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Title: A formal method for identifying distinct states of variability in time-varying sources: SGR A* as an example

Journal Article · · Astrophysical Journal
; ;  [1];  [2]
  1. Department of Physics and Astronomy, University of California, Los Angeles, CA 90095-1547 (United States)
  2. UCLA Anderson School of Management, University of California, Los Angeles, CA 90095-1481 (United States)

Continuously time variable sources are often characterized by their power spectral density and flux distribution. These quantities can undergo dramatic changes over time if the underlying physical processes change. However, some changes can be subtle and not distinguishable using standard statistical approaches. Here, we report a methodology that aims to identify distinct but similar states of time variability. We apply this method to the Galactic supermassive black hole, where 2.2 μm flux is observed from a source associated with Sgr A* and where two distinct states have recently been suggested. Our approach is taken from mathematical finance and works with conditional flux density distributions that depend on the previous flux value. The discrete, unobserved (hidden) state variable is modeled as a stochastic process and the transition probabilities are inferred from the flux density time series. Using the most comprehensive data set to date, in which all Keck and a majority of the publicly available Very Large Telescope data have been merged, we show that Sgr A* is sufficiently described by a single intrinsic state. However, the observed flux densities exhibit two states: noise dominated and source dominated. Our methodology reported here will prove extremely useful to assess the effects of the putative gas cloud G2 that is on its way toward the black hole and might create a new state of variability.

OSTI ID:
22365416
Journal Information:
Astrophysical Journal, Vol. 791, Issue 1; Other Information: Country of input: International Atomic Energy Agency (IAEA); ISSN 0004-637X
Country of Publication:
United States
Language:
English