Jet Physics - Experimental Aspects - Murilo Rangel ! ! New Trends in HEP and QCD School Outline
This lecture is an attempt to cover the experimental challenges of jet physics at LHC ! How to go from detector hits to theory comparison? ! What are the new ideas for jet tools? ! Notes: 1) jet algorithms⨁theory are not covered in this lectures 2) nice lectures already given (overlap is expected) Grégory Soyez, Albert de Roeck, Bruno Lenzi, Rikkert Frederix, Marc Besançon 3) some experimental aspects are not covered (ex: efficiencies, trigger)
Murilo Rangel - IF/UFRJ 2 Reminder
Reminder
TeV4LHC QCD WG - hep-ph/0610012
Murilo Rangel - IF/UFRJ 3 Is this really what is happening?
Murilo Rangel - IF/UFRJ 4 Epistemological Realism - Personal View
Truth is a place we can not go
Murilo Rangel - IF/UFRJ 5 Epistemological Realism - Personal View
Truth is a place we can not go But, we can take pictures of it
Murilo Rangel - IF/UFRJ 5 Epistemological Realism - Personal View
Truth is a place we can not go
Murilo Rangel - IF/UFRJ 5 Epistemological Realism - Personal View
Truth is a place we can not go And, we can paint how we think it is
Murilo Rangel - IF/UFRJ 5 Epistemological Realism - Personal View
Truth is a place we can not go
Murilo Rangel - IF/UFRJ 5 Epistemological Realism - Personal View
Truth is a place we can not go
Our job is to compare photographies with paintings Both photographers and painters are doing a great job
Murilo Rangel - IF/UFRJ 5 Jet evolution
‘’stable’' particles
decays
hadrons time hadronization
partons
proton proton
Murilo Rangel - IF/UFRJ 6 Jet Clustering
+ Algorithms that combine nearest particles ! o Cambridge/Aachen algorithm: combine particles nearest each other ! o “kT” algorithm: preference for combining lower-momentum particle pairs first, then moving on to higher-momentum pairs ! o “anti-kt” algorithm collects particles around the hardest particle first. It guarantees “cone-like geometry” with well-defined borders around the highest-kT particles and it maintains the infrared safety and collinear safety of sequential recombination family ! + These algorithms correspond to p=0, p=1 and p=-1. ! 2 2 2 ∆ ! d =min(k p,k p) ij ij ti tj R2
Murilo Rangel - IF/UFRJ 7 Jet evolution
“hadron-level” / “particle-level” jets or “stable” particles* used as inputs of the jet algorithm ‘’stable’' particles
decays
hadrons time hadronization
“parton-level” jets partons used as inputs of the jet algorithm
partons
proton proton
*Neutrinos are excluded Murilo Rangel - IF/UFRJ 8 Experimental Challenge
- Only “stable’’ particles are detected (� >10-8 s)* - Prior knowledge of their interactions are needed
http://www.particleadventure.org/
0 *These include �, K, p, n, e, �, � and KL . Neutrinos are invisible
Murilo Rangel - IF/UFRJ 9 Particle Interaction
R. Cavanaugh, HCPSS 2012
Murilo Rangel - IF/UFRJ 10 Particle Interaction
Nuclear Interaction Length Mean distance over which a hadron collides with a nuclei
� ~ 35 g cm-2 A1/3
R. Cavanaugh, HCPSS 2012
Murilo Rangel - IF/UFRJ 10 Particle Interaction
Nuclear Interaction Length Mean distance over which a hadron collides with a nuclei
� ~ 35 g cm-2 A1/3
Radiation length Mean distance over which the electron energy is reduced by a factor of 1/e due to radiation losses
R. Cavanaugh, HCPSS 2012
Murilo Rangel - IF/UFRJ 10 Particle Interaction
Nuclear Interaction Length Mean distance over which a hadron collides with a nuclei
� ~ 35 g cm-2 A1/3
Typical values for lead
� ~ 17 cm
X0 ~ 5.5 mm
