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Page 1: New  Photon  Results from CDF

New Photon Results from CDFCostas Vellidis

Fermilab

DIS 2012, Marseilles, April 22

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Photon analyses at CDF• Photon-related analyses have been hot topics at CDF• ~30 papers published using CDF Run II data on a

wide variety of photon-related topics.o Cross section measurementso Searches

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Hγγ

Xgg

Inclusive-g

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Diphoton cross sections

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p

g

g

p_

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Prompt gg production in hadron colliders

Born: a2

ggqq qgq gg

Compton+radiation asa2

Dg/q~a/as

Fragmentation: a2

Suppressed byisolation cut

“Box”: Dominantat the LHC

gggg

Hard QCD (“direct” gg production):

colinearsingularity

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Prompt gg production in hadron colliders

Born: a2

ggqq qgq gg

Compton+radiation asa2

Dg/q~a/as

Fragmentation: a2

Suppressed byisolation cut

“Box”: Dominantat the LHC

gggg

Hard QCD (“direct” gg production):

Possible heavy resonance decays:

H →γγ

G∗ →γγ

Higgs boson

colinearsingularity

Extra dimensions4/24/13

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• Identified the importance of resummation, qg fragmentation in the modeling of diphoton cross sections.

PRL 107 (2011) 102003PRD 84 (2011) 052006

5.4 fb-1

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Previously published results – CDF

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Previously published results – D0

• Sherpa describes data the best in the intermediate PT(gg) and low gg regions.

PT1(2)>18(17) GeV/c, |η1,2|<0.9R(g,g)>0.4, ET

iso<2.5 GeV

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arXiv:1301.4536Full Run II data set

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Previously published results – ATLASJHEP 1301 (2013) 086

PT1(2)>25(22) GeV/c,|η1,2|<2.37R(g,g)>0.4

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Previously published results – CMS

DIPHOX discrepancy for PT(gg)>30 GeV and (g,g)<π/2

JHEP 1201 (2012) 133

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Collinear diphoton production• Fragmentation – a higher-order effect

o The pQCD cross section is divergent when q and g are collinear logarithmic enhancement of the cross section

o Handled with a fragmentation function – MCFM, DIPHOXo Affects low m(gg), moderate PT(gg) and low gg regions

• Higher order subprocesses (23 at 1-loop and 24 at “tree” level) needed to describe the enhancement

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Resummation• Remove singularities [PT(gg)->0] by adding initial gluon radiation

o RESBOS: Low-PT analytically resummed calculation (NNLL) matched to high-PT NLO

o PYTHIA and SHERPA: Use parton showering to add gluon radiation in a Monte Carlo simulation framework which effectively resums the cross section (LL)

o Affects low PT(gg) and gg = p regions

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PRD 76, 013009 (2007)

a sn

PT2 γγ( )

lnm M 2 γγ( )PT

2 γγ( )

− α snδ

r P T γγ( )( )

n =1,...∞ m = 0,...,2n −1

or

Fixed-order calculation contains singular terms at and M(gg) ≠ 0 of the form

PT γγ( ) →0

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Updated diphoton cross section measurements

• Use the full 9.5 fb-1 CDF run II dataset• Select isolated diphoton events

o Background subtraction using track isolation information• Pythia evaluation of efficiency/acceptance/unfolding• Compare results with new predictions

dσdX

=Nγγ

ε ⋅ A⋅ L⋅ Δ

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The Tevatron and CDFTevatron:Proton-antiproton accelerator√s = 1.96 TeVDelivered ~12 fb-1

Recorded ~10 fb-1 for each experiment

CDFCollider Detector at FermilabTracking (large B field):

◦ Silicon tracking◦ Wire Chamber

Calorimetry:◦ Electromagnetic (EM)◦ Hadronic

Muon system

σ(E) / E =13.5% / E (GeV) ⊕1.5%4/24/13

A big thank you to Accelerator Division!

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Photon identification and event selection

Used dedicated diphoton triggers with optimized efficiency

Photons were selected offline from EM clusters, reconstructed in a cone of radius R=0.4 in the – plane, and requiring:

• Fiducial to the central calorimeter: ||<1.1

• ET 17,15 GeV (gg events)

• Isolated in the calorimeter: Ical = Etot(R=0.4) - EEM(R=0.4) 2 GeV

• Low HAD fraction: EHAD/EEM 0.055 + 0.00045Etot/GeV

• At most one track in cluster with pTtrk 1 GeV/c + 0.005ET

g/c

• Shower profile consistent with predefined patterns: 2CES 20

• Only one high energy CES cluster: ET of 2nd CES cluster 2.4 GeV + 0.01 ET

γ

CP2: pre-shower CES: shower maximum profile

EM Cal HAD Cal

Isolation cone: R=0.4 rad

Imply thatR(g,g) or R(g,j) 0.4

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Theoretical predictions• PYTHIA LO parton-shower calculation – including gg and gj with radiation [T. Sjöstrand et al., Comp. Phys. Comm. 135, 238 (2001)]

