study of g production in association with jets using the cms detector

Study of g production in association with jets using

the CMS detector

Michael Anderson

July 21, 2011

2/47Mike Anderson

July 21, 2011Outline• Standard Model of Particle Physics

• Events of photon + jets

• Large Hadron Collider

• Compact Muon Solenoid Detector

• Detector/Physics Simulation

• Measuring Jets and Photons

• Conclusions

Mike AndersonJuly 21, 2011

3/47Standard Model• Quarks interact via Strong

Force (g), leptons cannot• Quarks, e, m, t interact via

Electromagentic Force (g)• Both quarks and leptons

interact via Weak Force (W, Z)• Quarks are tightly bound can

cannot be detected individually

• Quarks combine to form composite particles– Examples:

Quarks (Fermions)

Leptons (Fermions)

Force Carriers (Bosons)

proton neutron pion

HHiggs


4/47Composite Particles• Probing composite particles (like

protons) at high energy will find gluons and “sea” quarks

• All quarks & gluons within hadrons referred to as “partons”

• Parton Distribution Functions (PDFs):– defined as probability density for

finding a particle with a certain momentum fraction, x, at a given momentum transfer

– Must be determined experimentally

– Needed as input to make theoretical predictions

proton

Simplified picture. In high-energy collisions, energy is available for finding “sea” quarks

x = p(parton)/p(hadron)


5/47Photons and Jets• Prompt Photons: come directly

from interaction– Energy & position can

be measured accurately– Prompt, Isolated photons

provide good probe of hard-scattering process (like pp collisions)

• Jets: quarks and gluons fragment into collimated collection of hadrons– Must measure jets to

determine momentum of original scattered parton

– Non-prompt photons produced within jets

(“jet”)

Jet example


6/47Motivation for g+jets• Motivation for study of

photon+jet events includes:– Test/Validate theoretical

predictions• Cross section calculations are

challenging as the number of jets increases

– Explore new kinematic regions in hadron-hadron collisions

– They are background to pp->Higgs->gg

– Also background for beyond standard model searches

– Ability to constraining PDFs of the proton

– Calibrate jet energy scales

Prompt Photons

Bremsstrahlung Photons

• Prompt photons produced from quark-gluon scattering & quark-anti-quark annihilation• Primary prompt photon background comes from neutral meson decays

7/47Mike Anderson

July 21, 2011Goal• Goal: measurement of the rate of events in

which a proton-proton collision produces a prompt photon and jets

• Prefer to measure inclusive rate of jets (rate of events with ≥ n jets), and to not correct for acceptance of the detector


8/47

Large Hadron Collider

• Circumference of 27 km• In 2010, collided protons with

center-of-mass energy of 7 TeV• Protons are organized into bunches (next slide)

9/47Mike Anderson

July 21, 2011Proton Collisions at LHC

Luminosity L = particle flux/time

Interaction rate

Cross section, = “effective” area of interacting particles

During 2010 run: Beam energy 3.5 TeV (7 TeV center of mass) Peak Luminosity, L = 2x1032 cm-2s-1

Recorded 36pb-1 of p-p collisions

dNdt

=L

Design Achieved1380 bunch/beam1.3*1011

3.5 TeV1.3*1033 cm-2s-1

10/47Mike Anderson

July 21, 2011Compact Muon SolenoidSolenoid (3.8T) Muon chambers

Silicon Strip & Pixel Tracker

Electromagnetic Calorimeter Hadronic Calorimeter

Brass/Scintillator

Forward calorimeter

Weight: 12,500 metric tonsDiameter: 15 mLength: 21.5 m

11/47Mike Anderson

July 21, 2011Compact Muon Solenoid← Surface assembly hall

CMS together underground ↓

Endcap Discs: Designed, assembled & installed by Wisconsin


12/47Detector Geometry• Pseudorapidity h = -ln(tan(q/2)

• Another common variable:– Radius: DR = ((Df)2 + (Dh)2)1/2

(used for sizes of jets, for example)

h=0.0

h=inf

h=1.5

h=3.0

One quadrant slice of CMS parallel to proton beam pipe

f=p/2

f=0

Slice of CMS perpendicular to beam pipe


13/47Particle Detection• Prompt Photons:

– Deposit of of Energy in ECAL

– Generally isolated from other energy deposits in Tracker, ECAL & HCAL

– Found by clustering energy of ECAL crystals

• Jets– Energy deposit in ECAL & HCAL– With tracks– Found by clustering tracks and

energy deposits in the calorimeters

• Detector returns quantities like transverse momentum, pT, and transverse energy, ET

pp collisionpoint

Slice of CMS perpendicular to proton beam pipe

14/47Mike Anderson

July 21, 2011Particle Flow Algorithm

• Particles are found using Particle-Flow (PF) Algorithm

• Collects information from all subdetectors– Tracker, ECAL, HCAL, and muon System

