ETD

• astrophysics
• black-holes
• detection
• double-white-dwarfs
• extreme-mass-ratio-inspirals
• global-fit-challenge
• gravitational-waves
• LISA
• signal-analysis

The work investigates how to identify and estimate the parameters of low-frequency gravitational-wave sources, specifically Double White Dwarfs (DWDs) and Extreme Mass Ratio Inspirals (EMRIs), in the context of the upcoming LISA mission. Because many signals will overlap in the data stream, the problem falls under the LISA Global Fit Challenge. The thesis injects simulated DWD signals into synthetic LISA data and uses the algorithm samsara, a trans-dimensional CTMCMC sampler, to reconstruct both the number of sources and their parameters. An unmodeled EMRI is then added to study its impact as a background disturbance. The PETRA algorithm is employed to resolve label-switching degeneracies. Results show that DWDs are successfully recovered, and the analysis explores how an EMRI with varying signal-to-noise ratio can introduce systematic biases depending on the correlation between signals. The work concludes with considerations on improvements and future developments toward a robust global-fit algorithm for LISA.