Variability, periodicity and contact binaries in WISE. (arXiv:2012.04690v2 [astro-ph.SR] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Petrosky_E/0/1/0/all/0/1">Evan Petrosky</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hwang_H/0/1/0/all/0/1">Hsiang-Chih Hwang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zakamska_N/0/1/0/all/0/1">Nadia L. Zakamska</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Chandra_V/0/1/0/all/0/1">Vedant Chandra</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hill_M/0/1/0/all/0/1">Matthew J. Hill</a>
The time-series component of WISE is a valuable resource for the study of
variable objects. We present an analysis of an all-sky sample of ~450,000
AllWISE+NEOWISE infrared light curves of likely variables identified in
AllWISE. By computing periodograms of all these sources, we identify ~56,000
periodic variables. Of these, ~42,000 are short-period (P<1 day), near-contact
or contact eclipsing binaries, many of which are on the main sequence. We use
the periodic and aperiodic variables to test computationally inexpensive
methods of periodic variable classification and identification, utilizing
various measures of the probability distribution function of fluxes and of
timescales of variability. The combination of variability measures from our
periodogram and non-parametric analyses with infrared colors from WISE and
absolute magnitudes, colors and variability amplitude from Gaia is useful for
the identification and classification of periodic variables. Furthermore, we
show that the effectiveness of non-parametric methods for the identification of
periodic variables is comparable to that of the periodogram but at a much lower
computational cost. Future surveys can utilize these methods to accelerate more
traditional time-series analyses and to identify evolving sources missed by
periodogram-based selections.
The time-series component of WISE is a valuable resource for the study of
variable objects. We present an analysis of an all-sky sample of ~450,000
AllWISE+NEOWISE infrared light curves of likely variables identified in
AllWISE. By computing periodograms of all these sources, we identify ~56,000
periodic variables. Of these, ~42,000 are short-period (P<1 day), near-contact
or contact eclipsing binaries, many of which are on the main sequence. We use
the periodic and aperiodic variables to test computationally inexpensive
methods of periodic variable classification and identification, utilizing
various measures of the probability distribution function of fluxes and of
timescales of variability. The combination of variability measures from our
periodogram and non-parametric analyses with infrared colors from WISE and
absolute magnitudes, colors and variability amplitude from Gaia is useful for
the identification and classification of periodic variables. Furthermore, we
show that the effectiveness of non-parametric methods for the identification of
periodic variables is comparable to that of the periodogram but at a much lower
computational cost. Future surveys can utilize these methods to accelerate more
traditional time-series analyses and to identify evolving sources missed by
periodogram-based selections.
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