Top Tagging at CLIC 1.4TeV Using Jet Substructure Alasdair Winter University of Birmingham 21/02/2017

Overview Aims: identify TTBar decays in boosted events select events without introducing bias to the top mass examine prospects for CPT violation measurements in the top sector Boosted topology makes conventional top tagging techniques a challenge- btagging alone no longer viable! Approach will be to use the concept of fat jets and look at jet substructure Only focusing on event selection- NOT reconstruction! (yet…)

Samples used 𝑠 =1.4TeV P(e-) = -80% for signal y=d,s,b x=u,c l=mu,tau Process ID CrossSection (fb) N Generated Events eeyyveyx 6589 31.9 790,000 eeyyxyev 6592 29.0 800,000 eeyyvlyx 6634 40.7 1,140,000 eeyyxylv 6637 1,130,000 eeqqlv 3249 4309.7 980,000 eeqqqq 2163 1328.1 300,000 eeqq 2091 4009.5 460,000 eeqqqqlv 2169 115.3 180,000 eeqqqqll 2166 71.7 210,000 eeqqqqvv 2152 24.7 230,000 Signal!

MC Sample Selection- Stage 1 Samples are 6 fermion final states consistent with TTBar final state- need to select only those that are truly from TTBar decays Check truth tables contain: 2 b quarks 2 light quarks Combination of light quarks and single b quark consistent with top mass- 168-178GeV Remove events where the isolated lepton is a Tau (will add these as background in future) Process Sample CrossSection (fb) Pass Rate(%) New CrossSection (fb) eeyyveyx 31.9 19.8 15.7 eeyyxyev 29.0 20.3 15.1 eeyyvlyx 40.7 12.5 13.7 eeyyxylv eeqqqqlv 115.3 39.4 45.4 Also need to remove signal from background sample

MC Selection- stage 2 Broad tail in energy spectrum Split analysis into boosted and low E regions, design separate selection for both Work presented here is mainly focused on the boosted region 1350GeV

MC Selection- stage 2 Process Whole Sample CrossSection (fb) Boosted Events CrossSection (fb) LowE CrossSection (fb) eeyyveyx 15.7 6.32 9.41 eeyyxyev 15.1 5.90 9.16 eeyyvlyx 13.7 5.10 8.61 eeyyxylv 5.08 8.59 High E BDT Discriminator BDT Score 1 Event Selection Final Discriminating Variable Event Variables Low Energy BDT Discriminator BDT Score 2

Analysis Strategy Find Isolated Lepton in TightSelectedPFOs 1 Find Isolated Lepton in TightSelectedPFOs 2 Cluster remaining PFOs into 2 Fat Jets 3 Determine FatJet structure- multiplicity, nSubJettiness, angular distribution of subjets 4 Apply 2 BDTs using kinematic and jet substructure variables. One looking for boosted top events, one for low energy tops. 5 Use scores from each BDT as discriminating variables for a second tier classifier that makes the final selection 6 Check top mass dependence on final selection before looking at physics studies

1) Lepton Identification Cluster all TightSelectedPFOs into 5 jets using ee_kt_algorithm 2 Select TightPFOs with Pandora PID=11 or 13, E>10GeV as isolated lepton candidates 3 Calculate 𝐸 𝑃𝐹𝑂 𝐸 𝐽𝑒𝑡 (where EJet is the energy of the jet the PFO was clustered into) for each candidate- provides an estimate for how isolated the candidate is 4 Select the PFO with the highest 𝐸 𝑃𝐹𝑂 𝐸 𝐽𝑒𝑡 ratio to be the isolated lepton 5 If no candidate is selected, repeat process without the inclusion of step 2 By construction, 1 lepton identified per event Purity= 85% High purity essential for many applications

2) Fat Jet Finding Combine remaining PFOs into two jets- ee_kt_algorithm and Valencia (1.5,1,1) tested ExclusiveNJets=2 Declare jet with the highest energy to be the hadronically decaying top Narrow peak at 174 GeV but peaks at low mass too... Broad peak at 184GeV ee kt algortihm Valencia Preferred for now Need to investigate distribution post selection

3) Jet Substructure MicroJet Counting- recluster fat jets into microjets with very small ΔR and count them to find jet multiplicity R increased from 0.001 to 0.5, supress possible effects of systematics arising from hadronization modelling NSubjettiness- estimate number of prongs by reclustering fat jet into N subjets and measuring distance of microjets relative to the jet axes Jet topology- recluster fat jet into three subjets and measure the relative angles between subjets Redefinition of microjets has an impact on many jet related variables: Jet variables no longer as highly ranked by BDT Nsubjettiness removed completely from selection process Need to examine systematics in detail to understand if this change is necessary Currently looking into using Herwig for hadronization rather than Pythia

