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PileUp Per Particle Id Daniele Bertolini MIT Philip Harris CERN Matthew Low University of Chicago Nhan Tran Fermilab/LPC
work soon to appear !
!Mitigation of pileup effects at the LHC
May 16, 2014
PUPPI Daniele Bertolini MIT Philip Harris CERN Matthew Low University of Chicago Nhan Tran Fermilab/LPC
work soon to appear !
!Mitigation of pileup effects at the LHC
May 16, 2014
May 16, 2014 3
• what is PUPPI? !
• defining the algorithm !
• setup and results !
• discussion and extensions
outline
May 16, 2014 4handles on pileup
•global event metric •local shape •tracking/vertexing •precision timing •depth segmentation
• (apologies, not a complete list!) !
• ρ correction/subtraction (area, 4-vector, shape, particle)
groomingtopoclustering
charged hadron subtractionjet cleansing pileup jet ID
• …
May 16, 2014 5handles on pileup
•global event metric •local shape •tracking/vertexing •precision timing •depth segmentation
• (apologies, not a complete list!) !
• ρ correction/subtraction (area, 4-vector, shape, particle)
groomingtopoclustering
charged hadron subtractionjet cleansing pileup jet ID
• …
May 16, 2014 6handles on pileup
•global event metric •local shape •tracking/vertexing •precision timing •depth segmentation
• (apologies, not a complete list!) !
• ρ correction/subtraction (area, 4-vector, shape, particle)
groomingtopoclustering
charged hadron subtractionjet cleansing pileup jet ID
• …
May 16, 2014 7handles on pileup
•global event metric •local shape •tracking/vertexing •precision timing •depth segmentation
• (apologies, not a complete list!) !
• ρ correction/subtraction (area, 4-vector, shape, particle)
groomingtopoclustering
charged hadron subtractionjet cleansing pileup jet ID
• …{•stand on the shoulders of giants!
May 16, 2014 8PUPPI
grooming/local shape
tracking/vertexing information
global event information
depth segmentation
precision timing
following the “jets without jets” paradigm…
May 16, 2014 9PUPPI
grooming/local shape
tracking/vertexing information
global event information
depth segmentation
precision timing
following the “jets without jets” paradigm… Define on a per particle basis, before jet clustering, a weight for how likely a particle (or jet constituent) is to be from pileup or the leading vertex, then rescale each particle four momentum by that likelihood
May 16, 2014 10PUPPI
grooming/local shape
tracking/vertexing information
global event information
depth segmentation
precision timing
in one algorithm: correct the jet pT
correct the jet mass/shapes
perform pileup jet ID classify particles for MET determination
following the “jets without jets” paradigm… Define on a per particle basis, before jet clustering, a weight for how likely a particle (or jet constituent) is to be from pileup or the leading vertex, then rescale each particle four momentum by that likelihood
May 16, 2014 11
• what is PUPPI? !
• defining the algorithm !
• setup and results !
• discussion and extensions
outline
May 16, 2014 12PUPPI overview
[1] define a local metric, α, that differs between pileup (PU) and
leading vertex (LV)
for a particle i with nearby particles jLet’s assume something similar to Particle
Flow inputs (not a requirement): neutral hadrons
charged hadrons from LVcharged hadrons from PU
May 16, 2014 13PUPPI overview
[1] define a local metric, α, that differs between pileup (PU) and
leading vertex (LV)
for a particle i with nearby particles j
[2] using tracking information (e.g. charged particles) “sample”
the event, define unique distributions of α for PU and LV
Let’s assume something similar to Particle Flow inputs (not a requirement):
neutral hadronscharged hadrons from LVcharged hadrons from PU
May 16, 2014 14PUPPI overview
[1] define a local metric, α, that differs between pileup (PU) and
leading vertex (LV)
for a particle i with nearby particles j
[2] using tracking information (e.g. charged particles) “sample”
the event, define unique distributions of α for PU and LV
[3] for the neutrals, ask “how PU-like is α for this particle?”, compute a weight
for how un-PU-like (or LV-like) it is
Let’s assume something similar to Particle Flow inputs (not a requirement):
neutral hadronscharged hadrons from LVcharged hadrons from PU
May 16, 2014 15PUPPI overview
[1] define a local metric, α, that differs between pileup (PU) and
leading vertex (LV)
for a particle i with nearby particles j
[2] using tracking information (e.g. charged particles) “sample”
the event, define unique distributions of α for PU and LV
[3] for the neutrals, ask “how PU-like is α for this particle?”, compute a weight
for how un-PU-like (or LV-like) it is
[4] reweight the four-vector of the particle by this weight, then proceed to cluster the
event as usual
Let’s assume something similar to Particle Flow inputs (not a requirement):
neutral hadronscharged hadrons from LVcharged hadrons from PU
May 16, 2014 16[1] define a metricthere are many possibilities, we have tested many and find these to be near-optimal
example: 2-body system, for a particle i, what does particle j tell us?
