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Experimental Aspects of Jet Reconstruction in Collider Experiments

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Experimental Aspects of Jet Reconstruction in Collider Experiments Powered By Docstoc
					Jet Reconstruction in ATLAS
             Peter Loch
        University of Arizona
        Tucson, Arizona, USA




      e-mail: loch-at-physics.arizona.edu
                            Outline
Jets at LHC
  Brief overview on sources for jets and jet features at LHC
  Physics environment: Underlying Event and Pile-up
Jet reconstruction in ATLAS
  Jet finders
  Tower and cluster jets
  Jet calibration strategies
  In-situ calibration
More from jets at LHC
  Jet reconstruction performance
  Mass reconstruction and jet substructure analysis
  Jet shapes
Summary & Outlook
         Seminar
                              Slide 2               Peter Loch
         October 31, 2007
                                         University of Arizona
         RAL
                              Jets at LHC
New kinematic regime for jet
physics
   Jets can be much harder
      Jets get more narrow in general
      (kinematic effect ~αs)
      Higher energies to be contained in
      calorimeters
Jet reconstruction challenging
   Physics requirements typically 1%
   jet energy scale uncertainty
      top mass measurement in ttbar
          LHC is a top factory!
      hadronic final states in at the end
      of long decay chains in SUSY
   Quality takes time
      Previous experiments needed up to
      10 years of data taking to go from
      ~4% down to ~1%
      Can often not be achieved for all
      kinds of jets and in all physics      W. Stirling, LHCC Workshop “Theory of LHC Processes” (1998)
      environments                          *annotation from J. Huston, Talk @ ATLAS Standard Model
                                            WG Meeting (Feb. 2004)



           Seminar
                                  Slide 3                          Peter Loch
           October 31, 2007
                                                        University of Arizona
           RAL
Jets from QCD Processes                            s = 1.8TeV
Jets at low pT most likely produced
by gluon fusion                                               s = 14 TeV
   Large phase space for radiation
   Expectation are multi-jet final states
   even for 2→2 processes
More likely quark jets at higher pT
   Less radiation (Sudakov suppression)
   Less jets in events
   Narrower jets (again)
Large kinematic range
   pT range 10-5000 GeV/c
   Di-jet mass reach several TeV/c2
Multitude of jet flavours
   Expect corresponding variety of jet
   shapes with possibly specific
   calibrations!


           Seminar
                               Slide 4                 Peter Loch
           October 31, 2007
                                            University of Arizona
           RAL
Physics Environments @ LHC
                                             Underlying event
                                                 Refers to multiple interactions
                                                 between the two colliding
                                                 protons
                                                 Typically correlated with hard
                                                 scatter
                                             Increased activity compared to
                                             Tevatron
                                                 more phase space
                                                                                        A.Moraes,
Number charged tracks in transverse region




                                                                                        HERA-LHC
                                                                                        Workshop,                                                              Δφ
                                                                                        DESY, March                             leading jet
                                                           LHC prediction:              2007
                                                           x2.5 the activity
                                                           measured at
                                                           Tevatron!
                                                                                            independent of                                    “toward”
                                                                                         luminosity → present                  “transverse”
                                                                                                                                              |Δφ|<60°
                                                                                                                                                           “transverse”
                                                       CDF data (√s=1.8 TeV)
                                                                                            at day 1 at LHC!                  60°<|Δφ|<120°
                                                                                                                                                “away”
                                                                                                                                                          60°<|Δφ|<120°

                                                                                                                                              |Δφ|>120°

                                              CDF data: Phys.Rev, D, 65 (2002)

                                                                                            Rick Field’s (CDF) view on
                                                                 pT leading jet (GeV)
                                                                                                          di-jet events

                                                                Seminar
                                                                                                  Slide 5                            Peter Loch
                                                                October 31, 2007
                                                                                                                          University of Arizona
                                                                RAL
           Pile-up                                      no pile-up added

                                                              Et ~ 81 GeV
                                                                                    LHC design luminosity pile-up
                                                                                    added


Large pp total cross-section (~75mb)                     Et ~ 58 GeV
   ~23 “minimum bias” (soft to medium hard)
   collisions between protons in the same
   bunch in addition to triggered hard
   scatter
       @ 1034 cm-2 s-1
       Poisson distributed
   Similar dynamics as UE
   Statistically independent
       No correlation to hard scatter!
   Generate lots of additional particles in
   addition to the underlying event




                                                                                                                P. Savard et al., ATLAS-CAL-NO 084/1996
       ~370 particles/unit rapidity per bunch
       crossing (3700 within ATLAS)
       ~1,800 charged tracks in ATLAS/bunch
       crossing
High crossing rate at LHC (40 MHz)
   Calorimeter signals typically to slow
   Short signal shaping, bi-polar shaping
   function (ATLAS)                                                                            R = 0.7
       Long signal history (~500-600 ns) generates
       “out-of-time” pile-up
   Canceling area → integrated effect 0
       Careful, only true in limit of continuous
       collisions
   Can be treated as noise in calorimeter
       Asymmetric!