Radiation length Mean distance over which the electron energy is reduced by a factor of 1/e due to radiation losses
R. Cavanaugh, HCPSS 2012
Murilo Rangel - IF/UFRJ 10 Calorimeters
a is the stochastic term b is the electronic noise c includes detector effects
Hadronic Calorimeter - high density material - total absorption of energy - � = 50-100% / √ET
EM Calorimeter - high-Z material - total absorption of energy - � = 1-10% / √ET
Murilo Rangel - IF/UFRJ 11 Calorimeters vs Tracker
R. Cavanaugh, HCPSS 2012
Murilo Rangel - IF/UFRJ 12 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! Muons
http://www.particleadventure.org/
Murilo Rangel - IF/UFRJ 13 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! Electrons / Photons
http://www.particleadventure.org/
Murilo Rangel - IF/UFRJ 14 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! Hadrons
http://www.particleadventure.org/
Murilo Rangel - IF/UFRJ 15 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! Converted photons
location of converted photons
Murilo Rangel - IF/UFRJ 16 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! Converted photons
location of converted photons
Murilo Rangel - IF/UFRJ 16 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! V0s (� ~ 10-10 s)
V0s live long enough to reconstruct its vertex
Murilo Rangel - IF/UFRJ 17 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! V0s (� ~ 10-10 s)
V0s live long enough to reconstruct its vertex
Murilo Rangel - IF/UFRJ 17 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! π0→γγ
Murilo Rangel - IF/UFRJ 18 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! Neutral Hadrons
neutral hadron
charged hadron
Primary Vertex
Murilo Rangel - IF/UFRJ 19 Particle Flow
Strategy to get most of detector is to match tracks with calorimeter clusters ! Track momentum is preferred over calorimeter energy ! Steps are ordered motivated by momentum resolution and particle identification purity ! Neutral Hadrons
neutral hadron
charged hadron
Primary Vertex
Murilo Rangel - IF/UFRJ 19 Particle Flow
Murilo Rangel - IF/UFRJ 20 Jets - Experimental Picture
Reconstructed “particles” are used as inputs of the jet algorithm - “detector-level” jets
“Detector-level” jets must be corrected/calibrated to compare with theory/models Calibration of jets to “particle-level” is necessary ! Calibrated jets - little dependence with detector effects (segmentation, response and resolution) - good resolution and no angle biases - good efficiency and low fake rate (Jet Identification) - stable with beam luminosity (pile-up) - computer time efficient ! Inputs - calorimeter cells/towers/clusters - tracks - tracks+calorimeter (particle flow) ! !
Murilo Rangel - IF/UFRJ 21 Jet Identification
At “detector-level”, the jet algorithm can reconstruct fake jet candidates: - Hadronic tau decays (electrons and photons too) - Cosmic ray - Detector noise - Pile-Up contribution
Murilo Rangel - IF/UFRJ 22 Jet Identification
At “detector-level”, the jet algorithm can reconstruct fake jet candidates: - Hadronic tau decays (electrons and photons too) - Cosmic ray - Detector noise - Pile-Up contribution
Jet characteristics can be used to reduce these background rate to O(1%) o Charged Fraction (from PV) o EM Fraction
Murilo Rangel - IF/UFRJ 22 Jet Identification
At “detector-level”, the jet algorithm can reconstruct fake jet candidates: - Hadronic tau decays (electrons and photons too) - Cosmic ray - Detector noise - Pile-Up contribution
Jet characteristics can be used to reduce these background rate to O(1%) o Charged Fraction (from PV) o EM Fraction
