Plan forThoughts on Optimization the ILC detector for jetjet mass resolution

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(Plan for/Thoughts on)Optimization the ILC
detector for jet-jet mass resolution
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Contributions to jet-jet mass resolution

• Physics missing neutrinos, final state radiation
• Jet finding (particles level) out of cone/jet,
  underlying event, combinatorics
• Jet finding (detector level) from cells to
  particles, jet mass reconstruction
• Detector imperfections cracks, dead areas,
  backscattering
• Calibration (measured signals to GeV)
• Detector linearity
• Difference in response to different species,
  detectable vs kinetic vs total energy
• EM/HAD calorimeters cross-calibration
• Detector resolution sampling fluctuations,
  leakage, fluctuations of loss due to nuclear
  binding energy

Reduced (not eliminated) by the PFA
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Goals of the exercise

• Produce a curve/table of jet-jet mass resolution
  (s) as a function of the detector parameters
• Overall thickness (containment)
• Absorber material (shower size)
• Active material (gas/silicon/scintillator)
• Sampling frequency (sampling fluctuations)
• Readout granularity
• Readout technology (digital/analog)
• In principle, the optimal performance of any
  particular detector may require tuning of the
  underlying procedures, thus requiring an immense
  effort and/or creating excuses. It is very
  important, whenever possible, to express the
  critical parameters of the analysis algorithms in
  terms of physical parameters (like
  interaction/radiation length) rather than
  detector geometry specific terms, like layers,
  cells.

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Particle Flow Algorithm

• What it is a recipe to improve jet energy
  resolution by minimizing the contribution hadron
  energy resolution by reducing the function of a
  hadron calorimeter to the measurement of neutrons
  and K0s only
• What it is not an universal and complete
  prescription for minimization of jet energy
  resolution. Examples detector linearity, EM/HAD
  cross calibration are factors above and beyond
  the PFA.
• Why bother with academic distinctions
  performance measures. Example total event energy
  measurement at the Z0 pole. It ignores the issues
  of PFA performance as a function of jet
  environment, but includes various
  detector-induced effects.

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PFA functional description

• List of reconstructed tracks and their momenta
• List of hits in EM and HAD calorimeters

PFA
List of hits which are NOT produced by charged
particles

• Final list of hit cells has, in general, two
  problems
• Missing hits, due to incorrect association with a
  charged track
• Spurious hit due to left-overs of charged
  particles showers. They can by isolated hits, or
  form clusters (a.k.a. fragments)

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PFA and post-PFA decisions

• EM clusters (a.k.a. gs) identification
• Inside or after the PFA algorithm? Does it
  matter?
• For jet energy measurement does one need a
  positive identification of a cluster as g (a.k.a.
  H-matrix) ?
• (Why?) is it important to remove the MIP trace at
  the very beginning of the PFA?
• Classification of the resulting energy clusters
  as primary neutral hadrons or charged
  particles left-overs
• It is (should be) an integral part of the PFA
• Is it possible to differentiate n/ñ/K0 showers
  (calibration!)
• Treatment of single isolated hits (or very
  small clusters)?

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PFA performance measures

• Ideal PFA would produce the list of hit cells
  hit by neutrals. Possible measures of the PFA
  algorithm performance, FOM
• Nhit(PFA) Nhit(true) is minimal
• Ehits(PFA) Ehits(true) is minimal
• Nhitclust(PFA) Nhitclust(true) is minimal
• Variance(Nhit(PFA) Nhit(true)) is minimal
• Variance(Ehits(PFA) Ehits(true)) is minimal
• Variance(Nhitclust(PFA) Nhitclust(true)) is
  minimal
• Note
• in cases 4-6 it is not necessary that the mean
  value is zero (effective calibration)
• These measures isolate the PFA performance from
  the detector-related contributions
  (non-linearities, calibrations)

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Inside the PFA

• Step 1 Cluster hits
• Given two hits, X and Y, define a distance
  between them (need not be symmetric!)
• Spatial separation
• Hit density weighted
• Gradient weighted
• Decide
• X?cluster(Y)
• Y?cluster(X)
• Dist(X,Y) cut-off , start a new cluster
• Step 2Associate cluster(s) with charged track
• Distance of cluster from a track
• Closest point
• Centroid
• Goble up clusters until SEcluster ptrack

