Pileup, substructure and other thoughts from CMS

Transcription

1 Pileup, substructure and other thoughts from CMS Nhan Tran Fermi National Accelerator Laboratory April 25th, 2013 Terascale Workshop on Substructure

2 outline Initially, discuss open question about the topic of substructure and its utility in mitigating pileup Last week, CMS had a substructure workshop where we also started thinking about future issues, post shutdown So, I will start by talking about pileup and then touch on a few other subjects Should we be worried about this? Is it something only CMS should worry about? Is it something experimentalists should worry about? Thinking about jet substructure applications beyond the classic boosted heavy object case N.B. this has a personal twist, not representative of all of CMS 2

3 particle flow CMS employs a particle flow algorithm translating detector measurements into physics objects Algorithm uses information from all subsystems, returns a list of muons, electrons, photons, and neutral and charged hadrons List is used as the building blocks for final physics objects in analyses: Jets and b-jets, taus, and missing transverse energy (MET) 3

4 jet clustering and corrections CMS standard are jets clustered with the Anti-kT (AK) algorithm with an R parameter R = 0.5 built from particle flow inputs (PF). In addition, we are able to subtract charged hadrons from jets which do not come from the primary vertex (PFCHS Jets) L1 PU, data or L1 PU, MC MANDATORY FOR DATA AND MC L2 Relative L3 Absolute L2L3 Res OPTIONAL L5 Flavor RESIDUALS MANDATORY FOR DATA L1 corrects for pileup using FastJet ρ A correction L2/L3 jet response versus pt, η Dedicated corrections for AK5 jets and AK7 jets 4

5 Pile-up measured with Zero Bias data and MC, then calibrated to QCD Random cone method allows to separate contribution per subdetector Most charged hadrons can be associated to pile-up vertices and remove typical pileup contribution Part that can be removed is labeled charge Hadrons Part that remains as PU after charge hadron substraction is labeled charge pt pile-up (GeV) A few plots to give you a scale of the pileup on average CMS preliminary, L = 1.6 fb η < 0.5 Average Offset (DATA) Average Offset (MC) Jet Area (DATA) Jet Area (MC) s = 8 TeV, GeV p New Level 1 Corrections T,offset (Offset), GeV T p New precision scaling in bins of (η, NPV) η CMS preliminary s = 8 TeV Offset/N PV CMS Preliminary s = 8 TeV PFJets The CMS collaboration Data Simulation JEC photons em deposits neutral hadrons hadronic deposits charged pile-up charged hadrons N PV =5 N PV =10 N PV =15 N PV =20 5 Anti-k T R=0.5 PFlow Number of primary vertices η 5

6 pileup jets 6

7 identifying jets from pileup Pileup jets are several lower energy jets not originating from the primary vertex which are top of each other Particular jet substructure variables are found to separate PU jets from real jets -- similar philosophy to quark/gluon separation β = fraction of jet pt from charged tracks coming from primary vertex (in tracking volume only) Jet width, shapes Charged track multiplicity real pileup β= Δ z(track, v0 )<0.2cm p T cand p T cand 7

8 sorry No public plots yet, this is what it would look like... pileup jet, quark/gluon jet β RMS Validation is done with Z+jets event balancing to isolate pileup jets Find hard recoil back-toback with Z and classify other jets as pileup N constituents frac ΔR 0.1 Warning: does not include quark/gluon differences 8

9 identifying jets from pileup example, technique applied in Hττ analysis very little contamination from pileup jets in 0-jet category, even for very low jet pt reduces experimental jet veto uncertainties Ratio Fraction of 0-jet events /fb 8 TeV, CMS Preliminary # vtx all # vtx [0,8] # vtx [9,12] # vtx [13,16] # vtx [17,20] # vtx [20,+] Jet p T threshold [GeV] 9

10 post LS1 Mike Lamont Potential performance Number of bunches Ib LHC FT[1e11] beta*x beta*sep Xangle Emit LHC [um] Peak Lumi [cm- 2 s -1 ] ~Pile-up Int. Lumi per year [fb -1 ] 25 ns /43/ e33 21 ~24 25 ns low emit /43/ e34 42 ~40 50 ns /43/ ns low emit /43/ e34 level to 0.9e34 2.2e34 level to 0.9e34 74 level to level to 40 ~45* ~45* 6.5 TeV 1.1 ns bunch length 150 days proton physics, HF = mb visible cross-section could have essentially double pileup vertices post LS2 could be > 100 PU * different operational model caveat - unproven All numbers approximate 40 10

11 post LS1 Mike Lamont Potential performance Number of bunches Ib LHC FT[1e11] beta*x beta*sep Xangle Emit LHC [um] Peak Lumi [cm- 2 s -1 ] ~Pile-up Int. Lumi per year [fb -1 ] 25 ns /43/ e33 21 ~24 This does not even take into account out-of-time pileup which CMS will be more affected by at 25 ns. 25 ns low emit /43/ e34 42 ~40 50 ns /43/ ns low emit /43/ e34 level to 0.9e34 2.2e34 level to 0.9e34 74 level to 40 Initial reaction, what s the future of GeV jets post LS1? in the forward region? post LS2? 109 level to 40 ~45* ~45* 6.5 TeV 1.1 ns bunch length 150 days proton physics, HF = mb visible cross-section could have essentially double pileup vertices post LS2 could be > 100 PU * different operational model caveat - unproven All numbers approximate 40 11

