Transcript Slide 1

Z Production associated with
jets @LHC (ATLAS)
Monica Verducci CERN/INFN
On behalf of Atlas Collaboration
MCWS Frascati (Rome)
Summary
 Introduction @ LHC (ATLAS Detector)
 Parton Density Function (PDFs) measurements @
LHC
 Physics motivations for Z+jet measurement
 Analysis on fully reconstructed MC samples
 Possible checks on systematics from data



b-tagging efficiency
background
Jet energy scale
 Conclusions and Outlook
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Large Hadron Collider
stot(pp) = 70 mb proton-proton
event rate R = s L = 109 eventi\sec
(high luminosity)
Energy per proton
7 TeV
Bunch spacing
25 ns
Bunch size
15 m  12
cm
Bunch-crossing frequency: 40 MHz
~ 20 collisions p-p per bunch crossing
Protons per
Bunch
1011
Bunches per ring
2835
109
events/s =>1GHz
1 event~ 1MB (~PB/s)
Hierarchical trigger
system
~MB/sec
~PB/year raw data
Z(ll)+jet (~2Hz)
γ+jet (~ 0.1 Hz)
At low luminosity
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Lifetime
Luminosity
Lenth of the ring
Number of
collisions per
bunch
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10 hours
1034 cm-2 s-1
27 Km
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ATLAS@LHC
Muon Spectrometer:
Pt measurements and muon
identification
Mounted on an air-core
toroid with B field
Calorimeters:
electromagnetic
and hadronic
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Inner Tracker: Pt
Measurements and
charge of the
particles with a
solenoidal magnetic
field of 2 T.
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Importance of PDFs at LHC
 At a hadron collider, cross
sections are a convolution of the
partonic cross section with the
PDFs.
 PDFs are important for
Standard Model physics, which
will also be backgrounds to any
new physics discovery: Higgs,
Extra Dimensions…
fa
pA
x1 s
X
x2
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fb
ˆ
B
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Parton Kinematic Regime@LHC
 The kinematic regime at the LHC
is much broader than currently
explored.
 At the EW scale (ie W and Z masses)
theoretical predictions for the LHC
are dominated by low-x gluon
uncertainty
 At the TeV scale, uncertainties in
cross section predictions for new
physics are dominated by high-x
gluon uncertainty
The x dependence of f(x,Q2) is determined by fits
to data, the Q2 dependence is determined by the
DGLAP equations.
Fits and evaluation of uncertainties performed by
CTEQ, MRST, ZEUS etc.
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Constraining PDFs at LHC
 Direct photon production
Studies ongoing to evaluate
experimental uncertainties (photon
identification, fake photon
rejection, backgrounds etc.)
(I.Dawson - Panic05,proc.)
Compton
~90%
Annihilation
~10%
ud  W   e n
du  W   e n
 W and Z rapidity
distributions
Impact of PDF errors on W->en
rapidity distributions investigated
using HERWIG event generator with
NLO corrections. Systematics < 5%
(A.Tricoli, hep-ex/0511020,PHOTON05)
(A.Tricoli, Sarkar, Gwenlan CERN-2005-01
(A.C.Sarkar, hep-ph/0512228, Les Houches)
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ud  W   e n
uu  Z
du  W   e n
dd  Z
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The measurement: Z+jet (b)
 Measurement of the b-quark PDF
 Process sensitive to b content of
the proton
(Diglio,Tonazzo,Verducci- ATL-COM-PHYS-2004-078
AIP Conf 794:93-96, 2005, hep-ph/0601164, CERN-2005-014)
(J.Campbell et al. Phys.Rev.D69:074021,2004)
 Tuning of the MonteCarlo tools for Standard Model
 Background of new physics signatures
 Calibration Tool (clean and high statistics signature)
(Santoni, Lefevre ATL-PHYS-2002-026) (Gupta et al. ATL-COM-PHYS-2005-067,
Mehdiyev, Vichou, ATL-COM-PHYS-99-054)
 Luminosity Monitor
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Why measure b-PDF?
 bb->Z @ LHC is ~5% of entire Z production -> Knowing
σZ to about 1% requires a b-pdf precision of the order of
20%
Now we have only HERA
measurements, far from this precision
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Z+b with different PDF sets
MRST5NLO, CTEQ5M1, Alehkin1000
 Differences in total Z+b cross-
section are of the order of 5%
 Some sensitivity from differential
distributions: jet energy calibration
crucial
Number of events
(with LHAPDF in Herwig)
 Other PDF sets predict larger differences
(e.g., MRST5NNL0 >10%)
 New studies are undergoing with
different sets of PDFs function using
NLO generators (Diglio, Farilla,
Verducci)
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Pt b (MeV/c)
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The D0 measurement of Zb/Zj
The D0 collaboration has recently measured:
s(Z+b)/ s(Z+jet) with Z→ and Z → ee
→ Phys.Rev.Lett.94:161801,2005
Analysis flow:
– select events with Z→ee or
Z→ + jet
– apply b-tagging
– extract content of b, c and light quarks
(assuming Nc/Nb from theory)
Fitted values for selected sample in 184 pb-1
 0.005
s Z  b 
= 0.024 0.005( stat)
( syst )
 0.004
s Z  j 
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NLO (J.Campbell et al.):
0.018 +/- 0.004
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LHC vs Tevatron
Cross Section (pb)
J.Campbell et al.
Phys.Rev.D69:074021,2004
TEVATRON
Processes
gb  Zb
gb  Zbb
gc  Zc
gc  Zcc
LHC
ZQ inclusive
13.40.9 0.8 0.8
 70 30
1040 70
60100 50
6.83
49.2
20.3 11..85  0.111..32
 40
1390  100 60
70 80
13.8
89.7
Zj inclusive
qq  Zg , gq  Zq
 9 7
1010 44
40 2 12
 60 300
15870900
600 300 500
The measurement of Z+b should be more interesting at LHC than at
Tevatron:
 Signal cross-section larger (x80), and more luminosity
 Relative background contribution smaller (x5)
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Z+jet: Impact to other measurements
 Background to Higgs search
In models with enhanced s(h+b)
and BR(h->

