Controlling Outlier Contamination In Multimessenger Time-domain Searches
  For Supermasssive Binary Black Holes
  Time-domain datasets of many varieties can be prone to statistical outliers
that result from instrumental or astrophysical anomalies. These can impair
searches for signals within the time series and lead to biased parameter
estimation. Versatile outlier mitigation methods tuned toward multimessenger
time-domain searches for supermassive binary black holes have yet to be fully
explored. In an effort to perform robust outlier isolation with low
computational costs, we propose a Gibbs sampling scheme. This provides
structural simplicity to outlier modeling and isolation, as it requires minimal
modifications to adapt to time-domain modeling scenarios with pulsar-timing
array or photometric data. We robustly diagnose outliers present in simulated
pulsar-timing datasets, and then further apply our methods to pulsar
J$1909$$-$$3744$ from the NANOGrav 9-yr Dataset. We also explore the periodic
binary-AGN candidate PG$1302$$-$$102$ using datasets from the Catalina
Real-time Transient Survey, All-Sky Automated Survey for Supernovae, and the
Lincoln Near-Earth Asteroid Research. We present our findings and outline
future work that could improve outlier modeling and isolation for
multimessenger time-domain searches.