• Information from each sub-detector is linked to find individual particles (e,g,m,charged & neutral hadrons)– Example: track is associated with ECAL deposit and so

found an electron • All particles found are then available to be clustered

with jet algorithms– Used “anti-kT” clustering algorithm


15/47g+jet Characteristics• Prompt Photon is

generally isolated deposit of energy in ECAL (red)

• Jet is collimated collection of tracks (green), and deposit of energy in the ECAL (red) and HCAL (blue)

• Events with 1 prompt photon and 1 jet have the photon and jet roughly opposite in f Photon

Jet

Slice of CMS perpendicular to beam pipe


16/47Silicon Tracker• Measures pT & path of

charged particles within |h| < 2.5

• Strip Tracker– 200 m2 coverage– 10m precision

measurements– 11M electronic channels

• Inner Pixel tracking system– 66M channels

• Used for isolating prompt photons, and finding jets & measuring their pT


17/47

h

h=-ln(tan(q/2)

Electromagnetic Calorimeter• Measures energy &

position of electrons and photons within |h| < 3

• PbWO4 crystals, very dense (8.3 g/cm3)– 23 cm long (26 radiation

lengths)– 61K in the barrel, 22 x 22

mm2– 15K in the endcaps, 28 x

28 mm2

• Resolution:


18/47Hadronic Calorimeter• Barrel and Endcap:

brass & scintillator• Coverage to || < 3• x =0.087x0.087• Hadron Forward: steel &

quartz fiber: coverage 3 < || < 5

• Also used for isolating photons and finding jets

• Resolution on energy of a single particle:


19/47Trigger• Level 1: Hardware trigger operating at bunch crossing rate

(40MHz at design luminosity)– Brings event rate down to 50-100 kHz

• Level 2: – Reconstruction done using High-Level Trigger (HLT) -- computer farm– Reduces rate from Level-1 value of up to 100 kHz to final value of

~300 to 400 Hz– Slower, but determines energies and track momenta to high

precision


20/47Level-1 Trigger• ~25 ns bunch

crossings*2.2 interactions/crossing– Not all events can

be stored/processed

• L1 trigger electronics select 50-100 kHz of interesting events

• e/g trigger:– 8 or 12 GeV threshold– ~100% efficient

HF HCAL ECAL RPC CSC DT

PatternComparator

Trigger

RegionalCalorimeter

Trigger

4 m

e, J, ET, HT, ETmiss

Muon Trigger

max. 100 kHz L1 Accept

Global Trigger

Global Muon Trigger

GlobalCalorimeter

Trigger

Local DT Trigger

Local CSC Trigger

DT TrackFinder

CSC TrackFinder

40 M

Hz

pipe

line,

lat

ency

< 3

.2 m

s

Calorimeter Trigger

21/47Mike Anderson

July 21, 2011Computing• CMS is dependent on computing for transmitting, storing, and processing

data– Every collision event ~0.2MB, and we record ~300 events/s– Needs to be shared with ~2000 collaborators around the world

• Uses “tiered” system to organize responsibilities among many computing facilities around the world– One Tier0: CERN– Several Tier1s: One per country, FNAL in US– Dozens of Tier2s: One is here at UW-Madison

• I was involved with Tier2 responsibilities (production of monte carlo simulations for collaboration, support of end-user analysis…)

Tier0: CERN

Tier1: Fermilab

Tier2: UW-Madison

Many other computing facilities not shown

22/47Mike Anderson

July 21, 2011Data• Data entirely collected in 2010• Total of 36 pb-1 of high quality data (all subdetectors

working well)• Required events to pass a trigger requiring the presence

of at least one high-energy photon• Trigger required a clustered deposit of energy that passes:

– ET > minimum thresh (20 GeV in early runs, and raised as luminosity increased)

– Ratio of Hadronic E to Electromagnetic E < 0.15– Energy shape ratio (called ‘R9’) < 0.98 in barrel of CMS (to

remove anomalous ‘spikes’ from ionization of APD’s in barrel of CMS)


23/47Monte Carlo Predictions• Data is compared to

predictions made by simulations of proton collisions– Simulations are made

by software called Monte Carlo event generators

• Two useful programs used in this analysis:– Pythia: simulates events

of g+1 jet.Pythia can only simulate processes of 2->2.

