AtomPy: An Open Atomic Data Curation Environment for Astrophysical Applications

Abstract

We present a cloud-computing environment, referred to as AtomPy, based on Google-Drive Sheets and Pandas (Python Data Analysis Library) DataFrames to promote community-driven curation of atomic data for astrophysical applications, a stage beyond database development. The atomic model for each ionic species is contained in a multi-sheet workbook, tabulating representative sets of energy levels, A-values and electron impact effective collision strengths from different sources. The relevant issues that AtomPy intends to address are: (i) data quality by allowing open access to both data producers and users; (ii) comparisons of different datasets to facilitate accuracy assessments; (iii) downloading to local data structures (i.e., Pandas DataFrames) for further manipulation and analysis by prospective users; and (iv) data preservation by avoiding the discard of outdated sets. Data processing workflows are implemented by means of IPython Notebooks, and collaborative software developments are encouraged and managed within the GitHub social network. The facilities of AtomPy are illustrated with the critical assessment of the transition probabilities for ions in the hydrogen and helium isoelectronic sequences with atomic number Z ≤ 10.

Atomic Data Assessment with PyNeb

“…We tend to agree with [64] that we have a laboratory astrophysics problem in hand with no foreseeable solution due to the endemic difficulties in computing, measuring, and evaluating the data products. As proposed in [16], open and fluid user-provider interactions and early data curation schemes in the research cycle are perhaps the best we can do.…”

“…This data-curation strategy allows modelers to estimate the uncertainties of the plasma diagnostics and chemical abundances arising from the scatter of the atomic parameters, and also provides a suitable environment for atomic data assessment, the central topic of the present report. This capability of the package is conceptually similar to AtomPy [11,16] but supported by a battery of class methods that facilitate the data revision procedures.…”

The XSTAR Atomic Database

“…As astronomical observations become ever more accurate (see, for example, Figure 1), the reliability of the atomic data used in spectral modeling is under recurrent scrutiny, and hence, data assessment activities are key. In this respect, we have seen three schemes: by a reputed institution such as NIST [92]; by appointed review panels (e.g., VAMDC [93]), and by an open community of both data producers and users (e.g., AtomPy [94]). Our research group comprises both computational atomic physicists and astrophysical modelers, so we favor the latter.…”

A benchmark of the He-like triplet for ions with 6 ≤Z≤ 14 in Maxwellian and non-Maxwellian plasmas

“…On the other hand, the two-photon decay rates of the 2 1 S 0 metastable level are by Drake (1986) who used a relativistic method based on correlated variational wave functions of the Hylleraas type. These transition-probability data sets have been shown to be accurate to better than 3% by Mendoza et al (2014). Therefore, the key benchmark is the accuracy of the effective collision strengths for these ionic species reported by Zhang & Sampson (1987), Delahaye & Pradhan (2002), Chen et al (2006), Aggarwal & Keenan (2008, 2012 and Aggarwal et al (2009Aggarwal et al ( , 2011.…”

“…The R(N e ) and G(T e ) uncertainty intervals have been estimated from the atomic data errors using the formalism developed in Section 3.3. The A-value errors have been globally estimated to be within 3% (Mendoza et al 2014), while a temperaturedependent error margin of the effective collision strength for each transition has been obtained from the ratios of Aggarwal & Keenan (2008) and the present values.…”