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D
- Calibration for jets
 Reminder on the DØ detector
 Jet Identification and Reconstruction
 Jet Energy Scale:
 results from Run 1
 b-jet calibration
ATLAS Ringberg workshop - 23/7/2002
Ursula Bassler, LPNHE Paris
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Run II Detector
ATLAS Ringberg workshop - 23/7/2002
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- Calorimeter
 Fine segmentation:
 semi-projective towers in 0.1
x0.1
 4 em layers: 2, 2, 7, 10 X0
 shower-max (EM3): 0.05 x 0.05
 4/5 hadronic (FH + CH)
 hermetic with full coverage
 || < 4.2 (  2o)
 int > 7.2 (total)
 Uranium absorber (Cu (CC) or
Steel (EC) for coarse hadronic)
 compensating e/  1
 to be studied with shorter
shaping
Beam Tests of the D0 Uranium Liquid Argon Calorimeter.
NIM A324, 53 (1993)
NIM A 338 185 (1994)
ATLAS Ringberg workshop - 23/7/2002
from test beam
data
e: sE/E = 15% /E + 0.3%
: sE/E = 45% /E + 4%
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SCA non-linearity
 functional form of SCA non-linearity
correction function
 correction important at low energies
 electronic noise translates into
higher energy
 jet become more narrow
250 MeV
0.25 ADC
count/MeV
0
~Energy/GeV
for energies
> 200MeV
non-linearity
introduces an
offset of
~250 MeV for
the gain 8
4 measurements
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1 GeV
Gain 8
Gain  1
0
10
20
30
40
50 GeV
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Central Jet Triggers
 L2 jet
Efficiency vs jet pT
CJT(1,3)
CJT(1,5)
CJT(1,7)
CJT(1,10)
Cluster 3x3 or 5x5 trigger
towers around L1 seed
towers

 L3 jet
Simple cone or tower NN
algo’s 0.1x0.1 towers
 3 single jet (tower) triggers:
 JT_LO L1: 5 GeV,
L3:10 GeV
 JT_HI
L1:10 GeV,
L3:15 GeV
 CJT40: L1:40 GeV
 Efficiency
 standard jet selection,
offline pT > 8 GeV
 very sharp turn on

L1 Trigger efficiency CJT(1,x)
L1 Trigger efficiency CJT(2,x)
 L1 single jet efficiencies
ask for one or two hadronic trigger towers
(0.2x0.2) above threshold
 use -trigger as unbiased reference to
measure turn-on
 ask for one and only one reconstructed jet
in ||<0.7
 L1 hadronic response about 40% low for
current data set

ATLAS Ringberg workshop - 23/7/2002
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NADA: noise reduction
 NADA = New Anomalous Deposit Algorithm
 identify isolated energy deposits in the calorimeter = “Hot Cells”

Source: electronics, Ur noise, beam splash, cosmics etc
 Improve object resolution and ETmiss
 Run 1: AIDA
Only examine neighbors in the same tower for Ecell > 10 GeV
 99% efficient, BUT 5-10% misidentification rate

 examine all cells with > 1 GeV
remove cells < -1 GeV & > 500 GeV
 ET < 5 GeV removed if no neighbor with E
> 100 MeV
 ET < 500 GeV removed if no neighbor with
E > 2% Ecell
 high efficiency (90%) and low
misidentification
 ET > 1 GeV : ~0.5%
 ET > 10 GeV : ~0%
 on average about 0.8 cells / event

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ETthresold
ETneighbour> 100 MeV or 0.02Ecell
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Jet Finding
 Calorimeter
jet (cone)
jet is a collection of energy deposits with a
given cone R: R  Δ 2  Δη2
 cone direction maximizes the total ET of the jet
 various clustering algorithms

 correct for finite energy resolution
 subtract underlying event
 add out of cone energy
 Particle jet
a spread of particles running roughly in the
same direction as the parton after hadronization

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Jet Algorithms: Cone
 Run 1 Legacy Cone
draw a cone of fixed size around a seed
 compute jet axis from ET-weighted mean and jet ET from ET’s
 draw a new cone around the new jet axis and recalculate axis
and new ET
 iterate until stable
algorithm is sensitive to soft radiation (split & merge)

 Improved Run 2 cone
use 4-vectors instead of ET
 add additional midpoint seeds between pairs of close jets
 split/merge after stable proto-jets found
algorithm is infrared safe

