Dijet Production in Polarized Proton-Proton Collisions at 200 GeV Matthew Walker April 22, 2011 STAR
Dijet Production in Polarized Proton-Proton Collisions at
200 GeVMatthew Walker
April 22, 2011
STAR
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Outline
✦ Theoretical Motivation
✦ Experimental Overview
✦ Cross Section Analysis
✦ Asymmetry Analysis
✦ Conclusions
2
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Theoretical Motivation✦ Polarized DIS tells us that the
spin contribution from quark spin is only ~30%.
3
D. de Florian et al., Phys. Rev. D71, 094018 (2005). D. de Florian et al., Phys. Rev. Lett. 101 (2008) 072001
-0.04
-0.02
0
0.02
0.04
-0.04
-0.02
0
0.02
0.04
-0.04
-0.02
0
0.02
0.04
10 -2 10 -1
DSSVDNS KREDNS KKP
DSSV !"2=1DSSV !"2=2%
x!u–
x!d–
x!s–
x
GRSV maxgGRSV ming
x!g
x
-0.2
-0.1
0
0.1
0.2
0.3
10 -2 10 -1 1
Without RHIC data With RHIC data
Substantial improvement for
0.05 < x < 0.2, but large
uncertainties at low x
x 110-110-2
8
parabola and the 1! uncertainty in any observable would correspond to !"2 = 1. In order to account for unexpectedsources of uncertainty, in modern unpolarized global analysis it is customary to consider instead of !"2 = 1 betweena 2% and a 5% variation in "2 as conservative estimates of the range of uncertainty.
As expected in the ideal framework, the dependence of "2 on the first moments of u and d resemble a parabola(Figures 3a and 3b). The KKP curves are shifted upward almost six units relative to those from KRE, due to thedi"erence in "2 of their respective best fits. Although this means that the overall goodness of KKP fit is poorer thanKRE, #d and #u seem to be more tightly constrained. The estimates for #d computed with the respective best fitsare close and within the !"2 = 1 range, suggesting something close to the ideal situation. However for #u, they onlyoverlap allowing a variation in !"2 of the order of a 2%. This is a very good example of how the !"2 = 1 does notseem to apply due to an unaccounted source of uncertainty: the di"erences between the available sets of fragmentationfunctions.
-0.2
0
0.2
0.4
-0.2
0
0.2
0.4
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
10-2
10-2
x(!u+!u–)
x(!d+!d–)
x!uv
x!dv
x!g–
x!u–
xBj
x!d–
xBj
x!s–
xBj
KRE (NLO)
KKP (NLO)unpolarizedKRE "
2KRE "
min+1
KRE "2
KRE "min
+2%
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
10-2
FIG. 4: Parton densities at Q2 = 10 GeV2, and the uncertainty bands corresponding to !!2 = 1 and !!2 = 2%
An interesting thing to notice is that almost all the variation in "2 comes from the comparison to pSIDIS data.The partial "2 value computed only with inclusive data, "2
pDIS , is almost flat reflecting the fact the pDIS data are
not sensitive to u and d distributions. In Figure 3, we plot "2pDIS with an o"set of 206 units as a dashed-dotted line.
The situation however changes dramatically when considering #s or #g as shown in Figures 3c and 3f, respectively.In the case of the variation with respect to #s, the profile of "2 is not at all quadratic, and the distribution is muchmore tightly constrained (notice that the scale used for #s is almost four times smaller than the one used for lightsea quarks moments). The "2
pDIS corresponding to inclusive data is more or less indi"erent within an interval aroundthe best fit value and increases rapidly on the boundaries. This steep increase is related to a positivity constraints on!s and !g. pSIDIS data have a similar e"ect but also helps to define a minimum within the interval. The preferredvalues for #s obtained from both NLO fits are very close, and in the case of KRE fits, it is also very close to thoseobtained for #u and #d suggesting SU(3) symmetry.
8
parabola and the 1! uncertainty in any observable would correspond to !"2 = 1. In order to account for unexpectedsources of uncertainty, in modern unpolarized global analysis it is customary to consider instead of !"2 = 1 betweena 2% and a 5% variation in "2 as conservative estimates of the range of uncertainty.