              Seminar
                                              Slide 6                             Peter Loch
              October 31, 2007
                                                                       University of Arizona
              RAL
                Experimenter’s View on Jets
                               longitudinal energy leakage
     detector signal inefficiencies (dead channels, HV…)
   pile-up noise from (off- and in-time) bunch crossings
                                          electronic noise
  calo signal definition (clustering, noise suppression ,…)
       dead material losses (front, cracks, transitions…)
             detector response characteristics (e/h ≠ 1)
                 jet reconstruction algorithm efficiency

                jet reconstruction algorithm efficiency
 added tracks from in-time (same trigger) pile-up event
                     added tracks from underlying event
                   lost soft tracks due to magnetic field

physics reaction of interest (interaction or parton level)

We like to factorize the calibration and corrections dealing with
these effects as much as possible!
                      Seminar
                                                 Slide 7                 Peter Loch
                      October 31, 2007
                                                              University of Arizona
                      RAL
Theoretical Requirements for Jet Finders
  Infrared safety
       Absence of additional
       radiation splits jets
       Presence of radiation
       merges jets
  Collinear safety                                        infrared sensitivity
                                                      (soft gluon radiation merges jets)
       Split seeds




    collinear sensitivity (1)                            collinear sensitivity (2)
  (sensitive to Et ordering of seeds)             (signal split into two towers below threshold)


                    Seminar
                                        Slide 8                        Peter Loch
                    October 31, 2007
                                                            University of Arizona
                    RAL
Experimental Requirements for Jet Finders
  Detector technology independence
     Minimal contributions to spatial and energy resolution
     Insignificant effects of detector environment
        Noise
        Dead material
        Cracks
     Easy to calibrate
        Well…
  Environment independence
     Stability with changing luminosity
     Identify all physically interesting jets from energetic partons in
     pertubative QCD (pQCD)
     High reconstruction efficiency
  Implementation
     Fully specified
        All selections and other configurations known
     Efficient use of computing sources



              Seminar
                                   Slide 9                         Peter Loch
              October 31, 2007
                                                        University of Arizona
              RAL
   Popular Jet Algorithms in ATLAS
Seeded cone                                     Recursive recombination (kT)
   Place cone with radius R around                 Calculate for all particles i and
   seed                                            pairs ij :
       pT > 1 GeV
                                                                               Rij
                                                                                  2

                                                   dij  min( pt ,i , pt , j ) 2
                                                               2       2

   Collect all particles in cone                                               D
   Re-calculate energy and                                                     yij  ij
                                                                                 2       2

                                                        min( pt ,i , pt , j )
                                                               2       2

   direction of cone                                                                D2
       4-momentum recombination                     di  pi2
   Find more particle in new cone
                                                   Combine any two pairs to jet if:
   Stop until no more particles to
                                                   dij  di
   be found
       Stable solution
                                                   Else remove i from list
       Particles can be shared between                  Is a jet
       jets                                        Calculate new combinations
   Is not infrared safe                                 Stop when all particles declared
       Needs split & merge (50%                         jets
       threshold)                                       Each particle is part of one jet
                                                        only (exclusive assignment)
   May miss signficant energy
       Dark jets
                                                   Infrared safe

             Seminar
                                     Slide 10                         Peter Loch
             October 31, 2007
                                                           University of Arizona
             RAL
    Jet Finders in ATLAS
    Alternative applications:




                                                                       N.Godbhane, JetRec June 2006
           CDF mid-point, Cambridge/Aachen
           recursive recombination (0th order kT),
           “optimal jet finder” (event shape fit)                                                          mW
           More options: move to FastJet libraries
                 CMS, theory
    No universal configuration or jet finder
           Narrow jets
                 W->jj in ttbar, some SUSY
           Wider jets
                 Inclusive jet cross-section, QCD
            Algorithm                  Rcone     D         Clients

Seeded Cone                                           W mass
                                       0.4
EtSeed = 1 GeV, fS/M = 0.5                            spectroscopy,
                                                      top physics,
Kt (FastKt)                                     0.4   SUSY
Seeded Cone
                                       0.7
EtSeed = 1 GeV, fS/M = 0.5                            QCD, jet
                                                      cross-sections
Kt (FastKt)                                     0.6
                                                                                                           P.-A. Delsart, June 2006


