In low-count discrete photon imaging systems, such as in high energy astrophysics, the spatial distribution of a very few (or no!) photons per pixel can indeed carry important information about the shape of interesting emission. Our Low-counts Image Restoration and Analysis package, LIRA, was designed to: 'deconvolve' any unknown sky components; give a fully Poisson 'goodness-of-fit' for any best-fit model; and quantify uncertainties on the existence and shape of unknown sky components. LIRA does this without resorting to χ2 or rebinning, which can lose high-resolution information. However, running it thoughtfully requires understanding of several key areas, since it combines a Poisson-specific multi-scale model for the sky with a full instrument response, within a (Bayesian) probablility framework, sampled via MCMC. To this end, we have created and are releasing a 'teaching' version of LIRA. It is implemented in R. The accompanying tutorial and R-scripts step through all the basic analysis steps, from simple multi-scale representation and deconvolution; to model-testing; setting quantitative limits; and even simple ways of incorporating uncertainties in the instrument response.
Astronomical Data Analysis Software and Systems XX
- Pub Date:
- July 2011