NASSP Masters 5003S - Computational Astronomy - 2009 Lecture 1 Aim: –Become familiar with astronomy data –Learn a bit of programming No set text – –Web

NASSP Masters 5003S - Computational Astronomy - 2009

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


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 www.python.org/doc/)– Easy to learn, easy to read– Lots of libraries and APIs.– But, you will learn other languages…

http://www.python.org/doc/

http://www.python.org/doc/


Astronomy data – binned:

• 1-d:– Time series or light curves– Spectra

• vs frequency…• wavelength…• energy…• recession velocity… etc

• 2-d:– Images

• 3-d:– Cubes!


Astronomy data – unbinned

• Lists of sources, spectral lines or other objects.


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’.


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


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:


HI spectra

Courtesy Anja Schroeder


HI spectra



HI spectra



HI spectra



Interferometry calibration

Courtesy Danielle Fenech


Interferometry calibration

Courtesy Danielle Fenech


UKIDSS



UKIDSS



DENIS



XMM-Newton

MOS pn


XMM-Newton



XMM-Newton



XMM-Newton


Documents

NASSP Masters 5003S - Computational Astronomy - 2009 Lecture 1 Aim: –Become familiar with astronomy data –Learn a bit of programming No set text – –Web