+jets production

modeled using ALPGEN + PYTHIA w/ matchingjet , , between leading jets corrected to match

dataZ+jets production

modeled using ALPGEN + PYTHIA Z+ heavy flavor corrected to theory, with ±14%

uncertaintydiboson production

modeled using PYTHIA Normalized to expected cross sections

11/2/2009

signal and background modelsmultijet background

jets mimic e, from semileptonic b-decaysestimates data drivenkeep small with selection cuts( 5%)

11/2/2009

3/24/2009 Meenakshi Narain 24

background normalizationbefore b-tagging

iterative fits to data in three variableslepton pT, MT, and missing pT

subject to constraint30% to 54% (multijet), 1.8% to 3.9% (W+jets)from max difference with 1-variable fit result

11/2/2009

multijetsjetsWbkgdata NNNN

background normalizationafter b-tagging

W + heavy flavor normalized to theory (MCFM @ NLO)

• 1.47 (Wbb,Wcc), 1.38 (Wcj)empirical correction from two-jet data and simulation

• 0.95 ± 0.13 (Wbb, Wcc)

11/2/2009

event yield (before b-tagging)

expected signal

backgrounds

s:b 1:250

observed

acceptance: 3.70.5% (tb) 2.50.3% (tqb)

event yield (after b-tagging)

expected signal

backgrounds

s:b 1:20

observed

Data/MC comparison(all channels combined)

11/2/2009

signal and background models

11/2/2009

pre tag

1 b-tag

2 b-tags

2 jets 3 jets 4 jets

signal and background modelstest background model in regions dominated by

one type of background

GeV 175tag-b 1 jets, 2

jetsplepton

p GeV 300tag-b 1 jets, 4

jetsplepton

pW+jets: tt pairs:

11/2/2009

matrix elementsmethod pioneered by DØ for top quark mass measurement use 4-vectors of all reconstructed leptons and jets use matrix elements of main signal and background processes compute a discriminant

define Psignal as a normalized differential cross section:

performed in 2-jets and 3-jets channels only split sample in high and low HT to improve performance (W+jets

and top quark pair dominated regions)

11/2/2009

matrix elements

2-jet channels

3-jet channels

matrix elements2-jet channels

11/2/2009

tb discriminant

tqb discriminant

matrix elementsstarting from 2dimensional s vs t-channel discriminant

rebin to ensure enough background events in each bin re-order bins according to highest-to-lowest signal:background

to obtain the 1dim tb+tqb discriminant split according to HT

11/2/2009

HT < 175 GeV HT > 175 GeV

11/2/2009

boosted decision treesdecision trees

widely used in social sciences idea: recover events that fail a cut find cuts with best separation between

signal and background repeat recursively on each branch stop when no further improvement or when too few events left terminal node is called a “leaf” decision tree output = leaf purity

adaptive boosting technique to improve any weak classifier used with decision trees by GLAST and MiniBooNE train a tree increase weight of misclassified events train again average over 20 boosting cycles dilutes discrete nature of output and improves performance

boosted decision trees64 input variables

rank variables to select the 50 most sensitive variables for each channel

adding more variables does not degrade the performance

reducing the number of variables reduces the sensitivity of the analysis

use 1/3 of all signal and background events as training sample

train 24 treese,2,3,4 jets1,2 b-tags2 detector configurations

boosted decision treeskinematics angular correlationsjet characteristics

top reconstruction

boosted decision treesvariables

boosted decision treesapply transformation to discriminant to ensure sufficient

number of background events in each bin provides stability in the final cross section measurement

11/2/2009

Bayesian neural networks neural networks are nonlinear functions

defined by weights associated with each node weights are determined by training on signal and

background samples Bayesian neural networks improve on this

average over many networks weighted by the probability of each network given the training samples

less prone to over-training network structure is less important – can use larger

numbers of variables and hidden nodes for this analysis:

18-28 input variables in each channel 20 hidden nodes 100 training iterations each iteration is the average of 20 training cycles backgrounds are combined for training

11/2/2009

Network output

Bayesian neural networkslist of variables (example from one channel)

final discriminant after binning transformation similar to BDT

11/2/2009

cross section measurement verify that calculation methods work as expected using ensembles

of pseudo-experiments select subsets of events from total pool of MC events randomly sample a Poisson distribution to simulate statistical

fluctuations background yields fluctuated according to uncertainties to reproduce

correlations between components from normalization each pseudo-experiment simulates one DØ experiment

