False detection rate control in time series coincidence detection

We study the problem of coincidence detection: given data from multiple sensors, each individual sensor may have a high false positive rate when searching for events or signals among background noise, but we can reduce the rate of false positives by searching for simultaneous detections in multiple sensors. The coincidence detection problem arises across many applications, and this particular work is motivated by applications in astrophysics, where the aim is to detect astrophysical events such as gravitational waves. A commonly used technique in that field is "time-shifting": the timeline of one data stream is randomly shifted relative to another, to determine whether nearly simultaneous detections might occur frequently simply due to random chance. This talk will present a theoretical analysis of the time-shifting methodology, examining whether it is able to offer false positive rate control in settings where the data streams may each have high temporal dependence, as is common in time series data.

This work is joint with Ruiting Liang, Samuel Dyson, and Daniel Holz.