12 detector considerations Pileup mitigation Depth segmentation in the HCAL, different depths in the hadronic calorimeter can be used to identify pileup contributions Currently in the HE only but by LS2 for the whole HCAL Timing information in HCAL (post LS2) - can help in identifying out-of-time pileup Really high pt jets - more prevalent at s = 13 TeV with more boosted objects When does tracking start to fail? At which momentum do the tracks become too close together The outer region of the HCAL is not being used currently (HO), how much will this improve the performance of really high pt jets? 12

13 pileup mitigation with grooming Grooming algorithms are used to clean up soft QCD and pileup contributions to the jet Can we use grooming algorithms to reduce the affect of pileup on our standard AK5 jets? Typically grooming algorithms reduce the jet area, reducing the size of the L1 pileup correction JEC Uncertainty (PFJets) Which grooming algorithm should we use? vs pt Dedicated studies -1 are needed, varying CMS preliminary, L = 11 fb s = 8 TeV grooming parameters Total uncertainty JEC uncertainty [%] Pruning is standard Extrapolation for searches with 7 Pile-up, NPV=14 7 Jet flavor Jet flavor substructure 6 Time but stability also the most 6 invasive 5 5 to jets, maybe trimming R=0.5 PF is a better choice 4 Are there 3 situations where a CMS 3 standard jet 2 2 in physics analyses was a groomed jet is better 1 1 than a 0 regular one? (GeV) e.g. Jet vetoes? Absolute scale Relative scale Anti-k T η =0 jet p T JEC uncertainty [%] 9 4 CMS preliminary, L = 11 fb p (GeV) T -1 Total uncertainty Absolute scale Relative scale Extrapolation Pile-up, NPV=14 Time stability -1 s = 8 TeV Anti-k T R=0.5 PF η =2.0 jet > groomed /pt groomed <pt > GEN ungroomed GEN /pt RECO ungroomed double ratio, RECO <pt JEC uncertainty [%] CMS Simulation, L = 5fb at <pt double ratio, <pt Ungroomed AK7 Trimmed AK7 Filtered AK7 Pruned AK7 s = 7 TeV, AK7 W+jets groomed RECO ungroomed RECO /pt /pt groomed GEN ungroomed GEN RECO CMS preliminary, L = 11 fb s = 8 TeV 10 Total uncertainty 9 Absolute scale 8 Relative scale Extrapolation 7 Pile-up, NPV=14 Jet flavor 6 Time stability 5 Anti-k T R=0.5 PF 4 η =2.7 jet p (GeV) T CMS preliminary, L = 11 fb s = 8 TeV CMS preliminary, L = 11 fb s = 8 TeV > > 13

14 scanning in grooming parameters Reconstructed vertex multiplicity (N ) Reconstructed vertex multiplicity (N ) It would be great if CMS does something like this. PV PV Does (c) Pruned it make anti-k t : 200 an apple effect p jet < 300 GeV for smaller R (d) jet? Pruned With anti-karea t : 600 applesubtraction? p jet < 800 GeV T T [GeV] [GeV] jet jet 1 m m ATLAS Preliminary Data -1 Data 2011, 2011, Ldt Ldt = 1 = fb1 fb C/A anti-k LCW t LCW jets with jets R=1.2 jet with R= p < jet300 GeV, η < T p < 800 GeV, η < 0.8 T No jet grooming No jet grooming f cut =0.01, R =0.3 sub f cut =0.03, µ =0.67 frac R =0.3 f =0.3 sub cut =0.05, R sub f cut =0.01, µ =0.33 frac µ =0.20 R =0.2 f =0.2 sub cut =0.03, R frac sub f cut =0.05, R =0.2 sub Reconstructed vertex multiplicity (N (N ) ) PV PV (e) (b) Filtered TrimmedC/A: anti-k 200 t : 600 apple papple jet p< jet 300 < 800 GeV GeV T T [GeV] jet 1 m ATLAS Preliminary Data 2011, Ldt = 1 fb C/A LCW jets with R=1.2 jet 600 p < 800 GeV, η < 0.8 T No jet grooming µ =0.67 frac µ =0.33 frac µ =0.20 frac Reconstructed vertex multiplicity (N ) PV (f) Filtered C/A: 600 apple p jet T < 800 GeV ] jet 6 14

15 inclusive measurements R V+jets and dijets final states All measurements are unfolded 5 fb -1 at 7 TeV, 2011 Dijet final state = unfolded, = detector level = 36 pb -1 at 7 TeV, 2010 = 5 fb -1 at 7 TeV, 2011, grooming parameters varied,ca CA,CA,CA,CA,CA 1.0,AK,CA AK&CA 0.5 N.B. jet finding with anti-kt (AK) unless otherwise indicated, alternative algorithm: Cambridge-Aachen (CA) jet mass jet mass [pruned] jet mass [trimmed] jet [(mass-drop)/filtered] kt splitting scale and N-subjettiness, τ2/τ1 and τ3/τ2 width, eccentricity, planar flow, angularity observable 15

