Quantum chromodynamics in the LHC era - …ryszard/8361/Nadolsky-PDF2.pdf · Quantum chromodynamics...
Transcript of Quantum chromodynamics in the LHC era - …ryszard/8361/Nadolsky-PDF2.pdf · Quantum chromodynamics...
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Quantum chromodynamics
in the LHC era
Pavel Nadolsky
Department of PhysicsSouthern Methodist University (Dallas, TX)
December 9, 2014
Lecture 2
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QCD factorization and PDFs
q
p
p
q
Z0µ
µ
+...
pp→ (Z0 → µµ)X: Feynman diagram at the leading order in QCD. Let's
now consider higher orders (...).
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QCD factorization and PDFs
q
p
p
q
Z0µ
µ
+...
According to QCD factorization theorems, typical cross
sections (e.g., for p(k1)p(k2)→[Z(q)→ `(k3)¯(k4)
]X)
take the form
σpp→`¯X =∑
a,b=q,q,g
∫ 1
0
dξ1
∫ 1
0
dξ2 σab→Z→`¯
(x1
ξ1,x2
ξ2;Q
µ
)fa/p(ξ1, µ)fb/p(ξ2, µ)
+O(Λ2QCD/Q
2)
� σab→Z→`¯ is the hard-scattering cross section
� fa/p(ξ, µ) are the PDFs
� Q2 = (k3 + k4)2, x1,2 = (Q/√s) e±yV � measurable quantities
� ξ1, ξ2 are partonic momentum fractions (integrated over)
� µ is a factorization scale (=renormalization scale from now on)
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QCD factorization and PDFs
q
p
p
q
Z0µ
µ
+...
According to QCD factorization theorems, typical cross
sections (e.g., for p(k1)p(k2)→[Z(q)→ `(k3)¯(k4)
]X)
take the form
σpp→`¯X =∑
a,b=q,q,g
∫ 1
0
dξ1
∫ 1
0
dξ2 σab→Z→`¯
(x1
ξ1,x2
ξ2;Q
µ
)fa/p(ξ1, µ)fb/p(ξ2, µ)
+O(Λ2QCD/Q
2)
� µ is naturally set to be of order Q
� Factorization holds up to terms of order Λ2QCD/Q
2
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QCD factorization and PDFs
q
p
p
q
Z0µ
µ
+...
According to QCD factorization theorems, typical cross
sections (e.g., for p(k1)p(k2)→[Z(q)→ `(k3)¯(k4)
]X)
take the form
σpp→`¯X =∑
a,b=q,q,g
∫ 1
0
dξ1
∫ 1
0
dξ2 σab→Z→`¯
(x1
ξ1,x2
ξ2;Q
µ
)fa/p(ξ1, µ)fb/p(ξ2, µ)
+O(Λ2QCD/Q
2)
Purpose of this arrangement:
� Subtract large collinear logarithms αns lnk(Q2/m2
q) from σ
� Resum them in fa/p(ξ, µ) to all orders of αs
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QCD factorization and PDFs
q
p
p
q
Z0µ
µ
+...
According to QCD factorization theorems, typical cross
sections (e.g., for p(k1)p(k2)→[Z(q)→ `(k3)¯(k4)
]X)
take the form
σpp→`¯X =∑
a,b=q,q,g
∫ 1
0
dξ1
∫ 1
0
dξ2 σab→Z→`¯
(x1
ξ1,x2
ξ2;Q
µ
)fa/p(ξ1, µ)fb/p(ξ2, µ)
+O(Λ2QCD/Q
2)
Purpose of this arrangement:
� Hard cross sections σ depend only on the partonic process. They are
computed.
� PDFs fa/h(ξ, µ) are universal functions. They are de�ned in QFTand �measured� for each pair of hadron h and parton a.
