A1454
Title: Separating states in astronomical sources using hidden Markov models
Authors: Robert Zimmerman - University of Toronto (Canada) [presenting]
David van Dyk - Imperial College London (United Kingdom)
Vinay Kashyap - Harvard-Smithsonian (United States)
Aneta Siemiginowska - Harvard University (United States)
Abstract: A new method is presented to distinguish between different states (e.g., high and low, quiescent and flaring) in astronomical sources with count data. The method models the underlying physical process as latent variables following a continuous-space Markov chain that determines the expected Poisson counts in observed light curves in multiple passbands. Several autoregressive processes are considered for the underlying state process, yielding continuous-space hidden Markov models of varying complexity. Under these models, the state that the object is in can be inferred at any given time. The continuous state predictions from these models are then dichotomized with the help of a finite mixture model to produce state classifications. These techniques are applied to X-ray data from the active dMe flare star EVLac, splitting the data into quiescent and flaring states. It is found that a first-order vector autoregressive process efficiently separates flaring from quiescence: flaring occurs over 30-40\% of the observation durations, a well-defined persistent quiescent state can be identified, and the flaring state is characterized by higher temperatures and emission measures.