(J.Campbell et al. Phys.Rev.D67:095002,2003)
 Background to MS Higgs search

In models where pp -> ZH con H -> bb
Simple spread of existing PDFs
gives up to 10% uncertainty on
prediction of Higgs cross section.
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Impact on New Physics
Black: ISAJET
Red: PYTHIA
 Susy Background:
Z(->nn jet
 Effective Mass distribution for No-
Leptons Mode after standard event
selection M(g)≈M(q)≈1TeV
Susy Atlas meetings
T.S.S.Asai U. of Tokyo
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Event Topology
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Z+jet(b) Analysis
Event selection: taking into account only Z→
 Two isolated muons with
•
•
•
Pt > 20 GeV/c
opposite charge
invariant mass close to Mz
(70 <M<110 GeV)
 Two different b-tagging algorithms have been considered:
•
•
Soft muon
Inclusive b-tagging of jets
Analysis presented @ ATLAS Physics Workshop 2005
ATL-COM-PHYS-2006-051 (Verducci, Diglio, Farilla, Tonazzo)
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How estimate the events…
 Backgrounds:
 Signal:
Acceptance Efficiency = 59.6%
Trigger Efficiency > 95%
Cuts Efficiency ~ 40%
Pythia
sother
= 2.6  106 mb
Nb = s
table
b
 BR ( Z   )  L  t   acc   cuts   b
Pythia
N other = s other
 L  t   acc   cuts   other
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Z+1 jet reconstruction (I)
# events (Rome) = 516550 (Layout for
Rome Atlas Physics Workshop 2005)
CSC
# events (CSC) = 139400 (Computing
System Commissioning 2006)
cuts
2 mu
# event
Rome
306129
# event
CSC
67737
Eff
Rome
59.3%
Eff
CSC
48.6%
pt>20
Eta cut
182628
67737
35.4%
48.6%
M
range
157764
50721
30.5%
36.4%
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ROME
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Z+1 jet reconstruction (II)
CSC5145
Algo
# event
Rome
Eff
Rome
# event
CSC
Eff
CSC
Cone
0.7
51487
10%
16600
11.9%
Cone
0.4
51864
10%
16137
11.6%
Kt
20118
3.9%
8808
6.3%
 Three different algorithms
to select the jets with
different radius.
 Jet: pT > 15 GeV,|η|< 2.5
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ATLANTIS DISPLAY (Rφ)
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BTagging
All Jets
30 fb-1
b jet
other
# events
176642
204265
B Jets
BTagging Efficiency 59.5%
Purity 60.7%
Soft Muon Tagging
30 fb-1
b jet
other
# events
22630
68088
All Muons
B Muons
Soft MuonTagging Efficiency 7.2%
Purity 37.2%
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Systematic Effects
 Efficiency of b-tagging
 To check b-tagging efficiency, we can use b-enriched
samples. Experience at Tevatron & LEP indicates
that we can expect:
 Δεb/εb = 5%
 Background from mistag
 Check mistagging on a sample where no b-quark jets
should be present
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Diglio
 We use W+jet events, where there
are not b jet