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Tuning of the PFA, I

• How many clustering algorithms? EM and HAD the
  same, or dedicated EM-shower finder exploiting
  known shower characteristics
• Clustering cut-off parameter
• Too high too large clusters, eat up energy from
  the neutrals
• Too low fragment the hadronic shower into
  several clusters. They will usually be associated
  with the charged tracks, but small clusters
  displaced laterally may be left over
• Track-cluster(s) association
• Too restrictive create fragments
• Too loose eat up neutral energy

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Tuning of the PFA, II

• PFA parameters need not to be fixed, they may
  depend on the charged track momentum (known at
  this point)
• PFA parameters may depend on the running
  conditions and/or physics analysis
  (ZH/tT/ZHH/other)
• hence
• Study FOM of PFA as a function of charged/neutral
  shower separation, charged shower energy
• Study the neutral/charged shower separation and
  their energy imbalance for different vs and
  physics processes to optimize the PFA parameters
  for the physics goal

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PFA performance

• The dominant contribution to the energy
  resolution, so far, seems to be due to the
  fluctuations in the left-over fragments of
  charged hadrons. It is a result of a tuning of
  the clustering/association parameters to balance
  the cluster growth of charged particles (hence
  loss of neutral energy) and the creation of
  spurious neutral clusters.
• These fragments are presumably clusters of
  energy deposited by neutral particles (fast
  neutrons and/or K0s) produced in the charged
  hadron shower
• Modeling of neutral component of hadronic shower
  is poorly constrained by the existing data, hence
  large variation between various simulation
  packages. It may lead to large error on the
  claimed PFA performance
• Perhaps Fine grained MINOS Calibration detector
  was exposed to hadron test beam 1-10 GeV. Perhaps
  one can compare the frequency and energy
  distribution of detached energy clusters in the
  data and simulation.
• Hadron shower simulation technology is making
  significant progress, but substantial differences
  exist at this point in time. One should compare
  the PFA performance with different showering
  codes. Ideally, the PFA would be robust against
  simulation changes. If not stand alone FLUKA is
  probably the best guess.

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Possible(?) improvements of the current algorithms

• Or what can be done differently.. The goal is to
  attach more hits to the charged clusters without
  loosing the neutral clusters.
• Use track more information. At the moment the hit
  clustering is done without any guidance from the
  tracking. But we want to remove the shower
  caused by known energy charged hadron. Add to
  the distance used for clustering a term
  involving the distance of the cell from the
  extrapolated trajectory?
• It may permit to relax the clustering cut-off and
  accumulate more of the energy deposition along
  the track while preventing a runaway cluser.
• It may also prevent clusters from growing
  sideways and gobbling up hits from neighboring
  showers
• (in the scintillator case) make use of the
  analog information in the distance definition ?
• Determine the angular resolution for neutral
  clusters. Use it to associate the neutral
  cluster with the charged hadron shower.

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Fundamentals of Clustering

• All of the algorithms used so far are of
  agglomerative nature clusters are grown by
  attaching cells up to a certain cut-off value of
  the distance between the cells. While it is the
  most popular class of algorithms, it has certain
  shortcomings
• It depends on the arbitrary choice of the
  starting seeds (this is probably of very little
  importance here)
• The cluster assignment (cell to a cluster)
  decision is irreversible, even if taken at the
  very early stages of the algorithm. Clusters can
  be merged, though.
• It makes no use of a global information (event
  topology) until the very late stages of
  association of found clusters with the charged
  particle trajectory.
• At the scale of granularity of the putative
  calorimeters our sets of hit cells represent a
  web of interconnected segments rather than
  isolated groups of closely packed points in phase
  space.

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Possible Alternative A Divisive Clustering
Algorithm

• General concept
• Put all hits into one cluster. Examine local
  cluster properties (configuration of links, local
  gradients) to break some links and divide the
  overall set of hits into several clusters.
• Potential advantages
• It allows to utilize the global event
  information the set of initial
  positions/directions and momenta of the charged
  hadrons. Positions and directions provide a good
  initial guess for the cluster seeds, whereas the
  momentum may provide cluster-dependent set of
  parameters for the divisive algorithm