Fake jets sample ATLAS Preliminary 5 after Looser cuts 10 Data 2011, s = 7 TeV after Loose cuts after Medium cuts 4 pjet>150 GeV, |η|<2 after Tight cuts 10 T Good jets sample
103 Number of jets / 0.05
102
10
1 0 0.2 0.4 0.6 0.8 1 1.2 1.4
fch
Murilo Rangel - IF/UFRJ 22 Jet Identification
At “detector-level”, the jet algorithm can reconstruct fake jet candidates: - Hadronic tau decays (electrons and photons too) - Cosmic ray - Detector noise - Pile-Up contribution
Jet characteristics can be used to reduce these background rate to O(1%) o Charged Fraction (from PV) o EM Fraction
Fake jets sample Fake jets sample ATLAS Preliminary 8 ATLAS Preliminary 5 after Looser cuts 10 after Looser cuts 10 Data 2011, s = 7 TeV after Loose cuts Data 2011, s = 7 TeV after Loose cuts 107 after Medium cuts jet after Medium cuts jet p >150 GeV 4 p >150 GeV, |η|<2 after Tight cuts 6 T after Tight cuts 10 T Good jets sample 10 Good jets sample 105 103
Number of jets / 0.05 Number of jets / 0.05 104 2 10 103 102 10 10 1 1 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.2 0.4 0.6 0.8 1
fch fEM
Murilo Rangel - IF/UFRJ 22 Jet Identification
At “detector-level”, the jet algorithm can reconstruct fake jet candidates: - Hadronic tau decays (electrons and photons too)
-1 - Cosmic ray ×103CMS Preliminary, s = 8TeV L=20 fb Z→µµ Data - Detector noise 600 All |η| < 2.5 Jet p > 25 GeV Gluon T Quark - Pile-Up contribution 500 PU Real Jet Events/0.025 400 Jet characteristics can be used to reduce these 300 background rate to O(1%) 200 o Charged Fraction (from PV) 100 o EM Fraction 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 β
Fake jets sample Fake jets sample ATLAS Preliminary 8 ATLAS Preliminary 5 after Looser cuts 10 after Looser cuts 10 Data 2011, s = 7 TeV after Loose cuts Data 2011, s = 7 TeV after Loose cuts 107 after Medium cuts jet after Medium cuts jet p >150 GeV 4 p >150 GeV, |η|<2 after Tight cuts 6 T after Tight cuts 10 T Good jets sample 10 Good jets sample 105 103
Number of jets / 0.05 Number of jets / 0.05 104 2 10 103 102 10 10 1 1 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0.2 0.4 0.6 0.8 1
fch fEM
Murilo Rangel - IF/UFRJ 22 Jet Composition
LHCb Generator
Jet at “particle level”
LHCb Simulation
Jet at "detector level” (uncorrected)
Murilo Rangel - IF/UFRJ 23 Jet Composition
Tests of parton-shower⨁hadronization models are necessary
particle energy fraction
Murilo Rangel - IF/UFRJ 24 Calibration Factorization
Atlas
CMS
Murilo Rangel - IF/UFRJ 25 Calibration Factorization
Atlas
CMS
Murilo Rangel - IF/UFRJ 25 Pile Up
Murilo Rangel - IF/UFRJ 26 Pile Up
CMS 78 collisions
Murilo Rangel - IF/UFRJ 27 Pile up
Pile Up Vertex Primary Vertex
Murilo Rangel - IF/UFRJ 28 Pile up Correction
In-time pile up activity depends on the number of Primary Vertices (PVs) Out-of-time pile up activity depends on the average number of PVs
larger R, larger PU
Murilo Rangel - IF/UFRJ 29 Pile up Correction
Jet “independent” PU correction is also possible, e.g., jet area method. ➡ adding “infinite” number of very soft 4-momentum vectors to cluster jets j jet area is defined (A ) as the space occupied by the very soft particles j j ➡ distribution of pT /A is related to the PU activity
NPVs=1
Number of PVs
Murilo Rangel - IF/UFRJ 30 Jet Energy Calibration - Simple view
Using simulation, we can match jets at “particle-level” and at “detector-level” ! ! ! ! ! ! Calibration factor is taken from the ratio pT(detector-level)/pT(particle-level) + factor is applied to 4-momentum: angle biases needed to be checked !
