4.6 Article

YOUNG STELLAR OBJECT VARIABILITY (YSOVAR): LONG TIMESCALE VARIATIONS IN THE MID-INFRARED

Journal

ASTRONOMICAL JOURNAL
Volume 148, Issue 5, Pages -

Publisher

IOP PUBLISHING LTD
DOI: 10.1088/0004-6256/148/5/92

Keywords

circumstellar matter; stars: pre-main sequence; stars: protostars; stars: variables: general

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The YSOVAR (Young Stellar Object VARiability) Spitzer Space Telescope observing program obtained the first extensive mid-infrared (3.6 and 4.5 mu m) time series photometry of the Orion Nebula Cluster plus smaller footprints in 11 other star-forming cores (AFGL 490, NGC 1333, Mon R2, GGD 12-15, NGC 2264, L1688, Serpens Main, Serpens South, IRAS 20050+2720, IC 1396A, and Ceph C). There are similar to 29,000 unique objects with light curves in either or both IRAC channels in the YSOVAR data set. We present the data collection and reduction for the Spitzer and ancillary data, and define the standard sample on which we calculate statistics, consisting of fast cadence data, with epochs roughly twice per day for similar to 40 days. We also define a standard sample of members consisting of all the IR-selected members and X-ray-selected members. We characterize the standard sample in terms of other properties, such as spectral energy distribution shape. We use three mechanisms to identify variables in the fast cadence data-the Stetson index, a chi(2) fit to a flat light curve, and significant periodicity. We also identified variables on the longest timescales possible of six to seven years by comparing measurements taken early in the Spitzer mission with the mean from our YSOVAR campaign. The fraction of members in each cluster that are variable on these longest timescales is a function of the ratio of Class I/total members in each cluster, such that clusters with a higher fraction of Class I objects also have a higher fraction of long-term variables. For objects with a YSOVAR-determined period and a [3.6]-[8] color, we find that a star with a longer period is more likely than those with shorter periods to have an IR excess. We do not find any evidence for variability that causes [3.6]-[4.5] excesses to appear or vanish within our data set; out of members and field objects combined, at most 0.02% may have transient IR excesses.

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