Speaker
Description
To fully exploit the rich SCIP observations, it is important to update the atomic data of observed lines. In this contribution, I will discuss the procedure to identify lines with poorly constrained parameters, such as the transition probability, and how these are inferred from observations, self-consistently with the physical parameters. For this purpose I use SCIP observations of an emerging flux region from the 10th of July consisting of the quiet sun and a few pores. The diverse features in the observed FOV allow us to disentangle blends and thus improve the inference of atomic parameters. The uncertainties in the inferred parameters are determined using a Bayesian method. The improvements in atomic parameters permit inference of complex atmospheric models, further revealing finer scales of the solar atmosphere through many-line inversions.