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Operator de�nitions for PDFs
To all orders in αs, PDFs are de�ned as matrix elements of certain
correlator functions:
fq/p(x, µ) =1
4π
∫ ∞−∞
dy−eiy−p+〈p
∣∣∣ψq(0, y−,~0T )γ+ψq(0, 0,~0T )
∣∣∣ p〉, etc.Several types of de�nitions, or factorization schemes (MS, DIS, etc.),exist
They all correspond to the probability density for �nding a in p at LO; they
di�er at NLO and beyond
To prove factorization, one must show that fa/p(x, µ) correctly captureshigher-order contributions for the considered observable
This condition can be violated for multi-scale observables
(e.g., DIS or Drell-Yan process at x ∼ Q/√s� 1)
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Exercise. Factorization in pp→ (Z → e+e−)X
The appendices contain
1. A derivation of the NLO cross section for pp→ ZX(on-shell Z boson production)using cut Feynman diagrams |M|2. A lecture by C.-P. Yuan.
2. A derivation of the Born cross section for pp→ (Z → e+e−)(Z boson production and decay) using helicity amplitudesMh1h2h3h4 .
Work out these derivations after the lectures.
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Derive the LO cross section for a spin-1 boson
Traditional path
Lagrangian⇒Feynman rules ⇒∑spin |M|
2⇒Tr (γα1 ...γαn)⇒cross section
Helicity amplitudes
Lagrangian⇒�Feynman rules� for helicity
amplitudes⇒M⇒∑spin |M|
2⇒ cross section
� E�cient computation of tree diagrams
� are building blocks in unitarity-based QCD
calculations
Feynman Rules
� Quark Propagator
i; � j; �
p
i(6p+m)
��
p
2
�m
2
+i�
Æ
ij
(i,j=1,2,3)
Take m=0 inour calculation
� Gluon Propagator
�; a
�; b
k
i(�g
��
)
k
2
+i�
Æ
ab
(a,b=1,2...,8)� Quark-W Vertex
i; �
j; �
W
�
i
g
W
p
2
(
�
)
��
(1�
5
)
2
Æ
ij
g
w
=
e
sin �
w
, weak oupling
� Quark-Gluon Vertex
i; �
; �
j; �
G
�ig (t
)
ji
(
�
)
��
t
is the SU(N)
N�N
generator
� Quark Color Generators
[t
a
; t
b
℄ = if
ab
t
X
t
2
= C
F
I
N�N
C
F
=
N
2
� 1
2N
=
4
3
, (N = 3)
Tr(
X
t
2
) = N C
F
� Many excellent reviews, e.g., Mangano, Parke, Phys. Rep. 200, 301; Dixon,hep-ph/9601359
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Factorization Theorem
σhh� =�
i,j� 10 dx1dx2 φi/h
�
x,Q2�
Hij
�
Q2
x1x2S
�
φj/h��
x2, Q2�
Nonperturbative,but universal,hence, measurable
IRS, Calculablein pQCD
Procedure:
(1) Compute σkl in pQCD with k, l partons
(not h, h� hadron)
σkl =�
i,j
� 1
0dx1dx2 φi/k
�
x1, Q2�
Hij
�
Q2
x1x2S
�
φj/l
�
x2, Q2�
(2) Compute φi/k, φj/l in pQCD
(3) Extract Hij in pQCD
Hij IRS ⇒ Hij indepent of k, l
⇒ same Hij with�
k → h, l → h��
(4) Use Hij in the above equation with φi/h, φj/h�
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Extracting Hij in pQCD
• Expansions in αs:
σkl =∞�
n=0
�αs
π
�nσ(n)kl αs =
g2
4π
Hij =∞�
n=0
�αs
π
�nH
(n)ij
φi/k (x) = δikδ (1 − x) +∞�
n=1
�αs
π
�nφ(n)i/k
↑φ(0)i/k (αs = 0 ⇒ Parton k “ stays itself ”)
• Consequences:
������ � �
����� � ������
������ � �
����� �
������� �
������ � �
������ �
�����
�
�������� ������� � �� � ��
�������� ������ ��������� ������� ���� � �������������� ��������
suppress "^" from now on
process dependent
process independent,
scheme dependent
� �
��
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Feynman Diagrams
• Born level α(0)s (qq�)Born
u
d
W
_
*
2
• NLO: (α(1)s ) virtual corrections (qq�)virt
+ +
12
+
12
2
• NLO: (α(1)s ) real emission diagrams (qq�)real
+
2
• NLO: (α(1)s ) real emission diagrams (qG)real
+
2
• NLO: (α(1)s ) real emission diagrams (Gq�)real
+
2
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In ”Cut-diagram” notation
• (qq�)Born
• (qq�)virt
2 × Re + 12 + 1
2
• (qq�)real
+ + +
• (qG)real
+ + +
• (Gq�)real
Same as (qG)real after replacing q by q�.
