RESEARCH TECHNIQUES IN ASTRONOMY - 2025/6

Module Overview

In this module, students will learn key methods adopted in astrophysics to carry out advanced research: scientific computing, statistics, data analysis, machine learning. Much of the course develops highly transferrable skills that apply to science research in general. The goal is to ensure that students are well-prepared for either their research year or their future careers.

Module provider

Mathematics & Physics

Module Leader

GUALANDRIS Alessia (Maths & Phys)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

Indicative content includes:

1. 
   Data handling and analysis:  Handling large astronomic data files, extracting physical quantities of interest, image analysis, coping with noise and systematic errors.
   


2. Numerical methods: Integration, Fitting, Sampling.


3. 
   Statistics: Probability density distributions and moments; Sampling, fitting, comparing data and models, Bayesian statistics, Monte Carlo Markov Chain
   

Assessment pattern

Alternative Assessment

None.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate understanding of the basic principles of statistics and data analysis, visualisation of big data, machine learning and data science methods, as detailed in the learning outcomes.

The summative assessment for this module consists of two pieces of coursework.

 

Formative assessment and feedback

Formative assessment: The Python refresher is assessed formatively and then additional formative feedback will be provided in subsequent laboratory sessions by members of academic staff. 

Detailed written feedback will be provided for each piece of submitted coursework. Additional feedback will be provided during lab sessions by means of verbal feedback from the academics.

 

Module aims

• Provide a clear perspective of how astrophysical research is conducted
• Provide an introduction and hands-on experience of numerical tools used in scientific research, including modern machine learning and data science methods in astronomy

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to help students gain a basic understanding of the main research techniques used in astrophysics and prepare them for a research year or future career in science.

 

The learning and teaching methods include:

• 
  Lectures
  


• 
  Computational lab 
  

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: PHY3054

Other information

The School of Mathematics and Physics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

• Digital Capabilities Throughout the module students will engage with large and complex datasets (‘big data’) and will develop their computational skills in analysing this data using Python and bespoke computational packages

•  Employability The module introduces learners to modern computational techniques in Python, including data handling and analysis, visualisation, Bayesian inference and machine learning

• Resourcefulness and Resilience Problem solving is a key component of this module with students given the opportunity to retrieve real astronomical data from large surveys and troubleshooting any ensuing difficulties

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.