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Collider Phenomenology at the UW: Looking for New (BSM) Physics at the LHC with Jets

Collider Phenomenology at the UW: Looking for New (BSM) Physics at the LHC with Jets. S. Ellis, J. Walsh & C. Vermilion. UW 10/30/08. Big Picture:. For the next decade the focus of particle physics phenomenology will be on the LHC. The LHC will be both very exciting and very challenging -

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Collider Phenomenology at the UW: Looking for New (BSM) Physics at the LHC with Jets

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  1. Collider Phenomenology at the UW: Looking for New (BSM) Physics at the LHC with Jets S. Ellis, J. Walsh & C. Vermilion UW 10/30/08

  2. Big Picture: For the next decade the focus of particle physics phenomenology will be on the LHC. The LHC will be both very exciting and very challenging - • addressing a wealth of essential scientific questions • with new (not understood) detectors • operating at high energy and high luminosity • most of the data will be about hadrons (jets). • NOTE: The recent HEPAP Subpanel Report says – progress will come only from the coordinated efforts of large numbers of theorists and experimentalists • We are working to maintain the necessary collaborative environment in the NW. We have an outstanding history of such collaboration at the UW. UW 10/30/08

  3. Outline: • UW Phenomenology Assets • Why jets? • Review of Jets – cone & recombination (kT) algorithms • Recent progress in understanding jets & substructure, especially masses Goal: using jets and their substructure to look for Beyond the Standard Model physics S. D. Ellis - DoE Site Visit 2008

  4. Local Assets • Theory/Pheno Faculty: Steve Ellis, Ann Nelson • Theory/Pheno Graduate Students: Jon Walsh (2007-08 LHC Fellow), Chris Vermilion Other (the environment matters): BSM: A. Nelson, D. Kaplan (INT) String Theory: A. Karch AdS/CFT – QCD L. Yaffe, D. Son (INT), A. Karch Collider QCD: W.-K. Tung (Affiliate Faculty) – pdf & heavy flavor expert Experiment: D0, ATLAS – T. Burnett, A. Goussiou, H. Lubatti, J. Rothberg, G. Watts (+ 3 PDs & 8 students) and we meet on a regular basis S. D. Ellis - DoE Site Visit 2008

  5. Not so Local Assets: Northwest Center for Terascale Physics:Joint with the University of Oregon PIs - A. Nelson, A. Goussiou, D. Soper and E. Torrence Theorists: N. Deshpande, S. Hsu, G. Kribs, D. Soper Experimentalists: J. Brau, R. Frey, D. Strom, E. Torrence The opportunities for synergy and collaboration are enormous, sharing ideas, people and resources. Physical proximity will add in the organization of useful, productive and sometimes transformative LHC conferences. S. D. Ellis - DoE Site Visit 2008

  6. Even more long distance Assets: CDF, ATLAS – J. Huston (working with Ellis on jets) ATLAS, CMS, LHCb – Ellis has just written review/preview paper “Jet in Hadron-Hadron Collisions” with J. Huston, K. Hatakeyama, P. Loch for Prog. Part. Nucl. Phys. 60 (2008) 484 (0712.2447) West Coast LHC Theory Network – participating Matt Strassler, now at Rutgers, has continued to collaborate S. D. Ellis - DoE Site Visit 2008

  7. Why JETS? Essentially all LHC events involve an important hadronic component, only Z’ + -avoids this constraintThe primary tool for hadronic analysis is the study of jets, to map long distance degrees of freedom (i.e., detected) onto short distance dof (in the Lagrangian)Jets used at the Tevatron to test the SM, will be used at the LHC to test for non-SM-ness UW has a long history of expertise with jets: S. Ellis – founding father of jet physics (also D. Soper) A. Karch & L. Yaffe – leaders in AdS/CFT jet studies A. Goussiou – expert in tagging tau jets G. Watts – expert in tagging b-quark jets H. Lubatti – expert in Hidden Valley to jets UW goal – learn to tag “non-QCD” jets to search for BSM physics S. D. Ellis - DoE Site Visit 2008

