Jet-Vertex Association at low-luminosity

1. David W. Miller SLAC ATLAS Forum 16 May, 2007 Jet-Vertex Association at low-luminosity • 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 David W. Miller SLAC ATLAS Forum

2. 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” David W. Miller SLAC ATLAS Forum

3. ATLAS pileup simulation High Luminosity: 1034 • 23 p-p interactions • Undefined number of cavern background Low Luminosity: 2•1033 • 4.6 p-p interactions • 5 cavern BG Very Low Lumi: 1033 • 2.3 p-p interactions • 2 cavern BG Simulated 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 David W. Miller SLAC ATLAS Forum

4. 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. David W. Miller SLAC ATLAS Forum

5. Jet-Vertex association with pileup • 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 CPF [PV] = 0 CPF [MB] = 1 CPF [PV] = f CPF [MB] = 1-f Min-bias Hard Scatter DØ & A. Schwartzman David W. Miller SLAC ATLAS Forum

6. Minimum-bias at low luminosity • The minimum bias (MB) events used as pileup Shown: 100 events, 3.3 MB int. per BC < 1 MB jet per event |η| distribution fairly central <Jet Et> ~ 15 GeV All Jets All Jets All Jets David W. Miller SLAC ATLAS Forum

7. 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 # Pileup Events David W. Miller SLAC ATLAS Forum

8. ttbar: 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 After cuts Et(jet) > 20 GeV | η(jet) | < 2.5 David W. Miller SLAC ATLAS Forum

9. 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 algorithm is to be able to distinguish which of these jets originate in the hard-scattering vertex, and which are simply high-energy minimum bias jets. David W. Miller SLAC ATLAS Forum

10. Jet-track matching & selection Pt > 0.4 GeV Pt < 50 GeV ΔR(jet,trk) < 0.4 ΔZ(rec,mc) < 100μm ΔR Jet-Trk < 0.4 • Track matching is critical to jet-vertex association. • Currently, only a ΔR cut • No explicit lepton-Jet overlap removal David W. Miller SLAC ATLAS Forum

11. 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 Hard scatter Tracks per jet Pileup tracks per jet Jet E vs Ntrks # MB PV per jet Significant David W. Miller SLAC ATLAS Forum

12. 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 David W. Miller SLAC ATLAS Forum

13. Charged particle energy fraction • 3 categories of jets: • Hard scatter • Min-bias • No tracks • Many min-bias jets do not have > 2 tracks. Requiring > 2 tracks David W. Miller SLAC ATLAS Forum

14. Jet rapidity distributions David W. Miller SLAC ATLAS Forum

15. 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 David W. Miller SLAC ATLAS Forum

16. 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 David W. Miller SLAC ATLAS Forum

17. 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 David W. Miller SLAC ATLAS Forum