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Immediate problems (Singularities)
• Ultraviolet singularity
k
(UV)
∼
∞�
d4k� k � k
(k2) (k2) (k2)→ ∞
• Infrared singularities
kp
+
2(IR)
�
→ ∞
as kµ → 0 (soft divergence)
or kµ � pµ (collinear divergence)
1
(p − k)2 − m2=
1
−2p · k(for m = 0 or m �= 0)
p · k → 0 as
k → 0 or kµ � pµ (for m = 0)
k → 0 (for m �= 0)
(Similar singularities also exist in virtual diagrams.)
• Solutions
Compute Hij in pQCD in n = 4 − 2ε dimensions
(dimensional regularization)
(1) n �= 4 ⇒ UV & IR divergences appear as 1ε
poles
in σ(1)ij (Feynman diagram calculation)
(2) Hij is IR safe ⇒ no 1ε
in Hij
(Hij is UV safe after ”renormalization”.)
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Virtual Corrections (qq�)virt(in Feynman Gauge )
•
= 0
1εIR
and 1εUV
poles cancel when εUV = −εIR ≡ ε
•
1εUV
cancel ⇒ Electroweak coupling is not
renormalized by QCD interactionsat one-loop order(Ward identity,a renormalizable theory)
1εIR
poles remain
•
σ(1)virt is free of ultraviolent singularity.
σ(1)virt = σ(0) αs
2πδ (1 − τ)
�4πµ2
M2
�εΓ (1 − ε)
Γ (1 − 2ε)
·
�
− 2
ε2− 3
ε− 8 +
2π2
3
�
· (CF)
− 2ε2: soft and collinear singularities
−3ε: soft or collinear singularities
CF : color factor
σ(0) ≡ π12s
g2w · (1 − ε)
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Real Emission Contribution (qq�)real
•
1ε
Collinear
1ε2 Soft and Collinear
•
σ(1)real
�qq�
�= σ(0) αs
2π
�4πµ2
M2
�εΓ (1 − ε)
Γ (1 − 2ε)· CF
·
�2
ε2δ (1 − τ) − 2
ε
1 + τ2
(1 − τ)+
+ 4�1 + τ2
�� ln (1 − τ)
1 − τ
�
+
− 21 + τ2
1 − τln τ
�
Note: [· · · ]+ is a distribution,
� 1
0
dz f (z)
�1
1 − z
�
+
=
� 1
0
dzf (z) − f (1)
1 − z, which is finite.
• In the soft limit, τ → 1�τ = M2
s
�,
σ(1)real
�qq�
�−→ σ(0) αs
2π
�4πµ2
M2
�εΓ (1 − ε)
Γ (1 − 2ε)· CF
·
�2
ε2δ (1 − τ) − 4
ε
1
(1 − τ)+
+ 8
�ln (1 − τ)
1 − τ
�
+
�
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�
qq��
virt+
�
qq��
realat NLO
•
σ(1)qq� = σ
(1)virt
�qq�
�+ σ
(1)real
�qq�
�
= σ(0) αs
2π
�4πµ2
M2
�εΓ (1 − ε)
Γ (1 − 2ε)· CF
·
�−2
ε
�1 + τ2
1 − τ
�
+
− 21 + τ2
1 − τln τ + 4
�1 + τ2
�� ln (1 − τ)
1 − τ
�
+
+
�2π2
3− 8
�
δ (1 − τ)
�
Where we have used
−2
ε
�1 + τ2
(1 − τ)+
+3
2δ (1 − τ)
�
=−2
ε
�1 + τ2
1 − τ
�
+
•
All the soft singularities�
1ε2 ,
1ε
�cancel in σ
(1)qq�
⇒ KLN theorem
(Kinoshita-Lee-Navenberg)
•
σ(1)qq� ∼ 1
ε(term) + finite (terms)
↑
Collinear Singularity
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Factorization Theorem
• Perturbative PDF
φ(0)i/k
= δikδ (1 − x)
αs
πφ
(1)i/k
can be calculated from the definition of PDF.
