Studies in astronomical time series analysis. V. Bayesian blocks, a new method to analyze structure in photon counting data

I describe a new time-domain algorithm for detecting localized structures ( bursts), revealing pulse shapes, and generally characterizing intensity var iations. The input is raw counting data, in any of three forms: time-tagged photon events (TTE), binned counts, or time-to-spill (TTS) data. The outpu t is the most probable segmentation of the observation into time intervals during which the photon arrival rate is perceptibly constant, i.e., has no statistically significant variations. The idea is not that the source is de emed to have this discontinuous, piecewise constant form, rather that such an approximate and generic model is often useful. Since the analysis is bas ed on Bayesian statistics, I call the resulting structures Bayesian blocks. Unlike most, this method does not stipulate time bins-instead the data det ermine a piecewise constant representation. Therefore the analysis procedur e itself does not impose a lower limit to the timescale on which variabilit y can be detected. Locations, amplitudes, and rise and decay times of pulse s within a time series can be estimated independent of any pulse-shape mode l-but only if they do not overlap too much, as deconvolution is not incorpo rated. The Bayesian blocks method is demonstrated by analyzing pulse struct ure in BATSE gamma-ray data.(2).