  8. Jet Physics: The Basis of QCD Collider Phenomenology Long distance physics = complicated (all orders showering of colored objects, nonperturbative hadronization = organization into color singlets) Measure this in the detector pdf Short distance physics = simple (perturbative) Fragmentation fct Connect this to the long distance – jet substructure Correlated by Underlying Event (UE) color correlations Stuck with this, small? More long distance physics, but measured in pdfs S. D. Ellis - DoE Site Visit 2008

  9. Since large momentum transfer is rare (small running coupling) and physics is dominated by collinear (or soft) emissions (gauge theory), spreading is limited in QCD  Jet Structure. Clear in data! real simulated S. D. Ellis - DoE Site Visit 2008

  10. A Review of Jets • Idea starts with Feynman and Bjorken, applied to hadron collisions by Ellis & Kislinger in 1974 • JETS I - Applied to data at the ISR, SpbarpS, and Run I at the Tevatron to map final state hadrons onto LO (or NLO) hard scattering, essentially 1 jet 1 parton (test QCD)Little attention paid to masses of jets or the internal structure, except for energy distribution within a jet • JETS II – Run II & LHC, starting to look at structureof jets: masses and internal structure – a jet renaissance S. D. Ellis - DoE Site Visit 2008

  11. Defining Jets • Map the observed (hadronic) final states onto the (short-distance) partons by summing up all the approximately collinear stuff, ideally on an event-by-event basis. • Need rules for summing  jet algorithm Start with list of particles/towers End with list of jets (and stuff not in jets) • E.g., • Cone Algorithms, based on fixed geometry – focus on core of jetWell suited to hadron colliders with Underlying Events (UE) (Snowmass, Ellis, et al., 1990; NLO, Ellis, Kunszt & Soper, 1989) • kT Algorithm, based on pairwise merging, lowest pT first – undo shower Tends to “vacuum up” soft particles, well suited to e+e- colliders (Hadron Collider application, Ellis & Soper, 1993) S. D. Ellis - DoE Site Visit 2008

  12. The good news about jet algorithms: • Render PertThy IR & Collinear Safe, potential singularities cancel • Simple, in principle, to apply to data and to theory • Relatively insensitive to perturbative showering and hadronization The bad news about jet algorithms: • The mapping of color singlet hadrons on to colored partons can never be 1 to 1, event-by-event! • There is no unique, perfect algorithm; all have systematic issues • Different experiments use different algorithms • The detailed result depends on the algorithm, especially true for jet substructure S. D. Ellis - DoE Site Visit 2008

  13. Different algorithms  different jets (same CDF event) S. D. Ellis - DoE Site Visit 2008 EM, Hadronic

  14. Cone Algorithm – focus on the core of jet • Jet = “stable cone”  4-vector of cone contents || cone direction • Well studied – several issues • Cone Algorithm – particles, calorimeter towers, partons in cone of size R, defined in angular space, e.g., (y,), • CONE center - • CONE i C iff • Cone Contents  4-vector • 4-vector direction • Jet = stable coneFind by iteration, i.e., put next trial cone at S. D. Ellis - DoE Site Visit 2008

  15. Negative systematic Issues for cone: • Stable Cones can and do overlap, need to define rules for merging and splitting, more parameters (but CDF and D0 choose different parameters, no analogue in NLO PertThy) • Seeds – experiments only look for jets near very active regions (save computer time)  problem for theory, IR sensitive (Unsafe?) at NNLO Don’t find “possible” central jet between two well separated proto-jets (partons)  Simulate with Rsep (eliminate ) • Dark Towers - Energy in secondary showers may not be clustered in any jet No seed Seed NNLO NLO • Expected stable cone not stable due to smearing from showering/hadronization (compared to PertThy) • Under-estimate ET (~ 5% effect for jet cross section) S. D. Ellis - DoE Site Visit 2008

  16. Fixes - • All experiments use the same split/merge parametersStill not true at the Tevatron • Use seedless cone algorithm (e.g., SIScone) or correct data for seed effects Small effect (1-2 %) in data, big issue in pert Thy • No good solution yet to Dark towers except to look for 2nd pass jets after removing the 1st pass jets from the analysis. Still the Cone algorithm is familiar and will be the starting point at the LHC S. D. Ellis - DoE Site Visit 2008

