Pipeline for searching and fitting instrumental glitches in LISA data

Instrumental artifacts, such as glitches, can significantly compromise the scientific output of the Laser Interferometer Space Antenna (LISA). Our methodology employs advanced Bayesian techniques, including reversible jump Markov chain Monte Carlo and parallel tempering to find and characterize glitches and astrophysical signals. The robustness of the pipeline is demonstrated through its ability to simultaneously handle diverse glitch morphologies and it is validated with a “Spritz”-type dataset from the LISA Data Challenge. Our approach enables accurate inference on massive black hole binaries, while simultaneously characterizing both instrumental artifacts and noise. These results present a significant development in strategies for differentiating between instrumental noise and astrophysical signals, which will ultimately improve the accuracy and reliability of source population analyses with LISA.