Separating States in Astronomical Sources Using Hidden Markov Models: With a Case Study of Flaring and Quiescence on EV Lac

TL;DR

This paper introduces a novel hidden Markov model-based method to distinguish flaring and quiescent states in astronomical light curve data, demonstrated on EV Lac's X-ray observations, revealing the duration and characteristics of each state.

Contribution

The paper develops a continuous-space hidden Markov model approach with autoregressive processes for state classification in astronomical sources, enhancing the analysis of variable phenomena.

Findings

Flaring occurs in 30-40% of observations.

A persistent quiescent state is identifiable.

Flaring states show higher plasma temperatures.

Abstract

We present a new method 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. For the underlying state process, we consider several autoregressive processes, yielding continuous-space hidden Markov models of varying complexity. Under these models, we can infer the state that the object is in 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. We apply these techniques to X-ray data from the active dMe flare star EV Lac, splitting the data into quiescent and flaring states. We find 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 plasma temperatures and emission measures.