Benchmarking the SiD - Www Group Slac Stanford by xiaopangnv

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									Benchmarking the SiD


     Tim Barklow
        SLAC
     Sep 27, 2005


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There is an effort underway at SLAC to do
physics benchmarking of the SiD. Activities
include:
– Evaluation and parameterization of the output
  of event reconstruction software applied to the
  fully simulated (Geant4) SiD detector
– Selection of physics benchmark reactions
– Physics analysis of reconstructed objects




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         Why Do Physics Benchmarking?

• Optimize individual detector designs
   – where is optimal physics/$ as function of component radius and
     length, B-field, etc.?
• Help evalulate performance of different detector
  concepts
   – we’re not at this point yet, but eventually we will have to do
     this.
• Further strengthen physics case for the ILC
   – despite 15+ years of research, not all processes have been studied
   – an in-depth look at a previously studied physics measurement
     can cut both ways: more realistic simulation of detector and
     background may worsen resolution, but inclusion of overlooked
     signal reactions and decay modes can lead to an improvement.




                                                                      3
     SiD Detector outline




Whole Detector ~ 12m X 12m X 12m
                                   4
                   SiD Detector outline




A high performance detector for the LC
Uncompromised performance
BUT Constrained & Rational cost



     This is
 simulated SiD00
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 Detector Design Issues to be Addressed by
         Physics Benchmarking (I)

• Physical dimensions & B-field
• Tracker (aka Momenter) performance
  – momentum resolution
     • how much is enough?
     • how much multiple scattering is acceptable?
  – tracking efficiency* as function of polar angle, track
    density, track origin
  – forward region behavior
• Calorimeter performance
  – granularity, Ejet resolution*, MIP tracking

   *Combined VTX+TRK+CAL performance                         6
                Fraction of the photon(s) energy per event , closer to
                a charged track than some distance

                      1.5 x the pad size
Fraction




                                 Distance in cm
           BR2 does not by itself set performance.
           Pixel size (and Moliere radius) are also very important.      7
           EMCAL
             Si/W pixel size:
            • prototypes are 16 mm2
            • readout chip: designed for 12 mm2
             How small can we go?? 2-4 mm2 ?
            Need a physics argument for smaller pixels.




ρ-> π+πo
                                                  8
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  Detector Design Issues to be Addressed by
         Physics Benchmarking (II)
• Vtx detector
  – inner radius, number of layers
  – mechanical design, sensor technology
• Alignment and Calibration
  – Is Z-pole running required?
  – Alternatives such as e e → e ν eW ,       e+ e− Z , γ Z
                           + −  −    +
                                                              ?
• Background
  – true and false track finding efficiency
  – timing-based background veto


                                                         10
                                 Illustration of bunch timing tag




Yellow = muons    Red = electrons     Green = charged hadrons
Black = Neutral Hadrons    Blue = photons with E > 100 MeV

        150 bunch crossings (5% of train)                           1 bunch crossing
        98 events
        920 GeV detected energy
        125 detected charged tracks


                                                                                       11
WWS (World Wide Study of Physics and Detectors for the ILC)
Formed Committee to Develop Physics Benchmark List:




                                                     12
Physics Benchmark Processes




                              13
           Physics Benchmark Processes
Reduced Benchmark List :




SiD plans initially to study all of these reactions plus

 e + e − → τ +τ − → ρ + ρ −ν τν τ at   s = 1 TeV*
*addresses issue of ultimate EM calorimeter granularity
                                                           14
               SiD Benchmarking Tools
• MC Data sets (stdhep files) of all SM processes at
  Ecm=500 GeV assuming nominal ILC machine parameters
   – About 50 fb-1 with e- pol=+/- 90% available at
      •   ftp://ftp-glast.slac.stanford.edu/glast.u32/simdet_output/simd401xx/whizdata.stdhep (-90% e- pol)
      •   ftp://ftp-glast.slac.stanford.edu/glast.u32/simdet_output/simd402xx/whizdata.stdhep (+90% e- pol)

   – 1 ab-1 on SLAC mass storage with all initial e+,e- polarization
     states
• Many Monte Carlos (Pythia, Whizard) for producing
  additional stdhep files
• Fast MC which takes stdhep files as input and outputs the
  same kind of reconstructed particle LCIO objects that full
  event reconstruction software produces (LCIO bindings
  exist for C++, JAVA, FORTRAN ).
                                                                                                              15
     Fast MC Detector Simulation (I)
• In the context of SiD benchmarking the Fast Monte Carlo
  should be considered a Fast Physics Object Monte Carlo.
  It emulates the bottom line performance of the event
  reconstruction software in producing the electron, muon,
  charged hadron, photon and neutral hadron physics objects.
• Status of Fast MC used by SiD:
   – Tracker simulation uses parameterized covariance matrices based
     on tracker geometry and material
   – Electron and muon id given by min energy + overall efficiency
   – Photon and neutral hadron energies & angles smeared using
     single particle EM & hadronic energy & angle resolutions.
     Photons and neutral hadrons also have min energy and overall
     efficiency within detector volume.
                                                              16
    Fast MC Detector Simulation (II)
• Fast MC with nominal single particle calorimeter
  response gives 17%/sqrt(E) jet energy resolution.
  This can be tuned to any value by varying the
  single particle EM & hadronic calorimeter energy
  resolutions and by replacing charged particle
  tracker momentum with calorimeter energy a
  certain fraction of the time.
• Will improve the parameterization of calorimeter
  response as we learn more from the particle flow
  algorithm studies.