100 Counts 80 If your detector is perfect => delta function
60
40
20
0 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 p (detector-level) / p(particle-level) T T
Murilo Rangel - IF/UFRJ 31 Jet Energy Calibration - Samples
We do not want to be simulation dependent. ! Data-driven methods are developed using production of well calibrated object with a jet photon+jet or Z(→µ+µ-/e+e-)+jet ! ! ! ! ! ! ! ! ! Two jet production are also very useful for relative jet energy calibration
Murilo Rangel - IF/UFRJ 32 Jet Energy Calibration - Dijet
Dijet sample ➡ Both objects are the subject for calibration - this sample can be used to calibrate one region of the detector relative to another one. ➡ Jet energy (pT) resolution can be measured
Murilo Rangel - IF/UFRJ 33 Jet Energy Calibration - V+jet
�/Z+jet The reference object energy resolution is much better than the jet energy resolution. ! ➡ At LO, the �/Z is balanced with the parton that originates the jet. ! ➡ Missing transverse energy projection fraction (MPF) is used to include effects like: - additional parton radiation - underlying-event (UE) contribution - out-of-cone contribution
Murilo Rangel - IF/UFRJ 34 Jet Energy Calibration - V+jet
�/Z+jet The reference object energy resolution is much better than the jet energy resolution. ! ➡ At LO, the �/Z is balanced with the parton that originates the jet. ! ➡ Missing transverse energy projection fraction (MPF) is used to include effects like: - additional parton radiation - underlying-event (UE) contribution - out-of-cone contribution
Murilo Rangel - IF/UFRJ 34 Jet Energy Calibration - V+jet
�/Z+jet The reference object energy resolution is much better than the jet energy resolution. ! ➡ At LO, the �/Z is balanced with the parton that originates the jet. ! ➡ Missing transverse energy projection fraction (MPF) is used to include effects like: - additional parton radiation - underlying-event (UE) contribution - out-of-cone contribution
Murilo Rangel - IF/UFRJ 34 Jet Energy Calibration - V+jet
�/Z+jet The reference object energy resolution is much better than the jet energy resolution. ! ➡ At LO, the �/Z is balanced with the parton that originates the jet. ! ➡ Missing transverse energy projection fraction (MPF) is used to include effects like: - additional parton radiation - underlying-event (UE) contribution - out-of-cone contribution
Murilo Rangel - IF/UFRJ 34 Jet Energy Calibration - V+jet
�/Z+jet The reference object energy resolution is much better than the jet energy resolution. ! ➡ At LO, the �/Z is balanced with the parton that originates the jet. ! ➡ Missing transverse energy projection fraction (MPF) is used to include effects like: - additional parton radiation - underlying-event (UE) contribution - out-of-cone contribution
Murilo Rangel - IF/UFRJ 34 Jet Energy Calibration - V+jet
�/Z+jet The reference object energy resolution is much better than the jet energy resolution. ! ➡ At LO, the �/Z is balanced with the parton that originates the jet. ! ➡ Missing transverse energy projection fraction (MPF) is used to include effects like: - additional parton radiation - underlying-event (UE) contribution - out-of-cone contribution
Murilo Rangel - IF/UFRJ 34 Jet Energy Calibration - ISR/FSR
Jet2 γ By vetoing additional jets in the sample (pT >α pT ), the effect of initial and final parton radiation can be studied
leading jet
2nd jet
Photon
Murilo Rangel - IF/UFRJ 35 Jet Energy Calibration - ISR/FSR
Jet2 γ By vetoing additional jets in the sample (pT >α pT ), the effect of initial and final parton radiation can be studied
Murilo Rangel - IF/UFRJ 36 Residual Correction
Calibration derived in data may need to be corrected by residual effects ➾ data-to-MC differences ➾ different MC can provide different corrections
Murilo Rangel - IF/UFRJ 37 Sanity check using W→jets
Good knowledge of the W boson mass can be used to test the jet energy calibration ➾ W from top quark decay ➾ sensitive to jets originating from quarks
Murilo Rangel - IF/UFRJ 38 Jet Calibration - Flavour dependence
How can the calibration vary by changing the initial parton flavour (gluon)?