⬆ for harder, collinear particles!⬇ for softer, wide angle particles
May 16, 2014 17[1] define a metricthere are many possibilities, we have tested many and find these to be near-optimal
example: 2-body system, for a particle i, what does particle j tell us?
⬆ for harder, collinear particles!⬇ for softer, wide angle particles
define 2 metrics, with and without using tracking information
R0 is the radius around particle i which is considered
!the log regularizes the scale
for event sampling (next slides)
“C” = charged or central, “F” = full or forward
May 16, 2014
Cα -5 0 5 10 15
frac
tion
of p
artic
les
0
0.02
0.04
0.06
LV particles
PU particles
18[2] sample the event
Fα -5 0 5 10 15
frac
tion
of p
artic
les
0
0.05
0.1
0.15
LV particles
PU particlesαF
charged particles with pT > 1 GeV, populated over 200 events
** dijet events, more details on simulation later
sample the charged particles in the event, get separate distributions of α for LV and PU particles
!Determine the median and (left-side) RMS values for the charged PU
(good) assumption: α for charged PU and neutral PU are similar need to extrapolate αF to the forward region (η-dependence)
αC
does not include particles with
αC = 0.0
May 16, 2014 19[3] compute weight
Use χ2 CDF to determine probability, pi:
weight, wi = 1 - pi
χ2 construction allows for additional information, use χ2 with N DOF)Cα weight (
0 0.2 0.4 0.6 0.8 1
frac
tion
of p
artic
les
-310
-210
-110
LV particles
PU particles
)Fα weight (0 0.2 0.4 0.6 0.8 1
frac
tion
of p
artic
les
-310
-210
-110
LV particles
PU particles
Use a χ2 approximation to derive a probability of pileup
grooming and local shapeglobal event metrics
tracking/vertexing information
signed information because we know LV particles lie to the right of
the PU distribution
May 16, 2014 20[4] recompute 4-vectors
•step 1: assuming perfect tracking for charged particles, set wChLV = 1 and wChPU = 0.
•step 2: for neutrals, re-weight 4-vectors, wi × pi •step 3: cut on neutrals with wi < wcut and wi*pTi < βcut
!•wcut and βcut are tunable parameters
•this study: wcut = 0.001•βcut = f(nPV) ≈ 0.7e-2 × npu + 0.1 GeV (central)•βcut = f(nPV) ≈ 1.1e-2 × npu + 0.2 GeV (forward)
•R0, PUPPI cone size, is a tunable parameter•this study: R0 = 0.3
May 16, 2014 21
• what is PUPPI? !
• defining the algorithm !
• setup and results !
• discussion and extensions
outline
May 16, 2014 22‘experimental’ setup
•dijet events, Pythia 8.176 •flat pT spectrum for populating higher pT bins
!•pileup scenarios, nPU = 20-140, every 15 nPU
•not Poisson-distributed !
