Controlling Outlier Contamination In Multimessenger Time-domain Searches For Supermasssive Binary Black Holes (DSI-SRP)
This DSI-SRP fellowship funded Qiaohong (Joanna) Wang to work in the laboratory of Professor Stephen Taylor in the Department of Physics and Astronomy during the summer of 2020. Qiaohong anticipates graduating in May 2022, and is majoring in Math and Physics.
The project funded by this fellowship aimed to understand a versatile outlier mitigation method tuned for multimessenger time-domain searches for supermassive binary black holes, which has yet to be fully explored. In an effort to perform robust outlier isolation with lower computational costs, we propose a Gibbs sampling approach. Our method provides structural simplicity to outlier modeling and isolation, as it requires only small alterations when adapting to each hierarchical time-domain model. We robustly diagnose outliers present in simulated pulsar-timing datasets, and then further apply our methods to pulsar J1909-3744 from the NANOGrav 9-yr Dataset. We also explore the periodic AGN candidate PG1302-102 using datasets from the Catalina Real-time Transient Survey (CRTS), All-Sky Automated Survey for Supernovae (ASAS-SN), and the Lincoln Near-Earth Asteroid Research (LINEAR). Joanna and Professor Taylor present their findings and outline future work that could improve outlier modeling and isolation for multimessenger time-domain searches.
In addition to receiving support through a DSI-SRP fellowship, this project was supported and facilitated by the DSI Data Science Team through their regular summer workshops and demo sessions.