11/2/2009

cross section measurementcheck discriminant in background dominated regions

W+jets: 2 jets, 1 b-tag, HT < 175 GeV

ttbar : 4 jets, 1-2 b-tags, HT > 300 GeV

11/2/2009

DØDØ

cross section measurement

11/2/2009

cross section is given by posterior density peak with 68% interval as uncertainty

cross section measurementbefore looking at the data

how well can we rule out the background-only hypothesis?fraction of the ensembles without single top signal that give a cross

section at least as large as the expected sm valueconvert p-value to “expected significance”

from the data how well do we rule out the background-only hypothesis?

fraction of the ensembles without single top signal that give a cross section at least as large as the observed value

convert p-value to “measured significance” what cross section do we measure? how consistent is the measured cross section with the SM?

fraction of the ensembles with SM-signal pseudo-datasets that give a cross section at least as large as the measured value to get “consistency with SM”

11/2/2009

systematic uncertainties

final discriminant outputs

cross section results

correlations between methodseven though all analyses use the same data, they are not

100% correlated

combined DØ resultsuse BNN to combine the three methods

input variables are output discriminants of individual analyses

11/2/2009

observed cross section = 3.90.9 pb

observed(expected) significance = 5.0σ (4.5σ)

DØ, PRL 103 092001 (2009)

s- and t-channel cross sections

pb 14.3

pb 81.005.194.080.0

significance = 4.8

submitted to Phys. Lett. B arXiv.org:0907.4259

measurement of |Vtb| use the measurement of the single top cross section to make the

first direct measurement of |Vtb| calculate a posterior in |Vtb|2 ((tb, tqb) |Vtb|2) general form of Wtb vertex:

assume sm top quark decay : |Vtd|2 + |Vts|2 << |Vtb|2 pure V–A : f1

R = 0 CP conservation : f2

L= f2R = 0

do not assume three quark families CKM matrix unitarity

(unlike for measurements using tt decays)

measure the strength of the V–A coupling |Vtb f1L|, which can be > 1

measurement of |Vtb|

11/2/2009

assuming f1L =1

combined Tevatron resultsuse BNN to combine CDF and DØ results

11/2/2009

pb 76.2 58.047.0

07.088.0|| tbV

08.091.0|| tbV

single top productioncross section

for cross section byKidonakis (NLO+soft gluon)

Harris & Sullivan (NLO)

11/2/2009

Wtb couplingsgeneral Lagrangian

in standard model: f1L = 1, f1

R = f2L = f2

R = 0anomalous couplings can change

kinematicsangular distributionscross section

11/2/2009

coupling cross section s:t channelf1

L = 1 or f1R = 1 3 pb 1:2

f2L = 1 or f2

R = 1 10 pb 6:1

Boos, Dudko, Ohl, Eur. Phys. J. C11, 472 (1999)

Wtb couplings train boosted decision trees

on models with f1L and one

other coupling >0compute 2dim posterior first limits on tensor

couplings f2L and f2

sm expectation measurement

DØ, PRL 101 221801 (2008)

Wtb couplings helicity of W from top decay

= 0 70% = 1 30% = 1 0

angle between down-type fermion and and top quark in W rest frame ()

constrains only ratio of couplings

combine with W helicity

W helicity prior combined results

DØ, PRL 102 092002 (2009)

LongitudinalLeft-handed

Right-handed

search for W’tb

DØ, PRL 100 211803 (2008)

W’L W’R

search for H+tb

DØ, PRL 102 191802 (2009)

non minimal Higgs sector 2 weak isospin doublet fields (e.g. MSSM) 5 physical Higgs bosons: h0, H0, A0, H

if mH < mtop then t H b affects top quark branching fractions in top-antitop quark pair decays

if mH > mtop then H tb

11/2/2009

summarysingle top production has been observed by the

DØ and CDF experimentsthe cross section is measured to be

consistent with the standard model implies that |Vtb| > 0.77 @ 95% CL

opens a new window to studying the top quark

pb 76.2 58.047.0

CKM matrix

4/16/2009 Ulrich Heintz 68

tbtstd

cbcscd

ubusud

VVVVVVVVV

tbV039.0008.0041.004.1230.0004.02255.09742.0

quarksleptons

1222 tbcbub VVV 9991.0tbV

unitarity

CKM matrix

039.0008.0041.004.1230.0004.02255.09742.0

btbtstdt

VVVVVVVV

222 tbtbcbub VVVV ?tbV

unitarity

quarksleptons

evidence versus observation

|x-x0| probability

>1 0.32>2 0.046>3 0.0027>4 6.3 x 10-5

>5 5.7 x 10-7

nothing to write home about

claim evidencestart a rumor, write to NY Times

it’s getting seriousdiscovery – collect Nobel Prize…

99.994%

99.99994%

single top quark production

Documents

Transcript of single top quark production

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