Characterising the i-band variability of YSOs over six orders of magnitude in timescale (article)
Sergison, D; Naylor, T; Littlefair, S; et al.Bell, C; Williams, C
Date: 4 December 2019
Journal
Monthly Notices of the Royal Astronomical Society
Publisher
Oxford University Press (OUP) / Royal Astronomical Society
Publisher DOI
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Abstract
We present an i-band photometric study of over 800 young stellar objects in the OB
association Cep OB3b, which samples timescales from 1 minute to ten years. Using
structure functions we show that on all timescales (τ ) there is a monotonic decrease in
variability from Class I to Class II through the transition disc (TD) systems to ...
We present an i-band photometric study of over 800 young stellar objects in the OB
association Cep OB3b, which samples timescales from 1 minute to ten years. Using
structure functions we show that on all timescales (τ ) there is a monotonic decrease in
variability from Class I to Class II through the transition disc (TD) systems to Class
III, i.e. the more evolved systems are less variable. The Class Is show an approximately
power-law increase (τ
0.8
) in variability from timescales of a few minutes to ten years.
The Class II, TDs and Class III systems show a qualitatively different behaviour
with most showing a power-law increase in variability up to a timescale corresponding
to the rotational period of the star, with little additional variability beyond that
timescale. However, about a third of the Class IIs show lower overall variability, but
their variability is still increasing at 10 years. This behaviour can be explained if all
Class IIs have two primary components to their variability. The first is an underlying
roughly power-law variability spectrum, which evidence from the infrared suggests is
driven by accretion rate changes. The second component is approximately sinusoidal
and results from the rotation of the star. We suggest that the systems with dominant
longer-timescale variability have a smaller rotational modulation either because they
are seen at low inclinations or have more complex magnetic field geometries.
We derive a new way of calculating structure functions for large simulated datasets
(the “fast structure function”), based on fast Fourier transforms.
Physics and Astronomy
Faculty of Environment, Science and Economy
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