Usually no extra correction is applied ! Differences go to the systematic error of the calibration
Murilo Rangel - IF/UFRJ 39 Jet Calibration for b-jets
Using a data sample enriched in b-jets, one can check possible additional corrections
Murilo Rangel - IF/UFRJ 40 Jet pT resolution
In dijet events, asymmetry distribution provides information about the jet energy resolution ! ⦿ Extra activity affects the resolution ⇒ resolution is evaluated with different veto thresholds of a third jet in the event ⦿ Contribution from balance between “particle-jets” need to be considered
Murilo Rangel - IF/UFRJ 41 Jet pT resolution
In dijet events, asymmetry distribution provides information about the jet energy resolution ! ⦿ Extra activity affects the resolution ⇒ resolution is evaluated with different veto thresholds of a third jet in the event ⦿ Contribution from balance between “particle-jets” need to be considered
Murilo Rangel - IF/UFRJ 41 Jet pT resolution
In dijet events, asymmetry distribution provides information about the jet energy resolution ! ⦿ Extra activity affects the resolution ⇒ resolution is evaluated with different veto thresholds of a third jet in the event ⦿ Contribution from balance between “particle-jets” need to be considered
Murilo Rangel - IF/UFRJ 41 Unfolding
To compare with theory, one needs to unfold measured distribution ➡ correction for bin migration effects due to detector resolution ➡ non-trivial mathematical operation
Figure from Mikko Voutilainen
Murilo Rangel - IF/UFRJ 42 Measurement
Murilo Rangel - IF/UFRJ 43 Measurement
Calibration Correction Unfolding
Murilo Rangel - IF/UFRJ 43 Measurement
Calibration Correction Unfolding
Murilo Rangel - IF/UFRJ 43 Measurement
24 orders of magnitude
Murilo Rangel - IF/UFRJ 44 Measurement
How the jet reconstruction parameter (R) affects cross section?
⦿ pQCD calculation considers the ratio directly, rather than each distribution separately, making the calculated ratio effectively one perturbative order higher than the individual cross sections
Murilo Rangel - IF/UFRJ 45 Missing Transverse Energy
Neutrinos and dark matter particles are identified with MET ➡ Use of calibrated objects: muons, photons, electrons, jets ➡ Pileup robust strategy ➡ Resolution can be used to quantify MET consistency
“Real” MET
“Fake” MET
Murilo Rangel - IF/UFRJ 46 b-tagging
⦿ b-hadrons can decay >~ 1 mm away from the PV. - Need secondary vertex resolution of O(30 µm) ! ⦿ c-hadrons have similar behaviour
p⊥ p⊥ mB d0 ∼ θLB ∼ LB ∼ (cτB )γB ∼ (cτB )γB ∼ (cτB ) ! p|| " ! p|| " ! pB "
Murilo Rangel - IF/UFRJ 47 Secondary Vertex
Murilo Rangel - IF/UFRJ 48 b-tagging
⦿ Use of tracks is mandatory ! ⦿ Several variables can be used for discrimination between b-jets and l-jets Multivariate techniques are often used
ATLAS Preliminary 104 JetProb SV0
IP3D
SV1 103 IP3D+SV1
Light jet rejection JetFitter
IP3D+JetFitter 102
10 tt simulation, s=7 TeV pjet>20 GeV, |ηjet|<2.5 T 1 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 b-jet efficiency
Murilo Rangel - IF/UFRJ 49 b-tagging
⦿ Use of tracks is mandatory ! ⦿ Several variables can be used for discrimination between b-jets and l-jets Multivariate techniques are often used ) 2 LHCb 4000 beauty charm
2000 Candidates / (20 MeV/c
di-b-jet sample 0 2 4 6 8 10 2 Mcorr [GeV/c ]
Murilo Rangel - IF/UFRJ 50 Quark-Gluon Tagging ⦿ Many measurements and searches can benefit from identifying jets parton origin ➯ Models that predict production of many quarks vs multi-jet QCD production ! ⦿ Colour factor ∝ radiation ∝ particle multiplicity ➯ CA/CF = 9/4 ! ⦿ Other variables can be used: width, number of subjets, etc.
