The thesis focuses on the development of rapid analysis methods for the detection and characterization of gravitational waves, particularly in the context of the upcoming LISA (Laser Interferometer Space Antenna) space mission planned by ESA around 2035. Data analysis involves several stages, one of the first being the rapid analysis “pipeline,” whose role is to detect new events and to characterize them. The final aspect concerns the rapid estimation of the sky position of the gravitational wave source and their characteristic time, such as the coalescence time in the case of black hole mergers. These analysis tools constitute the low-latency analysis pipeline.

Beyond its value for LISA, this pipeline also plays a crucial role in the rapid follow-up of events detected by electromagnetic observations (ground or space-based observatories, from radio waves to gamma rays). While fast analysis methods have been developed for ground-based interferometers, the case of space-borne interferometers such as LISA remains an area to be explored. Thus, a tailored data processing method will have to consider the packet-based data transmission mode, requiring event detection from incomplete data. From data affected by artifacts such as glitches, these methods must enable the detection, discrimination, and analysis of various sources.

In this thesis, we propose to develop a robust and effective method for the early detection of massive black hole binaries (MBHBs). This method should accommodate the data flow expected for LISA, process potential artifacts (e.g., non-stationary noise and glitches), and allow the generation of alerts, including a detection confidence index and a first estimate of the source parameters (coalescence time, sky position, and binary mass); such a rapid initial estimate is essential for optimally initializing a more accurate and computationally expensive parameter estimation.