                             Seminar
                                                        Slide 11                                                 Peter Loch
                             October 31, 2007
                                                                                                      University of Arizona
                             RAL
   The ATLAS Detector




Seminar
                   Slide 12              Peter Loch
October 31, 2007
                              University of Arizona
RAL
                                                                    Electromagnetic Calorimeters:
          ATLAS Calorimeters                                        
                                                                    
                                                                      Liquid Argon/Pb accordion structure;
                                                                      highly granular readout (~170,000
                                                                    channels);
                                    Electromagnetic Liquid Argon     0.0025 ≤ Δη ≤ 0.05, 0.025 ≤ Δφ ≤ 0.1;
                                            Calorimeters             2-3 longitudinal samplings;
                                                                     ~24-26 X0 deep
  Tile Calorimeters                                                  covers |η|<3.2, presampler up to
                                                                    |η|<1.8;
                                                                    Central Hadronic Calorimeters
η=1.475                                                              Scintillator/Fe in tiled readout;
                                                                     Δη x Δφ = 0.1 x 0.1
η=1.8                                                                3 longitudinal samplings,
                                                                     covers |η|<1.7;

                                                                    EndCap Hadronic Calorimeters
η=3.2                                                                Liquid Argon/Cu parallel plate absorber
                                                                    structure;
                                                                     Δη x Δφ = 0.1 x 0.1 (1.5<|η|<2.5),
                                                                    Δη x Δφ = 0.2 x 0.2 (2.5<|η|<3.2);
                                                                     4 samplings;

                                                                    Forward Calorimeters
                                                                     Liquid Argon/Cu or W absorbers with
                                                                    tubular electrodes in non-projective
                                                                    geometry;
                                             Forward Liquid Argon    Δη x Δφ ≈ 0.2 x 0.2 (3.2<|η|<4.9)
                                                 Calorimeters        3 samplings;
        Hadronic Liquid Argon
        EndCap Calorimeters

                          Seminar
                                                      Slide 13                     Peter Loch
                          October 31, 2007
                                                                        University of Arizona
                          RAL
   ATLAS Calorimeter Details                                                      Cryostat walls
                                                                                   (warm/cold)

Electromagnetic Barrel Module

                                                                      Hadronic
                                                                       EndCap
                                                                     (2 wheels)
                                            to interaction
                                                                                            p from LHC
                                                vertex




                                      Electromagnetic
                                          EndCap
                                                             FCal1            FCal3 Cu Shielding

                                                                      FCal2


 total ~200,000 channels, with hadronic coverage ~10 absorption lengths in full
 acceptance (|η|<5) and a typical level of non-compensation e/h≈1.3-1.6;

                   Seminar
                                      Slide 14                          Peter Loch
                   October 31, 2007
                                                             University of Arizona
                   RAL
Calorimeter Signals: Towers
                                                                 E           
                                                                           cell   2
                                                                                
                                                                                            wcell Ecell
                                                                           cell   2
 Imposes regular grid view                                                           
 on event                                                                           Acell
                                                                        wcell   
    Δη×Δφ = 0.1×0.1                                                                 
    Motivated by event ET flow
    Natural for trigger!
 Calorimeter cell signals are
 summed up in tower bins




                                                     φ
                                                          0.25   0.25                    wcell
    No cell selection, all cells are
    included                                              0.25   0.25
         Indiscriminatory signal sum
         includes cells without any
         true signal at all                          projective cells
    Sum typically includes                                                       non-projective
    geometrical weight                                                    1.0    cells
 Towers have fixed direction                             1.0
    Massless four-momentum
    representation
     ET ,,   E  p, px , py , pz                                                           η
                Seminar
                                          Slide 15                          Peter Loch
                October 31, 2007
                                                                 University of Arizona
                RAL
                                                       Electronic Noise in Calorimeter Cells
Calorimeter Signals:




                                                                                               S. Menke, ATLAS Physics Workshop 07/2005
 Topological Clusters
Attempt to reconstruct particle
showers
   Establish local signal correlations in
   (neighbouring) cells
   “energy blob” in 3-d
Growing volume algorithm using
seeds and signal thresholds
   Primary seeds start cluster
   Secondary seeds among neighbours                     Pile-up Noise in Calorimeter Cells




                                                                                               S. Menke, ATLAS Physics Workshop 07/2005
   control growth
   Basic cell selection threshold
   suppresses noise                                              L  1034 cm2s1
   All thresholds use signal-over-noise
   rather than signal
        Signal significance is above a constant
        (but complex) threshold
        Note: smallest reliably measurable
        energy is changing with noise!
        Avoids regional energy thresholds (lots
        of tuning)
        Uses experimentally accessible
        information
        Gets the best out of the calorimeter!



