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Estimating the Peak Magnification of a Microlensing Event

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Estimating the Peak Magnification of a Microlensing Event. Michael Albrow ... In the majority of cases these events turn out to be low magnification. OGLE-2003-BUL-331 ... – PowerPoint PPT presentation

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Title: Estimating the Peak Magnification of a Microlensing Event


1
Estimating the Peak Magnification of a
Microlensing Event
  • Michael Albrow
  • Department of Physics and Astronomy
  • University of Canterbury

2
Background
  • Fits based on early data often predict very large
    peak magnifications (usually with large
    uncertainties).
  • In the majority of cases these events turn out to
    be low magnification.

3
OGLE-2003-BUL-331
4
The problem
  • Methods based on ? 2 minimisation do not produce
    the most probable parameters.
  • Maximising the likelihood function (minimising ?
    2 ) gives the parameter values that make the data
    most probable.
  • We are interested in obtaining the most probable
    values of the parameters given the data.

5
Bayes theorem
  • P(?D,O) a P(D?,O) P(?O)
  • We have prior statistical knowledge about
  • the underlying distribution of ?.

Underlying probability of ? given an alert
Probability of ? given an alert and with data D
likelihood function exp(-? 2/2)
6
2002 OGLE III
7
OGLE-2003-BUL-331
8
2003 OGLE Distribution of Brightness Increases
All data
? 2 at alert
Bayesian at alert
Ibase Ipeak
9
2003 OGLE Distribution of u0
All data
? 2 at alert
Bayesian at alert
U0
10
2003 OGLE Distribution of Observed Predicted
Peak Magnitudes
? 2
Bayesian
I0-I0,predicted at alert
11
Typical low magnification eventOGLE-2003-BUL-171
12
Typical high magnification eventOGLE-2003-BUL-208
13
High magnification eventOGLE-2003-BUL-262
14
Suggested follow up strategy
  • Only follow events where the Bayesian
  • prediction A0 gt 4.
  • For all high magnification cases examined,
  • this occurs when A lt 3.

15
Generic Behaviour
  • ? 2 minimisation
  • Rapid parameter changes with new data
  • Biased estimate of parameters
  • Tends to overpredict peak magnification
  • Probability maximisation
  • Smooth parameter changes with new data
  • Unbiased estimate of parameters
  • Usually converges to the correct solution earlier
  • Tends to initially underpredict peak
    magnification for high magnification events
  • High magnification nature is identified early
    enough for observation

16
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18
2002 OGLE III
19
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