• SHERPA LO parton-shower calculation with improved matching between hard and soft physics [T. Gleisberg et al., JHEP 02, 007 (2009)]

• MCFM: Fixed-order NLO calculation including non-perturbative fragmentation at LO [J. M. Campbell et al., Phys. Rev. D 60, 113006 (1999)]

• DIPHOX: Fixed-order NLO calculation including non-perturbative fragmentation at NLO [T. Binoth et al., Phys. Rev. D 63, 114016 (2001)]

• RESBOS: Low-PT analytically resummed calculation matched to high-PT NLO [T. Balazs et al., Phys. Rev. D 76, 013008 (2007)]

• NNLO calculation with qT subtraction [L. Cieri et al., http://arxiv.org/abs/1110.2375 (2011)]

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Theoretical predictions• PYTHIA LO parton-shower calculation – including gg and gj with radiation [T. Sjöstrand et al., Comp. Phys. Comm. 135, 238 (2001)]

• SHERPA LO parton-shower calculation with improved matching between hard and soft physics [T. Gleisberg et al., JHEP 02, 007 (2009)]

• MCFM: Fixed-order NLO calculation including non-perturbative fragmentation at LO [J. M. Campbell et al., Phys. Rev. D 60, 113006 (1999)]

• DIPHOX: Fixed-order NLO calculation including non-perturbative fragmentation at NLO [T. Binoth et al., Phys. Rev. D 63, 114016 (2001)]

• RESBOS: Low-PT analytically resummed calculation matched to high-PT NLO [T. Balazs et al., Phys. Rev. D 76, 013008 (2007)]

• NNLO calculation with qT subtraction [L. Cieri et al., http://arxiv.org/abs/1110.2375 (2011)]

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Integrated cross section (pb)

Data (CDF) 12.3 ± 0.2stat ± 3.5syst

RESBOS 11.3

DIPHOX 10.6

MCFM 11.5

SHERPA 12.4

PYTHIA gg+gj 9.2

NNLO 11.8

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m(gg)

• Good agreement between data and theory for Mgg>30 GeV/c2 except PYTHIA gg

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PT(gg)

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PT(gg) - ratios

• RESBOS agrees with low PT(gg) data the best

• SHERPA agrees with low PT(gg) data well

• NNLO and SHERPA describe the “shoulder” of the data at PT(gg) = 20 – 50 GeV/c (the “Guillet shoulder”)

NB: Vertical axis scales are not the same

DIPHOX

RESBOS

PYTHIA NNLO

MCFM

SHERPA

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(gg)

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(gg)- ratios

• RESBOS and SHERPA describe (gg) = p region• Fixed order calculations do not describe (gg) = p region• NNLO describes (gg) = 0 region

NB: Vertical axis scales are not the same

DIPHOX

RESBOS

PYTHIA NNLO

MCFM

SHERPA

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Summary of diphoton cross sections• High precision gg cross sections are measured using the full CDF Run II dataset

• The data are compared with all state-of-the-art calculations

• The SHERPA calculation, overall, provides good description of the data, but still low in regions sensitive to nearly collinear gg emission (very low mass, very low Δϕ)

• The RESBOS calculation provides the best description of the data at low PT and large Δϕ, where resummation is important, but fails in regions sensitive to nearly collinear gg emission

• The NNLO calculation provides the best description of the data at low Δϕ, but still not very good at very low mass and at high PT

• More in PRL 110, 101801 (2013) (supplemental material online)

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Photon+heavy flavor (b/c) cross sections

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p

g

p_

b-jet

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g+b/c+X production• Photon produced in association with heavy

quarks provides valuable information about heavy flavor excitation in hadron collisionso LO contribution: Compton scattering (QgQg)

dominates at low photon pT

o NLO contribution: annihilation (qqQQg) dominates at high photon pT

Compton scattering ~ aaS Annihilation ~ aaS2

Q

g

q

q

- -

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-

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Previous results – D0PRL 102, 192002 (2009) − 1 fb-1

• Good agreement for g+b+X• Discrepancy for g+c+X

PLB 714, 32 (2012) – 8.7 fb-1

g+b+X

PLB 719, 354 (2013) – 8.7 fb-1

Discrepancies in both channels.

g+c+X

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Previous results – CDF

• Measure low pT cross section using a special trigger• g+b+X agrees with NLO up to 70 GeV