As expected in the ideal framework, the dependence of "2 on the first moments of u and d resemble a parabola(Figures 3a and 3b). The KKP curves are shifted upward almost six units relative to those from KRE, due to thedi"erence in "2 of their respective best fits. Although this means that the overall goodness of KKP fit is poorer thanKRE, #d and #u seem to be more tightly constrained. The estimates for #d computed with the respective best fitsare close and within the !"2 = 1 range, suggesting something close to the ideal situation. However for #u, they onlyoverlap allowing a variation in !"2 of the order of a 2%. This is a very good example of how the !"2 = 1 does notseem to apply due to an unaccounted source of uncertainty: the di"erences between the available sets of fragmentationfunctions.
-0.2
0
0.2
0.4
-0.2
0
0.2
0.4
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
10-2
10-2
x(!u+!u–)
x(!d+!d–)
x!uv
x!dv
x!g–
x!u–
xBj
x!d–
xBj
x!s–
xBj
KRE (NLO)
KKP (NLO)unpolarizedKRE "
2KRE "
min+1
KRE "2
KRE "min
+2%
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
10-2
FIG. 4: Parton densities at Q2 = 10 GeV2, and the uncertainty bands corresponding to !!2 = 1 and !!2 = 2%
An interesting thing to notice is that almost all the variation in "2 comes from the comparison to pSIDIS data.The partial "2 value computed only with inclusive data, "2
pDIS , is almost flat reflecting the fact the pDIS data are
not sensitive to u and d distributions. In Figure 3, we plot "2pDIS with an o"set of 206 units as a dashed-dotted line.
The situation however changes dramatically when considering #s or #g as shown in Figures 3c and 3f, respectively.In the case of the variation with respect to #s, the profile of "2 is not at all quadratic, and the distribution is muchmore tightly constrained (notice that the scale used for #s is almost four times smaller than the one used for lightsea quarks moments). The "2
pDIS corresponding to inclusive data is more or less indi"erent within an interval aroundthe best fit value and increases rapidly on the boundaries. This steep increase is related to a positivity constraints on!s and !g. pSIDIS data have a similar e"ect but also helps to define a minimum within the interval. The preferredvalues for #s obtained from both NLO fits are very close, and in the case of KRE fits, it is also very close to thoseobtained for #u and #d suggesting SU(3) symmetry.
1
2=
1
2!"+ Lq +!G+ Lg
x
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Theoretical Motivation✦ Extracting gluon polarization
4
-1
-0.75
-0.5
-0.25
0
0.25
0.5
0.75
1
-1 -0.8 -0.6 -0.4 -0.2 -0 0.2 0.4 0.6 0.8 1a L
Lcosθ*
gg ! gg
qg ! qg
qq ! qq qq̄ ! qq̄
gg ! qq̄
=!f1 ! !f2 ! !h · aLL ! Dh
f
f1 ! f2 ! !h ! Dhf
ALL =d!!
d!
long-range short-range long-range
!f1
!f2
!h
!G(Q2) =! 1
0!g(x,Q2)dxExtract ∆g(x,Q2) using a global fit
1
2=
1
2!"+ Lq +!G+ Lg
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Inclusive jets
✦ Run 6 results: GRSV-max/GRSV-min ruled out, a gluon polarization between GRSV-std and GRSV-zero favored
