Introduction to My Project

This next week my research is starting. My project is on using complex-valued neural networks to improve the phase estimation of interferometric signals. This essentially means that I’m using a machine learning model that is trained directly on the phase and magnitude of signals (since complex numbers are used to represent the phase and magnitude of signals as a number) and trying to improve how those signals are processed in regards to interferometry, an imaging/measurement technique that uses light waves to create interference patterns to measure surfaces or take precise images.

I decided on this project because I learned about interferometry in Astrophysics as it was used in the Michelson-Morley experiments. At the same time, I’ve always been fascinated by neural networks and their applications. While brainstorming about project ideas, I realized I could combine these two interests into one. With this project, I hope I can learn more about machine learning and the process of research. 

As my research proposal dictates, my first steps are to generate a synthetic interferometric dataset. Training my CVNN on the synthetic dataset will ideally give the model flexibility so it can tackle a range of datasets with varying signal to noise ratios (SNR). 

In addition to that, it is also in my proposal to work on preprocessing the data. For a neural network, it is important for data to abide to certain guidelines, which include the removal of outlier data, normalizing the data (dividing the data such that it scales between 0 to 1), and splitting the data into training, validation, or test sets. For my project, I plan to randomly divide 70% of my data as a training set, 15% as a validation set, and 15% for test sets.