Measurement of the tZq cross section at 13TeV with the CMS detector

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Measurement of the tZq cross section at 13TeV with the CMS

detector

Mar Barrio Luna

On behalf of the CMS Collaboration

IX CPAN days

23-25 October 2017

CMS-TOP-16-020

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tZq-SM search is sensitive to tZ(q) flavour changing neutral current interactions with similar final states.

A deviation from SM prediction could be an indication of new physics.

Previous analysis conducted by CMS at 8 TeV (no observation).

Recent analyses at 13TeV by both ATLAS and CMS (this talk).

tZq searches: motivation

Increasing luminosities and centre of mass energy at LHC motivate the search for rare Standard Model single top processes, such as the production of a single top in association with a Z boson.

tZ(ll)q expected cross section at 13TeV (NLO)

l : electron, muon, taus (m

ll>50GeV) Using

35.9 fb-1

data collected by CMS in 2016

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■ Predominantly occurs when the Z boson is radiated off one of the quark lines in the t-channel (sensitive to ttZ-coupling).

■ It is also related to the WZ production by crossing, therefore also sensitive to the WWZ coupling (triple gauge coupling).

■ Background in important SM analyses (ttH, tHq...).

■ It is an irreducible background for tZ and tZq FCNC processes.

provides stringent SM tests and constrains new physics

tZq in the SM

Z

W W q

b t

q’

q b

q’

W t

W W q

b t

q’

Z

q b

q’

W t

q Z

b

q’

W t Z

q b

q’

W t

Z

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Two different channels, depending on the decay of the W boson coming from the top quark:

■ Di-lepton channel: t -> W(qq)b

■ Tri-lepton channel: t -> W(lν)b

t W

b

ν , q’

l , q

■ Backgrounds in dilepton channel are much larger

■ Shape analysis more sensitive than simple cut and count. multivariate analysis trilepton channel

Why a 3-lepton shape analysis?

Smaller branching ratio, but cleaner signal.

( Z → 2leptons )

Towards highest sensitivity...

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A. Exactly 3 isolated hight p

_T

leptons

W

t b

missing E

_T

(MET) electron or muon

q jet

b-jet

Z

two opposite-sign same-flavour (OSSF) leptons

4 channels: ^μμμ ^μμe ^μee eee

tZq event topology

A

two of them compatible with

coming from a Z boson (OSSF)

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A.

B. Two or three jets.

W

t b

missing E

_T

(MET) electron or muon

q b-jet jet

Z

tZq event topology

B

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At least one of them originating from a

b-quark.

In single top production:

forward recoiling jet.

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A.

B.

C. Missing transverse energy (neutrino).

W

t b

missing E

_T

(MET)

electron or muon

q jet

b-jet

Z

C

tZq event topology

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Processes with prompt leptons

■ ttZ

■ WZ+jets

■ ttW

■ ttH

■ ZZ

■ tWZ

An optimal discrimination between signal and background is crucial in analysing processes with small cross section values.

WZ

Background sources (I)

TTZ

Identical final state if one of the b’s is untagged Identical final state if one of the b’s

is mistagged/from gluon splitting (+ b-tagged jet)

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Samples with prompt leptons contaminated by

■ leptons from other hadron decays

■ hadrons/jets identified as leptons Events containing 2 real leptons + 1 NPL.

The non-prompt lepton (NPL) background

(contribution from events with 2 NPLs is negligible)

NPL

background

coming primarily from DY+jets and ttbar (dilepton)

■ Instrumental: poorly modelled by MC.

■ Sample derived from data.

shape & normalization

estimated from data

Most challenging background

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● Define 3 statistically independent regions:

■ tZq signal region

■ ttZ enriched control region

■ WZ+jets & NPL enriched control region

Analysis Overview

bOOSTED dECISION tREES For signal/background separation.

Simultaneous Fit

The fit is applied to the three regions, so that normalization of the signal and the main backgrounds are better constrained to their respective control regions.

For signal extraction.

