LISA Global Fit Data Analysis with a Wavelet Domain Python Pipeline

The Laser Interferometer Space Antenna (LISA) will produce three TDI channels simultaneously containing all LISA gravitational-wave sources. The data from all astrophysical source classes will overlap, including galactic binaries (GBs), stellar origin black hole binaries (SOBHBs), supermassive black hole binaries (SMBHBs), and extreme mass ratio inspirals (EMRIs). Parameter estimation and search pipelines must fit all sources simultaneously in a global fit to the entire data stream. The limiting noise for LISA in some wavelengths will be the stochastic combination of astrophysical gravitational wave sources, making effective simultaneous global source extraction critical for all LISA science cases. Achieving high computational efficiency of the pipeline is essential to alert LISA's multi-messenger search partners as quickly as possible; slow alerts could result in irreversible loss of multi-messenger observations. Furthermore, efficient pipelines can substantially reduce expenditure on computational resources. In this work, I present an efficient wavelet-domain Python code with demonstrated flexibility to conduct parameter estimation for all these source classes within the same infrastructure. I will show the power of our pipeline to handle non-stationary noise and assess how the pipeline can provide value to LISA's multi-messenger and multi-wavelength observing partners.