(Process independent,but factorization scheme dependent)
•(1)
σ(0)
kl = H(0)ij
φ(0)i/k
φ(0)j/l
i
j
k
l
⇒ H(0)kl = σ
(0)kl
(2)
σ(1)
kl = H(0)ij
φ(1)i/k
φ(0)j/l
i
j
k
l
+ H(0)ij
φ(0)i/k
φ(1)j/l
i
j
k
l
+ H(1)ij
φ(0)i/k
φ(0)j/l
i
j
k
l
⇒
Factorizationscheme
dependent
H(1)kl = σ
(1)kl −
φ(1)i/kH
(0)il + H
(0)kj φ
(1)j/l
�
Finite Divergent
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Perturbative PDF
• In MS-scheme (modified minimal subtraction)
φ(1)q/q (z) = φ
(1)q/q (z) =
−1
ε
1
2
�
4πe−γE�ε
P(1)q←q (z)
φ(1)q/g (z) = φ
(1)q/g (z) =
−1
ε
1
2
�
4πe−γE�ε
P(1)q←g (z)
where the splitting kernel for φ(1)q←q
q
q
p
zp is
P(1)q←q (z) = CF
�
1 + z2
1 − z
�
+
= CF
�
1 + z2
(1 − z)++
3
2δ (1 − z)
�
,
and for φ(1)q←g
g
q
p
zp
is
P(1)q←g (z) = TR
�
z2 + (1 − z)2�
,
where CF = 43 and TR = 1
2.
Note:The Pole part in the MS scheme is1ε= 1
ε(4πe−γE)ε = 1
ε+ ln4π − γE
In the MS scheme, the pole part is just 1ε
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Find H(1)qq� (in the MS scheme)
• Take off the factor�
αsπ
�
σ(1)qq� = σ(0)
�
P (1)q←q (τ)
�
ln
�M2
µ2
�
− 1
ε+ γE − ln 4π
�
+CF
�
−1 + τ2
1 − τln τ + 2
�1 + τ2
�� ln (1 − τ)
1 − τ
�
+
+
�π2
3− 4
�
δ (1 − τ)
��
•
H(1)qq� (τ) = σ
(1)qq� −
�2φ
(1)q←qσ
(0)qq�
= σ(0) ·
�
P (1)q←q (τ) ln
�M2
µ2
�
+CF
�
−1 + τ2
1 − τln τ + 2
�1 + τ2
�� ln (1 − τ)
1 − τ
�
+
+
�π2
3− 4
�
δ (1 − τ)
��
where
τ =M2
s=
M2
x1x2S, σ(0) = σ(0) · (1 − ε) ,
σ(0) =π
12sg2
w =πg2
w
12S
1
x1x2
.
• pQCD prediction
σhh� =
!�
f=q,q�
�
dx1dx2φf/h
�x1, µ
2� �
σ(0)δ (1 − τ)
φf/h�
�x2, µ
2�
+�
f=q,q�
�
dx1dx2φf/h
�x1, µ
2�
αs
�µ2
�
πH(1)
ff(τ)
�
φf/h�
�x2, µ
2�
+�
f=q,q�
�
dx1dx2φf/h
�x1, µ
2�
αs
�µ2
�
πH(1)
fG (τ)
�
φG/h��x2, µ
2�+ (x1 ↔ x2)
"
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QCD factorization and PDFs
q
p
p
q
Z0µ
µ
+...
According to QCD factorization theorems, typical cross
sections (e.g., for p(k1)p(k2)→[Z(q)→ `(k3)¯(k4)
]X)
take the form
σpp→`¯X =∑
a,b=q,q,g
∫ 1
0
dξ1
∫ 1
0
dξ2 σab→Z→`¯
(x1
ξ1,x2
ξ2;Q
µ
)fa/p(ξ1, µ)fb/p(ξ2, µ)
+O(Λ2QCD/Q
2)
Once we computed partonic cross sections σab→(Z→`¯)X , we must
convolve them with proton PDFs fa/p(ξ1, µ) and fb/p(ξ2, µ).
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Operator de�nitions for PDFs
To all orders in αs, PDFs are de�ned as matrix elements of certain
correlator functions:
fq/p(x, µ) =1
4π
∫ ∞−∞
dy−eiy−p+〈p
∣∣∣ψq(0, y−,~0T )γ+ψq(0, 0,~0T )
∣∣∣ p〉, etc.The exact form of fa/p is not known; but its µ dependence is described by
Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) equations:
µdfi/p(x, µ)
dµ=
∑j=g,u,u,d,d,....
