NASSP Masters 5003S - Computational Astronomy Lecture 1 Aim: –Become familiar with astronomy data –Learn a bit of programming No set text – –Web resources –Library –My own books (maybe!) Programming environment: –Unix/Linux –Python (mostly) –Laptops not essential

NASSP Masters 5003S - Computational Astronomy Possible computing grumbles: Why Unix/Linux? –It is still pretty close to a default platform – certainly for any ‘serious’ computing. Mac: ok lots of people these days have mac laptops, but the command-line interface is (I’ve heard) similar, so it should not be too hard to port what you learn to mac. Windows: fool-featherbedding, closed-source philosophy makes it difficult to keep control of what you’re doing. Why python? –Good web doco (eg –Easy to learn, easy to read –Lots of libraries and APIs. –But, you will learn other languages…

NASSP Masters 5003S - Computational Astronomy Astronomy data – binned: 1-d: –Time series or light curves –Spectra vs frequency… wavelength… energy… recession velocity… etc 2-d: –Images 3-d: –Cubes!

NASSP Masters 5003S - Computational Astronomy Astronomy data – unbinned Lists of sources, spectral lines or other objects.

NASSP Masters 5003S - Computational Astronomy Astronomy data Mostly resolvable into: –Signal –Background –Noise Gaussian or ‘white’ noise (thermal) Poisson (quantum) 1/f or ‘red’ noise (fractal Nature) Other filtered noise Note: difference between signal and background is often an ‘academic question’.

NASSP Masters 5003S - Computational Astronomy Astronomy data Two sorts of problem: –Want to find things. Involves concepts of detection probability –signal-to-noise ratio –significance –null hypothesis –chi squared and friends sensitivity selection biases dynamic range –Want to measure things after you’ve found them. Concepts: parameter fitting –F test uncertainty confidence intervals

NASSP Masters 5003S - Computational Astronomy Astronomy data – words of wisdom: “If you can’t be perfect, the next best thing is to know how imperfect you are.” –That’s why estimation of uncertainties is vital. “Sometimes no data is better than bad data.” –What is ‘bad data’? Data which is either so difficult that it isn’t worth working with, or data which doesn’t allow you to estimate uncertainties well. Some examples:

NASSP Masters 5003S - Computational Astronomy HI spectra Courtesy Anja Schroeder

NASSP Masters 5003S - Computational Astronomy HI spectra Courtesy Anja Schroeder

NASSP Masters 5003S - Computational Astronomy HI spectra Courtesy Anja Schroeder

NASSP Masters 5003S - Computational Astronomy HI spectra Courtesy Anja Schroeder

NASSP Masters 5003S - Computational Astronomy Interferometry calibration Courtesy Danielle Fenech

NASSP Masters 5003S - Computational Astronomy Interferometry calibration Courtesy Danielle Fenech

NASSP Masters 5003S - Computational Astronomy UKIDSS Courtesy Anja Schroeder

NASSP Masters 5003S - Computational Astronomy UKIDSS Courtesy Anja Schroeder

NASSP Masters 5003S - Computational Astronomy DENIS Courtesy Anja Schroeder

NASSP Masters 5003S - Computational Astronomy XMM-Newton MOSpn

NASSP Masters 5003S - Computational Astronomy XMM-Newton Courtesy Anja Schroeder

NASSP Masters 5003S - Computational Astronomy XMM-Newton Courtesy Anja Schroeder

NASSP Masters 5003S - Computational Astronomy XMM-Newton Courtesy Anja Schroeder