Shooting Jupiter's Moons

It was a nice clear night, so I cracked the camera out to see how clear a picture I could get of the moon.

 Not bad, considering my 5 year old camera really isn't a telescope!

It really wasn't much of a challenge, though in some ways you wouldn't expect it to be. The moon is quite close to Earth in the grand scheme of things; 'only' 249,986 miles on this particular night. It is also normally the biggest and brightest object in the night sky, running at about magnitude -10 on that night.

So I set myself a more difficult challenge... How about aiming for a picture of some moons around a different planet? Jupiter is the obvious choice because it has four huge moons (the Galilean moons Ganymede, Callisto, Io and Europa) and sits relatively close to the sun. Jupiter is also nice and bright and easy to spot in the night sky, although is still about 200 times fainter than our moon.

The problem with taking a picture of Jupiter's moons lies in their sheer distance from Earth. Jupiter was 531,833,620 miles (over half a billion miles) from Earth on that night. This causes two problems: Firstly because the moons are over 2000 times further away from Earth than our moon they appear much much smaller in the sky. Secondly Jupiter's moons are also about 5 times further from the sun than ours, which means they are illuminated much more weakly by the sun than our moon... Together this means that Jupiters moons appear about 2-5 million times fainter than our moon in the night sky. A proper challenge!

So what kind of picture can you get of Jupiter and its moons? It took some tweaking to get a good picture (I had to capture 20 images, align them and average them together to remove the background noise) but here it is:

Huh, that looks plausible... A big blob (Jupiter) and four smaller blobs (its moons?). It was easy to check where Jupiter's moons are expected to be:

The positions of the Galilean moons: 16/04/2013 20:58

A perfect match! Even down to the brightnesses of individual moons with Ganymede appearing brightest and Callisto faintest. This really is quite incredible; with a standard, modern, off-the-shelf camera and lens you can get a clear picture of the Galilean moons. In comparison in 1610, when Galileo discovered these moons, he was at the cutting edge of optical technology. 403 years for technology to go from a cutting edge revolutionary idea, to a cheap consumer commodity.

At the moment astronomers are just about able to image some planets around other stars. Now imagine in 403 years time, the year 2416; will people be able to buy some consumer camera, pop out into the garden one evening and take a picture of planets around another star?

Software used:
ImageJ: Image processing
Stellarium: Simulated images

The geeky details:
Canon EOS 450D
Sigma 18-200mm f/3.5-6.3 DC OS HSM
The lens was used at 200mm, maximum aperture (f/6.3), with focus set manually to infinity. 20 images of Jupiter were captured at ISO 800 with a 2.5 exposure time. Short exposures and high ISO have to be used because through a telephoto lens the stars drift rapidly through Earth's rotation. The moons were visible in the raw images, but to get a clear and less noisy image they had to be aligned and averaged in ImageJ.

Visualising Large Data Sets - Exoplanets - The Video

Exoplanet discovery, from 1988 to 2010...

Watch it on YouTube! Over a decade of data, 300 planetary systems and 50 million cubic light years in just over 2 minutes.

Software used:
ImageJ - scripting and rendering of the video frames
FFMpeg - video transcoding

Visualising Large Data Sets - Exoplanets

Extremely large data sets pose extremely large challenges... A data set like all confirmed planets around stars other than the Sun (518) and where they sit among the 70000 stars that lie within 200 parsecs (670 light years) of the Sun is a challenge. What better way to show it than with an animation which tracks the discovery of each exoplanet, which star it orbits, where that star is in 3D space relative to the Sun, and the various orbital properties of the planet...

The data
The data I have used is from two totally open, and very useful, data sets: http://exoplanets.org/ for panetary data and http://astronexus.com/node/34 for star data.

Step 1: The timeline.
Exoplanet discovery streaches from 1988 to now. Back in 1988 the first report of evidence for a planet around another star, gamma Cephei, was published and was eventually confirmed as correct in 1996. This sparked huge new interest in exoplanets and since then the number of exoplanets discovered each year has shot up, reaching nearly 100 in 2010. As data from NASA's Kepler mission is confirmed over the course of 2011 this number is likely to shoot up again.
Each planet is added to the final diagram, showing both its 3D location and orbital properties, in the year of its discovery. The entire animation lasts 3200 frames; roughly 2 mins. In 2010 a new planet appears nearly every frame of the animation!

Step 2: The 3D location
The 3D location of many stars near Earth are known, their position is calculated by the position they lie in the sky and the distance to the star as calculated from stellar parallax. This gives fairly accurate 3D locations for many stars within aroun 200 parsecs of Earth. Data about the star's brightness (absolute magnitude) and colour (from the B-V index) can also be used to make a nice looking picture of the Sun's neighbourhood. Unfortunately not many programs can cope with this kind of complex plotting, so I wrote my own:

I use the camera location (rotation around the z axis (theta), angle of elevation (phi) and image scale) and the x, y, z location of a star to project the location of the star onto a 2D image.
a=x*sin(theta)+y*cos(theta)
b=-x*cos(theta)+y*sin(theta)
c=b*sin(phi)+z*cos(phi)
The projected position of the star in the final image is a, c. The star's brightness and colour were then calculated to choose the pixel colour at the star's position.

This picture shows a section of the final 3D starmap. It is animated by rotating it gradually around the z axis to highlight the 3D effect.

Step 3: The planetary system
The most interesting thing about the exoplanet star systems is their arrangement; how big the planets are, how far they are from their parent star and what shape their orbits are. All these properties are summarised in just 3 numbers: estimated minimum mass (measured in Jupiter masses), semi-major axis (a measure of orbit size, normally measured in astronomical units) and orbit ellipticity (which describes how far from circular the orbit is). With a bit more maths a to-scale diagram of the planetary system is drawn in the animation as each new exoplanet is discovered.

The animation
The full animation should be finished soon, I will post it here when it is done...