               Seminar
                                            Slide 16                        Peter Loch
               October 31, 2007
                                                                 University of Arizona
               RAL
  Principle of Topological Clustering

                        Ecell                                      1 1         1
  Primary seeds                    4                          1   1 1 1       1 1
                          noise




                                            φ
                           cell                                1   1 1 1       1 1
                                                               1   1 1 1       1 1
                                                                               1
                                                                               4
                                                                                 1
                                                                                 4      4
                                                                                        1
                        Ecell
Secondary seeds                    2                              1 1 1         4
                                                                               1 1      1
                                                                                        4   1
                                                                                            4
                                                                   5   5   5   15
                                                                               1
                                                                   1 1 1       4


                        cell
                         noise
                                                               2   1 1 1
                                                                   5
                                                                   2 5 5       1 1
                                                                               1
                                                                               5
                                                                               4 4      4
                                                                                        1   1
                                                                                            4
                                                       2   2   2   1
                                                                   5
                                                                   2   1
                                                                       5
                                                                       2


                         Ecell                     2   2   2   2   2 2 2            3   3   3
 Basic threshold                   0              2   2   2   2   2 2 2            3   3   3   3
                          noise
                           cell                        2   2   2       2 2          3   3   3   3
                                                                                        3   3   3


                                                                                        η
 Cluster splitting introduces geometrical weights!
           Seminar
                                        Slide 17                          Peter Loch
           October 31, 2007
                                                               University of Arizona
           RAL
Calorimeter Signals: It’s All In The Pictures…




         Seminar
                            Slide 18              Peter Loch
         October 31, 2007
                                       University of Arizona
         RAL
             CaloCells
              (em scale)
                                                                   Tower Jets in ATLAS
          Tower Building                              Sum up electromagnetic scale calorimeter cell signals into
 (Δη×Δφ=0.1×0.1, non-discriminant)
                                                      towers
                                                               Fixed grid of Δη x Δφ = 0.1 x 0.1
           CaloTowers
              (em scale)                                       Non-discriminatory, no cell suppression
                                                               Works well with pointing readout geometries
   Tower Noise Suppression                                             Larger cells split their signal between towers according to the overlap
(cancel E<0 towers by re-summation)                                    area fraction
                                                      Tower noise suppression
             ProtoJets
            (E>0,em scale)
                                                               Some towers have net negative signals
                                                               Apply “nearest neighbour tower recombination”
             Jet Finding                                                Combine negative signal tower(s) with nearby positive signal towers
         (cone R=0.7,0.4; kt)                                          until sum of signals > 0
                                                                        Remove towers with no nearby neighbours
       Calorimeter Jets                                        Towers are “massless” pseudo-particles
              (em scale)
                                                      Find jets
Jet Based Hadronic Calibration                                 Note: towers have signal on electromagnetic energy scale
(“H1-style” cell weighting in jets etc.)              Calibrate jets
                                                               Retrieve calorimeter cell signals in jet
       Calorimeter Jets
      (fully calibrated had scale)                             Apply signal weighting functions to these signals
                                                               Recalculate jet kinematics using these cell signals
 Jet Energy Scale Corrections                                           Note: there are cells with negative signals!
(noise, pile-up, algorithm effects, etc.)
                                                               Apply final corrections
          Physics Jets                                 In-situ Calibration                    Refined Physics Jet
     (calibrated to particle level)         (underlying event, physics environment, etc.)    (calibrated to interaction level)



                                             Seminar
                                                                                            Slide 19                                        Peter Loch
                                             October 31, 2007
                                                                                                                                 University of Arizona
                                             RAL
Determination of Tower Jet Calibration
 Sample of fully simulated QCD di-jet events from hard scatter
 pT>17 GeV/c to kinematic limit
    Electronic noise included in simulation
 Match reconstructed calorimeter jet with close-by particle jet
    Both jets reconstructed with seeded cone R=0.7
        pTseed>1 GeV/c
        Overlap threshold 50%
    Match exclusive: only accepted if only one jet close by
    Calorimeter jets are based on tower signals in a grid of ΔηxΔη = 0.1x0.1
 Access cell signals in jet
    H1 motivated cell signal weighting strategy
    Determine cell signal weights in resolution optimization fit using truth
    particle jet energy as normalization
        Weights are function of cell location and cell signal density
             Dense signals – em, less dense signals hadronic
 Re-calculate jet four-momentum using cell weights
    Jet energy and direction change




