The MSc course in Computational Astronomy will concentrate on data and how to process it.
General lore:
- Good vs bad data
- Some traps:
- Poorly-orthogonal bases
- Splines
Software:
- When to trust it
- Good vs evil (= how to write maintainable code)
- Scripting (perl, python)
Statistics and source/signal detection:
- Gaussian vs Poisson
- Bayesian vs frequentist
- Significance
- Reliability vs completeness
- Sensitivity
- Surveys: volume- vs flux-limited
- Which is the best detection method?
- Confusion
Radio Interferometry:
- - Basic theory
- - Continuum vs spectral-line
- - Packages (AIPS, Miriad, CASA)
- - Project: dynamic range issues - weighting, w-term
X-ray:
- - Instrumentation overview
- - Event-oriented data
- - Packages (SAS, Ciao, xspec, ftools, ds9)
- - Project: trawl for flashers in XMM data