✦ Run 9 results: good agreement with DSSV, GRSV-std and GRSV-zero excluded
5
D. de Florian et al. PRL 101 (2008) 072001.
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Correlation Measurements✦ Reconstructing multiple physics
objects (di-jets, photon/jet) provides information about initial parton kinematics
✦ STAR well suited for correlation measurements with its large acceptance
6
M =!x1x2s
!3 + !4 = lnx1
x2
x1 =1!s(pT3e
!3 + pT4e!4)
x2 =1!s(pT3e
!!3 + pT4e!!4)
STAR Collaboration PRL 100 (2008) 232003
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Experimental Setup
✦ RHIC produces polarized proton beams up to 250 GeV in energy
✦ Siberian snake magnets in the AGS and RHIC help protect beam from depolarizing resonances
7
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
STAR Detector
8
Not shown:Zero-degree calorimeters, time-of-flight, polarimeters
!=-1!=0
!=1
TPC
BEMC
YellowBlue
West
East
Tai Sakuma
BBC
Tai Sakuma, Thesis, MIT (2010)
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Jet Terminology
9
Partons
Hadrons, Leptons
Tracks, Energy Depositions
Parton Branching, Hadronization, Underlying Event
Detector Effectspa
rton
part
icle
dete
ctor
Jet
π0
π+
g
q
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Data✦ 2005 Data: 2.27 pb-1 taken during RHIC Run 5
✦ 2009 Data: 10.3 pb-1 taken during RHIC Run 9
10
✦ Jet Patch Trigger:
✦ 1x1 in φxη patch of towers in the BEMC (400 towers)
✦ Midpoint Cone Algorithm with Split-Merge
✦ Cone Radius: 0.4, 0.7
✦ Seed 0.5 GeVTai Sakuma, Thesis, MIT (2010)
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Cross Section Formula
11
dσi/dMi = differential cross section in bin iΔMi = bin width in invariant mass of bin iL = LuminosityAvert = vertex acceptanceϵreco,vert = reconstruction + vertex efficiencyαij = matrix element for unfolding method (more later)ϵmisreco = efficiency for reconstructing dijets that have an associated particle dijetϵtrig = trigger efficiencyJj = reconstructed yield in bin j
d!i
dMi
=1
!Mi
1
L
1
Avert
1
"reco,verti
!
j
#ij"misrecoj
1
"trigj
Jtrigj,reco
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2005 Data/Simulation Comparison
12
20 30 40 50 60 70 80 90 100 110
10
210
310
Data
Simulation
-0.2 0 0.2 0.4 0.6 0.8 1
10
210
310
0 0.05 0.1 0.15 0.2 0.25 0.3
310
20 30 40 50 60 70 80 90 100 1100.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
-0.2 0 0.2 0.4 0.6 0.8 10.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
0 0.05 0.1 0.15 0.2 0.25 0.30.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
2005 STAR Preliminary
data
/si
mul
atio
nno
rmal
ized
yi
elds
M =!x1x2s !34 =
!3 + !42
=1
2ln
x1
x2cos !! = tanh
"3 ! "42
✦ Run 5 di-jet data shows good agreement with simulations
✦ Asymmetric pT cut applied to the jets for comparison with more stable NLO calculations
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Unfolding
13
Used a matrix unfolding scheme based on G. D’Agostini, NIM A 362 (1995), p. 487.Purpose of unfolding - undo “smearing” caused by
Detector effects, eg:Double counting electronsHadronic responseLost tracksEnergy Leakage
Falling spectrum
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Uncertainties
14
Statistical - besides data statistics, account for effects from MC finite statistics in correction factors and unfoldingSystematic -
Jet Energy Scale - more next slideBeam Background - < 0.5% from varied neutral energy cutNormalization
Luminosity - 8% from MB cross section uncertaintyAcceptance - 6% from difference in timebin distributions between MB and BJP2
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Jet Energy Scale Uncertainty
15
Neutral Energy Uncertainty - 4.9 %BEMC Scale Uncertainty - 4.8 %BEMC Efficiency Uncertainty - 1 %
Charged Energy Uncertainty - 5.6 %Track Scale Uncertainty - 1%Track Finding Efficiency Uncertainty - 5%Hadron Response of the BEMC - 2.3 %
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Jet Energy Scale Uncertainty
16
Data
High
Nominal
Low
High; High
High; Nominal
High; Low
Nominal; High
Nominal
Nominal; Low
Low; High
Low; Nominal
Low; Low
Yield
BEMC Min
BEMC Max
Reconstruction TriggerCorrections
UnfoldingOther Efficiencies
High; High
High; Nominal
High; Low
Nominal; High
Nominal
Nominal; Low
Low; High
Low; Nominal
Low; Low
Matthew WalkerSTAR Thesis DefenseApril 22, 2011 17
2005 Dijet Cross Section
Invariant Mass (GeV)20 30 40 50 60 70 80 90 100 110
)2 (p
b/G
eV/c
dMσd
-110
1
10
210
310
410
510
Invariant Mass (GeV)20 30 40 50 60 70 80 90 100 110
)2 (p
b/G
eV/c
dMσd
-110
1
10
210
310
410
510
2005 STAR Data with Statistical Uncertainties
Systematic Uncertainties
NLO Calculation (de Florian, et al.)
