Stages M2 / Internship positions
This internship focuses on the development of new semi-blind deconvolution methods based on recent advances in Machine Learning.
In preparation for the future gravitational wave data that the LISA observatory will provide, we will explore with this internship how Machine Learning can be used to provide a fast and reliable estimation of Galactic Binaries parameters.
In the future LISA data, investigating the properties of Galactic Binaries (e.g. binaires of Black Holes) is challenging since it requires disentangling between those that can be extracted individually and those that are mixed and compose a so-called confusion noise. The goal of this internship is to investigation a statistical method to separate out detectable galactic binaries, confusion noise and instrumental noise.
It is expected that thousands of galactic binaries will be observed with the LISA gravitational wave observatory. However estimating the parameters of such a large number of signals in a fast and reliable way is a challenging task. In this internship, we investigate a two-step approach where signal are first detected and then identified separately in a parallel manner thanks of MCMC methods.
Thèses / PhD positions
- Unmixing LISA data with Machine Learning.
No open position for 2021.