              Seminar
                                           Slide 20                       Peter Loch
              October 31, 2007
                                                               University of Arizona
              RAL
                   Tower Jet Performance
 Signal linearity                                     Energy resolution
      Relative to matched MC                            Relative to truth
      truth jet!
2.35  y  2.55  10  pt  13 GeV / c                             calibrated
                                                                   σ = 49%         3.4%
                                                                    E      E[GeV]




                                                         S. Padhi, ATLAS Physics Workshop 07/2005


       Kristin Lohwasser, September 2007


                                                        S. Padhi, ATLAS Physics Workshop 07/2005


                   Seminar
                                           Slide 21                          Peter Loch
                   October 31, 2007
                                                                  University of Arizona
                   RAL
Tower Jet Uniformity
 Signal linearity as function
 of jet direction
    Cracks less visible for wider
    jets, as expected
    No strong jet energy
    dependence                                 Kristin Lohwasser, September 2007
 Relative energy resolution
 as function of jet direction
    Cracks deteriorate signal
    Relative effect stronger
    dependend on jet energy,
    less on jet size



                                               Kristin Lohwasser, September 2007



             Seminar
                                    Slide 22                            Peter Loch
             October 31, 2007
                                                             University of Arizona
             RAL
       CaloCells
         (em scale)
                                             Cluster Jets                                                        Attempt to factorize
                                                                                                                         Noise suppression
Topological Clustering
(includes noise suppression)
                                              in ATLAS                                                                            Noisy cells are removed
                                                                                                                         Hadronic calibration
                                                                                                                                  Signal weighting in cluster
     CaloClusters                                  Cluster Classification                                                         context, no jet bias
                                                  (identify em type clusters)
                                                                                                                         Dead material corrections
         (em scale)


                                             Jet Finding                     CaloClusters                                         Limited to vicinity of clusters
        Jet Finding                       (cone R=0.7,0.4; kt)            (em scale, classified)                                  Cannot correct if no signal at
                                                                                                                                  all nearby
    (cone R=0.7,0.4; kt)


                                                                                                                         Out-of cluster corrections
                                                                     Hadronic Cluster Calibration
           Calorimeter Jets                           (apply cell signal weighting dead material corrections, etc.)               Efficiency correction for
                  (em scale)
                                                                                                                                  clustering algorithm
                                                                                                                 Provides calibrated input to
   Jet Based Hadronic Calibration
                                                                             CaloClusters
                                                                       (locally calibrated had scale)
                                                                                                                 jet finding
    (“H1-style” cell weighting in jets etc.)
                                                                                                                         Relative mis-calibration
                                                                                                                         O(5%)
           Calorimeter Jets                               Jet Finding                                                             Instead of O(30%)
                                                                                                                         Clusters can be interpreted
         (fully calibrated had scale)                 (cone R=0.7,0.4; kt)

                                                                                                                         as massless pseudo-particles
    Jet Energy Scale Corrections
   (noise, pile-up, algorithm effects, etc.)
                                                                                                                                  ATLAS convention, see later!


              Physics Jets                                       In-situ Calibration                     Refined Physics Jet
         (calibrated to particle level)           (underlying event, physics environment, etc.)         (calibrated to interaction level)



                                             Seminar
                                                                                        Slide 23                                      Peter Loch
                                             October 31, 2007
                                                                                                                           University of Arizona
                                             RAL
            Why Cluster Jets At All?
Reduce noise contribution
   Fixed cone tower jet                           Fixed cone cluster jet
                                                 
                                                                1

                                                          4     2
                                                                         5
                                                            3
                                                                    6


                                                                            




    Iacopo Vivarelli, September 2006                  Iacopo Vivarelli, September 2006


               Seminar
                                       Slide 24                      Peter Loch
               October 31, 2007
                                                          University of Arizona
               RAL
 Determination of cluster calibration
Classification
   Use measurable cluster variables to determine if cluster looks
   electromagnetic, hadronic, or noisy
       Cluster location (“early”) and average cell signal density useful to classify
       electromagnetic and hadronic clusters
       Noisy clusters are typically seeded by negative signal
            Often negative total cluster energy
Hadronic weighting
   Apply only to hadronic clusters
       Uses cluster direction, cluster energy, cell signal density and cell location
       (sampling layer) to find cell signal weights
Dead material corrections
   Apply to hadronic and electromagnetic clusters
       Uses DM corrections parametrized as function of cluster shapes
Out of cluster corrections
   Account for lost true signal due to clustering
All corrections are derived from single pions and photons
   Detailed simulations are needed for most of them
   Can all be benchmarked with test beam data!
   Cannot correct for everything at cluster level
       No signal, no correction!


