Speaker
Fabrizia Guglielmetti
(MPE Garching)
Description
A method to solve the long-lasting problem of disentanglement of the
background from the sources is given by Bayesian mixture modelling
(Guglielmetti F., et al., 2009, MNRAS, 396,165).
The technique employs a joint estimate of the background and detection of
the sources in astronomical images.
Bayesian probability theory is applied to gain insight into the
coexistence of background and sources through a probabilistic
two-component mixture model. Uncertainties of the background and source
signals are consistently provided. Background variations are properly
modelled and sources are detected independent of their shape. No
background subtraction is needed for the detection of sources. Poisson
statistics is rigorously applied throughout the whole algorithm.
The technique is general and applicable to count detectors.
Practical demonstrations of the method will be given through simulated
data sets and data observed in the X-ray part of the electromagnetic
spectrum from ROSAT and Chandra satellites.