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Jet Algorithms: kT
For each object and pair of objects:
dii  k 2T,i
order all dii and dij:
If dmin=dij
2
ΔR
ij
dij  min(k 2T,i ,k 2T, j ) 2
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Soft
Collinear
 merge particles
(if R<<1 )
Resolution
parameter
(D=1)
 theoretically favored, no split-merge
 to reduce computation time, start
with 0.2 x 0.2 pre-clusters
 x-section measurement differ from
cone-jet
If dmin=dii
 jet
(JETRAD)
DØ
Subjet multiplicity of gluon and quark jets reconstructed using the kT algorithm in pbarp collisions
Phys. Rev. D65 052008 (2002) hep-ex/0108054
The inclusive jet cross section in pbarp collisions at sqrt(s)=1.8 TeV using the kT algorithm
Phys. Lett. B {525}, 211 (2002) hep-ex/0109041
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Hadronization effects?
• particle jets are more (less) energetic
than parton jets with kT (cone)
 kT collects more energy
 cone looses energy
 kT jets are 7 (3)% more energetic at
60 (200) GeV than cone jets:
• consistent with HERWIG at high pT,
at 2s at low pT
applying correction to
cone-jets improves
agreement between
the 2 algorithms
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Jet Algorithms: CellNN & Flow
 Cell Nearest Neighbor

layer-by-layer clustering starting with EM3

each local maximum starts a layer-cluster then add in neighbors

energy sharing according to transverse shape parameterization

angular matching of floor clusters
search for minima in longitudinal energy distribution to separate EM
and hadronic showers

 Energy Flow algorithm
use tracking information to better characterize the contributions from
charged particles


in development
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Jet Selection
DØ Run 2 Preliminary
 central jets (Run 2 cone, R=0.7)
 event quality cuts
number of jets  1
 Etotal in the calorimeter  2 TeV
 missing ET  70% of leading jet pT
 Zvtx < 50 cm

CHF
EMF
 leading Jet Cuts
Jet pT > 8 GeV (offline cut)
 0.05  EMF  0.95
 CHF  0.4 (0.25 tight)
 HotF  10 (5 tight)
(HotF = ET1st cell / ET2nd cell )
 n90 > 1
(number of towers that
contain 90% of jet ET)

 efficiencies from MC
loose: ~100%
 ~Flat in 

tight: ~ 98%
HotF
n90
 Data
— MC
 Non-linearity of SCA included in MC
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Jet Energy Scale
jet

 correct Jet Energy to the particle level
ptcl
E jet

E meas
jet  E offset
shower
R calo
R
jet
jet
 Eoffset energy offset from underlying event,
pile-up, noise
determined from Min. Bias Events
 Rcalo calorimeter response
using -jet events: Missing ET
Projection Fraction Method
 Rshower energy contained in jet
corrections from MC - energy in
cones around the jet axis
 depending on jet algorithm!
Determination of the Absolute Jet Energy Scale in the D0
Calorimeters. NIM A424, 352 (1999), hep-ex/9805009
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Run I: Offset corrections
 subtract contributions not associated to the high pt interaction:
 Ur noise, pile-up, multiple interaction, underlying even
 measured as ET densities D, to be multiplied by the area of a jet in
 
E
EOFF= EUE +NZB EUE +Enoise+Epile-up
 measurement of the ET density D in zero bias event
 measurement of DUE from minimum bias events
DUE=DMB-DZBno HC
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Run I: Offset corrections
Ur noise, pile-up, multiple events
underlying event contribution
ICR
 measured for different luminosities
 depends on s and process
 dominant error from occupancy
dependence
 associated to a single event
 independent of luminosity
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Run I: response correction
 using -jet events
 ideal calorimeter :