             Seminar
                                         Slide 25                        Peter Loch
             October 31, 2007
                                                              University of Arizona
             RAL
Cluster Jet Signal Linearity
  Flat response in Et and rapidity
     Forward region problems under
     study
  Missing ~8% jet energy
     ~3% cluster mis-classification
                                                                S.Menke/G. Pospelov
                                                                  March 2007 T&P
         Cluster classified as em, but really
         is hadronic
     ~3% signal efficiency
         Signal of low energetic particles
         below cluster threshold
     ~2% electromagnetic calibration
         Em clusters need own calibration
              Basic scale insufficient
     Remember: calibration so far
     comes from single particle!
         No jet context whatsoever!
  Studies under way…                                 S.Menke/G. Pospelov
                                                       March 2007 T&P
     We are now looking at topology
     based on clusters
     Also started to look at jets

               Seminar
                                         Slide 26              Peter Loch
               October 31, 2007
                                                    University of Arizona
               RAL
      Jet Energy Scale Corrections
Use of kinematic constraints in                      Normalization strategy
pp                                                      Match reconstructed pT balance
   Photon/Z+jet pT balance                              with particle level balance
       Central value model dependent!                   Unfold topology dependences
       Topology
           Hard cuts on back-to-back no
         problem at LHC!
       Kinematic limit ~400 GeV/c
       pT(photon) for 1%
       First shot at jet energy scale!
   W mass
       Powerful but very special jets
            W color-disconnected
            Narrow jets
   Di-jet balance
       Extrapolation tool to high pT
       Also detector uniformity
       Topology dependence
                                                          Sigrid Jorgensen, September 2006



              Seminar
                                          Slide 27                         Peter Loch
              October 31, 2007
                                                                University of Arizona
              RAL
Photon+Jet
 Missing Et Projection




                                              Doug Schouten, ATL-COM-PHYS-2007-057,September 2007
 Fraction (MPF)
     Pioneered by DØ
     Low sensitivity to pile-up
     No jet context needed
          Can use clusters, towers,
          even cell signals
                                                                                                    uncalibrated clusters


                              ptcalo  pt
                 
              calo signals        pt
 R jet
         
                             pt
  calo




                  Seminar
                                             Slide 28                                                          Peter Loch
                  October 31, 2007
                                                                                                    University of Arizona
                  RAL
         Jet Energy Scale From W
Challenge: pile-up and W boost
   Pile-up can “improve” jet energy scale!


                       η(W) ~1.8




                                   L  1034 cm2s1
                                               P. Savard, P. Loch, CALOR97


   W colour-disconnected from rest of event
      Cannot expect the same particle flow around jet
      Not straight forward to carry over corrections based on W mass to
      other jets at 1% level


           Seminar
                                    Slide 29                          Peter Loch
           October 31, 2007
                                                           University of Arizona
           RAL
Very High pT Jets
 Reconstruction concern:
 leakage
    Find indicators in jet signal




                                                                       Frank Paige, ATLAS T&P Week February 2006
    to tag leakage
       Late showering in
       calorimeter
    Use muon spectrometer hits
    behind jet
       No energy measurement, but
       good tag
 Studies underway to validate
 jet energy scale at very
 high pT
    pT balance in systems with
    very high pT jet balancing
    several lower energetic jets


             Seminar
                                    Slide 30              Peter Loch
             October 31, 2007
                                               University of Arizona
             RAL
             Jet Finder Efficiencies in ATLAS
     Efficiency                                                  Purity
             Only free parameter: matching                              Relates to fake rate
             radius Rm
                   No kinematics matching!
             # matches reconstructed and truth jets                     # matches reconstructed and truth jets
 ( Rm )                                                   ( Rm ) 
                          # truth jets                                          # reconstructed jets
            jets                                                       jets
          N m ( Rm )                                                 N m ( Rm )
                jets
                                                                           jets
                                                                                  1   fake ( Rm )
            Ntruth                                                     N reco




                              Jets in VBF!                                Jets in VBF!