2 Gev per bin
Jets will cover the whole Pt
range
Statistics 30x Z+j (after
selection of decays to
muons)
5 Gev per bin
 The relative error on background
from mistagging can be kept at the
level of few-% in each bin of the
Pt range
5-2 Gev per bin
Full Simulation Rome Sample
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Calibration in Situ
  and Z0 are well calibrated objects
at EM scale balancing the recoiling
hadronic system potentially large
statistics available: L=1033cm-2s-1
pT range from 20 GeV to ~60 GeV
 Calibration in situ of the jet energy
scale -> jet energy absolute scale
within 1%
This means calibrate the calorimeters
using jets reconstructed in the exp.
 Z+jet (b 5%)
high statistic -> 380pb
 pjetT = pZT balance criteria on
transverse plan

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-0.16 ± 0.01
(pT jet – pT zeta)/ pT zeta
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pT balance =
(pT jet – pT boson)/ pT boson
(p TRaw  p TZ )
Cal 0
Raw
~
1 =

p
=
p
T
T (1   1 )
Raw
pT
0
(p Cal
 p TZ )
Cal
Cal 0
T
~ )
2 =

p
=
p
(
1


T
T
2
0
p Cal
T
p TZ bi n
0
p TZ  p Cal
T
bi n
2
Df
Jet
Z
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Conclusions I
 Precision Parton Distribution Functions are crucial for new physics discoveries
at LHC and to tune MonteCarlo studies:
 PDF uncertainties can compromise discovery potential (HERA-II:
significant improvement to high-x PDF uncertainties)
 At LHC the major source of errors will not be statistic but systematic
uncertainties
 To discriminate between conventional PDF sets we need to reach high
experimental accuracy ( ~ few%) and to improve the detector performance
and resolution
 Standard Model processes like Direct Photon, Z and W productions are good
processes:
 to constrain PDF’s at LHC, especially the gluon
 to calibrate the detector
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Conclusions II
 Z+b measurement in ATLAS will be possible with high
statistics and good purity of the selected samples with two
independent tagging methods
 We will have data samples to control systematic errors related
to b-tagging at the few-% level over the whole jet Pt
distribution


b-tagging efficiency
Mistagging: from W+jet
 Jet Calibration in situ: error within 1%
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Backup
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Inclusive b-tagging Algorithm
Inclusive jet b-tagging
Identification of a single jet in the event with b flavour
•pT > 15 GeV
•|η|< 2.5
Primary Vertex
d
•Number of tracks > 0
•Secondary vertex >3
(weight)
Impact
Parameter
Secondary Vertex,
B-hadron decays
Extrapolated track
Life time of a bottom hadron is about t ~ 1.5 ps
long enought to permit to a hadron of 30 GeV
of energy to do a distance of L ~ 3 mm before
decaying
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Calibration in Situ (II)
 Cone DR=0.7
 Et> 15 GeV
 Et(cell)=1.5 GeV
 E,,: pt>5GeV
(p TRaw  p TZ )
Cal 0
Raw
~ )
1 =

p
=
p
(
1


T
T
1
p TRaw
2 =
(p
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p )
Cal 0
T
Cal 0
T
p
Z
T
p
Cal
T
=p
Cal 0
T
~ )
(1  
2
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p TZ bi n
0
p TZ  p Cal
T
bi n
2
ISR
Correction
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Calibration in Situ (III)



BiSector Method
Measurement of the resolution
via estimation of the ISR
contribution
Transverse plane:
1.
η depends only on ISR
2.
 depends on both
resolution and ISR
K T = ( p
jet
T
f jet  f Z
 p ) sin (
)
2
K T = ( p
jet
T
f jet  f Z
 p ) cos(
)
2
Z
T
Z
T
s D = s 2  s 2
s(pT ) = a  pT  b  pT  c
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