Video/Audio Open Access
STAR-MELT Python Package
Accretion is a fundamentally important process for pre-main-sequence stars, affecting disk stability and evolution, stellar rotation and activity, and planet formation and migration. The main observational challenge is probing the sub-au scales of the innermost disk, which is not yet possible via interferometry. Such young stars, however, possess a wealth of metallic emission lines that can reveal the nature of these accretion-related processes.
Our analysis involves emission line tomography of time-resolved, high-resolution spectra of young stars. This technique uses the time domain to investigate distortions in the stellar emission line profiles and radial velocity signatures. Local temperatures and densities can be determined for the various emission line species. With both temporal and spatial information, we can then infer a tomographic map of the accretion structures, activity spots, and the innermost hot atomic gas; down to scales smaller than those achievable with direct imaging. Our analysis allows for new science results to be obtained from archival data, as well as facilitating timely analysis of new data as it is obtained.
In this talk, we will give a demonstration of the STAR-MELT Python package. Directly from the FITS files, the emission lines are automatically extracted and identified, via matching to a compiled reference database of lines. Line profiles are fitted and quantified, allowing for calculations of physical properties across each individual observation. Temporal variations in lines can readily be displayed and quantified. Our STAR-MELT python package would also be useful for different applications of spectral analysis, where emission line identification is required. Standard data formats for spectra are automatically compatible, with user-defined custom formats also available. Any reference database (atomic or molecular) can also be used for line identification.
For further details on the emission line tomography results, see the poster by Aurora Sicilia Aguilar.
Sicilia-Aguilar, A., et al. (2020b), A&A 643, 29
Sicilia-Aguilar, A., et al. (2020a), A&A 633, 37
Sicilia-Aguilar, A., et al. (2015), A&A, 580, A82
Sicilia-Aguilar, A., et al. (2012), A&A, 544, A93