•tracker exists to |η| < 2.5, all particles out to |η| < 5 •assume perfect tracking to determine if particles are LV or PU
!•neutrals are reconstructed in cells (Δη×Δφ = 0.1×0.1)
•cut on neutrals, pT > 0.1 GeV
May 16, 2014 23input collections
•LV •only the particles from the leading vertex of interest
•PF: like particle flow •all charged and neutral particles
•PFCHS: like particle flow with charged hadron subtraction •all charged and neutral particles except for charged particles from PU in the tracking volume
•PUPPI: like particle flow •particles after the PUPPI algorithm
!•PF and PFCHS jet collections are corrected using fastjet 4-vector grid ρ subtraction
•PFCHS uses its own ρ inside tracker volume and PF ρ outside
May 16, 2014 24event displays
η-4 -2 0 2 4
φ
0
2
4
6
pT (G
eV)
-110
1
10
210
η-4 -2 0 2 4
φ
0
2
4
6
pT (G
eV)
-110
1
10
210
η-4 -2 0 2 4
φ
0
2
4
6
pT (G
eV)
-110
1
10
210
η-4 -2 0 2 4
φ
0
2
4
6pT
(GeV
)
-110
1
10
210
colored cells = LV process, black cells = pileup
LV PF
PFCHS PUPPI
nPU = 80 scenario
May 16, 2014 25jet kinematicsnPU = 80 scenarioNumber of jets with
pT > 25 GeV per event
eta-4 -2 0 2 4
jets
/eve
nt0
0.1
0.2
0.3
0.4LVPFlowPFlowCHSPUPPI
= 80PUAnti-kT (R=0.7), n
n jets 0 5 10 15 20
jets
/eve
nt
0
0.1
0.2
0.3
0.4 LVPFlowPFlowCHSPUPPI
= 80PUAnti-kT (R=0.7), n
PUPPI performs pileup jet identification reducing the amount of spurious jets formed from overlapping pileup events
May 16, 2014 26jet pT resolutionnPU = 80 scenario
LV)/pT
LV (pT - pT
-0.2 0 0.2
frac
tion
of je
ts
0
0.1
0.2PFlow
= 0.03x = 0.071σ
PFlowCHS = -0.01x = 0.043σ
PUPPI = 0.001x = 0.026σ
= 80PUAnti-kT (R=0.7), n
| < 2.5ηpT = [100-200] GeV, |
LV)/pT
LV (pT - pT
-0.4 -0.2 0 0.2 0.4
frac
tion
of je
ts
0
0.05
0.1 PFlow = 0.108x = 0.206σ
PFlowCHS = 0.003x = 0.154σ
PUPPI = -0.025x = 0.098σ
= 80PUAnti-kT (R=0.7), n
| < 2.5ηpT = [25-50] GeV, |
lower pT jets (25-50 GeV) higher pT jets (100-200 GeV)
match PF, PFCHS, PUPPI jets to LV jets and compare the jet pT resolution
May 16, 2014 27jet pT resolution vs. pT
nPU = 80 scenario
pT (GeV)0 100 200 300 400 500
LV)/p
TLV
, (pT
- pT
σ fi
tted
0
0.1
0.2
0.3PFlowPFlowCHSPUPPI
= 80PUAnti-kT (R=0.7), n
pT (GeV)0 100 200 300 400 500
LV)/p
TLV
fitte
d m
ean,
(pT
- pT
-0.2
-0.1
0
0.1
0.2PFlowPFlowCHSPUPPI
= 80PUAnti-kT (R=0.7), n
jet pT scale as a function of LV jet pT
jet pT resolution as a function of LV jet pT
May 16, 2014 28jet massnPU = 80 scenario
mass (GeV) 0 50 100
fract
ion
of je
ts
0
0.1
0.2
0.3 LVPFlowPFlowCHSPUPPI
= 80PUAnti-kT (R=0.7), n
| < 2.5ηpT = [100-200] GeV, |
trimmed mass (GeV) 0 50 100
fract
ion
of je
ts0
0.1
0.2
0.3 LVPFlowPFlowCHSPUPPI
= 80PUAnti-kT (R=0.7), n
| < 2.5ηpT = [100-200] GeV, |
(GeV)LV m - m-50 0 50
frac
tion
of je
ts
0
0.1
0.2PFlow
= 17.335x = 10.149σ
PFlowCHS = 5.926x = 7.191σ
PUPPI = 2.115x = 3.913σ
= 80PUAnti-kT (R=0.7), n
| < 2.5ηpT = [100-200] GeV, |
(GeV)LV m - m-50 0 50
frac
tion
of je
ts
0
0.1
0.2 PFlow = 27.503x = 11.0σ
PFlowCHS = 12.716x = 9.145σ
PUPPI = 0.408x = 4.069σ
