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| Paper: |
LIRA — The Low-Counts Image Restoration and Analysis Package: A Teaching Version via R |
| Volume: |
442, Astronomical Data Analysis Software and Systems XX (ADASSXX) |
| Page: |
463 |
| Authors: |
Connors, A.; Stein, N. M.; van Dyk, D.; Kashyap, V.; Siemiginowska, A. |
| Abstract: |
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. |
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