August 31, 2001Ph.D. Thesis Defence

Search for mSUGRA signature at DØ in

TeV 8.1at collisions spp

John Zhou

Iowa State University

Outline

• Introduction to myself• Introduction to this

analysis• Data Selection• Event Simulation• Background Estimation• Signal Analysis• Results• Conclusion

Broken Symmetry

Introduction to myself

What’s the Point?

High Energy Particle Physics studies the smallest pieces of matter the elementary particles.

It investigates (among other things) the nature of the universe immediately after the Big Bang, i.e., what the universe is made of, how the particles interact, and why they interact the way as is.

It’s a lot of fun and pain.

u

du

u

d d

Proton NeutronStandard Model: a quantum theory that includes theoryof strong interactions (Quantum Chromodynamics, QCD) and the unified theory of weak and electromagnetic interactions (electroweak theory).

It is well tested experimentally to a very high precision.

The Standard Model

Standard Model

A quantum theory that includes theoryof strong interactions (Quantum Chromodynamics, QCD) and the unified theory of weak and electromagnetic interactions (electroweak theory).

It is well tested experimentally to a very high precision.

Limitation of the Standard Model

• There are minimal 18 parameters in the SM that have to be put in hand (highly undesirable for an “ultimate” theory.

• Electroweak Symmetry Breaking (EWSB) through an ad hoc way: The Higgs Mechanism. It is responsible for generating mass for SM particles.

• The hierarchy problem:Higgs potential:

Higgs mass:

422)( mV222 2 cmmH

EW scale:

If SM valid to GUT scale: GeV 1019

2222 ) 1( ,) 100( TeVmGeVm H

H

f

H

s

Supersymmetry

Supersymmetry incorporates additional symmetry between fermions and bosons. For each SM particle, there is a SUSY partner with spin differ by 1/2.

0~~~~

~~~~

~~~~~~~~

B

We

gbsd

tcu

e

Squark

Slepton

Gaugino

),( du HHHiggisno

Why supersymmetry?

• Solves the fine tuning problem by exact cancellation of each fermion loop with a scalar loop.

• Higgs potential turns negative at EW scale by evolving the SUSY parameters down from GUT scale, naturally gives EWSB.

• GUT candidate Main motivation.

Introduction to mSUGRA

Parameters of mSUGRA– m0 - common scalar mass at MSUSY

– m1/2 - common fermion mass at MSUSY

– A0 - common soft trilinear coupling

– tan(β) - ratio of the Higgs vacuum expectation values

– sign(μ) - μ is a Higgsino mass parameter

Supersymmetry

breaking origin

(Hidden sector)

MSSM

(Visible sector)

Gravity

R-parity: +1 for SM particles, -1 for SUSY particles.

R-parity is assumed to be conserved in this analysis.

Enrico

Fermi

The Dzero Detector

Dzero Coordinate

)ln(tan 2

A “single” muon tt event

Current mSUGRA limit

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ALEPH Limit DØ Dilepton Limit

This analysis: Search for mSUGRA in single electron channel

Signal: electron + 4 jets + ET

g~ g~ q q

q

g~

g~

q

g 1

~

q

q

ee

q

01

~

01

~

'q

is the LSP.01

~

g~

q~

q

01

~

q

Motivation:

• Search for parameter space which is sensitive chargino and neutralino decays to W and Z respectively large jet multiplicity (not as sensitive in other channels).

• Complements other mSUGRA search channels at DØ.

Event selection

DØ 1994-95 data ELE_JET_HIGH(A) trigger– Luminosity = 92.7 pb-1

– 1 isolated electron [elike<0.5(0.3) in CC(EC), fiso<0.1]

• GeV, • or

– 4 jets [cone size = 0.5, pass EM and CH fraction cuts]• GeV,

– Missing Energy

• ET > 25 GeV

– No second loose electron [elike<1.0, fiso<0.3 (dilepton signal electron definition)]

– No good muon with

• We observed 72 events.

2.5 || ηjet

d

11.| |ηe

d 2.5 || 5.1 η

e

d

0.2| |e0.20e

TE

0.15jetTE

0.4TP

Backgrounds

• Physics– Standard Model e + 4 jets + ET

• W + 4 jets

• t t

• WW + 2 jets

• Instrumentation– QCD 5 or more jets with one jet faking an electron and

inaccurately measured jet energies leading to ET

q

q

g

t

t

We

e

W

b

b

q

q

Event simulation: Fast MC, to explore the large parameter space

PhysicsEvent

Generator(PHTHIA)

particles Jet(RECO)

elecsjetsmuonsET

ObjectSmear

s_elecss_jetss_muonss_ET

Kinematiccuts

EventWeighting(trigger)

EventWeighting(object ID)

KinematicNtuple

acceptance

Structure of FMCØ:Signal, ttbar, and WW bkgd are simulated with FMCØ.