               Martin Schmitz, September 2007                                 Martin Schmitz, September 2007


                           Seminar
                                                      Slide 31                             Peter Loch
                           October 31, 2007
                                                                                University of Arizona
                           RAL
 Pile-Up in Clone Cluster Jets
     Expect cluster to suppress noise




                                                             Doug Schouten, ATL-COM-PHYS-2007-057,September 2007
           Works for pile-up as well
           Flucutations can be suppressed if
                                                                                                                   0.8 1034 cm2s1
           correct noise RMS used in cluster
           finder
                 Cluster noise cuts are symmetric!
     Some energy offset observed
           Pile-up is asymmetric
           Baseline larger for correct RMS
                 Bias toward positive signals by noise
                 selection


                                    Et Pile-up
                                    in R=0.2
                                    cone
                                 0.8 1034 cm2s1                                                                 0.8 1034 cm2s1




Doug Schouten, ATL-COM-PHYS-2007-057,September 2007


                        Seminar
                                                         Slide 32                                                                 Peter Loch
                        October 31, 2007
                                                                                                                       University of Arizona
                        RAL
Jet Masses                                                                                                                   cluster jets
                                                                                                                             tower jets




                                                PL & Chiara Paleari, Poster @ SLAC ATLAS Workshop, August 2007
 Gained interest at LHC
    Heavily boosted top decays                                                                                                                 y  0.8
       All decay products reconstructed in
       one jet
                                                                                                                             cluster jets
       Jet mass one observable indicating top
       decay                                                                                                                 tower jets
       Jet substructure also sensitive to
       source!                                                                                                                           1.7  y  2.5
 Mass measurement challenging
    Particle jet level mass is reference
                                                                                                                             cluster jets
       Simulations only!
                                                                                                                             tower jets
    Mass of calorimeter jet is affected by
    shower spreads
                                                                                                                                         3.7  y  4.2
       Enters: signal definition dependence,
       cluster shapes, noise,…
    No significant attempt at Tevatron or
    elsewhere                                                                                                        relative mass difference

            Seminar
                                 Slide 33                                                                                   Peter Loch
            October 31, 2007
                                                                                                                 University of Arizona
            RAL
  Mass Reconstruction Sensitivities
Contribution from low energetic particles lost
  Dead material and magnetic field
  Overall effect depends on signal definition
  How about effect on mass?
Exercise: remove particles below pT threshold
from jet and re-calculate mass
  Remember: towers are not calibrated
     More severe effect of cut in tower jets
  Clusters are calibrated
     More similar to particle selection in jets




          Seminar
                              Slide 34                       Peter Loch
          October 31, 2007
                                                  University of Arizona
          RAL
Mass
Sensitivity



              change of mass

                                    QCD kT jets, D = 0.6




                               log10(least biased reconstructed mass/GeV)

          Seminar
                                    Slide 35                   Peter Loch
          October 31, 2007
                                                    University of Arizona
          RAL
Jet Substructure
 Mass too complex?




                                             J. Butterworth et. al,, ATL-COM-PHYS-2007-077,October 2007
    Can be too sensitive to small
    signals in jets
       UE, pile-up, other noise
 Use YSplitter to detect
 substructure
    Determines scale y for splitting a
    giving jet into 2,3,… subjects, as
    determined by ycut, from
                                                                                                          Not very
    y  ycut  p     T
                      jet
                                                                                                          sensitive to
                                                                                                          calorimeter
    More stable as only significant                                                                       signal details!
    constituents are used ?
    At least additional information to
    mass
 Other option:
    Look at mass of 2…n hardest
    constituents (Ben Lillie,ANL)
            Seminar
                                  Slide 36                                                                               Peter Loch
            October 31, 2007
                                                                                                              University of Arizona
            RAL
Jet Shapes (1)
 1st question: any
 relation between number
 of particles, towers,
 clusters in jets?
   Most interesting for kT
      D = 0.6 here
   Look at matching
   callorimeter/truth jets
   Note: not the most
   important variable!
      We already expect change
      of “jet picture” by
      detector signal definition
      Hints on resolution power
      for jet shape variables
      and mass

          Seminar
                              Slide 37              Peter Loch
          October 31, 2007
                                         University of Arizona
          RAL
                         Jet Shapes (2)
We expected clusters to represent
indivdual particles
   Cannot be perfect in busy jet
   environment!
      Shower overlap in finite calorimeter
      granularity
   Some resolution power, though
      Much better than for tower jets!
   ~1.6:1 particles:clusters in central region
   ~1:1 in endcap region
      Best match of readout granularity, shower
      size and jet particle energy flow
      Happy coincidence, not a design feature of
      the ATLAS calorimeter!