CDF: PRD 81, 052006 (2010) - 340 pb-1

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Analysis overview• Measure g+b/c+X cross section using 9.1 fb-1 inclusive photon

data collected with CDF II detector• Use ANN (artificial neural network) to select photon

candidateso Fit ANN distribution to signal/background templates to get photon

fraction• Use SecVtx b-tag to select heavy-flavor jets

o Fit secondary vertex invariant mass to get light/c/b quark fractions• Use Sherpa MC to get efficiency/unfolding factor

o Photon ID efficiency, b-tagging efficiency, detector acceptance and smearing effects

• Cross sectiono

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dσdET

γ =N⋅ fγ ⋅ fb / c

ε ⋅ A⋅ L⋅ ΔETγ

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4 theoretical predictions• NLO – direct-photon subprocesses and fragmentation subprocesses

at O(aas2), CTEQ6.6M PDFs [T.P. Stavreva and J.F. Owens, PRD 79,

054017 (2009)]

• kT-factorization – off-shell amplitudes integrated over kT-dependent parton distributions, MSTW2008 PDFs [A.V. Lipatov et al., JHEP 05, 104 (2012)]

• Sherpa 1.4.1 – tree-level matrix element (ME) diagrams with one photon and up to three jets, merged with parton shower, CT10 PDFs [T. Gleisberg et al., JHEP 02, 007 (2009)]

• Pythia 6.216 – ME subprocesses: gQgQ, qqgg followed by gluon splitting: gQQ, CTEQ5L PDFs [T. Sjöstrand et al., JHEP 05, 026 (2006)]

__

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g+b+X cross sections

• NLO fails to describe data at large photon Et – perhaps gluon splitting is treated at LO• kT-factorization and Sherpa agree with data reasonably well• Pythia with doubled gluon splitting rate to heavy flavor describes the shape

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NB: Vertical axis scales are not the same

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g+c+X cross sections

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• NLO fails to describe data at large photon Et – perhaps gluon splitting is treated at LO• kT-factorization and Sherpa agree with data reasonably well• Pythia with doubled gluon splitting rate to heavy flavor describes the shape

NB: Vertical axis scales are not the same

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Summary of photon+b/c cross sections• High precision g+b/c cross sections are measured using the full CDF Run II

dataset

• The data are compared with parton shower, fixed-order and kt-factorization calculations

• NLO does not reproduce data most likely because of its limitation in modeling gluon splitting rates.

• kT-factorization and Sherpa agree with data reasonably well

• Pythia with doubled gluon splitting rates to heavy flavor describes the data shape

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Conclusions• The CDF experiment has produced a wealth of QCD physics

results and analysis techniques, which is a legacy for the current and future high energy physics experiments

• We have achieved an unprecedented level of precision for many photon-related observables

• Those results provide valuable information to the HEP community, e.g. the diphoton results can help the precision measurements of H boson in the gg channel.

• … and we are not done yet!!

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Interesting kinematic variables•

o Search for resonances.•

o Sensitive to activity in the event.•

o Sensitive to production mechanism.

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m(γγ) = (E1 + E2)2 − (p1 + p2)2

PT (γγ) = PT1 + PT 2

φ(γγ )

PT1

PT2

=0

g1

g2

p p_

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Interesting kinematic variables•

o Search for resonances.•

o Sensitive to activity in the event.•

o Sensitive to production mechanism.

• Fragmentation/higher order diagramso Two g’s go almost collinearo Low m(gg), intermediate PT(gg), low (gg)

• Resummationo Low PT(gg), high (gg)

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=0

g1

g2

p p_

m(γγ) = (E1 + E2)2 − (p1 + p2)2

PT (γγ) = PT1 + PT 2

φ(γγ )

Special case=0

g1

g2

p p_

PT1

PT2

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Background subtraction using track isolation

• Sensitive only to underlying event and jet fragmentation (for fake g)

• Immune to multiple interactions (due to z-cut) and calorimeter leakage

• Good resolution in low-ET region, where background is most important

• Uses charged particles only

Signal: direct diphotonsBackground: jets misidentified as photons – jg, jj

trkT

cmzz

Rintrackstrk pI

trkvtx

5

4.0

Signal Probability (Itrk<1 GeV)

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Background Probability (Itrk<1 GeV)

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Background subtraction• For a single g, a weight can be defined to characterize it as signal or

background:o = 1 (0) if Itrk () 1 GeV/c

o s = signal probability for Itrk 1 GeV/c

o b = background probability for Itrk 1 GeV/c

• For gg, use the track isolation cut for each photon to compute a per-event weight under the different hypotheses (gg, g+jet and dijet):