CMS Preliminary, L = 18.3 fb -1 at s = 8 TeV CMS Simulation Preliminary, s = 8 TeV 0.2 80 < p < 100 GeV Z+Jets 40 < p < 50 GeV 1800 T 0.18 |η| < 2 T |η| < 2 Data 1600 0.16 Quark Jets Quark Gluon Jets Events / (1) 1400 Gluon 0.14 1200 Unmatched+PU 0.12
1000 Unity NormalizedTo 0.1 800 0.08 600 0.06 400 0.04 200 0.02 0 0 5 10 15 20 25 30 35 40 0 0.2 0.4 0.6 0.8 1 Number of Constituents Quark-Gluon Likelihood Discriminant
Murilo Rangel - IF/UFRJ 51 Quark-Gluon Tagging ⦿ Many measurements and searches can benefit from identifying jets parton origin ➯ Models that predict production of many quarks vs multi-jet QCD production ! ⦿ Colour factor ∝ radiation ∝ particle multiplicity ➯ CA/CF = 9/4 ! ⦿ Other variables can be used: width, number of subjets, etc.
Murilo Rangel - IF/UFRJ 51 Quark-Gluon Tagging ⦿ Many measurements and searches can benefit from identifying jets parton origin ➯ Models that predict production of many quarks vs multi-jet QCD production ! ⦿ Colour factor ∝ radiation ∝ particle multiplicity ➯ CA/CF = 9/4 ! ⦿ Other variables can be used: width, number of subjets, etc.
Murilo Rangel - IF/UFRJ 51 Tau-Jets
Tau-jet is the first use of jets to tag other particles than quarks/gluons It is massive enough to decay hadronically (M~1.8 GeV) Tau-jets are different than quark/gluon jets ⦿ ”displaced” tracks: decay in beam pipe cτ=87 µm ⦿ narrow jets with 1 or 3 tracks, possibly with neutrals
Murilo Rangel - IF/UFRJ 52 Tau-Jets
Tau-jet is the first use of jets to tag other particles than quarks/gluons It is massive enough to decay hadronically (M~1.8 GeV) Tau-jets are different than quark/gluon jets ⦿ ”displaced” tracks: decay in beam pipe cτ=87 µm ⦿ narrow jets with 1 or 3 tracks, possibly with neutrals
Murilo Rangel - IF/UFRJ 52 Tau-Jets
Tau-jet is the first use of jets to tag other particles than quarks/gluons It is massive enough to decay hadronically (M~1.8 GeV) Tau-jets are different than quark/gluon jets ⦿ ”displaced” tracks: decay in beam pipe cτ=87 µm ⦿ narrow jets with 1 or 3 tracks, possibly with neutrals
Murilo Rangel - IF/UFRJ 52 Tau-Jets
Tau-jet is the first use of jets to tag other particles than quarks/gluons It is massive enough to decay hadronically (M~1.8 GeV) Tau-jets are different than quark/gluon jets ⦿ ”displaced” tracks: decay in beam pipe cτ=87 µm ⦿ narrow jets with 1 or 3 tracks, possibly with neutrals
Murilo Rangel - IF/UFRJ 52 Boosted High Mass Particles
⦿ At higher LHC energies, high mass particles (W,Z,H,top) move boosted regimes This can be used to reduce the backgrounds for signals, e.g., ⇒ WH measurements
