Physics projects for Y3 and Y4 students

Project description

Parametrising Transient Sunspot Properties

(supervisor: Tom Knight)

Nature of project: software, data analysis

Available to students on full-time physics degree schemes or joint students.

Project description and methodology

Transient phenomena captured in video files (or image sequences) are commonly studied by physicists and other scientists. Extracting information from these sequences can be challenging and is always time-consuming. The project focuses on automatic identification and parametrisation of the properties of sunspots.

The dynamics of sunspots, and in particular their evolution, are linked to the complexities of the Sun's magnetic field. On a global scale, the number and relative position of sunspots are used to estimate the Sun's medium-term future activity. At a more local level, the interplay of clusters of sunspots may correlate to eruptive solar atmospheric events.

The objective is to write software that analyses the image sequences to identify sunspots, quantify properties (at the very least, position) and track those properties for each sunspot through the image sequence.

Data would be acquired from online sources.

Is it possible to extrapolate from the observed properties to correctly identify an entity that was hidden for part of the image sequence, such as a sunspot rotating to and from the far side of the Sun?

Can the software identify and map the relative positions of clusters of sunspots over time?

Do any of the features of the extracted data correlate to eruptive events?


Additional scope or challenge if taken as a Year-4 project: Additional properties of the tracked entities would need to include parametrisation of the shape of the sunspots, and using that to analyse the area and/or rotation of the entities.

Please speak to Tom Knight if you consider doing this project.

Initial literature for students:

  1. Alexander Mordvintsev & Abid K., 2013, OpenCV Python Tutorials
  2. John C. Russ 2011, The image processing handbook, 6th ed.
  3. Module PH24010 (Data Handling and Statistics)
  4. http://scikit-image.org

Novelty, degree of difficulty and amount of assistance required

Programming heavy project. Previous python (or other object orientated language) experience would be useful though not necessary.

Difficulty is dependent on the student's pace through the project and the chosen application.

Project milestones and deliverables (including timescale)

milestoneto be completed by
Familiarity with python and external librariesend of October
Simple application of library to simplified example image/image sequenceend of November
Position (and other properties?) extractionend of February
Analysis of Parameters and/or Refinement of extractionEaster