                                                17
Full Reconstruction/Analysis Overview
• Java based reconstruction and analysis package
   – Runs standalone or inside Java Analysis Studio (JAS)
   – Fast MC → Smeared tracks and calorimeter clusters
   – Full Event Reconstruction
      •   detector readout digitization (CCD pixels & Si μ-strips)
      •   ab initio track finding and fitting for ~arbitrary geometries
      •   multiple calorimeter clustering algorithms
      •   Individual Particle reconstruction (cluster-track association)
   – Analysis Tools (including WIRED event display)
   – Physics Tools (Vertex Finding, Jet Finding, Flavor Tagging)
   – Beam Background Overlays at detector hit level

                                                                           18
   Full Reconstruction/Analysis
• Java Analysis Studio (JAS) provides a
  framework for event visualization (with
  WIRED) and reconstruction.




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                 Software CD
• We have developed a CD containing simulation
  and reconstruction software as well as
  documentation and tutorials. In addition, a small
  amount of data is available on this CD.
• Full Detector simulation is available through slic
  (GUI available for Windows).
• Reconstruction/analysis via org.lcsim & JAS.




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Examples of Tracker (Momenter)
  Performance Benchmarking




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                               a = 2.0 × 10−5           a = 1.0 ×10−5
                               b = 1.0 × 10−3           b = 1.0 ×10−3
                               ΔM h = 103 MeV           ΔM h = 85 MeV

e + e − → ZH
       → μ+μ− X

   s = 350 GeV
 L = 500 fb−1             Recoil Mass (GeV)       Recoil Mass (GeV)


δ pt         b                  a = 4.0 × 10−5         a = 8.0 × 10−5
     = a⊕
 pt2      pt sin θ              b = 1.0 × 10−3        b = 1.0 × 10−3
                                ΔM h = 153 MeV         ΔM h = 273 MeV
Δσ Zh and ΔBR(h → X)
 relatively independent
of a, b since you must
use events outside peak
to maximize statistics
                          Recoil Mass (GeV)      Recoil Mass (GeV)
                                                                 22
                                   δ pt         b
                                        = a⊕
e + e − → ZH                        pt2      pt sin θ
      → μ+μ− X
                                                                   ΔM h vs a
   s = 350 GeV
                                          ΔM h vs b
 L = 500 fb−1

         ΔM h (MeV)


Recoil technique provides
best Higgs mass measurement
if there is signficant branching
ratio for decays with invisible
particles

                                          a × 10−5 or   b × 10−3
                                                                           23
                     Beam Energy Profiles        E beam (incoming) = 250 GeV

  Before Collision                After Collision                    Lumi Weighted
                                          bias
                               50 ppm < E CM < 250 ppm



σ Ecm ≈ 0.1%                    δ B ≈ 4.3%                       δ B ≈ 1.6%




     Ebeam (GeV)                   Ebeam (GeV)                         Ebeam (GeV)




Center of Mass Energy Error Requirements
   • Top mass:              200 ppm (ΔMt=35 Mev)
   • Higgs mass:            200 ppm (ΔMH=60 MeV for 120 GeV Higgs)
   • Giga-Z program:         50 ppm
                                                                                     24
Measure Ecm with
Detector using                                                ΔEμμ vs a
                          angles only
e e → μ μ (γ )
 + −       +     −

                     ΔEZγ vs b

                                                              ΔEZγ vs a
                      ΔEμμ vs b

       ΔE cm (MeV)


   s = 350 GeV                                  δ pt         b
                                                     = a⊕
  L = 100 fb−1                                   pt2      pt sin θ




                                  a × 10−5 or    b × 10−3
                                                                     25
Examples of Calorimeter Performance
          Benchmarking




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                           δ E jet                 δ E jet
                                     = 0.3                   = 0.4
                             E jet                   E jet

                          ΔM h = 42 MeV            ΔM h = 46 MeV

e + e − → ZH
     → qqbb

                      Mbb (GeV)              Mbb (GeV)

   s = 350 GeV             δ E jet                 δ E jet
               −1                    = 0.5                   = 0.6
  L = 500 fb                 E jet                   E jet

                          ΔM h = 48 MeV            ΔM h = 50 MeV



ΔE/ E = 60% → 30%
equiv to 1.4 × Lumi

                      Mbb (GeV)              Mbb (GeV)

                                                              27
       g HHH = −6 μ 2 / v


Standard Model:
                                             1 2 2 1 4
M = 2λ v = −2 μ
   2            2           2        V (φ ) = μ φ + λφ
   H                                         2     4


e + e − → ZHH → qqbbbb
  s = 500 GeV, L=1000 fb -1
                                         (ΔE / E = 60%)
ΔE/ E = 60% → 30%
equiv to 4 × Lumi
C. Castanier et al. hep-ex/0101028

                                                          28   (%)
                Summary

• Physics benchmarking is an important part
  of the ILC detector design process.
• SiD Benchmarking project would be an
  excellent entry point into ILC physics and
  detector studies
• Please contact any of the SLAC SiD people
  if you are interested. My email is
         timb@slac.stanford.edu .



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