∫ 1
x
dy
yPi/j
(x
y, αs(µ)
)fj/p(y, µ)
Pi/j are probabilities for j → ik collinear splittings;
are known to order α3s (NNLO):
Pi/j (x, αs) = αsP(1)i/j (x) + α2
sP(2)i/j (x) + α3
sP(3)i/j (x) + ...
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Universality of PDFs
To all orders in αs, PDFs are de�ned as matrix elements of certain
correlator functions:
fq/p(x, µ) =1
4π
∫ ∞−∞
dy−eiy−p+〈p
∣∣∣ψq(0, y−,~0T )γ+ψq(0, 0,~0T )
∣∣∣ p〉, etc.PDFs are universal � depend only on the type of the hadron (p) andparton (q, q, g)
... can be parametrized as
fi/p(x,Q0) = a0xa1(1− x)a2F (a3, a4, ...) at Q0 ∼ 1 GeV
... predicted by solving DGLAP equations at µ > Q0
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Example of DGLAP evolution
Compare µ dependence of uquark PDF and the gluon
The u, d PDFs have a
characteristic bump at
x ∼ 1/3 � reminiscent of
early valence quark models of
the proton structure
The PDFs rise rapidly at
x < 0.1 as a consequence of
perturbative evolution
Durham PDF plotter, http://durpdg.dur.ac.uk/hepdata/pdf3.html
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Example of DGLAP evolution
As Q increases, it becomes
more likely that a high-xparton loses some momentum
through QCD radiation
⇒ u(x,Q) reduces atx & 0.1, increases at x . 0.1
Durham PDF plotter, http://durpdg.dur.ac.uk/hepdata/pdf3.html
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Example of DGLAP evolution
As Q increases, it becomes
more likely that a high-xparton loses some momentum
through QCD radiation
⇒ u(x,Q) reduces atx & 0.1, increases at x . 0.1
Durham PDF plotter, http://durpdg.dur.ac.uk/hepdata/pdf3.html
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Example of DGLAP evolution
As Q increases, it becomes
more likely that a high-xparton loses some momentum
through QCD radiation
⇒ u(x,Q) reduces atx & 0.1, increases at x . 0.1
Durham PDF plotter, http://durpdg.dur.ac.uk/hepdata/pdf3.html
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Example of DGLAP evolution
As Q increases, it becomes
more likely that a high-xparton loses some momentum
through QCD radiation
⇒ u(x,Q) reduces atx & 0.1, increases at x . 0.1
Durham PDF plotter, http://durpdg.dur.ac.uk/hepdata/pdf3.html
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Example of DGLAP evolution: u and gluon PDF
g(x,Q) can become negative
at x < 10−2, Q < 2 GeV
may lead to unphysical
predictions
This is an indication that
DGLAP factorization
experiences di�culties at such
small x and Q
Large lnk(1/x) in Pi/j(x)break PQCD expansion at
x ∼ Q/√s� 1
Linear scale
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Example of DGLAP evolution: u and gluon PDF
g(x,Q) can become negative
at x < 10−2, Q < 2 GeV
may lead to unphysical
predictions
This is an indication that
DGLAP factorization
experiences di�culties at such
small x and Q
Large lnk(1/x) in Pi/j(x)break PQCD expansion at
x ∼ Q/√s� 1
Logarithmic scale
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Example of DGLAP evolution: u and gluon PDF
As Q increases, g(x,Q) growsrapidly at small x
αs(Q) becomes small enoughto suppress lnk(1/x) terms
small-x behavior stabilizes
Logarithmic scale
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Example of DGLAP evolution: u and gluon PDF
As Q increases, g(x,Q) growsrapidly at small x
αs(Q) becomes small enoughto suppress lnk(1/x) terms
small-x behavior stabilizes
Logarithmic scale
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Example of DGLAP evolution: u and gluon PDF
As Q increases, g(x,Q) growsrapidly at small x
αs(Q) becomes small enoughto suppress lnk(1/x) terms
small-x behavior stabilizes
Logarithmic scale
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Where do the PDFs come from?