NLO with Hadronization and UE Corrections
STAR Run 5 Data
= 200 GeVs Jet + Jet + X at →p+p = 0.4coneR
| < 0.5ηΔ < 0.8, |η0.2 < | > 2.0φΔ|
10% Normalization Uncertainty (not shown)
)2Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
(dat
a-th
eory
)/the
ory
-1
-0.5
0
0.5
)2Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
(dat
a-th
eory
)/the
ory
-1
-0.5
0
0.5 = 2M, M/2µScale Uncertainty on NLO,
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Cone Radius Dependence
18
Tai Sakuma, Thesis, MIT 2010
M (GeV/c2)
Comparison of data with theory for different cone
radii shows clear dependence
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2006 Cross Section
19
Mjj [GeV]
d3!/dMd!
3d!4
[pb/
GeV
]
1
10
102
103
104
105
30 40 50 60 70 80 90
Systematic Uncertainty
TheoryNLO pQCD + CTEQ6M
Had. and UE. Corrections
STAR Run-6
Dijet Cross Sectionpp @ 200 GeVCone Radius = 0.7max(pT) > 10 GeV, min(pT) > 7 GeV-0.8 < ! < 0.8, |!!| < 1.0|!!| > 2.0
!!Ldt = 5.39pb!1
d3!
dMd!3d!4
✦ Unpolarized differential cross section between 24 and 100 (GeV/c2)
✦ NLO theory predictions using CTEQ6M provided by de Florian with and without corrections for hadronization and underlying event from PYTHIA
✦ Statistical Uncertainties as lines, systematics as rectangles
STAR Preliminary
Tai Sakuma, Thesis, MIT (2010)
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2006 Cross Section
✦ Comparison to theory (including hadronization and underlying event correction) shows good agreement within systematic uncertainties
✦ Agreement confirms our use of pQCD to extract gluon polarizations
20
Mjj [GeV]
(Dat
a - T
heor
y) /
Theo
ry-1.0
-0.5
0.0
0.5
1.0
30 40 50 60 70 80 90
Systematic UncertaintyTheoretical Uncertainty
STAR Run-6
Theory: CTEQ6M NLO pQCD Had. UE. Corrections
Data-theory Comparisonof Dijet Cross Sectionpp @ 200 GeVCone Radius = 0.7max(pT) > 10 GeV, min(pT) > 7 GeV-0.8 < ! < 0.8, |!!| < 1.0, |!!| > 2.0
!!Ldt = 5.39 pb!1 STAR Preliminary
Tai Sakuma, Thesis, MIT (2010)
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Asymmetry Measurment
✦ Asymmetry formula:
✦ N++: like sign dijet yields
✦ N+-: unlike sign dijet yields
✦ R: relative luminosity
✦ P: polarization
21
ALL =1
PBPY
N++ !RN+!
N++ +RN+!
✦ 2009 Data: 10.3 pb-1
analyzed from Run 9
✦ Significant increase in data size over previous years and small increase in polarization
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2009 Asymmetry
✦ The value of ALL in a bin j is given by the above formula
✦ αjk are the matrix elements for the unfolding
✦ Changing the jet energy scale results in different unfolding matrices
✦ Formula above represents unpolarized unfolding, could also introduce a second unfolding matrix for polarized unfolding
22
!k !jk(
!i PB,iPY,i(N
++i,k +N!!
i,k )! PB,iPY,iRi(N+!i,k +N!+
i,k ))!
k !jk(!
i P2B,iP
2Y,i(N
++i,k +N!!
