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Photon energy smearing from events. Glen Cowan , Sudan Paramesvaran, David Hopkins, Henning Flaecher Royal Holloway, University of London. EMC Calibration Meeting 6 Sept 2006. Context. Follows on from resolution studies by David, Henning, Sudan. - PowerPoint PPT Presentation
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Glen CowanRHUL Physics
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Photon energy smearing from events
Glen Cowan, Sudan Paramesvaran, David Hopkins, Henning Flaecher
Royal Holloway, University of London
EMC Calibration Meeting 6 Sept 2006
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Context
Follows on from resolution studies by David, Henning, Sudan.
Same n-tuples and selection as before, see e.g. David Hopkins talk 5 April 06 at EMC software meeting:
2 ‘good’ measured tracks 1 ‘good’ measured photon 1 identified (loose) Kinematic fit 2 prob > 0.05
184 fb→ 1.2 million events
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Goal of study
For the photon in the events we have
Emeas measured by the calorimeter
Efit from the kinematic fit
Goal: (Roodman, Kocian, ...) try smearing MC photon energies to give better data/MC agreement.
Histograms of x = Emeas/Efit
found to have high-x tail, especially for higher E
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Method (1)
Find Try to smear MC so that it looks like data, i.e.,
find a pdf s(z) such that y + z ~ f(x)
Scale to samearea as data histogram
For x = y + z
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Method (2)
Find
For x = y + z we have
In terms of the histograms this is where
For a parameterized pdf s(z;) we therefore have
original MC
smearing matrix
smeared MC
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Method (3)
Try for smearing pdf: Gaussian
Student’s t
Use binned ML but for now ignore MC statistical errors,equivalent to minimizing
For Student’s t, controls extent of tails
= ∞ is Gaussian, = 1 is Cauchy
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit Student’s t fit
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Gaussian fit parameters
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Student’s t fit parameters
For 1.0 < E < 1.5 GeV, →∞(consistent with Gaussian).
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Andrew Wagner, 29 Aug 06 Neutrals meeting, showedsimilar study with L/R asymmetric Gaussian smearing:
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Andrew Wagner, 29 Aug 06 Neutrals meeting:
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From here,
Parameterize the fitted parameters vs energy, (e.g. polynomial),then for each photon
generate z ~ s(z;(E)) and replace E → E (1 + z)
Resulting distribution will not be exactly equal to smeared MCfrom fit due to several approximations made, but should be close.
To what extent are the tails due solely to EMC response? Are theyin part caused by, e.g., modeling of tracking, backgrounds,...?
Some further steps:
investigate angular dependence, refine E binning,
need more MC data (also use more real data?),
check effect on other quantities, e.g., 0 peak