b

b

s

w

w jj

w jγ

wγj

wγγ

⎜ ⎜ ⎜ ⎜ ⎜

⎟ ⎟ ⎟ ⎟ ⎟

= E −1 ×

N ff

N fp

N pf

N pp

⎜ ⎜ ⎜ ⎜ ⎜

⎟ ⎟ ⎟ ⎟ ⎟

Both photons fail

Leading fail, trailing passes

Leading passes, trailing fails

Both photons pass

N ff

N fp

N pf

N pp

⎜ ⎜ ⎜ ⎜ ⎜

⎟ ⎟ ⎟ ⎟ ⎟

=

0010

⎜ ⎜ ⎜ ⎜

⎟ ⎟ ⎟ ⎟

e.g. leading passes/trailing fails

4/24/13Transfer matrixFunction of s and b

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• Average 40%• Better at high mass:

o 60-80% for m() 80-150 GeV/c2

o 80% for m( )>150 GeV/c2

• Better at high PT():o 70% for PT() >100 GeV/c

• 15-30% sys. errors4/24/13

Signal fractions

Signal fraction =Nγγ

Ndata

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Efficiency×Acceptance• Estimated using detector-

and trigger-simulated and reconstructed PYTHIA events

• Procedure iterated to match PYTHIA kinematics to the data

Uncertainties in the efficiency estimation:• 3% from material uncertainty• 1.5% from the EM energy scale• 3% from trigger efficiency uncertainty• 6% (3% per photon) from underlying

event (UE) correction• Total systematic uncertainty: ~7-15%

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Experimental systematic uncertainties

• Total systematic uncertainty 15-30%, smoothly varying with the kinematic variables considered

• Main source is background subtraction, followed by overall normalization (efficiencies: 7%; integrated luminosity: 6%; UE correction: 6%)

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Integrated cross section (pb)

Data (CDF) 12.3 ± 0.2stat ± 3.5syst

RESBOS 11.3 ± 2.4

DIPHOX 10.6 ± 0.6

MCFM 11.5 ± 0.3

SHERPA 12.4 ± 4.4

PYTHIA gg+gj 9.2

NNLO 11.8 + 1.7 – 0.6

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Comparison with D0

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dσdX

=Nγγ

ε ⋅ A⋅ L⋅ Δ

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A closer look at fragmentation: DIPHOX isolation study

iso < 2 GeViso < 2 GeViso < 2 GeV

Fragmentation strength is missing from the DIPHOX calculation possibly because of the approximate application of the isolation requirement at the parton level

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TotalDirect1-frag2-frag

ETiso < 2 GeV

ETiso < 10 GeV

A closer look at fragmentation: DIPHOX isolation study

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Event selection• Use inclusive photon trigger to select photon events

o Trigger efficiency is approximately 100% for g ET>30 GeV

• Interaction vertex in the fiducial region• Photon candidate must pass a neural-net based photon

IDo ANN>0.75o ||<1.05, 30<ET<300 GeV, divided into 8 ET bins

• Jets are reconstructed with JetClu cone size 0.4 and must be positively tagged. o ||<1.5, ET>20 GeV

• R(g,jet)>0.4

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ANN photon ID• Trained with TMVA (Toolkit for Multivariate Data Analysis)• 7 input variables to take into account difference between g and p0/: isolation

(2), lateral shower shape (3), Had/Em, CES/CEM• ANN ID improves signal efficiency by 9% at the same background rejection

compared with the standard cut-based ID.• Use MC with full detector simulation to get templates

o Signal – prompt photonso Background – jets with prompt photons removed

prompt photons

p0,

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True photon fraction• Fit data ANN distribution using signal and

background templates to get true photon fraction

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True photon fraction (continued)

• Systematicso Photon energy scaleo Vary inputs to photon ID ANN according to their uncertaintieso Vary Photon ID ANN template binning to test sensitivity to shapeso 6% at low ET, 2% at high ET.

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Standard b-jet identification

• B-hadrons are long-lived – search for displaced vertices

• Fit displaced tracks and cut on Lxy significance (σ ~ 200 mm)

• Charm hadrons have similar tag behavior but lower efficiency

• Use “tag mass” to deduce the flavor composition of a sample of tagged jetso Mass of the tracks forming the

secondary vertexo B-hadrons are heavy: will have

higher mtag spectrum than charm or light jet fakes

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Light/c/b-jet fractions

• Fit data secondary vertex mass using MC templates• Shape of secondary vertex mass for event with fake photon is

taken from di-jet data4/24/13

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Light/c/b-jet fractions (continued)

Results from fitter.4/24/13

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Systematics on b/c-jet fractions

• Jet energy scale: affects acceptance• Uncertainty in tracking efficiency: scale secondary vertex

mass templates by ±3%o Dominant systematic effect

• Difference between single-quark and di-quark jets• Total systematic error is ~20%

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Efficiency×Acceptance

• Use Sherpa MC to unfold photon ID efficiency, b-tagging efficiency, detector acceptance and smearing effects.

• Systematic effects evaluatedo photon energy scale and IDo jet energy scaleo b-tagging efficiencyo Generatoro PDF4/24/13