Module Catalogue 2024/25

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

To introduce the students to the basic techniques and skills of modern observational astronomy, tools and statistical techniques for analysing astronomical data and an understanding of the science that can be obtained from it.

The course will be based around two assignments. These will be written assignments, which will be a report on primarily computational based problems. The problems will make use of astronomical data (both real and simulated). During the lectures the students will be provided the tools and knowledge necessary to engage with the essential data analysis and to understand and explain the scientific implications of the results.

Outline Of Syllabus

The module is made up from two mini-project (assignment) areas.
The first half of the lectures and first assignment will cover:
1.       Basic knowledge of mapping/tracking astronomical objects on the sky and how to plan to observe them with telescopes. Using measurements derived from photometric data and spectroscopic data of stars and galaxies to establish their properties (colours, ages, masses, central black holes etc.).
The second half of the lectures and the second assignment will cover:
2.       Using accurate statistical tools on measurements from simulated and real observed data to investigate the properties of galaxy populations and study their connections to dark matter haloes.

Intended Knowledge Outcomes

-       How we use co-ordinate systems to map and track astronomical objects for observations
-       Different types of astronomical observations (e.g., photometry and spectroscopy).
-       How to extract physical properties of astronomical objects (stars, galaxies, and dark matter haloes) from observational datasets.
-       How to use statistical techniques to obtain meaningful results from astronomical datasets, accounting for various issues with the data (incompleteness etc.).
-       How to estimate errors from astronomical data sets, and propagate this error to model parameters

Intended Skill Outcomes

Students will gain experience using Python coding to perform computational analysis and visualisations of astronomical data. They will further develop skills in statistical techniques to perform data analysis and extract scientific results.

Students will develop skills across the cognitive domain (Bloom's taxonomy, 2001 revised edition): remember, understand, apply, analyse, evaluate and create.

Teaching Rationale And Relationship

The teaching methods are appropriate to allow students to develop a wide range of skills, from understanding basic concepts and facts to higher order thinking.

Lectures are used for the delivery of scientific theory and explanation of methods, illustrated with examples, and for giving general guidance/feedback on the assignment problems. The assignments will put these skills and knowledge into practise in a practical way.

Assessment Rationale And Relationship

The two mini-projects allow the students to demonstrate their mastery of the techniques for analysing data from physical sciences and understanding the science that comes from it. The computer-based activities ensure that students have the expertise working with the tools, and the assignments focus on the data analysis in the context of scientific processes and conceptual interpretation.

General Notes

N/A