SIMBAD references

We investigate the efficacy of recursive Bayesian estimation of regularized and irregular astrophysical time series using particle filters to understand latent dynamics. We begin by regularizing a MACHO (massive compact halo object) quasar light curve using linear interpolation techniques. This is subsequently modelled using a variety of autoregressive and autoregressive-integrated moving average models. We find that we can learn regularized astrophysical time series using particle filters. Motivated by this result, we proceed by working on raw, irregular light curves. Accurately modelling the underlying dynamics as a continuous autoregressive stochastic process, calibrated using an MCMC we find that the scale variable, τ, is in fact first-order stable across 55 MACHO quasar light curves and thus not correlated with the black hole mass. We show that particle filters can be used to learn regularized and irregular astrophysical light curves. These results can be used to inform classification systems of stellar type and further study variability characteristics of quasars.