atlas su per sy mmetry wg journée de réflexion – sept. 14 th 2007

ATLAS Supersymmetry WGJournée de réflexion – Sept. 14th 2007

Till EifertDPNC – ATLAS group

What’s going on there ?■ Currently, people concentrate on the so-called CSC* notes …

○ 1 & 2: Data-driven estimations of Z/W & top backgrounds Generator & detector uncertainties Many analyses, most data-driven

○ 3: Data-driven estimations of QCD backgrounds Fake MET rejection MC, data-driven estimates

○ 4: Estimation of Heavy Flavor backgrounds and associated systematic○ 5: Searches and inclusive SUSY studies

RPC, no GMSB, no split SUSY Study signatures; scan parameter space

○ 6: Exclusive measurements for SUSY events DiLepton edge, lepton+jet edge Mass reconstruction Extract susy parameters

○ 7: Gaugino direct productions○ 8: ‘Studies for Gauge mediated SUSY’ -> ‘Photonic and long-lived SUSY signatures’

2007 Sept 14 ATLAS SUSY WG 2

* Computing System Commissioning

Till

Andree, TuanClemencia, Moritz

Supersymmetry (SUSY)■ The light scalar Higgs boson is unprotected at GUT/ Planck scales■ On the contrary, all the other light particles of the SM are protected against large scales:

○ Due to chiral symmetry, their mass corrections are logarithmic in E (and not quadratic)

○ Gauge symmetry protects the bosons (no correction to photon or gluon masses)

■ Fermion and boson loops contribute with different signs to the Higgs radiative corrections:if there existed a symmetry relating these two, this could protect the masses of the scalar !

■ Supersymmetry realises this by transforming bosons fermions○ SUSY transforms for example a scalar boson into a spin-½ fermion, whose mass is protected

○ Hence, the scalar mass is also protected

○ This solves the naturalness and the hierarchy problems of the SM

■ Local gauge invariance of SUSY requires existence of spin-3/2 and spin-2 particles○ This naturally introduces the spin-2 graviton, assumed to mediate the gravitational force


Fermion loop

Boson loop

Minimal Supersymmetric Standard Model (MSSM)

■ To create supermultiplets, we need to add one superpartner to each SM particle

■ Need to introduce an additional Higgs doublet to the non-SUSY side■ Mutual superpartners have equal masses and couplings

Spin 0 Spin 1/2 Spin 1 Spin 3/2 Spin 2

Higgs Higgsino Gravitino Graviton

sLepton Lepton

sQuark Quark

Gluino Gluon

Photino Photon

Zino Z

Wino W


SM

SUSY

Minimal SuperGravity (mSUGRA)

Reduce the ~ 105 parameters of MSSM to 5 !

mSUGRA assumes that at the GUT scale all scalars (squarks, sleptons, and Higgs bosons) have a common mass m0,

all gauginos and Higgsinos hava a common mass m1/2,

and all the trilinear Higgs-sfermion-sfermion couplings have a common value A0

Remaining two parameters (at GUT scale): SUSY conserving Higgs mass => sign Ratio of Higgs vacuum expectation values tan = 1/2

Renormalisation group equations (RGEs) govern the running to the EW scale

Lightest neutralino is LSP

R-parity is conserved R = (-1)( 3(B-L) + 2S) where B, L, and S are the baryon number, lepton number, and spin respectively.=> R=+1 for SM particles

R=-1 for SUSY particles


RG evolution of unified mSUGRA mass parameters


Characteristic SUSY “Cascades” at the LHC

Conserved R-parity requires existence of a lightest stable SUSY particle = “LSP”. Since no exotic strong or EM bound states (isotopes) have been observed, the LSP should be neutral and colourless WIMP !

The experimental signature of the LSP would be just as the one of a heavy neutrino !

The LSP is typically found to be a spin-½ “neutralino”, a linear combination of gauginos (in much of the SUSY parameter space the neutralino is a mixture of photino and zino)

p pg

Lq 02

R

01

qq

“Typical” SUSY decay chain at the LHC

escapes detection missing ET

qX


Inclusive SUSY Searches

The precise signatures of the SUSY “cascades” are driven by the masses of the SUSY particles

To good generality we can expect:

High-pT jets from squark & gluino decays

Leptons from gaugino & slepton decays

Missing energy from LSPs

This lays out an inclusive search strategy

Detector requirements:

Excellent jet-energy measurement

Excellent lepton identification

Hermeticity of the detector (good acceptance)

Run II V. Shary @ CALOR04

Measuring missing energy is a tough task !


