Disk tomography and dynamics: a time-dependent study of known mid-infrared variable young stellar objects

Abraham, Peter; Acosta-Pulido, Jose; Dullemond, Cornelis P.; Grady, Carol A.; Henning, Thomas; Juhasz, Attila; Kiss, Csaba; Kospal, Agnes; Kun, Maria; Miller, David Westley; Moor, Attila; Sicilia-Aguilar, Aurora
Referencia bibliográfica

Spitzer Proposal ID #60167

Fecha de publicación:
4
2009
Número de autores
12
Número de autores del IAC
0
Número de citas
0
Número de citas referidas
0
Descripción
Most of our knowledge on young stars comes from snapshot observations: spectra and images taken at a single epoch, or at different epochs at different wavelengths. It is, however, known that many of the systems are variable. Variability at optical and near-infrared wavelength is mostly related to the central star itself. Mid-infrared flux changes, on the other hand, are in most cases due to varying emission of the circumstellar material, either via varying accretion rate (and thus changing thermal emission), or varying extinction along the line-of-sight (shadowing effects). If the illuminated disk area varies with time, measuring the variable integrated flux offers a tomographic analysis. Monitoring and interpreting variability provide a powerful 'extra dimension' of information on the structure of the circumstellar material. The Spitzer Warm Mission is a unique opportunity for the systematic establishment of mid-infrared variability studies of young stars. Following an extensive preparatory work, we compiled a list of young stellar objects with variable mid-infrared brightness. We propose to conduct a multi-epoch survey of these carefully selected pre-main sequence stars with Spitzer. We plan to complement the Spitzer observations with simultaneous optical and near-infrared photometry from ground-based telescopes. Our aim is to document the mid-infrared brightness evolution of our targets, examine the possible reasons of the observed variability, model disk structure and dynamics for different scenarios and confront the data with model predictions.