N

gena

N

idtriggen

p

p

N

Na

1

Error

1

Acceptance

Data, GEANT, and FMCØ comparison

Total trigger efficiencies: Total acceptance of t t:

Conclusion: These and many other checks have concluded thatFMCØ models the detector and the trigger system very well. Ithas also indicated that the ID efficiencies are accurately measured.

QCD backgroundTitle:plots/qcd_bkgd_cc.epsCreator:HIGZ Version 1.23/07Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.

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Fake electron ET distributions are normalized to the good electron ET distribution in thelow ET region (dominant by QCD background). Tails in the fake sample in the high ET region(the signal region is ET > 25 GeV) models the QCD background in the signal region.Result: 19.1 4.7 events

CC EC

W + 4 jet background

• We do not rely on Monte Carlo cross section calculation because it suffers large high order correction. Yet event can be generated for kinematic and detector acceptance calculation.

• We do not use data exclusively to calculate the W+ 4 jet background because of the small amount of available data events.

• The method to calculate W+ 4 jet background uses both MC and data. The method is based on the assumption that the number of W+ jet events should follow the power law: scaled by s.

W

WWW

a

aNN

3

434

• is measured from data with the aid of MC (smaller stat. err.).

• is measured for different jet multiplicity and is inferred.

• is measured using FMCØ.

WN3

W

W

a

a

3

4

WiN

Measuring

Method:– from Monte Carlo and Neural Network, find a kinematic region where the

W+3 jet events dominate;

– keep the total number of W+3 jet events afloat and match its spectrum + background spectrum to the data in that kinematic region (defined in terms of NN output);

– from the matching we estimate .

WN3

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WN3

Introduction to Neural Network

• Neural Network mimics human to do pattern recognition.

• For a feedforward NN with one hidden layer (multilayer perceptron, MLP) and one output node, the output represents the Bayesian posterior probability of signal. – Pattern recognition in HEP usually means to distinguish signal from background.– Signal and background distribute differently in a multi-variable space. A function

(usually non-linear) exists to map the variables to a binary output:

is the vector of input variables.

– NN tries to approximate this mapping function through linear and non-linear transformation of the input variables. Mathematically, it has been proven that MLP can approximate any mapping function to arbitrary accuracy if it has enough hidden nodes.

bkgd 1

signal 0)(

xf x

1x

2x

3x

4x

jkw

jw

j

kx O

hidden inputn

j

n

kjkjkj xwgwgO ))((

xeg

1

1Sigmoid Function (non-linear)

Measuring using NNWN3

NN Variables:

• ;

• ;

• ;

• ;

• ;

• ;

• ;

• Aplanarity.

eTE

jetTT EH

TEe ,))cos(1(2 , TEeT

eTT EEM

TE

TEj ,1

TEj ,2

NN structure: X-2X-1

Data and MC matching: NN output 0.5 - 1.0

Result:

0.188.2413 WN

Measuring the scaling factor

• measure for i = 1, 2, 3, and 4 inclusive jet multiplicity ( is the number of W+i jet events measured from a sample with minimal jet trigger bias);

• fit to obtain .

WiN

WiN

NN training Data/MC matching

WiN

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4.74.27

1.271.230

2.791.1283

2.1313.7210

4

3

2

1

W

W

W

W

N

N

N

N

007.0172.0

7.52.323

434

W

WWW

a

aNN

012.0774.03

4 W

W

a

a

• t t : 17.4 5.5– PYTHIA generator + FMCØ

• WW + 2 jets : 1.4 0.3– PYTHIA generator + FMCØ

• QCD: 19.1 4.7– Estimated from data

• W + 4 jets : 32.2 5.7– Estimated from data and MC

• Total background: 70.1 9.2

Total number of background events

Observed: 72 events

Data agrees with the StandardModel background very well!