– Madgraph/Madevent: used to simulate g+n jets (n = 1 to 3).Madgraph does fixed order matrix element calculations of cross sections.Madgraph is interfaced to use Pythia for jet hadronization.

• Both simulators used as input the same parton distribution functions – from the CTEQ collaboration

Example diagram of a generated event

24/47Mike Anderson

July 21, 2011Analysis Steps• Select events with at least one photon passing

selection, then count number of jets above a pT threshold

• Select signal from data:– Determine fraction of signal & background by fitting a

distribution in which signal & background have different shapes

• Correct for selection efficiency– efficiency = (number of photons passing some selection) / (all

true hard-scattering photons)• Unsmear the measured jet distributions to obtain a

distribution that may be compared directly with theoretical predictions (called “unfolding”)


25/47Analysis Flow• Final Plots:

– σ(γ + ≥n jets) / σ(γ + ≥1 jets)– σ(γ + ≥n jets) / σ(γ + ≥(n-1) jets)

• Where:

– Ns=number of events with a prompt photon and n jets

– U=Unsmearing (‘Unfolding’) correction

– ε=Efficiency– Lint = integrated luminosity

Events

Event Selection

Exclusive Njet distributions

Find signal fraction

Correct Njet dist. for efficiency

Unfold Njet dist.

Change Njet binning from exclusive to inclusive

26/47Mike Anderson

July 21, 2011Event Selection• Pass single photon trigger • Photon passing:

– pT > 75 GeV– |h|<1.4442 or 1.566<|h|<2.5

• Measuring photons is problematic in boundary region

– Energy Isolation [next slide]• Jet, if present:

– pT > 30 GeV– |h| < 2.4– Loose Jet Identification [next slide]

• Standard selection to selection high-quality proton-proton collision events– Removes events where beam interacted with beam pipe– The presence of a vertex close to nominal interaction point (|z|<24cm)

Barrel EndcapEndcap

27/47Mike Anderson

July 21, 2011

Tracker

ECALHCAL

Photon Selection• Photon Isolation quantities:

– Sum of energy in cones aligned with a line from primary vertex to center of photon energy deposit in ECAL

– Sums do not include small central region to avoid including photon energy itself

– Radius of cone = 0.4• Selection on isolation sums:

– Track Iso < 2.0 GeV– Ecal Iso < 4.2 GeV– Hcal Iso < 2.2 GeV

• Selection on photon energy itself:– Ratio of Hadronic E to Electromagnetic E < 0.05

To measure isolation of photon, energy is summed around the photon


28/47Photon Selection• Isolation sums

around photons– True photons

generally have lower values while Jets have higher values

Remove > 4.2 GeV

Remove > 2.2 GeVRemove > 2.0 GeV

29/47Mike Anderson

July 21, 2011Jet Selection• Jets are collimated, clustered energy in the

Tracker, ECAL, and HCAL within a maximum cone size of R = 0.5

• Jet selection is very loose, simply to remove noise or anomalous signals

• Additional selection for jets:– Photon must not overlap with jet, DR(jet,lead g) > 0.5– Jets not from the same pp collision were removed by

requiring distance between jet vertex and event vertex < 0.2 cm

Charged Hadron EnergyFraction > 0.0Charged Em Energy Fraction < 0.99

Charged Multiplicity > 0Neutral Hadron Energy Fraction < 0.99

Neutral EmEnergy Fraction < 0.99

30/47Mike Anderson

July 21, 2011Number of Events• Number of events left after each selection

• Jets leave deposits of energy in ECAL which are background to true photons– Isolation requirements removes a significant

amount of these

Selection Number of EventsEvents in original “Photon” dataset 25 MTrigger passed; Vertex cuts; Photon passing pT and h cuts

645867

Photon passing Iso cuts and H/E cut 51905Jet passing selection & pT(jet)>30 GeV 42248


31/47Lead photon; jet pT• pT distribution of lead

photon and lead jet (if found) for both data and Madgraph MC

• Used from Madgraph to scale to data– Madgraph is leading-order

and underestimates yield– Scaled by ~1.6 to better

compare shapes– Will fit to a variable to

determine amount of signal & background in data (shown in 2 slides)

Prompt Photon

Jet


32/47Jet Multiplicity• Exclusive number of jets

above pT threshold– Madgraph samples simulated

up to g+3 jets

• pT distribution for 2nd and 3rd leading jet is modeled by Madgraph reasonably well

Jet Multiplicity

2nd Jet pT3rd Jet pT


33/47Signal Extraction• To measure number of signal

event must measure fraction of signal in data

• We use a shower-shape variable of the lead photon defined as sum over ECAL crystals in photon’s cluster:

• Where:

• Signal shower shape comes from MC, but background shape comes from data with a sideband selection

Photon in Barrel

Photon in Endcap


34/47

Fitting template variable• Fit σiηiη to determine

fraction of signal in data– Used Extended

Maximum-Likelihood fits

– Fits are performed separately in Barrel and Endcap, and for each # of jets

• Jet distributions then scaled by these fractions

Photon in Barrel

Photon in Endcap


35/47Signal Fraction• Results of fits to σiηiη

• Signal fraction was found to be generally higher in the barrel

• Too few stats in the endcap for higher jet multiplicity– Used average of lower

multiplicity bins

Number of Jets (with pT>30GeV) Barrel Endcap1 68 ± 1 58 ± 1

2 73 ± 2 54 ± 2

3 73 ± 4 64 ± 6

4 84 ± 8 -

5 80 ± 3 -

36/47Mike Anderson

July 21, 2011Efficiency Correction• Efficiency = number of prompt photons passing selection /

all prompt photons• Photon isolation selection efficiency depend on number of

jets– Ultimately had to use efficiency from MC for high energy photons,

but checked efficiency from MC against data for low energy photons

• Pure sample of photons is hard to get with amount of data available

• However, electrons & photons leave similar energy deposits in ECAL, so it is reasonable to use sample of electrons to find efficiency– We can use events of ‘pp -> Z -> ee’ for very pure sample of

electrons


37/47Tag & Probe Fits• Found photon selection efficiency

using events of pp -> Z -> ee– First, require the presence of

electron passing tight selection– Next, require another electron which

also came from the Z (the invariant mass of the electron pairs satisfy 60 < Minv(ee) < 120 GeV)

• This method is called ‘Tag and Probe’– Tag: Election passing tight selection &

pT>20GeV

– Probe: Electron with pT> 30 GeV– Also counted number of jets

• Fits to invariant mass performed to determine amount of electrons before & after requiring probe electron to pass photon selecton


38/47Eff of Loose Photon Iso• Efficiency of

photon selection vs number of jets with pT>30GeV– Using fits to Z mass– For photons with

pT > 30 GeV

• Efficiency decreases by a few % as number of jets increases

N Jets

Data T&P Madgraph T&P

Madgraph g+jets

1 87 ± 1 88 ± 0.1 86 ± 0.12 81 ± 3 85 ± 0.3 79 ± 0.23 - 84 ± 0.8 74 ± 0.44 - - 68 ± 0.8

N Jets

Data T&P Madgraph T&P

Madgraph g+jets

1 90 ± 3 91 ± 0.3 89 ± 0.22 89 ± 7 89 ± 8 85 ± 0.43 - 90 ± 2 83 ± 0.84 - - 80 ± 2

Probe/g in Barrel

Probe/g in Endcap


39/47Unsmearing Jet Multiplicity• Due to detector resolution, number

of jets found by detector may differ from number of generated jets

• Must unsmear or ‘unfold’ to remove effects of measurement resolutions, systematic biases, and detection efficiency to determine “true” distribution

• Shown here is a matrix from MC of number of generated jets vs number of measured jets– Called ‘response matrix’

• These are used to unsmear measured number of jets to obtain a distribution that can be compared to theory

• Response matrices here from Pythia and Madgraph signal MC– rows are normalized to they sum to 1

for easy comparison

Madgraph g + jet Response Matrix


40/47Unsmearing Jet Multiplicity• Performed unsmearing

using Bayesian (“iterative”) method with 4 iterations

• Unfolding has an effect of a few % at 1 or 2 jet multiplicities, up to 50% at highest multiplicities


41/47Incl. Jet Multiplicity• Inclusive jet multiplicity,

ratio of “≥njets” to “≥1jets”

• Data is after all corrections and unfolding

• Madgraph and Pythia comparison are with generated particles before any detector simulation– Data agrees well with

madgraph up to ≥3jets– Pythia only simulates g+1

jet, and simulates showering


42/47Incl. Jet Mult Ratio• Inclusive jet multiplicity

ratio of “≥njets” to “≥(n-1)jets”

• Data is after all corrections and unfolding

• Madgraph and Pythia comparison are with generated particles before any detector simulation– Data agrees well with

madgraph up to ≥3jets– Pythia only simulates g+1

jet, and simulates showering

43/47Mike Anderson

July 21, 2011Systematics• Largest Uncertainty is from uncertainty in jet energy

– Affects the counting of jets above an pT threshold

• Uncertainty of jet energy scale arises from:– Uncertainty in flavor composition between jets used to determine

energy corrections and jets in γ+jets will add ~2% on uncertainty– Subtraction is performed remove 500 MeV to jets in events without

pile–up, so we must add this to systematic uncertainty in jet energy– Uncertainty in energy corrections as function of jet η and pT