⇒ WW measurements and high-mass searches (p~MX/2) ⇒ Boosted top quark decays ⦿ Hadronic decays of W bosons may be boosted into a single (fat) jet ⇒ Typical size of this jet is ΔR>2/γ, where γ is boost factor of W ⇒ How can we separate these “W-jets” from light uds jets and b-jets? ⦿ Several well-motivated handles to quantify substructure ⇒ Main observable is the mass of the boosted (fat) jet ⇒ Jet pruning techniques serve to reduce the mass of QCD light jets ⇒ Mass drop observable contrasts fat jet mass with subjet masses ⇒ Jet variables must be intended to be robust against pileup contributions
Murilo Rangel - IF/UFRJ 53 Jet Pruning
C/A jet (R>0.8)
Figure from JHEP09(2013)076
recluster using distance between particles and ignoring softer “proto-jets” if z Murilo Rangel - IF/UFRJ 54 Jet Pruning Murilo Rangel - IF/UFRJ 55 Fat Jet Variables Jet variables can be used to discriminate between W-jets from parton-jets: ! ⦿ Mass drop: Undoing the last clustering step, the highest mass jet should have mass much lower than the W-jet. µ = msub1/mjet CMS Preliminary Simulation, s = 8 TeV, W+jets X → W W , Pythia6 0.4 CA R=0.8 L L 250 < p < 350 GeV + 0.2 Normalized Distribution 0.1 0 0 0.2 0.4 0.6 0.8 1 mass drop Murilo Rangel - IF/UFRJ 56 Fat Jet Variables Jet variables can be used to discriminate between W-jets from parton-jets: ! ⦿ N-subjettiness: For W→jets,�2/�1 is a good discriminant CMS Preliminary Simulation, s = 8 TeV, W+jets X → W W , Pythia6 CA R=0.8 L L 250 < p < 350 GeV + Normalized Distribution 0.1 0 0 0.2 0.4 0.6 0.8 1 τ2/τ1 Murilo Rangel - IF/UFRJ 57 Fat Jet Variables Jet variables can be used to discriminate between W-jets from parton-jets: ! ⦿ Charge: Neutral jets are background-like vs W-jets Murilo Rangel - IF/UFRJ 58 Fat Jet Variables Jet variables can be used to discriminate between W-jets from parton-jets: ! ⦿ Charge: Neutral jets are background-like vs W-jets Murilo Rangel - IF/UFRJ 58 W-Jet Data studies show promising results. Murilo Rangel - IF/UFRJ 59 W-Jet Data studies show promising results. Murilo Rangel - IF/UFRJ 59 Top Quark Tagging Boosted top quarks can be produced in decays of ultra-high-mass resonances ➯ one big fat jet can contain the top quark decays ! HEPTopTagger has been proposed to tag top quarks with hadronic W boson Murilo Rangel - IF/UFRJ 60 Top Quark Tagging Figure from JHEP09(2013)076 Murilo Rangel - IF/UFRJ 61 Top Quark Tagging Boosted top quarks can be produced in decays of ultra-high-mass resonances ➯ one big fat jet can contain the top quark decays Data studies show promising results. Murilo Rangel - IF/UFRJ 62 Future - High Pile Up Jet substructure methods must be robust against pile up for the next run. Murilo Rangel - IF/UFRJ 63 Future - High Pile Up Jet substructure methods must be robust against pile up for the next run. So far, simulation studies are promising! after grooming Murilo Rangel - IF/UFRJ 63 Summary Jets are key ingredients of measurements and new physics searches ! Understanding jets improves impact of data ! Jet algorithms can be used to tag boosted objects Murilo Rangel - IF/UFRJ 64