i,k )! P 2B,iP
2Y,iRi(N
+!i,k +N!+
i,k ))
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2009 Simulation
✦ New simulation needed:
✦ Different detector components installed in 2009 than in previous simulations
✦ Different trigger algorithms used
✦ Simulation geometry bugs fixed
✦ Previous simulation effectively integrated 5.3 x 10-4 pb-1
✦ Goal for new simulation: 1 pb-1
✦ Solution: use cloud computing resources
23
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Cloud Computing Projects
24
date Facility toolstype of task
# of VMs
# jobs/VM
CPU days
calendar days
input (TB)
output (TB)
remarks
2009, March Amazon EC2Nimbus Globus PBS batch
simu 100 1 500 5 0 0.3works like normal globus GK grid site
2009, November Amazon EC2 EC2 simu 10 1 or 2 1 1 0 0.01use commercial interface
2010, FebruaryGLOW Madison Uni Wisconsin
CondorVM simu 430 1 130 0.6 0 0.1 call home model
2010, JulyClemson Uni, SC
Kestrel, QEMU-KVM
simu 1000 1 17,000 20 0 7 VM lifetime 24 h, no ssh to VM
2011, FebruaryMagellan NERSC
Eucalyptus data reco 20 6 or 7 600+ 20+ 2 1 almost real-time
processing
Clemson
STARAmazonEC2
GLOW
NERSC
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2009 Simulation
✦ Prepare a VM Image✦ Start with a KVM image of Scientific Linux 5.3✦ Add ~50 additional required packages✦ Install STAR libraries, ~2.5M lines of code✦ Setup grid toolkit and credentials✦ Install database server✦ Setup scripts to interact with job manager
✦ Setup monitoring scheme✦ Design HTTP based API for jobs to record messages in a
database✦ Write monitoring software
25
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2009 Simulation
26
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2009 Simulation
27
✦ 9 STAR MC productions with partonic pT > 2 GeV (lower than before)
✦ PYTHIA 6.4.23, proPt0 (latest and greatest in PYTHIA and tuning)
✦ Over 12 billion events generated by PYTHIA, filtered to allow only 36 million to undergo detector simulation (GEANT3), and 10 million through full reconstruction
DateJul17 Jul24 Jul31
N M
achi
nes
0
200
400
600
800
1000
1200
1400
Available Machines
Working Machines
Idle Machines
✦ Took over 400,000 CPU hours and generated 7 TB of files transferred to BNL
✦ Expansion of 25% of STAR computing resources
✦ Would have taken over one year without cloud resources
✦ Largest physics simulation on cloud, largest STAR simulation (CPU hours, output size)
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Data/Simulation Run 9
✦ Run 9 data simulation agreement is good
28
20 30 40 50 60 70 80 90 100
Norm
aliz
ed Y
ield
s
310
410
510
610 Data Simulation
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
410
510
0 0.1 0.2 0.3 0.4 0.5 0.6
510
)2Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100
(Dat
a-Si
mu)
/Sim
ulat
ion
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
34η
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
*)θcos(0 0.1 0.2 0.3 0.4 0.5 0.6-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
STAR Run 9 Data Preliminary Jet + Jet + X→p+p
= 200 GeVs
= 0.7coneR < 0.8η-0.8 <
| < 1.0ηΔ || > 2.0φΔ|
STAR Preliminary
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Uncertainties
✦ Statistical - Uncertainties from all sources of statistical combined using a Monte Carlo that samples the distributions: data, simulation, polarization, relative luminosity
✦ Systematic
✦ Jet energy scale: change unfolding matrices
✦ Non-longitudinal effects: 0.025 x ALL
✦ Relative Luminosity: δR = 1x10-3
✦ Theory Scenario dependent trigger efficiencies
29
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
False Asymmetries
30
)2Invariant Mass (GeV/c20 30 40 50 60 70 80
-0.1
-0.05
0
0.05
0.1
LLRaw ALLRaw A
)2Invariant Mass (GeV/c20 30 40 50 60 70 80