= 80PUAnti-kT (R=0.7), n
| < 2.5ηpT = [100-200] GeV, |
jet mass trimmed jet mass
PF and PFCHS jets corrected doing trimming, then ρ
4-vector subtraction
May 16, 2014 29jet mass resolutionnPU = 80 scenario
pT (GeV)100 200 300 400 500
(GeV
)LV
, m -
mσ
fitte
d
0
10
20
30PFlowPFlowCHSPUPPI
= 80PUAnti-kT (R=0.7), n
pT (GeV)100 200 300 400 500
(GeV
)LV
fitte
d m
ean,
m -
m
0
20
40
60PFlowPFlowCHSPUPPI
= 80PUAnti-kT (R=0.7), n
jet mass scale as a function of LV jet pT
jet mass resolution as a function of LV jet pT
May 16, 2014 30results vs. PU
nPU50 100
(GeV
)G
ENm
- m
5
10
15
20
PUPPIPFlowPFlowCHS
Anti-kT (R=0.7)
| < 2.5ηpT = [100-200] GeV, |
nPU50 100
GEN
/pT
GEN
pT-pT
0.05
0.1
0.15
0.2
PUPPIPFlowPFlowCHS
Anti-kT (R=0.7)
| < 2.5ηpT = [100-200] GeV, |
jet pT resolution as a function of nPU
jet mass resolution as a function of nPU
May 16, 2014 31
• what is PUPPI? !
• defining the algorithm !
• setup and results !
• discussion and extensions
outline
May 16, 2014
!•iPUPPI •isolation of leptons and photons using PUPPI-weight particles should have improved stability in high pileup environments!
!•PUPPET •missing transverse energy (MET) determination using input particles from PUPPI algorithm should improve MET resolution and tails
32discussion and extensions
MissX E∆
-100 -50 0 50 100
frac
tion
of e
vent
s
0
0.05
0.1
0.15PFlow
PFlowCHS
PUPPI
= 80PU
Z+jet, n
T EΣ ∆ 0 500 1000 1500 2000
frac
tion
of e
vent
s
0
0.2
0.4
0.6
0.8PFlow
PFlowCHS
PUPPI
= 80PU
Z+jet, n
May 16, 2014 33discussion and extensions•not an algorithm set-in-stone, but rather a framework
•takes some ideas from existing algorithms, uses as much information as possible
•classify particles (or smallest detector units) before clustering •can be used for lepton isolation and MET determination
•can combine experimental and theoretical considerations •include probabilistic information about tracking, depth segmentation and timing !
•needs more rigorous comparisons against other methods •looking forward to working sessions!
•
May 16, 2014 34discussion and extensions
•For the future… !
•would like to understand the definition of the metrics more rigorously from the QCD point-of-view
•while α is IRC-unsafe under collinear splitting of particle i, the weight is IRC-safe !
•consider algorithm outside of the PF framework •i.e. in topoclustering, consider nearby tracks
!•extend jet shape studies beyond jet mass !
•the “M” word: MVA particle classification using several metrics shows modest improvement
May 16, 2014 35summary
•a new algorithm is presented for pileup mitigation at the LHC !
•aims to classify particles before jet clustering •using local information, global event metrics and charged particle tracking •can be extended to use further experimental information !
•showing good performance for jet pT and mass resolution, comparing to ρ-subtracted PF and PFCHS
•need to exercise in detector environment! !
•not the final story, algorithm can be tuned for different experiments or improved further
•