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Machine Learning Algorithms for Analysis of Radio Data from Satellites

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  • Full or part time
    Dr C Jackman
    Prof S Gunn
    Dr Mervyn Freeman
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Space Weather describes environmental conditions in space that can have an impact on Earth, and severe Space Weather is listed as one of the highest priority natural hazards in the UK Government’s National Risk Register. Conditions in Earth’s magnetosphere are constantly changing over what’s called the substorm cycle: a build-up and explosive release of magnetic flux and energy from the interaction between the solar wind and the planetary field. At substorm onset, enormous amounts of energy are released, and much of this energy makes its way toward the polar regions, dissipating into the upper atmosphere.

We now have access to long time series of Auroral Kilometric Radiation (AKR) – measurements of Earth’s radio emissions which respond to substorms and serve as an excellent remote proxy of this cycle of energy build-up and deposition. Preliminary work in Southampton’s Space Environment Physics group has involved the development of simple algorithms to pick out the main features in the AKR spectrum. This proposed project will take automation of radio data to the next level, in turn advancing our understanding of the impact of Space Weather on the Earth’s upper atmosphere.

It has long been known that radio emissions are a key tool for monitoring Space Weather and magnetospheric changes, with direct connection to Earth’s atmosphere. Individual case studies have highlighted the relevant changes to search for: intensity increases, and shifts in frequency linked to motion of radio sources to different altitudes in the ionosphere.

This project will involve handling large datasets from spacecraft in orbit around Earth which have been monitoring the radio emissions quasi-continuously since the 1990s. These datasets will be systematically probed for changes in power, polarization and frequency which are all characteristic signatures of this energy cycling process.

The student will build domain knowledge of radio emissions from examination of classic case studies which show the response to substorm onset. This will then be used to build machine learning training libraries. Important challenges include exploring how radio signatures alter as they propagate through the ionosphere, and monitoring by multiple spacecraft in different positions will be critical here (with research results directly relevant to the satellite communications sector). The main focus and deliverable at the end of the project will be sophisticated automated algorithms to select relevant features from large catalogues of radio data.

The NEXUSS CDT provides state-of-the-art, highly experiential training in the application and development of cutting-edge Smart and Autonomous Observing Systems for the environmental sciences, alongside comprehensive personal and professional development. There will be extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners. The student will be registered at University of Southampton, and hosted at Department of Physics and Astronomy. Specific training will include:

At the start of their PhD, the student will attend a national summer school in solar-terrestrial physics. An advanced summer school is also available in the second year. Further opportunities for knowledge development will be provided through the postgraduate lecture series and research seminar series in Physics & Astronomy. The student will have access to the training programme for DISCNet, a Southampton-led Data-Intensive Science Initiative which covers machine learning and big data. Development of programming skills will be a key element of the training provided, as computer programming will be integral to the data analysis undertaken.


Funding Notes

To be eligible for a full NEXUSS award (stipend and fees) a student must have:

No restrictions on how long they can stay in the UK
Been 'ordinarily resident' in the UK for 3 years prior to the start of the grant.
Not been residing in the UK wholly or mainly for the purpose of full-time education. (This does not apply to UK/EU nationals)

Potential PhD students are requested to apply using the University of Southampton postgraduate application form. For information on the application process and documents required please refer to the following webpage: http://noc.ac.uk/education/gsnocs/how-apply

References

Reed, J., C.M. Jackman, L. Lamy, W. Kurth, D.K. Whiter, Low frequency Extensions of the Saturn Kilometric Radiation as a proxy for magnetospheric dynamics, J. Geophys. Res., submitted, June 2017.

Jackman, C.M., L. Lamy, M.P. Freeman, P. Zarka, B. Cecconi, W.S. Kurth, S.W.H. Cowley, M.K. Dougherty (2009), On the character and distribution of lower-frequency radio emissions at Saturn, and their relationship to substorm-like events, J. Geophys. Res., 114, A08211, doi:10.1029/2008JA013997.

Related Subjects

How good is research at University of Southampton in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 68.62

Research output data provided by the Research Excellence Framework (REF)

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