Single Top Quark Production Single Top Quark Production at the - - PowerPoint PPT Presentation

▶
single top quark production single top quark production
SMART_READER_LITE
LIVE PREVIEW

Single Top Quark Production Single Top Quark Production at the - - PowerPoint PPT Presentation

Feb 20, 2023 134 likes •315 views

Single Top Quark Production Single Top Quark Production at the Tevatron at the Tevatron W t Reinhard Schwienhorst on behalf of the D and CDF collaborations Rencontres de Moriond EW 2008 SM single top quark production t-channel

slide-1
SLIDE 1

t W

Single Top Quark Production Single Top Quark Production at the Tevatron at the Tevatron

Rencontres de Moriond EW 2008

Reinhard Schwienhorst

  • n behalf of the DØ and CDF collaborations
slide-2
SLIDE 2

2

Reinhard Schwienhorst, Michigan State University

SM single top quark production

q q' W t b s-channel t-channel u d b t W SM cross section: σtot = 3 pb σt = 1.98 pb

σs = .88 pb

Tevatron Goals: – Discover single top quark production – Measure production cross sections σs, σt – First direct measurement CKM matrix element Vtb – Study top quark spin polarization – Understand as background to many searches – Establish techniques that will also be used in Higgs searches

slide-3
SLIDE 3

3

Reinhard Schwienhorst, Michigan State University

New physics in single top

Flavor Changing Neutral Current

q t q u, c g, Z, γ q W' t b

New heavy boson

s-channel t-channel q'

  • Recent results:

– Limits on W' from DØ and CDF:

  • M(W') > 800 GeV to 825 GeV, depending on couplings and decays

– FCNC gluon coupling limits from DØ:

  • limit coupling κc/Λ < 0.15 TeV-1 and κu/Λ < 0.038 TeV-1

PRL 99:191802 (2007) DØ: PLB 641:423-431 (2006)

slide-4
SLIDE 4

4

Reinhard Schwienhorst, Michigan State University

DØ

CDF

Batavia, Illinois

Fermilab Tevatron

Proton-antiproton collider CM energy 1.96TeV → Energy frontier Instantaneous luminosity reaching 300E30cm-2s-1 → Luminosity frontier

3.5 fb-1 delivered

slide-5
SLIDE 5

5

Reinhard Schwienhorst, Michigan State University

Single top event selection

  • Expect ~ 50 signal events per fb-1

– After b-tagging – S:B ~ 1:20

W+jets Top quark pairs Single top

Event sample composition

multijet

  • Basic event signature (e or µ)

– Single lepton trigger

  • r lepton+jets trigger

– One high-ET leptons

  • ET > 20 GeV or 15 GeV

– Missing transverse energy

  • Missing ET > 25 GeV
  • r 15 GeV

– 2-3 high-ET jets (2-4 jets)

  • ET > 15 GeV

– At least one b-tag

slide-6
SLIDE 6

6

Reinhard Schwienhorst, Michigan State University

Single top analysis

  • Classifiers:

– Likelihood function – Neural network – Bayesian neural networks – Boosted decision trees – Matrix Element

  • Systematic uncertainties:

– Normalization uncertainties, for example background composition (10-30%) – Shape uncertainty, for example jet energy scale, b-tagging – Implement as nuisance parameters

signal likelihood discriminating variables

Event kinematics Object kinematics Angular correlations .....

multivariate classifier

Reconstructed top mass

statistical analysis

slide-7
SLIDE 7

7

Reinhard Schwienhorst, Michigan State University

  • Update to 0.9 fb-1 analysis (3.4 σ, PRL 98, 181802 (2007))

– Improved Bayesian Neural Network analysis – Improved Matrix Element analysis

slide-8
SLIDE 8

8

Reinhard Schwienhorst, Michigan State University

Bayesian neural networks

Bayesian neural networks Single network

integrate

  • ver possible

network parameters

slide-9
SLIDE 9

9

Reinhard Schwienhorst, Michigan State University

Matrix element

  • Include ME for s, t, Wbb, Wcg, Wgg
  • In 3-jet bin also tt→l+jets

Parton level matrix elements

integrate

  • ver

measurement uncertainties

Signal discriminant

L= P sig P sigP bkg

slide-10
SLIDE 10

10

Reinhard Schwienhorst, Michigan State University

Summary

3.6 σ evidence for single top

(2.3 σ expected)

  • Combination using BLUE method

– Using large sets of ensembles for weights and correlations σ(s+t) = 4.7 ± 1.3 pb σ(s) = 1.0 ± 0.9 pb σ(t) = 4.2 +1.8-1.4 pb

based on DT tbtqb filter submitted to PRD

slide-11
SLIDE 11

11

Reinhard Schwienhorst, Michigan State University

CKM matrix element |Vtb|

Vtb Vtb CKM Matrix

  • Measurement: |Vtb × fL1|

– Based on DT result – Assume top decays to b (Vtb ≫ Vts, Vtd)

  • No constraint on # of generations
  • Assume fL1 =1

→ lower limit on Vtb

– At the 95% C.L.:

|Vtb| > 0.68

|Vtb × fL1|2

slide-12
SLIDE 12

12

Reinhard Schwienhorst, Michigan State University

  • Analyses based on 2.2 fb-1
  • Increased acceptance

– MET trigger – more muons

  • Now including 3-jet channel
  • Improved background model
slide-13
SLIDE 13

13

Reinhard Schwienhorst, Michigan State University

Multivariate likelihood function

  • Likelihood functions built from 7 variables (10 for 2-tags)

– Kinematic variables, b-tag NN, t-channel ME, kinematic solver

Measured cross Measured cross section: section: σ σ( s+t) =1.8 pb ( s+t) =1.8 pb

+0.9 +0.9 − −0 0.8 .8

slide-14
SLIDE 14

14

Reinhard Schwienhorst, Michigan State University

Neural Networks

  • 4 separate s+t networks built from 10-14 variables each

– Including b-tag NN, kinematic variables, angular correlations

Measured cross Measured cross section: section: σ σ( s+t) =2.0 pb ( s+t) =2.0 pb

+0.9 +0.9 − −0 0.8 .8

slide-15
SLIDE 15

15

Reinhard Schwienhorst, Michigan State University

Matrix element

– Analyze 2-jet and 3-jet events

  • Include ttbar matrix element for both 2-jet and 3-jet events
  • Include b-tag NN as weight in likelihood ratio

Measured cross section: Measured cross section:

σ σ( s+t) =2.2 pb ( s+t) =2.2 pb

+0.8 +0.8 − −0 0.7 .7

slide-16
SLIDE 16

16

Reinhard Schwienhorst, Michigan State University

Summary for s+t

slide-17
SLIDE 17

17

Reinhard Schwienhorst, Michigan State University

Conclusions/Outlook

  • The search for single top quark production is turning into

measurements in the single top final state

– Both experiments have seen 3 σ evidence – |Vtb| measurement to better than 15%

  • Further improvements in progress

– CDF combination – DØ update with larger dataset