Information & Tutorials

Below are several links on how the Level 2 astroLab is run, as well as some tutorials that will help you with data analysis. Remember knowledge has no limits, and Google is your best friend!

Most of what can be found here is based off all the effort Prof. Mark Swinbank, Prof. Alaistar Edge and Prof. John Lucey have put over the years in maintaining the telescopes and building the astroLab teaching programme for students. When you see them around the department, say thanks!

Make sure you are familiar and up to date with all important summatitive and formative submission deadlines for all assesment related to this module through ULTRA.

Aims & Goals

The overall aim of the Level 2 AstroLab RLI is to allow students to build on the knowlwdge and experience gained during previous components of the Laboratory Skills and Electronics course to design and carry out an observational astronomy project. During this you will:

  • Choose a topic/project. Possibly from the available list, or suggest your own and discuss this with a supervisor.
  • Formulate your hypothesis. Doing some literature research on the topic will absiolutley help for this step. Make sure to be realistic in defining your hypothesis, taking into account the available time you have to work on this.
  • Gather your data. You have the option to take some of your own data using the telescopes on the roof of the Physics building, or to use some previous observatrions taken in previous years (if your targets have already been observed and you are happy with the data quality).
  • Reduce and analyse your data. You can take this as far as you want. You can choose to do the entire data reduction yourself (bias subtraction, flat fielding, astrometry, etc), or use the pre-processed daata at hand. You will then need to extract the relevant information from your dataset and perform some sort of analysis. This may include fitting your data to a model to test your initial hypothesis (or more than one).
  • Write your report. Tell a story, and think of the "big picture". Why was your initial hypothesis interesting. How did you approach the challenge? What data reduction steps did you do, and why? Is your model fit to the data reasonable? Can you rule out the null-hypothesis, and oi not what is your main implication?

Observing sessions

Taking your own data at the telescopes is not mandatory, but can be really fun! If you'd like to have a go at taking your own data using the telescopes on the roof of the Physics building then sign up for the available sessions using the link below. Of course keep an eye out for the weather. We will notify students a few hours before the start of an observing session if this has to be cancelled due to poor weather.

Please come prepared with targets to observe. Ensure they are visible and bright enough. Also remeber to dress up: it's cold on the roof at night!

Learn Linux

With great power comes great responsibility

Reducing and analysing your data

Depending on how far into data reduction and analysis you want to go, you can choose to do this in Python and Gaia (a.k.a. "the harder way"), or use AstroImageJ (a.k.a. "the easier way")

Software

More information