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Recent CT10 NNLO PDFs[arXiv:1302.6246]
10-4 0.001 0.01 0.1 10.0
0.2
0.4
0.6
0.8
1.0u-vald-val0.1 g0.1 sea
Q = 2 GeV
10-4 0.001 0.01 0.1 10.0
0.2
0.4
0.6
0.8
1.0u-vald-val0.1 g0.1 sea
Q = 3.16 GeV
10-4 0.001 0.01 0.1 10.0
0.2
0.4
0.6
0.8
1.0u-vald-val0.1 g0.1 sea
Q = 8 GeV
10-4 0.001 0.01 0.1 10.0
0.2
0.4
0.6
0.8
1.0u-vald-val0.1 g0.1 sea
Q = 85 GeV
x fHx ,QL versus x
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CT10 NNLO describes well LHC 7 TeV experiments
|lep
η|0 0.5 1 1.5 2 2.5
+ X
) [p
b]
ν l
→ +
W→
| (p
p
lep
η/d
|σ
d 500
520
540
560
580
600
620
640
660
680
RESBOS CT10NNLO1ATLAS 35 pb
|lep
η|0 0.5 1 1.5 2 2.5
+ X
) [p
b]
ν l
→ W
→| (p
p
lep
η/d
|σ
d 300
320
340
360
380
400
420
440
460
480
RESBOS CT10NNLO1ATLAS 35 pb
|lep
η|0 0.5 1 1.5 2 2.5
chA
0.1
0.15
0.2
0.25
0.3
0.35
RESBOS CT10NNLO1ATLAS 35 pb
|ηlep|η0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4
chA
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26 > 35 GeVe
Tp
data
CT10NNLO PDF err.
=7 TeVS, 1
L dt=840 [pb]∫CMS electron charge asymmetry,
0<ÈyÈ<0.3
0.85
0.90
0.95
1.00
1.05
1.10
1.15ATLAS inc. jet H2010, R=0.6L
Ratio w.r.t. CT10 NNLO
using FASTNLOv2
PDF unc.H68%L
Shifted Data
+Hsta.&unc.L
Scale unc.
0.3<ÈyÈ<0.8
0.85
0.90
0.95
1.00
1.05
1.10
1.152.1<ÈyÈ<2.8
0.85
0.90
0.95
1.00
1.05
1.10
1.15
0.8<ÈyÈ<1.2
0 200 400 600 800 1000 1200
0.85
0.90
0.95
1.00
1.05
1.10
1.15
PT HGeVL
2.8<ÈyÈ<3.6
0 100 200 300 400 500
0.85
0.90
0.95
1.00
1.05
1.10
1.15
PT HGeVL
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NNLO gluon PDF xg(x,Q) from 5 groups
Linear x scale
Logarithmic xscale
x0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
= 0.118sα) 2 = 25 GeV2xg(x, Q
NNPDF2.3 NNLO
CT10 NNLO
MSTW2008 NNLO
NNPDF2.3 NNLO
CT10 NNLO
MSTW2008 NNLO
NNPDF2.3 NNLO
CT10 NNLO
MSTW2008 NNLO
= 0.118sα) 2 = 25 GeV2xg(x, Q
x0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
= 0.118sα) 2 = 25 GeV2xg(x, Q
NNPDF2.3 NNLO
ABM11 NNLO
HERAPDF1.5 NNLO
NNPDF2.3 NNLO
ABM11 NNLO
HERAPDF1.5 NNLO
NNPDF2.3 NNLO
ABM11 NNLO
HERAPDF1.5 NNLO
= 0.118sα) 2 = 25 GeV2xg(x, Q
x5
10 4103
10 210 110 10
5
10
15
20
25
30
35
40
= 0.118sα) 2 = 25 GeV2xg(x, Q
NNPDF2.3 NNLO
CT10 NNLO
MSTW2008 NNLO
NNPDF2.3 NNLO
CT10 NNLO
MSTW2008 NNLO
NNPDF2.3 NNLO
CT10 NNLO
MSTW2008 NNLO
= 0.118sα) 2 = 25 GeV2xg(x, Q
x5
10 4103
10 210 110 10
5
10
15
20
25
30
35
40
= 0.118sα) 2 = 25 GeV2xg(x, Q
NNPDF2.3 NNLO
ABM11 NNLO
HERAPDF1.5 NNLO
NNPDF2.3 NNLO
ABM11 NNLO
HERAPDF1.5 NNLO
NNPDF2.3 NNLO
ABM11 NNLO
HERAPDF1.5 NNLO
= 0.118sα) 2 = 25 GeV2xg(x, Q
R. Ball, et al., 1211.5142
Several PDF groups provide their parametrizations of PDFs. How are these
parametrizations obtained?
