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More universal curve fits

More universal curve fits. Matthew Jones. May 4, 2004. New stuff: Parameterize using Z = log(E measured /E predicted ) Compare with K 0 S from TTT. Distribution of dE/dx. E measured – E predicted is not Gaussian but Z=log(E measured /E predicted ) is.

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More universal curve fits

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  1. More universal curve fits Matthew Jones May 4, 2004 • New stuff: • Parameterize using Z = log(Emeasured/Epredicted) • Compare with K0S from TTT

  2. Distribution of dE/dx • Emeasured – Epredicted is not Gaussian but Z=log(Emeasured/Epredicted) is. • Mostly affects the descrption of the tails:

  3. Distribution of dE/dx • Z is used by other experiments (eg, STAR) (not in itself a good reason to use it…) • Tails are more important in likelihood analyses. • Better (still not perfect) description of inclusive particle fractions

  4. Fitted distributions in Z • Comparison: New Old Smaller value of -log(likelihood) for Z-fit.

  5. Biases in K0S from TTT • Not large, but significant: Mean = 0.0450§ 0.0006 Mean = 0.0436§ 0.0005

  6. Biases in K0S from TTT • Pull distributions: Mean=0.411§ 0.005 RMS = 0.875§ 0.004 Mean=0.437§ 0.005 RMS = 0.921§ 0.004

  7. Current issues • Is the “standard CDF” parameterization adequate? • Better way to parameterize resolution? • I think it is currently overestimated • Probably would benefit from constraints at higher momentum (eg, e+e-,K0S+-) • Need to compare with K0S from minimum bias data

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