  17. Recombination or kT Algorithms– focus on undoing the shower pairwise • Merge partons, particles or towers pairwise based on “closeness” defined by minimum value of • If kT,(ij)2is the minimum, merge pair and redo list;IfkT,i2is the minimum →i is a jet! (no more merging for i), Then repeat!parameterD  jet angular “size” (~ R) = 1, ordinary kT, recombinesoft stuff first = 0, Cambridge/Aachen (CA),controlled by angles only = -1, Anti-kT, justrecombinestuff around hard guys – cone-like • Jet identification is unique – no merge/split stage • Everything in a jet, no Dark Towers • Resulting jets are more amorphous, energy calibration difficult (subtraction for UE?), Impact of UE and pile-up not so well understood, especially at LHC • Analysis can be very computer intensive (time grows like N3, recalculate list after each merge) S. D. Ellis - DoE Site Visit 2008

  18. The good news - • New version (Gavin Salam) goes like N ln N (only recalculate nearest neighbors), plus has scheme for doing UE correction – the LHC collaborations will use kT algorithms • More importantly for us – the algorithm naturally defines a “tree” or daughter substructure (just undo the recombinations) S. D. Ellis - DoE Site Visit 2008

  19. At the Tevatron jet studies have been driven by “testing” QCD, comparing data and PertThy for inclusive jet cross section – [Cone, DØ] Ratio data/NLO theory Inclusive Jet cross section Uncertainty ~ 10%(1 % goal at the LHC) Range ~ 108 S. D. Ellis - DoE Site Visit 2008

  20. Similar situation for kT jets [kT, CDF] S. D. Ellis - DoE Site Visit 2008

  21. Goals at LHC Different  Different Figure of Merit for Jet algorithm? • Find Physics Beyond the Standard Model • Want to select events/jets by non-QCD-ness • Event structure likely different from QCD, more jets? • Different structure within jets? Highly boosted SM and non-SM particles – W, Z, top, Higgs, SUSY  focus on substructure in jets, especially masses S. D. Ellis - DoE Site Visit 2008

  22. Recent progress in understanding/using jets • Improved tools and understanding of algorithms – eg. G. Salam • Improved analytic descriptions – eg. G. Sterman and collaborators, SCET community (C. Lee, I. Fleming, S. Stewart, et al.) • Jet selection schemes to isolate W/Z, top quarks or Higgs as single jets – J. Butterworth and collaborators – kT jets UCB Group (J. Thaler, et al.) – kT jets Johns Hopkins Group (D. Kaplan, et al.) – kT jets Stony Brook Group (G. Sterman, et al.) – cone jets • Perturbative results for masses – UW • Generic search/pruning techniques for BSM searches with jets – focus on masses comparing algorithms - UW S. D. Ellis - DoE Site Visit 2008

  23. Jet Masses in QCD: To compare to non-QCD Dimensions Phase space from pdfs, f ~ 1 • In NLO PertThy Jet Size, R ~ , determined by jet algorithm Useful QCD “Rule-of-Thumb” S. D. Ellis - DoE Site Visit 2008

  24. Mass for fixed PJ at NLO For Cone, R = 0.7or kT, D = 0.7 Peaked at low mass, cuts off for (M/P)2 > 0.25, M/P > 0.5 S. D. Ellis - DoE Site Visit 2008

  25. Compare to (simulated) LHC data: (Rsep scales R) Various algorithms applied to simulated LHC data (diamond, square, circle) NLO Cone Theory, various Rsep values (lines, triangles) } } Rsep = 2, SnowmassRsep =1.3 EKSRsep= 2, kT Theory Brackets “data” S. D. Ellis - DoE Site Visit 2008

  26.  QCD exhibits smooth distributions in masses (and other substructure) with scale provided only by pT  BSM physics has inherent (large) mass scale and will exhibit bumps in distributions (~ independent of pT) S. D. Ellis - DoE Site Visit 2008

  27. Focus on kT algorithm – naturally defines subjets • Think of a jet as a tree with jet as trunk and subjets(daughters) as branches, eg. Baobab tree • Compare to simulated jets withRadius of branching = kT,(ij)2thickness, color of branch ~ pTangle ~ detector angle Note: the outer branches are always the same ttbar, More energetic jets S. D. Ellis - DoE Site Visit 2008 QCD, Fewer energetic jets