( , ) 1.5 TeVm q g ( , ) 1.0 TeVm q g

fully inclusive 1 Lepton

Inclusive SUSY Searches … continued

A sensitive variable to detect SUSY decays is the “effective mass”: eff ,missjets, leptons

T TM E p

Requiring at least one lepton reduces QCD background by factor of 20–30, with signal loss of only factor of ~3 better signal-to-background ratio than fully inclusive analysis

Meff

Eve

nts 10 fb1


Inclusive SUSY Searches … continued

Most SUSY searches are prepared by studying few “characteristic” points:

At the limit of experimental exclusion (SU4)

“Typical” point (SU3)

Special-feature points (SU1, SU2, SU6) m0

(GeV

)

SU1

SU1

SU3

SU4

SU6

SU3

SU4

no neutral LSP

SU6

“focus point”

“funnel region”

“coannihilation point”

“bulk region”

“low mass point”

SU2

SU2

m½ (GeV)

Idea of this study:

Simulate MC signals for a grid in the m0,

m1/2 space

Require ≥ 1 lepton (inclusive 1 lepton)

Find 1 optimal set of cuts for the whole grid

TDR SUSY analysis■ ATLAS TDR vol. II, page 820

■ Reach for S/sqrt(S+B) > 5 for various SUSY signatures in the mSugra parameter space

■ TDR Selection○ Transverse mass (l, MET)

≥ 100 GeV “..reduce W+jet bkg..”

○ Jet cut ≥ 2 Jets pT ≥ 100 GeV optimize pT cut for each point

○ MET ≥ 100 GeV optimize cut for each point

○ transverse sphericity > 0.2 “ .. To reduce dijet background .. “

○ Lepton pT > 20 GeV Eta < 2.5

■ Integrated lumi = 10 fb-1

■ Each point is separately optimized to yield the min p-value (max sigma)

■ As in TDR analysis, except for the missing ST cut ..

■ .. different datasets■ .. different detector

simulations …■ .. different isajet

version -> different susy spectra

■ Can we do better□ Other/more

variables ?□ Other methods ?

All opt resultAfter preSelection


■ Start from pre-selection (as before)■ Choice of variables for NN

○ MET

○ TransverseMass (l, MET)

○ JetLepPt = ΣEl_pT+ΣMu_pT+ΣJet_pT

… less correlated to MET as allMeff

○ Jet_C4_N … total number of jets

○ TopInvMass … ttbar-veto analysis t -> jet + W -> jet + lepton + nu (MET)

1 lep case: assume lep is boosted -> η(lep) = η(nu)

2 lep case: share MET b/w 2 nu, η, φ from lep

Future: use kinematic fit (HITFIT) Split analysis into 1, 2 lepton

channel

New analysis1 lep channel

2 lep channel


All points optimized

■ L=10fb-1

■ Each point is optimized!

■ Opt. against T1, W bkgs

Sign-plot from TDR (box-cuts on JetPt 1,2, MET)

Sign-plot (NN on MET, TM, Jet_N, JetLepPt, TopMass)


Conclusions

■ Contribution to CSC 5 note○ Lep (electron) ID in SUSY environment○ mSugra study (presented here)○ SM background validation with first data ○ common tools developments

■ Need to find out best (most sensitive) cut approach (single cut, cut as function of integrated lumi, multiple cut regions) including systematics

■ Also follow non-box-cut approaches


Backup slides


Data samples■ mSugra signal

□ Grid in parameter space● A0 = 0● tan = 10● sign ● scalar mass m0 = 0 .. 3TeV● Gauginos mass m1/2 = 0 .. 1.5 TeV

□ 5k events on each par. Point□ All AtlFast 12.0.6

■ SM Backgrounds□ Consider various SM bkg samples,

see next slide□ All AtlFast 12.0.6+

■ Software□ Isajet 7.75 (for the mSugra

spectra) + HERWIG/Jimmy□ AtlFast (Athena) 12.0.6□ HighPtView

■ Production□ LCG grid□ Private productions


SM Background Samplesprocess description gen. Vers. σgen (pb) EventFiler εEF σEF (pb) # evtsdisk

Wenu 8270 Wenu + q/gckin(3)=80 (lower pt W)

Pythia 12.0.6.1 343 MissingEtFilter MET≥ 80 & TruthJetFilter: N≥2, minPt≥80,40, maxEta=5

14.3% 49 48k

Wmunu 8271 Wmunu + q/gckin(3)=80 (lower pt W)

Pythia 12.0.6.1 343 as above 8.35% 29 54k

Wtaunu 8272 Wtaunu + q/gckin(3)=80 (lower pt W)

Pythia 12.0.6.1 343 as above 16.3% 56 54k

Znunu 8190 Znunu + q/gckin(3)=80 (lower pt Z)

Pythia 12.0.6.1 246 as above 16.8% 41 26k

Zee 8194 Zee + q/gckin(3)=80 (lower pt Z)

Pythia 12.0.6.1 46.2 none 100% 46.2 20k

Zmumu 8195 Zmumu + q/gckin(3)=80 (lower pt Z)

Pythia 12.0.6.1 46.4 TruthJetFilter as above 20.7% 9.6 61k

Ztautau 8191 Ztautau + q/gckin(3)=80 (lower pt Z)