ET  ETjet  0
 jet response (with calibrated ): R jet  1 
E
 T nˆT
ET
 Ejetmeas:
dependent on
energy response and
resolution, threshold effects
and smearing
 better: E= ET cosh jet
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Run I: jet response
 comparison
of jet
response in different
cryostat regions
 CC ||<0.7
 ICR 0.7<||<1.8
 EC 1.8 <||<2.5
 effect of finite jet
resolution at E = 10GeV
 lowest response in
ICR: int < 6
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Run I: EC/CC correction
 independent of E as EC/CC
similar in construction
 derived from overlap region
of CC and EC response at
60<E<180 GeV
 Fncry/Fscry=0.997 0.003
 EC response 2% below CC
 compared to the ratio of a
fit to the 2 response
functions
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Run I: ICR correction
 inhomogeneous detector material: correction as function of and ET
high ET: jet-jet events
low ET: -jet events
  or leading jet required to be central (| |< 0.5)
 fit of response as Rjet =  + b ln ET+ b ln (cosh )
 correction derived from difference between measurement and the expectation
for an ideal detector, extrapolated from fit at ||<0.5 and 2 < | |<2.5
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Run I: low ET bias
 Etjet
> 8 GeV
 jet resolution ~50%
 migration of low ET jets
 jets fluctuating below Etmin
are not reconstructed
 bias of the response
towards higher values
• as response determined
from Etmiss and , bias
correction determined from:
Rbias 
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R jet(1)
R jet(0)
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Run I: Response function
 fit of the measured
response function
Rjet(E)=a+b ln E+c (ln E)2
 logarithmic terms
justified by non
compensation at low E
 fit of CC and EC
measurement for ET>30
GeV
 at highest energy
prediction from MC after
tuning response on data
in measured region
 error band derived
taking into account
correlations
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Run I: Showering correction
 corrects for out-of-
1%
cone energy
belonging to the jet
 scales reconstructed
jet to particle level:
S=Ejet/(Ejet+EshoMC)
4%
10%
 parameterizations for
different cone sizes
 errors at low energy:
offset subtraction; at
high energy: stat
2.5%
5%
10%
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 shower correction
depend on jet
profiles, but not on s
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Run II: +jet / Z+jet
/Z+jet
QCD (udsg)
signal:152 M evts
bkgd: 47.1 M evts
signal: 64.8k evts
bkgd: 650 evts
QCD (cbt)
W+jet, Z+X,

• +jet: Run I method – jet calibration possible up to 250 GeV
• Z+jet: lower statistics, but clean sample, useful at low energies, x-check!
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b-jet calibration
 naïve reconstruction
of Z-mass shows a
lower mass for selected
b-jets than light quark
jets.
 energy losses from
semi-leptonic b decays
(, )
 wider b-jets (due to
the large b-mass)
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Z  bb
peak: 82.6
Z  qq
peak: 86.8
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Z bb vs  + b-jet
 + b-jet :
Zbb:
 high statistics, allows for a tight b-jet selection (btagging).
 expected number of tagged events: 1.2 M
but: sensitive fractional imbalance I= (pT() - ET(jet))/ pT()
 systematics closer to physics processes (H or
Top) at high pT
 resonance mass independent of
multiple interactions.
 but: signal/noise~10-3 requires special trigger
(Silicon Track Trigger – operational end 2002)
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CDF Run 1: Z bb selection
 about 120 000 Z  bb events produced in Run 1
 expected to be observed ~ 50-100
 Trigger: central muon (pT> 7.5 GeV)  5.5 M evts
 Offline: request 2 tagged (0.7 cone) jets  5479 evts
 QCD background rejection based on event topology:
> Z is produced by a time-like q-qbar anihilation,
> QCD produced color flow between initial and final
partons
> Z is expected to have soft radiation between the jets
> background will also have strong radiation between IS
and FS partons.
http://www-cdf.fnal.gov/physics/ewk/zbb_new.html
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CDF Run 1: 3 ET and 12
Use 2 kinematic variables to discriminate:
 3 ET : sum of ET of the clusters outside the 2
leading jets
 12 : azimuthal angle difference between the 2 jets
cuts derived: 3 ET < 10 GeV, 12>3 rad
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CDF Run 1: Z bb Signal
 after cuts:
S/N=1/6 at the Z
mass peak
 select/antiselect
w.r.t. the 2
variables to
determine the
tagging probability
 3.2 s exces
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CDF Run 1: Likelihood fit
Results:
MZ=90.0 2.4 GeV
sZ = 9.4  3.5 GeV
NZ=91  30(stat) 19(sys.)
Pythia: expect 12414
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First Run 2 QCD Physics
Inclusive jet pT spectrum at 1.96 TeV
Dijet mass spectrum at 1.96 TeV
Ldt = 1.9 ± 0.2 pb-1
Ldt = 1.9 ± 0.2 pb-1
Only statistical errors
Only statistical errors
Highest 3-jet event
ETjet1 : 310 GeV
Etjet2 : 240 GeV
ETjet3 : 110 GeV
Etmiss : 8 GeV
 not fully corrected distributions:
preliminary correction for jet energy scale
(but no unsmearing or resolution effects)


30-50% systematic error in cross-section

no trigger selection efficiency corrections
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