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The �ow of the PDF analysisPDFs are not measured
directly, but some data
sets are sensitive to
speci�c combinations of
PDFs. By constraining
these combinations, the
PDFs can be disentangled
in a combined (global) �t.
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The �ow of the PDF analysisData sets and χ2/d.o.f.in CT10 NNLO andCT10W NLO analyses
Modern �ts involve up to 40 experiments, 5000+ data points,
and 100+ free parameters
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The �ow of the PDF analysisWe are interested not just
in one best �t, but also in
the uncertainty of the
resulting PDF
parametrizations and
theoretical predictions
based on them.
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Stages of the PDF analysis
1. Select valid experimental data
2. Assemble most precise theoretical cross sections and verify their
mutual consistency
3. Choose the functional form for PDF parametrizations
4. Implement a procedure to handle nuisance parameters (>200 sources
of correlated experimental errors)
5. Perform a �t
6. Make the new PDFs and their uncertainties available to end users
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Requirements for PDF parametrizationsPDF parametrizations for fa/p(x,Q) must be ��exible just enough� toreach agreement with the data, without violating QCD constraints (sum
rules, positivity, ...) or reproducing random �uctuations
good fit
F2(x, Q2)
x
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Requirements for PDF parametrizationsPDF parametrizations for fa/p(x,Q) must be ��exible just enough� toreach agreement with the data, without violating QCD constraints (sum
rules, positivity, ...) or reproducing random �uctuations
bad fit
F2(x, Q2)
x
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Requirements for PDF parametrizationsPDF parametrizations for fa/p(x,Q) must be ��exible just enough� toreach agreement with the data, without violating QCD constraints (sum
rules, positivity, ...) or reproducing random �uctuations
good fit
F2(x, Q2)
x
Traditional solution
�Theoretically motivated� functions with
a few parameters
fi/p(x,Q0) = a0xa1(1− x)a2
×F (x; a3, a4, ...)
� x→ 0: f ∝ xa1 � Regge-like behavior
� x→ 1: f ∝ (1− x)a2 � quark
counting rules
� F (a3, a4, ...) a�ects intermediate x;just a convenient functional form
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Requirements for PDF parametrizationsPDF parametrizations for fa/p(x,Q) must be ��exible just enough� toreach agreement with the data, without violating QCD constraints (sum
rules, positivity, ...) or reproducing random �uctuations
F2(x, Q2)
x
Radical solutionNeural Network PDF collaboration
� Generate N replicas of the
experimental data, randomly scattered
around the original data in accordance
with the probability suggested by the
experimental errors
� Divide the replicas into a �tting
sample and control sample
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Requirements for PDF parametrizationsPDF parametrizations for fa/p(x,Q) must be ��exible just enough� toreach agreement with the data, without violating QCD constraints (sum
rules, positivity, ...) or reproducing random �uctuations
F2(x, Q2)
x
Radical solutionNeural Network PDF collaboration
� Parametrize fa/p(x,Q) byultra-�exible functions � neural networks
� A statistical theorem states that any
function can be approximated by a neural
network with a su�cient number of
nodes (in practice, of order 10)
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Requirements for PDF parametrizationsPDF parametrizations for fa/p(x,Q) must be ��exible just enough� toreach agreement with the data, without violating QCD constraints (sum
rules, positivity, ...) or reproducing random �uctuations
new good fit
old good fit
F2(x, Q2)
x
Radical solutionNeural Network PDF collaboration
� Fit the neural nets to the �tting
sample, while demanding good
agreement with the control sample
� Smoothness of fa/p(x,Q) is preserved,despite its nominal �exibility
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Hessian error PDFs (CT10)
x f(x,Q) vs. x
10-4 0.001 0.01 0.1 10.0
0.2
0.4
0.6
0.8
1.0u-vald-val0.1 g0.1 sea
Q = 2 GeV Neural network PDFs
x5
10 4103
10 210 110
2
1
0
1
2
3
4
5
6
7
)2xg(x, Q
NNPDF2.3 NLO replicas
NNPDF2.3 NLO mean value
error bandσNNPDF2.3 NLO 1
NNPDF2.3 NLO 68% CL band
