Project overview
Machine learning is a very hot topic in computer science these days. As a world we are generating ever greater volumes of data, and we need to find effective ways to gather and analyse that data, often by searching for regular patterns in data sets. The human eye is very good at picking out patterns either from images or from simple time series graphs. However, the human eye comes with its own biases: if you are trying to pick out blips in a single line trace on a screen your selection may not always be the same, but may depend on what has come before. Reproducibility is a huge issue here and one which impacts any kind of data science: if we are to do an experiment, or pick out interesting features from data, we want to make sure we get the same result every time given the same initial input. Furthermore, as our input data streams get bigger and bigger, it is extremely time consuming (and a bit boring!) to look through all the data by eye to pick out the kind of features that we want. This is where the extremely powerful tool known as machine learning can help. In this work we propose to use machine learning to pick out particular signatures from large catagloues of Space Physics data - but the computer analysis methods that we will develop will be applicable across multiple disciplines. The Space Physics problem we are interested in is called magnetic reconnection: it is a very energetic process which can take place when two oppositely directed magnetic field lines meet, come together, and break. Right before reconnection happens the field lines are holding lots of energy, but as soon as they break this energy can be released into multiple forms including kinetic energy and thermal energy (heating). The field lines change shape after they break and these newly shaped field lines can ping away from the site of reconnection, much like an elastic band that has been snapped. The field lines also carry with them charged particles, and these particles can heat up or change their flow direction as a result of the transfer of energy. In the solar system everything happens on a giant scale, and magnetic reconnection can involve the magnetic field lines and plasma of the Sun and of several magnetised planets, including, but not limited to Mercury, Earth, Jupiter and Saturn. Spacecraft flying through the solar system have instruments which can measure magnetic fields and plasmas, and thus can sample any changes associated with reconnection. The changes in the shape and orientation of magnetic fields and in the temperature and flow characteristics of charged particles can be observed by spacecraft. When scientists examine spacecraft data to search for evidence of this reconnection process, they know what they are looking for in the field and plasma data. There is a huge amount of spacecraft data: years and years' worth, with measurements taken several times a second. Reconnection can happen every few minutes at some planets. It would be impossible for a human being to look through all the data and pick out every time reconnection happened in our enormous catalogue. The purpose of this research is to teach the computer what reconnection signatures look like to a human eye, and to train the computer to pick these signatures out itself. This technique is called machine learning, and it has many advantages, because computers can be taught to work more quickly than humans, to give the same answer every time, and to not show biases. The ultimate goal at the end of this project is to have trained the computer to select reconnection signatures, and to be able to roll out this technique on multiple data sets from the solar system. This will be particularly useful for scientists who want to conduct large studies of the behaviour of magnetic fields and plasma across the solar system, under different conditions and over multiple years.