The STAR-MELT PYTHON package for emission-line analysis of YSOs

We introduce the STAR-MELT PYTHON package that we developed to facilitate the analysis of time-resolved emission-line spectroscopy of young stellar objects. STAR -MELT automatically extracts, identifies, and fits emission lines. We summarize our analysis methods that utilizes the time domain of high-resolution stellar spectra to investigate variability in the line profiles and corresponding emitting regions. This allows us to probe the innermost disc and accretion structures of YSOs. Local temperatures and densities can be determined using Boltzmann statistics, the Saha equation, and the Sobolev large velocity gradient approximation. STAR-MELT allows for new results to be obtained from archival data, as well as facilitating timely analysis of new data as it is obtained. We present the results of applying STAR-MELT to three YSOs, using spectra from UVES, XSHOOTER, FEROS, HARPS, and ESPaDOnS. We demonstrate what can be achieved for data with disparate time sampling, for stars with different inclinations and variability types. For EX Lupi, we confirm the presence of a localized and stable stellar-surface hotspot associated with the footprint of the accretion column. For GQ Lupi A, we find that the maximum infall rate from an accretion column is correlated with lines produced in the lowest temperatures. For CVSO109. we investigate the rapid temporal variability of a redshifted emission wing, indicative of rotating and infalling material in the inner disc. Our results show that STAR-MELT is a useful tool for such analysis, as well as other applications for emission lines.

Automated summary

The STAR-MELT PYTHON package facilitates the analysis of time-resolved emission-line spectroscopy of young stellar objects by automatically extracting, identifying, and fitting emission lines. Utilizing high-resolution stellar spectra in the time domain, it allows for the investigation of variability in line profiles and emitting regions. Results from applying STAR-MELT to various YSOs demonstrate the tool's effectiveness in analyzing disparate data sets.