But wait …

Data-Model Comparison

NN variables to separate mSUGRA signal from background

Variables:

• ;

• ;

• ;

• ;

• ;

• Aplanarity;

• ;

• .

eTE

jetTT EH

3jTE

))cos(1(2 , TEeTeTT EEM

TE

)cos( *j)cos( *

e

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q

q

g

t

t

We

e

W

b

b

q

q

tt 3C fit

Conclusions of Data-Model Comparison

Conclusions:– The observed data are well explained by the

Standard Model backgrounds.

– The existence of signal is not conclusive based on the observed number of data events and the estimated number of background events and error.

– We proceed to set limit on the signal.

– Remember these 72 events survived only the initial cuts. More optimized cuts can enhance the signal sensitivity. We use NN to do this.

tt 3C fit

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q

q

g

t

t

We

e

W

b

b

q

q

of signal and t t)cos( and )cos( **ej

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NN training result

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Signal: m0=170 GeV m1/2=60 GeV tan() = 3 < 0 A0 = 0

= 31.5 pb (SPYTHIA) a = 0.0056 (FMCØ)

Nevents = 16.3 2.9

Significance and NN cut

.2

1

!)|(

)|(

22

dttke bbk

bnFbnSnk

e

and S(n|b) the number of standard deviation thatbackground must fluctuate to at least n events:

The expected significance:

,)|()|()|(0

n

bnSbsnPbnS

where

,!

)()|(

)(

nebs

bsnPbsn

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• NN cuts at where the significance is maximal.

• For this particular param. set:– NN cut = 0.80

– Nsignal=9.51.7, Nbkgd=4.5±0.9

Result

• 95% confidence cross section upper limit:

where is the probability of signal production cross section given k observed events.

• If the model is excluded.

,),|(95.00

dIkPul

),|( IkP

ul PYTHIA

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Conclusions of mSUGRA search

• We search for mSUGRA with D Run I data in the electron + 4 jets + channel.

• 72 events are observed with 70.1 9.2 expected SM background events.

• No signal is observed in our data.• We use Neural Network to enhance signal sensitivity. • We obtain an improved limit from dilepon analysis in

moderate m0 region.

TE

New measurement of

• We can turn the problem around to measure .

• Same QCD and WW background as in the mSUGRA search.

• Same method in measuring the W+ 4 jet background, except

– no mSUGRA signal involved in NN training;

– is used as a parameter in obtaining and .

tt

tt

tt WN3

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• WW + 2 jets : 1.4 0.3– PYTHIA generator + FMCØ

• QCD: 19.1 4.7– Estimated from data

• W + 4 jets : 36.4 7.3– Estimated from data and MC

• Total background: 56.9 8.7

Observed: 72 events.

NN variables to separate tt signal from background

Variables:

• ;

• ;

• ;

• ;

• ;

• Aplanarity;

• ; 2 of 3C fit .

eTE

jetTT EH

3jTE

))cos(1(2 , TEeTeTT EEM

TE

)cos( *j

q

q

g

t

t

We

e

W

b

b

q

q

tt 3C fit

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ResultttTitle:ttbar_bkgd_1.epsCreator:HIGZ Version 1.23/07Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.

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pb 24.224.7 tt

pb 13.294.2 oldtt

• More optimized kinematic cut (8 variables vs. 2 variables);

• Better consistency (expect 72-56.9=15.1 vs. 39-35.5=3.5 t t events before optimization);

• More observed candidates with similar background rate: 19 observed with 4.550.76 expected background events vs. 9 observed with 4.510.91 expected background events.

Grand Finale

Would you grant me the Ph.D.?

Thank you all for being on my committee!

Electron ID efficiency (method)

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parent

daughtereid N

N

• Di-EM data sample

• Clean up the sample with a relative tight tag electron

• Plot M(ee) of the probe and tag electrons

• Subtract background (mostly Drell-Yan) using the side-band method

• Apply eid cuts and do the above steps again

eid = 5(4)-variable electron likelihood in CC(EC) and fiso

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075.0242.0 ,039.0674.0 ECeid

CCeid

Jet ID cuts (definition)

• Universal ID cuts:• Additional CC cuts:

• Additional EC cuts:

4.095.0 , chfemf

GeV 35 if,05.0

GeV 3530 if,10.0

GeV 3025 if,15.0

GeV 2515 if20.0

,

jT

jT

jT

jT

Eemf

Eemf

Eemf

Eemf

05.0emf

• emf should be moderate for quark or gluon jets. It is large for electron and small for hadronic noise.

• Large chf usually associates with noise from main-ring beam loss.

