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Abstract

Instrumental artefacts, such as glitches, can significantly compromise the
scientific output of 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 data set from the LISA Data Challenge. Our approach enables
accurate inference on Massive Black Hole Binaries, while simultaneously
characterizing both instrumental artefacts 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.