Defined to be as close as possible to the signal region Control Regions To determine main background normalization.

signal, NPL(e) and NPL(µ) free parameters in the fit

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Event Selection & Control Regions

Object Selection

“OR” of tri/di/single-lepton triggers Trigger logic

Leptons

Require exactly 3 isolated leptons:

■ p_T(jets)>30GeV

■ |η(recoil jet)|<4.5

■ B-tagging: ε_btag≈ 83%, ε_miss≈ 10%,

|η(bjet)|<2.4

Three different regions defined according to

N jet and N bjet multiplicities.

Jets

■ p_T>25GeV

■ |η(μ)|<2.4 & |η(e)|<2.5

■ |m_ll(OSSF) - m_Z| < 15GeV

■ Additional lepton ↔ top decay product

Signal & Control Regions

no veto leptons allowed

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Event Selection & Control Regions

Leptons

Require exactly 3 isolated leptons: ■ p_T(jets)>30GeV

■ B-tagging: ε_btag≈ 83%, ε_miss≈ 10%, |η(bjet)|<2.4

Three different regions defined according to N_jet and N_bjet multiplicities.

Jets

■ p_T>25GeV

■ |η(μ)|<2.4 & |η(e)|<2.5

■ Additional lepton ↔ top decay

SR (“1bjet”)

2-3 jets exactly 1 bjet Signal enriched Signal & Control Regions

W t b

MET electron or muon

q jet

b-jet

Z

two OSSF leptons Signal “1bjet”

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Event Selection & Control Regions

Leptons

Jets

■ p_T>25GeV

■ |η(μ)|<2.4 & |η(e)|<2.5

SR (“1bjet”)

W t b

q jet

b-jet

Z

two OSSF leptons

CR (“2bjet”)

>1 jet >1 bjet Mainly ttZ, some signal

Signal “1bjet”

Control “2bjet”

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Event Selection & Control Regions

Leptons

Jets

■ p_T>25GeV

■ |η(μ)|<2.4 & |η(e)|<2.5

SR (“1bjet”)

W t b

q jet

b-jet

Z

two OSSF leptons

CR (“2bjet”)

>1 jet >1 bjet Mainly ttZ, some signal

CR (“0bjet”)

>0 jet 0 bjet Mainly WZ, NPL

Signal “1bjet”

Control “2bjet”Control “0bjet”

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The templates

Cross section extracted from a simultaneous fit on 12 templates.

Two Boosted Decision Trees (BDTs) trained against all backgrounds (except non-prompt).

■

1bjet SR Highest sensitivity to signal

■

2bjet CR Constrain ttZ

3(regions) x 4 (channels)

W transverse mass ( m_TW ) distribution

■

0bjet CR Constrain WZ and non-prompt lepton background 0bjet CR 2bjet CR 1bjet SR

eee eeµ eµµ µµµ

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BDT input variables

The set of variables used to train and test the two BDTs includes

■ masses

■ kinematic distributions

■ angular distributions

■ b tagging related information.

* recoiling jet * reconstructed top quark

* reconstructed Z boson * decay products (t,Z)

involving

Matrix Element Method discriminators used during training to enhance discriminating power.

Most discriminating variables

Increases the expected significance by almost 20%

1bjet SR2bjet CR

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∗ the number of events passing the selections

∗ shape of the BDT response

∗ both

Systematics

■ Non-prompt lepton shape estimation

■ Theoretical uncertainties

● Renormalization and factorization scales at ME

● Parton Distribution Function (PDF)

● Scale variation effect on Parton Shower (PS)

■ Normalization of MC backgrounds

■ Luminosity

■ Lepton selection

Uncertainties can either affect _■ _Trigger

■ B tagging

■ Jet Energy Scale and Resolution

■ Pile Up (PU)

● Additional interactions in same bunch crossing.

(More details in backup!)