            Seminar
                                 Slide 38                     Peter Loch
            October 31, 2007
                                                   University of Arizona
            RAL
                                                                                                                     cluster jets
Jet Shapes (3)




                                           Fraction of energy outside cone around jet axis (Rcone=0.3)
                                                                                                                     tower jets
                                                                                                                     hadron jets




                                                                                                                                           PL & Chiara Paleari, Poster @ SLAC ATLAS Workshop, August 2007
 Jet density
   Calculate energy outside of                                                                                                   y  0.8

   cone R = 0.3 as function of
   pT and direction
   Classic Tevatron
   measurement
      Experimental indication of                                                                                         1.7  y  2.5
      transition from (low pT)
      gluon to (high pT) quark jets
 Example: kT jets in QCD
   D = 0.6
                                                                                                                         3.7  y  4.2




                                                                                                          log10(pTjet/GeV)
             Seminar
                                Slide 39                                                                            Peter Loch
             October 31, 2007
                                                                                                         University of Arizona
             RAL
                              Summary
General
   Everything you have seen here from ATLAS is based on simulations
   and thus very preliminary
      Real data can bring us surprises (good and bad)
Jet signals
   Cluster signal (~200/event) good basis for jet finding in physics
   analysis context
   Final jet energy scale corrections depend on analysis choices for jet
   finders, -configurations, selected event topologies…
What we can get from jets
   Strong interest to go beyond Tevatron jets
   Jet masses and substructure analysis of great interest for boosted
   heavy particle decays
   Jet shapes can test basic jet formation dynamics ??
   New dimension added: jet signal shapes in calorimeters can improve
   calibration jet by jet
           Seminar
                                 Slide 40                      Peter Loch
           October 31, 2007
                                                    University of Arizona
           RAL
                            Outlook
Refined jet calibration
  Use calorimeter jet signal shapes
     We used cluster shapes already, now look at cluster distribution
     in jet
  Use inner detector tracks
     Large momentum fraction of jet in tracks indicates a very
     hadronic jet, i.e. more corrections
Jet origins
  Use inner detector tracks and vertices to separate jets from
  hard scattering from jets from UE and/or pile-up (A.
  Schwartzman)
All this has not been used much in the past, but
we have to address a 1% systematic error
requirement somehow!
We are more than ready for data!
         Seminar
                              Slide 41                   Peter Loch
         October 31, 2007
                                              University of Arizona
         RAL
Backup Slides
        Jet Energy Scale                                          Process                 σ (nb)
                                                                                                     Evts/year
                                                                                                    ( =10 fb-1)

             (JES)                                                W → eν                    15         ~108
                                                                 Z → e+ e―                 1.5         ~107
      JES error very quickly                                          tt                   0.8         ~107
      systematically dominated                                         pt > 200 GeV        100         ~109
                                                          Inclusive
           Large statistics in unexplored                   Jet
                                                                           pt > 1 TeV      0.1         ~106
           kinematic range already at low                Production        pt > 2 TeV      10-4        ~103
           luminosity                                                      pt > 3 TeV    1.3×10-6       ~10

      Calibration channels quickly
      accessible
           pT balance
                                               
                Photon + jet(s)          40  pT  400 GeV c
                Z+jet(s) later           50  pT  200 GeV c
                                               Z


           Mass spectroscopy
                W->jj in ttbar
                                            q                γ

Dominant direct photon
production gives access to gluon
structure at high x
(~0.0001-0.2) (precision ?)                 g                q




                      Seminar
                                                  Slide 43                             Peter Loch
                      October 31, 2007
                                                                            University of Arizona
                      RAL
       Measurements with Jets
             Strong Coupling

                                                       just from cross-section,
                                                       can be improved by 3/2 jet
                                                       ratio, but no competition
            s                                         for LEP/HERA!



                                  s ( M z )  10%




test of QCD at very small
scale ( s  0.08 )

        PDFs                                                 Compositeness
                            di-jet cross section and                                     sensitivity to compositeness scale
                            properties (Et,η1,η2)                                        Λ up to 40 TeV @ 300 fb-1 (all
                            constrain parton
                                                                  Deviation from SM
                                                                                         quarks are composites)
                            distribution function




                        Seminar
                                                       Slide 44                              Peter Loch
                        October 31, 2007
                                                                                  University of Arizona
                        RAL
“H1” Style Cell Signal Weighting in ATLAS
  Fit constraint:
                                              2
         
      N jets    N cells
                                              
       
  w j 1   i 1
                   w( i , X i ) Ei   Etruth   0
                                          jet

                                              

  Jet four-momentum calculation after fit
                                                                                    “massless
   Ereco , preco    w( i , X i )  Ei , pi , with Ei  pi
                                  N   cells

      jet     jet                                                                   pseudo-
                                                                                    particles”
                                   i 1
  Final corrections for residual signal non-linearities
       Algorithm dependencies
               Available for seeded cone R=0.4, kT D=0.4, D=0.6
       Signal dependencies (cluster/tower)

               f a ,s ( , ptjet )  Ereco  Etruth
                                       jet     jet



                          Seminar
                                              Slide 45                 Peter Loch
                          October 31, 2007
                                                            University of Arizona
                          RAL

				
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