Jet-Vertex Association at low-luminosity
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Transcript of Jet-Vertex Association at low-luminosity
16 May 2007 David W. MillerSLAC ATLAS Forum
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Jet-Vertex Association at low-luminosity
David W. MillerDavid W. Miller
SLAC ATLAS ForumSLAC ATLAS Forum
16 May, 200716 May, 2007
David W. MillerDavid W. Miller
SLAC ATLAS ForumSLAC ATLAS Forum
16 May, 200716 May, 2007
• Pileup at ATLAS and the LHC– Composition and generation
• Jet reconstruction in the context of pileup
• The jet-vertex association algorithm– Method and implementation
• First results– Small dataset of ttbar events
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ATLAS Environment at high luminosity
• We expect ~ 25 interactions per bunch crossing– For L ≈ 1034 cm-2 s-1 and
σpp≈100 mb
• “Hard physics” will only begin at 10-4 per bunch crossing level– And of course, what we are
after is much, much further down…
• We must be able to identify the hard interaction physics from among the soft “pileup”
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ATLAS pileup simulationSimulated pileup has 2 components:
– Multiple p-p interactions per bunch-crossing
• Poisson distribution with mean determined by luminosity
– Cavern background• Uniformly distributed across
all bunch-crossings with mean determined by luminosity
• Thermal n’s, K's and low-E ɣ's escaping the detector and beam
High Luminosity: 10103434
– 23 p-p interactions – Undefined number
of cavern background
Low Luminosity: 2•102•103333
– 4.6 p-p interactions– 5 cavern BG
Very Low Lumi: 10103333
– 2.3 p-p interactions– 2 cavern BG
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Data samples• Minimum bias & cavern events are overlayed onto top
sample using the ATLAS job transforms– DC3.005200.T1_McAtNlo_Jimmy– misal1_csc11.005001.pythia_minbias – misal1_csc11.007903.cavernbg_sf05
• In addition, a sample with no signal events (only min-bias overlayed onto min-bias) is generated for comparison
• More details:– Geometry: ATLAS-CSC-01-02-00– Production Release: 12.0.6.4– Also: https://twiki.cern.ch/twiki/bin/view/Atlas/PileupDigitization
• Note that when data becomes available, we will use real minimum bias events to overlay onto simulated data.
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Jet-Vertex association with pileup
Min-bias Hard Scatter
CPF [PV] = fCPF [PV] = f
CPF [MB] = 1-fCPF [MB] = 1-f
CPF [PV] = 0CPF [PV] = 0
CPF [MB] = 1CPF [MB] = 1
• Isolate and remove those jets not originating in hard scatter interaction
• Match jets to tracks using ΔR association
• Use these jet-tracks to associate jets to vertices
• Define the charged particle energy fraction (CPF): fraction of track energy in jet for each PV
vtx trkskit
trkskit
k vtxtrkp
vtxtrkpvtxCPF
),(
),()(
DØ & A. Schwartzman
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Minimum-bias at low luminosity• The minimum bias (MB)
events used as pileupShownShown: 100 events, 3.3 MB int. : 100 events, 3.3 MB int.
per BCper BC< 1 MB jet per event|η| distribution fairly central<Jet Et> ~ 15 GeV
All Jets
All JetsAll Jets
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ttbar events at low luminosity• 50 events @
lumi = 1033
– 2.3 MB PV– 1 signal PV– 2 cavern BG
• Good PV resolution even with pileup– σXY ≈ 15 μm
– σZ ≈ 55 μm
• 50 events @ lumi = 1033
– 2.3 MB PV– 1 signal PV– 2 cavern BG
• Good PV resolution even with pileup– σXY ≈ 15 μm
– σZ ≈ 55 μm
# Pileup Events
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ttbar: Jet kinematics and topology
EEtt(jet) > 20 GeV(jet) > 20 GeV
| | ηη(jet) | < 2.5(jet) | < 2.5
EEtt(jet) > 20 GeV(jet) > 20 GeV
| | ηη(jet) | < 2.5(jet) | < 2.5
• Higher jet multiplicity than ttbar alone (as expected)
• Most MB jets will have been removed by Et cut, but not all
After cuts
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Jet kinematics and topology• Higher jet multiplicity than
ttbar alone (as expected)• Most MB jets will have been
removed by Et cut, but not all
The goal of this The goal of this algorithm is to be algorithm is to be able to distinguish able to distinguish which of these jets which of these jets originate in the originate in the hard-scattering hard-scattering vertex, and which vertex, and which are simply high-are simply high-energy minimum energy minimum bias jets.bias jets.
The goal of this The goal of this algorithm is to be algorithm is to be able to distinguish able to distinguish which of these jets which of these jets originate in the originate in the hard-scattering hard-scattering vertex, and which vertex, and which are simply high-are simply high-energy minimum energy minimum bias jets.bias jets.
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Jet-track matching & selection
ΔR Jet-Trk < 0.4
• Track matching is critical to jet-vertex association. – Currently, only a ΔR cut
• No explicit lepton-Jet overlap removal
Pt > 0.4 GeVPt > 0.4 GeV
Pt < 50 GeVPt < 50 GeV
ΔΔR(jet,trk) < 0.4R(jet,trk) < 0.4
ΔΔZ(rec,mc) < 100Z(rec,mc) < 100μμmm
Pt > 0.4 GeVPt > 0.4 GeV
Pt < 50 GeVPt < 50 GeV
ΔΔR(jet,trk) < 0.4R(jet,trk) < 0.4
ΔΔZ(rec,mc) < 100Z(rec,mc) < 100μμmm
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Track & vertex multiplicities per jet
• MC Truth flags a vertex as “hard-scatter” or “pileup”
• Reconstruction also “chooses” the hard scatter– Mis-reco: few x %
• The reconstructed tracks and vertices are thus also flagged
• Can identify– Min bias tracks, jets– Fraction jet E from
pileup• 30% Jets with at least
1 associated MB PV
Pileup tracks per jetHard scatterTracks per jet
Jet E vs Ntrks # MB PV per jet
SignificantSignificantSignificantSignificant
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Min-bias and Hard-scatter Jets
• A simple jet Et cut of 40 GeV would remove nearly all minimum bias jets from the sample
• However, the goal is retain a lower Et cut (say, 20 GeV) and instead use tracks to identify min-bias/pileup jets
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Charged particle energy fraction
Requiring > 2 tracks
• 3 categories of jets:– Hard scatter– Min-bias– No tracks
• Many min-bias jets do not have > 2 tracks.
ttotal
kttotal
k p
vtxpvtxCPF
)()(
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Jet rapidity distributions
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First estimate of CPF efficiency• Crude estimate of
efficiency:
– < 4% mis-identified– Are “composite” jets
(track contributions from hard scatter and MB)
• Really, must use full truth info (not just PV) and evaluate the efficiency as a function of jet Pt and average track Pt
PV) (true
PV) reco(
CPF
CPF
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Jet Selection using CPF• CPF allows a lower jet Et cut
by using track information• Should remove dependence of
jet multiplicity on # vertices– Expect increasing jet mult. due to
MB
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Summary and outlook
• A first look at studying jet vertexing in the context of pileup at ATLAS
• Implementation of DØ (A. Schwartzman) CPF algorithm– Further refinements and studies ongoing
• Tasks:– Electron overlap removal– Utilize MC truth in detail to understand contributions to
jets at the particle level– Evaluate CPF efficiency in different samples– Optimization of cuts and parameters in algorithms– Understand why jet multiplicity does not increase as a
function of # vertices as expected