We are using few petabytes worth of time series astronomy data. The general aim is to find very faint periodic signals within it.
Our current method of processing this data is to do a Fast Fourier transform of the time series and look for peaks in the Fourier space. However, most of the periodic signals are false positives (what we call Radio Frequency interference). Generally 1 in 10,000 periodic signals is a true candidate. So basically, the task of the neural network is to find which of them are true candidates.
Supervised learning methods have been explored in the past with mixed success, but generally well labelled training data is hard to come by. The Interference environment also depends on which telescope was used and it could change with time. My question is if reinforcement learning methods are well-suited for this problem? Do you think the Monte-Carlo tree search algorithm could help here? Any advice would be appreciated.
P.S. In case it is important to know, for the current processing of the data, there are several steps after the fast fourier transform as well. In case this needs to be expanded upon, please let me know.