16 inclusive measurements R V+jets and dijets final states All measurements are unfolded 5 fb -1 at 7 TeV, 2011 Dijet final state = unfolded, = detector level = 36 pb -1 at 7 TeV, 2010 = 5 fb -1 at 7 TeV, 2011, grooming parameters varied 1.0,CA,CA,CA CA,CA,CA,AK only one overlapping point,ca12, but unfortunately here CMS did filtering only and ATLAS did split/filtering method #facepalm,ca AK&CA 0.5 Would grooming help here? N.B. jet finding with anti-kt (AK) unless otherwise indicated, alternative algorithm: Cambridge-Aachen (CA) jet mass jet mass [pruned] jet mass [trimmed] jet [(mass-drop)/filtered] kt splitting scale and N-subjettiness, τ2/τ1 and τ3/τ2 width, eccentricity, planar flow, angularity observable 16

17 Number of Jets example: very high PU We can even glance into the far future with Snowmass studies for very high PU scenarios (Phase 2, 140 PU) Grooming more than just a improvement, rather it maybe become a necessity Snowmass setup: ttbar sample with 0 and 140 PU using the DELPHES detector Attempt to pick out the merged W s for moderately boosted tops W peak is washed out without grooming orkshop - April 5, 2013 J. Dolen PU - no grooming More details: PU - no grooming 140 PU - pruned Jet mass (GeV/c ) 2 17

18 high pt and high PU What happens at both high pt and high pileup? Which tagging techniques work in the region we also worry about detector resolution? / d / d Marzani et al. quark jets: m [GeV], for p t = 3 TeV plain jet mass Trimmer (z cut =0.05, R sub =0.2) Pruner (z cut =0.1) MDT (y cut =0.09, µ=0.67) = m 2 /(p t 2 R 2 ) Jets: C/A with R=1. MC: Pythia 6.4, DW tune, parton-level (no MPI), qq qq, p t > 3 TeV / d / d gluon jets: m [GeV], for p t = 3 TeV plain jet mass Trimmer Pruner 0.2 MDT = m 2 /(p t 2 R 2 ) Jets: C/A with R=1. MC: Pythia 6.4, DW tune, parton-level (no MPI), gg gg, p t > 3 TeV 18

19 example: Q-jets, Q-event, telescoping Could Qjets be a good measure for identifying pileup jets? Speed can be problem... Q-events, telescoping: It should be particularly useful in high multiplicity signatures (SUSY) It could be useful in identifying VBF tag jets where we have no tracking information and lots of pileup 19

20 new observables Q-jets, etc what observable would you like to see that you aren t an author of? (Jon) jet charge shower deconstruction N-jettiness as a veto, algorithm? Energy correlation functions... for theorists, what kind of measurements would you like to see being done? 20

21 MC tools Not optimal right now Only Pythia6 is tuned by CMS, but does not describe jet substructure What's the status/timescale of NLO ME+PS MCs? Sherp 2.0 POWHEG + MINLO, the GENEVA project Still, we wouldn t be able to use these for all samples We are moving to Pythia8 post LS1, no tune exists at the moment CMS/ATLAS have unfolded jet mass distributions This is our chance to impact the parton shower tunes to get better modeling of jet substructure Request from theorists of a CMS paper on jet substructure Compare data to different types calculations LO+PS, LO+Matching+PS, NLO+Matching+PS, alternate PS+UE +Hadronization+PU May systematically help understanding which calculations (don t) work Compare generator level and detector level Theorists want to know which information in their variables survive the detector reconstruction 21

22 summary A lot of open questions about pileup post LS1 Experiments will start to answer some of these questions soon, samples are becoming available to do detailed studies In the meantime, get input on what are most important to think about CMS has started to apply substructure ideas in identifying pileup jets, how much further can we take it? Thinking about other applications for substructure beyond classic heavy object tagging 22

23 CMS: Compact Muon Solenoid Total+weight t+ Overall+diameter+++15+m+ Overall+length m+ 76k scintillating ECAL! PbWO 4 crystals HCAL!Scintillator/brass Interleaved ~7k ch 3.8T Solenoid MUON'ENDCAPS! 473 Cathode Strip Chambers (CSC) 432 Resistive Plate Chambers (RPC) IRON'YOKE' Preshower Si Strips ~16 m 2 ~137k ch YBO YB1-2 YE1-3 Foward Cal Steel + quartz Fibers 2 ~k ch Pixels'&'Tracker! Pixels (100x150 µm 2 ) " ~ 1 m 2 ~66M ch" Si Strips ( µm)" ~200 m 2 ~9.6M ch! MUON'BARREL! 250 Drift Tubes (DT) and 480 Resistive Plate Chambers (RPC) 23