• Uncertainty in efficiency of photon selection as function of number of jets

• Uncertainty in signal fraction from different background template selection

44/47Mike Anderson

July 21, 2011Systematics Details• Systematics on ratio “(njet)/ ((n-1)jet)”

– Jet Energy Scale (+/- 1 to jet energies)– Background Template (different selection for efficiency extraction)– Selection Efficiency (using Pythia vs Madgraph)– Unfolding (using response matrix from Pythia vs Madgraph)

≥2 / ≥1 jets ≥3 / ≥1 jets ≥4 / ≥1 jets ≥5 / ≥1 jetsJES (+/-1) +13/-4 % +23/-5 % +15/-18% +27/-36 %Signal Fraction +4 % +8 % -8 % -10 %Efficiency ±4 % ±4 % ±4 % ±4 %Unfolding -1 % +7 % +8 % +9 %

Total +14/-6 % +26/-6 % +17/-20 % +29/-38 %

≥2 / ≥1 jets ≥3 / ≥2 jets ≥4 / ≥3 jets ≥5 / ≥4 jetsJES (+/-1) +13/-4 % +9/-1 % +7/-13 % +11/-22 %Signal Fraction +4 % +4 % -15 % -3 %Efficiency ±4 % ±4 % ±4 % ±4 %Unfolding +2 % +4 % +1 % -4 %

Total +14/-6 % +11/-1 % +8/-26 % +12/-23 %


45/47Jet Multiplicity Results• Inclusive jet multiplicity

agrees well with Madgraph in 2 &3 jet bins, data is higher than Madgraph in higher jet multiplicities– Systematics are higher

for larger number of jets primarily due to jet energy uncertainty

Jet Multiplicity

σ ratio Stat Sys

≥ 2 / ≥1 jets 0.36 ±0.003 +0.05/-0.02≥ 3 / ≥1 jets 0.09 ±0.001 +0.02/-0.005≥ 4 / ≥1 jets 0.02 ±0.005 +0.003/-

0.004≥ 5 / ≥1 jets 0.005 ±0.0003 +0.001/-

0.001

Jet Multiplicity

σ ratio Stat Sys

≥ 2 / ≥1 jets 0.36 0.003 +0.05/0.02≥ 3 / ≥2 jets 0.25 0.004 +0.03/-

0.003≥ 4 / ≥3 jets 0.26 0.007 +0.02/-0.07≥ 5 / ≥4 jets 0.23 0.01 +0.03/-0.05


46/47From W/Z+Jets• Another CMS analysis

found number of jets in events with W or Z

• They also measured ratio of number of events of“x+ ≥n jets” to “x+ ≥(n-1) jets” where x=W or Z

• Mine was the first to use photon + jets

47/47Mike Anderson

July 21, 2011Summary• Presented first measurement of jet rate in association with a high-pT

photon• Results with 36pb-1 of data

– Loose photon isolation selection– Jets with pT > 30 GeV, R=0.5, using anti-kT algorithm

• Rates of jets in agreement with Madgraph simulations for ≥1 and ≥2 jets, but higher than predicted for ≥3 and ≥4– This is expected because Madgraph sample used contains matrix elements

for up to g+3jets– Data disagreement with Pythia is also expected because Pythia only

simulates photon+1 jet, and uses showering to create more jets

• Tuned Monte Carlo event generators can be used for new physics searches. Ex:– g+1 jet: background to H->gg– g+jets+missing ET: a signature for super-symmetry

48/47Mike Anderson

July 21, 2011

Backup

49/47Mike Anderson

July 21, 2011MC Simulations• Events simulated with MadGraph

– Fixed order matrix element calculations of cross sections– Generates multi-parton processes in hadronic collisions.

• Hadronization simulation performed with Pythia 6– Simulates development of underlying event– Generates jets from hadronization, also simulates parton showers, and initial

and final state radiation• Detector simulated using GEANT4

– Toolkit for the simulation of the passage of particles through matter

Hard scattering

MadGraph

Hadronization, showers, IFSR

PYTHIA

Detector simulation

GEANT4

Reconstruction of event

CMSSW

study of g production in association with jets using the cms detector

Documents

quarks gluons

electromagnetic force

mevfree quarks

weak force w

single electroweak force

strong force g

isolated photons

jetsprompt photons