  28. Substructure - Variables describing jet (trunk) and first (going outward from trunk) branching of tree - • MJ daughter massesANGLES:In Lab frame in QCD expect z,  << 1 (soft, collinear shower)In jet rest frame (think top decay) • Angular distributions are strongly shaped by the algorithm, choosing the algorithm is important!  z 0 S. D. Ellis - DoE Site Visit 2008

  29. Kinematics – Cascade Decay: Use t →Wb→(ud)b as example, i.e., parton level(treat tWb & W→ud as isotropic in t & W frame)Consider Reconstruction efficiency in D = 1 jet versus cos0 W angle wrt jetBlack line = TOTAL, Blue dots = Correct (ud)b, Red dots = Incorrect (ub)d or (db)u kT CA Anti-kT Reconstruction efficiency increaseswith  Transverse Boost  = 2 TransverseBoost  = 3 TransverseBoost  = 10 S. D. Ellis - DoE Site Visit 2008 Correct reconstruction increases with cos0

  30. Reconstruction Fractions integrated over cos0 kT> Anti-kT CA Total fraction increases with boost Boost > 2 correct reconstruction fraction ~ 1/3 (and ~ constant) S. D. Ellis - DoE Site Visit 2008

  31. 1st suggested generic search for BSM physics – look for bumps in jet and first daughter mass distributions (cascade decays) • Extra radiation from showering and UE smears the distributions – clean up with pruning procedure –Find jets in usual way and then prune the tree, i.e.For branchings withDiscard the softer branch (remove from subsequent analysis – prune the soft radiation) However - S. D. Ellis - DoE Site Visit 2008

  32. Pythia simulated top data – kT & CA w & w/o pruning Pruned CA has sharpest JET MASS peaks kT CA S. D. Ellis - DoE Site Visit 2008

  33. Mass of heavier daughter for jets in top mass bin  pruned CA has narrowest DAUGHTER MASS peaks kT CA CA + mtop cut kT + mtop cut S. D. Ellis - DoE Site Visit 2008

  34. Cos 0 Distributions – shaped by algorithm and jet and daughter masses (~ same for signal and background) CA + mtop cut CA parton level CA +mtop cutwith QCD BKG CA + mtop cut+mW cut S. D. Ellis - DoE Site Visit 2008

  35. Numbers – Signal (top) vs BKG (QCD) FWHM window sizes - kTpkT 189.5-154.5 = 35 180.5 – 156.5 = 24 CA pCA 186.5-160.5 = 26 177.5 - 159.5 = 18** LO cross section, large uncertainties; no detector simulation S/B ~ 2 for 100 pb-1  factors of 2 improvement matter S. D. Ellis - DoE Site Visit 2008

  36. Continuing to look for more improvements to produce generic BSM jet techniques, expect the future to evolve like Exp Techniques Formal calce.g., SCET UW Jet Substr MC tools, detector simsmatched data setsNLO MCs Improved Algorithms UW S. D. Ellis - DoE Site Visit 2008

  37. Summary: • Jet substructure is likely to be part of the answer to finding BSM physics at the LHC • Recombination algorithms provide natural definition of substructure (cone may not and has systematic problems) • Looking at jet and subjet masses in pruned CA jets definitely can help Future • More worked needed to get past “shaping” of angular distributions • Also need better detector simulations and better data (matched) sets S. D. Ellis - DoE Site Visit 2008

  38. Extra Detail Slides S. D. Ellis - DoE Site Visit 2008

  39. Example Lego & Flow S. D. Ellis - DoE Site Visit 2008

  40. NLO Perturbation Theory – R= partonseparation, z= p2/p1Simulate the missed middle cones withRsep Naïve Snowmass Cone With Rsep Rsep*R r r R R ~10% of cross section here S. D. Ellis - DoE Site Visit 2008

  41. Why Dark towers?Include smearing (~ showering & hadronization) in simple picture, find only 1 stable cone r S. D. Ellis - DoE Site Visit 2008

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