  The proposed Laser Interferometer Space Antenna (LISA) mission is tasked with
the detection and characterization of gravitational waves from various sources
in the universe. This endeavor is challenged by transient displacement and
acceleration noise artifacts, commonly called glitches. Uncalibrated glitches
impact the interferometric measurements and decrease the signal quality of
LISA's time-delay interferometry (TDI) data used for astrophysical data
analysis. The paper introduces a novel calibration pipeline that employs a
neural network ensemble to detect, characterize, and mitigate transient
glitches of diverse morphologies. A convolutional neural network is designed
for anomaly detection, accurately identifying and temporally pinpointing
anomalies within the TDI time series. Then, a hybrid neural network is
developed to differentiate between gravitational wave bursts and glitches,
while a long short-term memory (LSTM) network architecture is deployed for
glitch estimation. The LSTM network acts as a TDI inverter by processing noisy
TDI data to obtain the underlying glitch dynamics. Finally, the inferred noise
transient is subtracted from the interferometric measurements, enhancing data
integrity and reducing biases in the parameter estimation of astronomical
targets. We propose a low-latency solution featuring generalized LSTM networks
primed for rapid response data processing and alert service in high-demand
scenarios like predicting binary black hole mergers. The research highlights
the critical role of machine learning in advancing methodologies for data
calibration and astrophysical analysis in LISA.
