Outline of an Analysis of an Expected Signal (with blinding)

Analysis Outline

Define the Analysis

  • Signal: what physics process is being studied (e.g., H -> ZZ, Z->mu mu, Z->mu mu)
    • Characteristics of the signal
      • Event topology and kinematics
      • Detector elements
    • Selection criteria
      • Particle identification
      • Kinematics
      • Jet reconstruction
    • Reconstruction algorithm
  • Background processes that mimic the signal
  • Discriminate between signal and background
    • Goodness criteria

Implement the Analysis

  • write/beg/borrow/steal code to implement the selection and reconstruction procedure
    • most analyses start with someone else's code

Inputs: source code

Outputs: source code

Tools: source browser (lxr), version control system (cvs), IDE, development tools

Test the analysis on Simulated Data

  • Use simulated signal and background data samples to test the implementation
    • "Generic" simulated data sample provided by the experiment, signal or analysis specific simulated data generated for the analysis
    • Usually test backgrounds individually
    • Statistically combine weighted results from individual signal and background tests to simulate full result

Inputs: simulated data

Outputs: test procedures, test results

Tools: data discovery (DBS), software framework (CMSSW, FWLite), analysis environment (ROOT), simulated data production workflow management (ProdAgent), analysis workflow management (CRAB)

Tune the analysis on Simulated Data

  • classification/optimization problem
    • systematically vary selection criteria and measure the results on the various signal and background samples
    • may include neural net training, decision trees, bayesian classifiers, etc.

Inputs: simulated data

Outputs: tuning procedure, tuning results

Tools: same as previous step

Validate the analysis

  • Verify that the overall characteristics of the simulated analysis results correspond to expectations
  • Probe for errors, anomalies, etc.
    • Hypothesize potential problems with the analysis
    • Develop data samples to test for the hypothesized problems
    • Some tests will use real data with no signal
      • samples where no signal is expected
      • samples with signal analyzed outside the signal region
      • analysis modified to look for "impossible" signal signature (e.g., impossible particle charge combinations)
  • On failure, iterate back to analysis definition or implementation as appropriate

Inputs: simulated data, real data

Outputs: validation procedure, validation results

Tools: same as previous step

Determine Efficiencies needed to reconstruct physics process

  • Usually determined via a combination of simulated and real data
    • real data is preferred to avoid systematic uncertainties/biases in simulated data
  • Efficiencies for a difficult analysis may be very low

Inputs: simulate data, real data

Outputs: efficiency analysis procedure, reconstruction efficiency results

Tools: same as previous step

Run on real data with blinded analysis

  • Blinding obscures the value of the final measurement so that the analysis is unbiased by tuning for a desired value

Verify the characteristics of the result

  • Verify that results other than the signal matches the expected distributions (e.g., check that fit residuals or backgrounds outside the signal region are reasonable).

Unblind the signal

-- DanRiley - 21 Sep 2007
Topic revision: r2 - 01 Oct 2007, DanRiley
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