The Astrophysical Journal (Jan 2024)

Mid-infrared Variability of Young Stellar Objects on Timescales of Days to Years

  • Sieun Lee,
  • Jeong-Eun Lee,
  • Carlos Contreras Peña,
  • Doug Johnstone,
  • Gregory Herczeg,
  • Seonjae Lee

DOI
https://doi.org/10.3847/1538-4357/ad14f8
Journal volume & issue
Vol. 962, no. 1
p. 38

Abstract

Read online

Variability in the brightness of young stellar objects (YSOs) is a common phenomenon that can be caused by changes in various factors, including accretion, extinction, disk morphology, interactions between the disk and the stellar photosphere, and the rotation of hot or cold magnetic spots on the stellar photosphere. Analyzing the variability on different timescales provides insight into the mechanisms driving the changes in the brightness of YSOs. We investigate the variability of YSOs on both long and short timescales using two mid-IR data sets: the Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) 7.5 yr W2 (4.6 μ m) data and the YSOVAR 40 day Spitzer/IRAC2 (4.5 μ m) data, respectively. We classify the variability types in each timescale following Park et al. We find a higher detection rate of variable sources in the short term (77.6%) compared to the long term (43.0%) due to the higher sensitivity of the Spitzer observations. In addition, the higher cadence of the YSOVAR data results in the weeks-long short-term variability being mostly secular, while the years-long long-term variability explored with the coarsely sampled NEOWISE data is mostly stochastic. By crossmatching the two catalogs, we statistically analyze the variability types exhibited by YSOs across both timescales. The long-term variability amplitude is mostly three times (up to 10 times) greater than the short-term variability. Furthermore, we evaluate variability on very short (1–2 days) timescales and recover a trend of the increasing amplitude of variability as the timescales increase. By comprehensively analyzing the variability of YSOs over various timescales, we contribute to a deeper understanding of the underlying mechanisms driving their variability.

Keywords