4) Variables Used for BDT-22 Leptonic Top Properties Jet Mass Jet Energy Jet Multiplicity Jet Pt Lep Charge Lep Energy Lep Pz Lep Pt Lep PID Hadronic Top Properties Fat Jet Energy Fat Jet Multiplicity Fat Jet Pt Angle between fat jet and beam axis W Mass W Energy W Pt Angle between highest and mid E subjets General Event Properties Visible Energy Visible Pt Visible Pz Y45 (switch from 4-5 jet topology) Angular separation of isolep and hadronic top

BDT Response

Significance Plot from TMVA

Expected number of events after selection Taking L = 1.5ab-1, BDT cut= 0.19 Significance = 104 ( 𝑆 𝑆+𝐵 ) Signal Efficiency = 52% Background efficiency = 0.07% Purity after selection = 62% Process CrossSection (fb) N Generated Events Selection Efficiency (%) Expected Events Passing for 1.5ab-1 eeyyveyx 6.32 790,000 42 4000 eeyyxyev 5.90 800,000 44 eeyyvlyx 5.10 1,140,000 64 4900 eeyyxylv 5.08 1,130,000 63 4800 eeqqlv 4309.7 980,000 0.02 1000 eeqqqq 1328.1 300,000 0.04 900 eeqq 4009.5 460,000 0.03 1600 eeqqqqlv 45.4 180,000 9.6 6500 eeqqqqll 71.7 210,000 0.78 800 eeqqqqvv 24.7 230,000 0.16 100 Need to examine this in more detail- is it really all background??

Very Preliminary Results….

Low E Selection Naively using same BDT variables as for boosted events, but train with low E events Maximum Significance = 144 Signal Efficiency = 67 % Background efficiency = 0.18 % Purity after selection = 57% Process CrossSection (fb) N Generated Events Selection Efficiency (%) Expected Events Passing for 1.5ab-1 eeyyveyx 6.32 790,000 60 8500 eeyyxyev 5.90 800,000 8200 eeyyvlyx 5.10 1,140,000 73 9500 eeyyxylv 5.08 1,130,000 76 9800 eeqqlv 4309.7 980,000 0.05 3200 eeqqqq 1328.1 300,000 0.18 3600 eeqq 4009.5 460,000 0.07 4400 eeqqqqlv 45.4 180,000 18 11800 eeqqqqll 71.7 210,000 2.8 3000 eeqqqqvv 24.7 230,000 1.9 700

Combined Selection Accept any events that pass either of the first tier BDT cuts- not optimal way to combine BDTs but quickest! Significance = 195 Signal Efficiency = 66 % Background efficiency = 0.2 % Purity after selection = 65% Process CrossSection (fb) N Generated Events Selection Efficiency (%) Expected Events Passing for 1.5ab-1 Signal 58.2 3,800,000 66 58,000 eeqqlv 4309.7 980,000 0.06 3,800 eeqqqq 1328.1 300,000 0.02 4,100 eeqq 4009.5 460,000 0.09 5,100 eeqqqqlv 45.4 180,000 20 13,300 eeqqqqll 71.7 210,000 3.2 3,400 eeqqqqvv 24.7 230,000 1.9 700 Improved performance appears to arise from boosted signal events that are missed in the boosted BDT selection being picked up in the low E selection

Still to do…. Implement more sophisticated second tier of selection (secondary BDT?) Examine systematics from jet related variables Ongoing effort to produce events in Whizard then switch to Herwig for hadronization Look at dependence on the MC mass window we choose for the top Top mass reconstruction after event selection Reinvestigate use of Valencia algorithm for top reconstruction based on work by Lars, Martin… Determine prospects for CPT measurements Extend approach to look at 3TeV stage

Summary Switching to new analysis approach- two tiered selection looking for both boosted and low energy tops New lepton finder implemented based on jet isolation ee_kt_algorithm still preferred for fat jet finding Additional backgrounds added to BDT, though necessary to further examine the effect of MC selection to make sure there is no mixing between signal and background samples Microjet parameters have been adjusted to reduce possible systematics, however this reduced the discriminating power of many variables previously used for event selection

Overtraining checks