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
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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
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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
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RG evolution of unified mSUGRA mass parameters
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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
“Typical” SUSY decay chain at the LHC
escapes detection missing ET
qX
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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 !
2007 Sept 14 ATLAS SUSY WG 8
( , ) 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
2007 Sept 14 ATLAS SUSY WG 9
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
2007 Sept 14 ATLAS SUSY WG 11
■ 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
2007 Sept 14 ATLAS SUSY WG 12
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)
2007 Sept 14 ATLAS SUSY WG 13
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
2007 Sept 14 ATLAS SUSY WG 14
Backup slides
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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
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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
?
2007 Sept 14 ATLAS SUSY WG 17
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
2007 Sept 14 ATLAS SUSY WG 18
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… ?
2007 Sept 14 ATLAS SUSY WG 19
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
2007 Sept 14 ATLAS SUSY WG 20
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
2007 Sept 14 ATLAS SUSY WG 21
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
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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
2007 Sept 14 ATLAS SUSY WG 23
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
2007 Sept 14 ATLAS SUSY WG 24