-0.1
-0.05
0
0.05
0.1
YellowAYellowA
)2Invariant Mass (GeV/c20 30 40 50 60 70 80
-0.1
-0.05
0
0.05
0.1
BlueABlueA
)2Invariant Mass (GeV/c20 30 40 50 60 70 80
-0.1
-0.05
0
0.05
0.1
Like-signALike-signA
)2Invariant Mass (GeV/c20 30 40 50 60 70 80
-0.1
-0.05
0
0.05
0.1
Unlike-signAUnlike-signA
Consistent with zero
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Jet Energy Scale Uncertainty
31
Neutral Energy Uncertainty - 2.1 %BEMC Scale Uncertainty - 1.9 %BEMC Efficiency Uncertainty - 1 %
Charged Energy Uncertainty - 5.4 %Track Scale Uncertainty - 2%Track Finding Efficiency Uncertainty - 5%
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Scenario Dependent Efficiencies
32
)2Invariant Mass (GeV/c20 30 40 50 60 70 80
LL,u
npol
- A
LL,p
olA
-0.02
-0.01
0
0.01
0.02
0.03
0.04
DSSV
GRSV STD
GRSV Zero
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Run 9 Asymmetry
33
]2M [GeV/c20 30 40 50 60 70 80
LLA
-0.02
0
0.02
0.04
0.06
0.08
East - East and West - West Barrel
MC GS-C(pdf set NLO)
2009 STAR Data
Systematic Uncertainties
]2M [GeV/c20 30 40 50 60 70 80
LLA
-0.02
0
0.02
0.04
0.06
0.08
East Barrel - West Barrel
Scale uncertaintyGRSV stdDSSV
]2M [GeV/c20 30 40 50 60 70 80
LLA
-0.02
0
0.02
0.04
0.06
0.08
Full Acceptance
p+ p ! jet + jet +X
!s = 200 GeV
PreliminarySTAR
East West East West
η=0η=0 η=1η=-1 η=1η=-1
Matthew WalkerSTAR Thesis DefenseApril 22, 2011 34
Kinematic Sensitivity
-210 -110 1-210
-110
1
10
210
310
410
510
-210 -110 1-210
-110
1
10
210
310
410
510
)2Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100
x
-110
1
)2Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100
x
-110
1
East Barrel - East Barrel East Barrel - West Barrel
1x
2x
: 20.0 < M < 30.01x
: 20.0 < M < 30.02x
: 70.0 < M < 80.01x
: 70.0 < M < 80.02x
PreliminarySTAR
p+ p ! jet + jet +X
!s = 200 GeV
X X
XX
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Summary✦ 2006 Dijet cross section (5.39 pb-1) shows good agreement with
NLO calculations, validating pQCD as an analysis framework
✦ 2009 asymmetry strongly favors DSSV over GRSV std and GS-C; separation into different acceptances allows for first constraints on the shape of Δg(x)
35
]2M [GeV/c20 30 40 50 60 70 80
LLA
-0.02
0
0.02
0.04
0.06
0.08
East - East and West - West Barrel
MC GS-C(pdf set NLO)
2009 STAR Data
Systematic Uncertainties
]2M [GeV/c20 30 40 50 60 70 80
LLA
-0.02
0
0.02
0.04
0.06
0.08
East Barrel - West Barrel
Scale uncertaintyGRSV stdDSSV
]2M [GeV/c20 30 40 50 60 70 80
LLA
-0.02
0
0.02
0.04
0.06
0.08
Full Acceptance
p+ p ! jet + jet +X
!s = 200 GeV
PreliminarySTAR
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Backup
36
7 October 2009 Collaboration Meeting
UNFOLDING
Consider the true bin with 49 < M < 64The spectrum at right represents the contributions to this true bin from each of the reconstructed mass binsThe red bin is the contribution from the same bin in reconstructed massThe contributions from blue bins is ~50%
37
)2Reconstructed Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
Un
fold
ed
Yie
ld
0
20
40
60
80
100
120
140
160
< 64.15particle
Contributions to corrected bin 48.83 < M < 64.15particle
Contributions to corrected bin 48.83 < M
There are important off diagonal components that must be considered
7 October 2009 Collaboration Meeting
UNFOLDING
38
)2Reconstructed Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
Un
fold
ed
Yie
ld
0
200
400
600
800
1000
1200
< 24.25particle
Contributions to corrected bin 20.00 < M < 24.25particle
Contributions to corrected bin 20.00 < M
)2Reconstructed Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
Un
fold
ed
Yie
ld
0
200
400
600
800
1000
1200
1400
1600
< 30.01particle
Contributions to corrected bin 24.25 < M < 30.01particle