(Except fakes, free in the fit)

scale variation at PS level b-tagging efficiency ttZ normalization Largest systematics

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Signal strength

Signal extraction

Binned Maximum Likelihood fit performed simultaneously on 12 templates.3(regions)*4(channels)

The fit maximizes

Fitted nuisance parameter for each systematic

N^postfit/N^pref

it

Postfit yields “1bjet” region FROM THE FIT

Parameters

NPL normalization for lepton flavour “k” k

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Results

After ℒ(data|μ,θ) maximization

Post-fit templates

Evidence

Observed significance:

3.7σ

(expected 3.1σ) Of this SM rare process

Dominated by statistical uncertainties

followed by non-prompt background normalization lepton = e, µ,

Using reference cross section

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Conclusions

■ Analysis performed using 35.9 fb^-1 of data collected by CMS during 2016.

■ Measurement of tZq cross section provides interesting test of the Standard Model

■ Studying this process is important for several analyses (ttV, ttH, tHq ...)

■ Evidence for this rare top process

3.7σ

(3.1σ expected) !

■ Uncertainty still dominated by statistics.

in good agreement with SM prediction

Need more data Measured cross section

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Thanks for your

attention

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BACKUP

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Matrix Element Method

MEM weight

Transfer functions Evaluated in MC Parton Distr. Function

LHAPDF interface NNPDF 2.3 LO Phase Space enforcing

4-momentum conservation:

analytic (same as in Madweight)

Integration VEGAS in ROOT

Matrix Element MadGraph C++ standalone

■ Custom framework in C++

■ Categories in which 1 or 2 jets are not reconstructed are included: integrate over missing jet momenta

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Matrix Element Method

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Simulations

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Trigger

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■ Instrumental backgrounds: poorly modelled by MC.

■ Sample derived from data: inverted isolation + looser ID for one of the three leptons.

■ Non-prompt leptons are either associated to the top quark (“additional lepton”) or the Z boson (“OSSF pair”).

The non-prompt lepton (NPL) background

shape + normalization estimated from data

Non-prompt lepton background is the most challenging.

Normalization

■ Pre-normalization: SF from m_TW distribution in WZ control region in data.

■ Final normalization: using previous SF as input, but let free (together with signal) in the final fit.

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Data-driven estimation:

■ Shape distribution: provided by the templates

■ Normalization: performed in two steps

1. The MTW distribution in the 0bjet region is used to provide normalization of all NPL components (all channels).

2. The non-prompt electrons and muon yields are treated as 2 free independent parameters in the simultaneous fit on the three regions.

NPL background estimation

fix the relative NPL normalization of the templates in the 4 channels

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Systematics

Luminosity ±2.5% Normalization

Trigger ±1-2% Normalization

Pile Up ±4.6% Shape

Lepton selection SFs ± 1σ Shape + Normalization

JES & resolution ± 1σ Shape + Normalization

B-tagging SFs ± 1σ Shape + Normalization

MC background norm. ±30% Normalization

NPL background shape

uncertainties variation of iso. criterion

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Systematics

Renorm. and fact. scales at ME

level factor ½ and 2

Renorm. and fact. scales at PS

level factor ½ and 2

Parton Distribution Function RMS of the 100 NNDPF variations (following PDF4LHC recommendations)

Theoretical uncertainties

These uncertainties affect the shape of the signal as well as the shape and normalisation of the simulated background samples, except for tWZ, for which only normalisation uncertainties from scale and PDF were considered.

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Signal strenght per channel

■ μμμ 1.22 +0.75 -0.63

■ eee 1.32 +1.14 -0.99

■ eeμ 0.66 +0.78 -0.63

■ μμe 0.01 +0.97 -0.01

Channel with highest observed (expected) significance

2.07 (1.94)

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Comparison with ATLAS

Signal modelling with LO generator, scaled to NLO cross-section (no off-shell Z)

Fit only SR (1 category) with all channels summed

WZ (no flavour-splitting) and ttbar SFs

from CRs

DY data-driven shape; SF from a region orthogonal to SR by inverting mTW cut, included in training

CERN-EP-2017-18

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