Jet ID efficiency (derivation)

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highlow

jetemf NNN

N

N: number of entries between the cuts

Nlow: area under the curve below the lower cut

Nhigh: area under the curve above the upper cut

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2210 )( jet

Tjet

Tjet

total EpEpp

Jet ID efficiencies are parameterized as:

ELE_JET_HIGH(A) trigger

• Level 1:– 1 EM tower:

– 2 jet towers:

• Level 2:– 1 electron:

– 2 jets (0.3 cone):

– Missing ET:

6.2 GeV, 0.12 EMEMTE

0.2 GeV, 0.5 JTJTTE

) 1CRun ofmost ( 1BRun for

,5.2 GeV, (17.0) 0.15 eeTE

5.2 GeV, 0.10 jjTE

GeV 0.14 calTE

Electron trigger efficiency (method)

• Two assumptions:– Shape of turn-on curve does

not change over a few GeV

– Offline electron ET scales as trigger level ET

• Method:– Obtain a turn-on curve of a

hypothetical EM trigger threshold T2=qT1, where T1 is the trigger of interest and q>1.

– Scale the offline electron ET by 1/q.

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Level 1 electron trigger efficiency

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)106.0894.0( 17151elecelecelec

Lelectrig

Single jet trigger efficiency

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• Single jet trigger efficiency is needed to calculate the trigger efficiency for events with multi-jets.

• Method:– Use EM1_EISTRKCC_MS

trigger which has minimum trigger bias

– Require one jet and a full trigger efficiency on the EM part

– Count the number of events passing ELE_JET_HIGH as a function of jet ET

Total trigger efficiency

• Total trigger efficiency on the JT/JET part:

where p0 is the probability that an EM object fires the JT/JET trigger, and p1-5 are the single jet trigger efficiency for the 5 leading jets in the event.

• Total trigger efficiency:

,)1(15

0

i

ijet

trig p

5

017151 ))1(1()106.0894.0( ielecelecelec

L

Etrig

jettrig

electrig

totaltrig

p

T

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T

jT

MET E

ER

1

emf ratio after and before RMET cut

End on view top + antitop

Algorithm

Algorithm+

Reality

e e

Jets in “God Mode”

Jets in “Our Mode”

Don’t know who goes with what

Know

(1) Mt = Manti-t=175 GeV

(2) t W + b anti-t W + b Jete

jet jet jet

Note:

combinations

121

3

1

4

Guess!!!!

Jet permutations in single lepton tt events

Now (15 billion years)

Stars form (1 billion years)

Atoms form (300,000 years)

Nuclei form (180 seconds)

Protons and neutrons form (10-10 seconds)

Quarks differentiate (10-34 seconds?)

??? (Before that)

Fermilab4×10-12 seconds

LHC10-13 Seconds

The needle in the haystack: Run I

• There are 2,000,000,000,000,000 possible collisions per second.

• There are 300,000 actual collisions per second, each of them scanned.

• We write 4 per second to tape.

• For each top quark making collision, there are 10,000,000,000 other types of collisions.

• Even though we are very picky about the collisions we record, we have 65,000,000 on tape.

• Only 600 are top quark events.

• We’ve identified 50 top quark events and expect 50 more which look like top, but aren’t.

Run II

×10

We’re in luck!

Quarks can’t exist, except when they are confined

Miracleq As quarks leave a collision, they change into a ‘shotgun blast’ of particles called a

‘jet’

q

Calorimetry: measuring energy

E 2×E/2 4×E/4 8×E/8 16×E/16

Dense Stuff Undense Stuff

A particle hits some dense stuff (like metal) and creates more particles, each of which have less energy. In the undense material you count particles. The number of particles is proportional to the energy.

January 2001

550 scientists involved 17 countries63 institutions400 authors

DØ History1985 1990 1995 2000

First MeetingStony Brook

July 1983

Baseline Approval from

DOENovember

1984Design CollisionHall, Do Detector

R&D (First Calorimeter Using

Liquid Argon)1985-1987

Peak Construction 1988-1991

Roll In

February 1992

Good Beam

September 1992

Fall 1993, First DØ Paper (Leptoquarks)

March 1995, Discovery of Top

Fall 2000, 100th DØ Paper (W Boson)

130th Ph.D.

Data Taking 1992-1996

Upgrade Detector

to Utilize Main

Injector Upgrade

1996-2000

Roll In January 2001