Pythia 12.0.6.1 46.3 MET & TruthJetFilter as above

9.72% 4.5 47k

T1 5200 ttbar "l+jets" (l=e,mu,tau) MC@NLO 12.0.6 854 TTbarLepton : 1 charged lepton from W decay

54% 461 720k

J4 8090 ckin(3)=140, ckin(4)=280 Pythia 12.0.6.1 3.16x105 JetMETEstimator > 100 & TruthJetFilter

0.29% 917 32k

J5 8091 ckin(3)=280, ckin(4)=560 Pythia 12.0.6.1 1.25x104 as above 2.85% 356 8k

J6 8092 ckin(3)=560, ckin(4)=1120

Pythia 12.0.6.1 344 as above 19.6% 67 18k

J7 8093 ckin(3)=1120, ckin(4)=2240

Pythia 12.0.6.1 5.3 none 100% 5.3 26k

J8 8094 ckin(3)=2240 Pythia 12.0.6.1 2.21x10-2 none 100% 0.02 42k

?


PreSelection■ Put samples on an equal

basis & reduce #evts○ Lepton cut

≥ 1 lepton (El / Mu) pT ≥ 20GeV

○ Jet cut ≥ 2 Jets pT ≥ 80, 40 GeV

○ MET ≥ 100 GeV

■ Add some variables○ AllMeff = MET+ΣJet_pT○ TransverseMass of

hardest lepton + MET

proc. Lep ε Jet ε MET ε Tot ε σPS(pb) # evtsPS #evts100pb-1

Wenu 62.8% 41.5% 68.5% 17.8% 8.7 8k 874

Wmunu 50.8% 88.0% 68.9% 30.8% 8.8 16k 881

Wtaunu 12.6% 62.6% 75.2% 5.9% 3.3 3k 330

Znunu 0.005 % 33% 100% 0.002% 7x10-4 1 0.1

Zee 83.0% 28.7% 0.2% 0.05% 0.02 12 2

Ztautau 30.5% 70.9% 66.8% 14.4% 0.65 3k 65

Zmumu 83.7% 84.0% 2.4% 1.7% 0.16 1k 16

T1 54.5% 60.7% 20.3% 6.7% 30.9 48k 3092

J4 0.1% 90.0% 3.8% ~0.003% 0.03 1 3

J5 0.05% 100 % 67 % ~ 0.12 0.12 2 12

J6 0.02% 100 % 0 % 0

J7 0.01 % 100 % 0 % 0

J8 0.02 % 100 % 40 % ~0.009% 2-6 4 0

out of statistics

out of statistics

Background efficiencies


Optimizing each point ?

■ Optimizing each point separately effectively means having one analysis per point…○ decreases rate of the statistical type-II error

(missing a true signal) ○ increases the rate of the statistical type-I error

(finding a wrong signal)

■ One needs to find a balance○ Divide parameter region into regions with

different signatures => optimize on as few points as possible… ?


A single optimization point■ Apply set of optimized cuts of signal @

■ m0=300, m1/2=150

■ 5-sigma region smaller, see sigma plot

■ High-sigma points stay

■ Low-sigma points gone

Ratio of significance w.r.t. “all optimized points” plots


A single optimization point .. II■ Try lower-sigma point:

■ Apply set of optimized cuts of signal@

■ m0=1500 m1/2=450

■ High-sigma points go down, but …

■ Keep some more low-sigma points

Ratio of significance w.r.t. “all optimized points” plots


Details @ m0=300 m1/2=150

Signal & Bkg variable dists

NN output variable => we run out of stats!

Input vars not strongly correlated

Sample TMVA NN eff. Events

Signal @ m0=300, m12=150 0.34 69420

Wenu 0.049 4242

Wmunu 0.053 4630

Wtaunu 0.065 2156

Zee 0.18 35

Zmumu 0.019 30

Ztautau 0.1 649

Znunu 0.0 0

Js 0.0 0

T1 0.11 34287

Bkg sum 46029


Details @ m0=1500 m1/2=750

Signal & Bkg variable distsJet_N & JetLepPt 75% corr.

NN output variable =>better seperation power

Sample TMVA NN eff. Events

Signal @ m0=1500, m12=750 0.078 7

Wenu 0.0 0

Wmunu 0.0 0

Wtaunu 0.0 0

Zee 0.0 0

Zmumu 0.0 0

Ztautau 0.0 0

Znunu 0.0 0

Js 0.0 0

T1 0.0 0

Bkg sum ~ 0


A single optimization point

■ Apply set of optimized cuts of signal @

■ m0=300, m1/2=150 (left)

■ m0=1500, m1/2=750 (right)

■ Net result: quite good coverage with 2 optimized NN (w.r.t. all points opt.)

Study of systematics -> need more stats


atlas su per sy mmetry wg journée de réflexion – sept. 14 th 2007

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