Contributions to corrected bin 24.25 < M
)2Reconstructed Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
Un
fold
ed
Yie
ld
0
200
400
600
800
1000
1200
< 37.90particle
Contributions to corrected bin 30.01 < M < 37.90particle
Contributions to corrected bin 30.01 < M
)2Reconstructed Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
Un
fold
ed
Yie
ld
0
100
200
300
400
500
600
< 48.83particle
Contributions to corrected bin 37.90 < M < 48.83particle
Contributions to corrected bin 37.90 < M
)2Reconstructed Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
Un
fold
ed
Yie
ld
0
20
40
60
80
100
120
140
160
< 64.15particle
Contributions to corrected bin 48.83 < M < 64.15particle
Contributions to corrected bin 48.83 < M
)2Reconstructed Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
Un
fold
ed
Yie
ld
0
2
4
6
8
10
12
14
16
18
< 85.92particle
Contributions to corrected bin 64.15 < M < 85.92particle
Contributions to corrected bin 64.15 < M
)2Reconstructed Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
Un
fold
ed
Yie
ld
0
0.1
0.2
0.3
0.4
0.5
0.6
< 117.29particle
Contributions to corrected bin 85.92 < M < 117.29particle
Contributions to corrected bin 85.92 < M
Here are the same plots for all of the binsThe last bin has contributions from ONLY other bins
7 October 2009 Collaboration Meeting
UNFOLDINGMethod used based on G. D’Agostini, NIM A 362 (1995), p. 487.Also used by (along with H1, ZEUS, HARP, and others):
IceCube: arXiv:0811.1671L3: arXiv: hep-ex/0507042D0: arXiv: hep-ex/9807029
Use PYTHIA to populate the unfolding matrix A (in the naming convention of D’Agostini) using the reconstructed invariant mass and the particle invariant massNormalize so that A does not change the integral of the spectrumThe following equation describes the matrix elements of A:
39
!ij =J(reconstructed bin j|particle bin i)
J(reconstructed bin j)
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2005/2006 Comparison
40
Invariant Mass (GeV)20 30 40 50 60 70 80 90 100 110
)2 (p
b/G
eV/c
4ηd 3ηdM
dσd
-110
1
10
210
310
410
510
2005 STAR Data with Statistical UncertaintiesSystematic UncertaintiesTai Run 5 BHT2 DataTai Run 6 BHT2 DataTai Run 5 BJP2 DataTai Run 6 BJP1 Data
)2Invariant Mass (GeV/c20 30 40 50 60 70 80 90 100 110
(Dat
a-M
att)/
Mat
t
-0.50
0.51
1.52
2.53
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2006 Asymmetry
✦ Run 6 Longitudinal double helicity asymmetry
✦ Systematic uncertainties show effects on trigger efficiency from different theory scenarios
✦ Scale uncertainty (8.3%) from polarization uncertainty not shown
41
Mjj [GeV]
ALL
-0.02
0.00
0.02
0.04
0.06
0.08
30 40 50 60 70 80
Dijet ALLpp @ 200 GeVCone Radius = 0.7max(pT) > 10 GeVmin(pT) > 7 GeV-0.8 < ! < 0.8, |!!| < 1.0|!!| > 2.0
Data Run-6
Sys. Uncertainty
GRSV STDDSSV
GRSV !g = 0GRSV !g = !g !!Ldt = 5.39pb!1
STAR Preliminary
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
2009 Projections
42
East West East West
Wed Sep 22 15:18:55 2010 ]2M [GeV/c20 30 40 50 60 70 80
LLA
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
east barrel - east barrel and west barrel - west barrel
MCGRSV stdGRSV m03GRSV zeroGS-C(pdf set NLO)2009 STAR Data
]2M [GeV/c20 30 40 50 60 70 80
LLA
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06east barrel - west barrel
Scale uncertaintyGRSV stdDSSV
Projected PrecisionSTAR
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Data/Simulation Run 6
43
✦ 2006 Simulation:
✦ 11 STAR MC productions producing 4M events with partonic pT between 3 GeV and 65 GeV
✦ PYTHIA 6.410, CDF Tune A
✦ Run 6 data and simulation agreement is good
Tai Sakuma, Thesis, MIT (2010)
Matthew WalkerSTAR Thesis DefenseApril 22, 2011
Dijet Run 9 Projected
44