)2xg(x, Q
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Multi-dimensional error analysis
aiai0
χ20
χ2
� Minimization of a likelihood
function (χ2) with respect to
∼ 30 theoretical (mostly PDF)
parameters {ai} and > 100experimental systematical
parameters
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Multi-dimensional error analysis
a+ia−i aiai0
∆χ2
χ20
χ2� Establish a con�dence region
for {ai} for a given tolerated
increase in χ2
� In the ideal case of perfectly
compatible Gaussian errors,
68% c.l. on a physical
observable X corresponds to
∆χ2 = 1 independently of the
number N of PDF parameters
See, e.g., P. Bevington, K. Robinson, Data analysis anderror reduction for the physical sciences
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Multi-dimensional error analysis
ai
χ2 Pitfalls to avoid
� �Landscape�
I disagreements between the
experiments
In the worst situation, signi�cant
disagreements between Mexperimental data sets can
produce up to N ∼M ! possiblesolutions for PDF's, with
N ∼ 10500 reached for �only�
about 200 data sets
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Multi-dimensional error analysis
ai
χ2 Pitfalls to avoid
� Flat directions
I unconstrained combinations
of PDF parameters
I dependence on free
theoretical parameters,
especially in the PDF
parametrization
I impossible to derive reliable
PDF error sets
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Multi-dimensional error analysis
χ2
ai
The actual χ2 function shows
� a well pronounced global
minimum χ20
� weak tensions between data
sets in the vicinity of χ20
(mini-landscape)
� some dependence on
assumptions about �at
directions
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Multi-dimensional error analysis
χ2
ai
The actual χ2 function shows
� a well pronounced global
minimum χ20
� weak tensions between data
sets in the vicinity of χ20
(mini-landscape)
� some dependence on
assumptions about �at
directions
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Multi-dimensional error analysis
χ2
ai
The actual χ2 function shows
� a well pronounced global
minimum χ20
� weak tensions between data
sets in the vicinity of χ20
(mini-landscape)
� some dependence on
assumptions about �at
directions
The likelihood is approximately described by a quadratic χ2 with a revised
tolerance condition ∆χ2 ≤ T 2
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Multi-dimensional error analysis
χ2
ai
The actual χ2 function shows
� a well pronounced global
minimum χ20
� weak tensions between data
sets in the vicinity of χ20
(mini-landscape)
� some dependence on
assumptions about �at
directions
The likelihood is approximately described by a quadratic χ2 with a revised
tolerance condition ∆χ2 ≤ T 2
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Multi-dimensional error analysis
∆χ2
ai0 a+ia−i
χ2
ai
The actual χ2 function shows
� a well pronounced global
minimum χ20
� weak tensions between data
sets in the vicinity of χ20
(mini-landscape)
� some dependence on
assumptions about �at
directions
The likelihood is approximately described by a quadratic χ2 with a revised
tolerance condition ∆χ2 ≤ T 2
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Con�dence intervals in global PDF analyses
Monte-Carlo sampling of the PDF parameter space
PDFs
Unweighted
ai
χ2
Average
A very general approach that
� realizes stochastic sampling of the
probability distribution
(Alekhin; Giele, Keller, Kosower; NNPDF)
� can parametrize PDF's by �exible
neural networks (NNPDF)
� does not rely on smoothness of χ2
or Gaussian approximations
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Modern parton distribution functions
...are indispensable in computations of inclusive hadronic reactions at
CERN and other laboratories
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Conclusions
� QCD theory at all energies undergoes rapid developments, with
much attention paid to
I ingenious perturbative computations for multi-particle states, fully
di�erential cross sections
I new factorization methods for di�erential cross sections and all-order
resummations
I sophisticated analysis of nonperturbative hadronic functions
� The global analysis help us to understand rich interconnections
between perturbative and nonperturbative features of QCD processes
and make sense of rich LHC dynamics
Pavel Nadolsky (SMU) 2014-12-09 35