Katharina Lutz is a postdoc at the Centre de Données astronomiques de Strasbourg (CDS). She works on gas and star formation in nearby galaxies, and the dissemination of the Virtual Observatory (VO) and the CDS services. This involves maintaining and developing tutorials, and mentoring at VO Schools. This post was written in collaboration with the CDS Python developers, who are currently working on further MOCpy and astroquery developments, and the CDS dissemination team, who are preparing for the next VO training events.
Welcome back to the last article in our CDS and Python miniseries. A quick recap of what we’ve learned so far: in our previous posts, we introduced the Aladin Lite widget for Jupyter notebooks granting access to billions of image pixels at all wavelengths, we talked about nifty coverage maps, and looked at all of the possibilities for querying the VizieR database. Last but not least, this post will scratch the surface of the seemingly hidden treasures in the SIMBAD database and explore how to start unearthing them.
SIMBAD is a database of more than 11 million astronomical objects ranging from stars to galaxy clusters. For each object, at least one identifier, set of coordinates, and a bibliographic reference are recorded. This is the bare minimum, and more often than not, additional data such as magnitudes, distances/redshifts/proper motions, alternative identifiers, object and morphological types, sizes (for galaxies), and hierarchical relations (e.g., cluster membership) are also available. So, SIMBAD can answer your questions such as is M101 the same galaxy as NGC5457? Which articles contain information about NOVA Cyg 2014? What kind of object is M13 and where is M13 located?
In the Python world, you can access SIMBAD — similarly to VizieR — either with the pyVO TAP module (Figure 1) or the SIMBAD module in astroquery (Figure 2).
We already touched on TAP in our previous post. It is a protocol to run ADQL (a friendly astronomy flavour of SQL) queries on databases. Under the hood, SIMBAD is a large relational database with quite a few tables, each containing information on alternative identifiers, flux measurements, or related journal articles. In the example below, we join the tables on references with the tables on identifiers, to get all papers about M13. This query will even return those papers that use 2MASX J16414163+3627407 instead of M13.
If you prefer astroquery, fear not, their SIMBAD module is also equipped with plenty of query options. This example is taken from one of our tutorials, in which we search for brown dwarf candidates. At the end of this tutorial, we get a set of coordinates and lo and behold, SIMBAD is able to confirm that we rediscovered a brown dwarf:
We hope you enjoyed our introduction to all things CDS and Python. If you would like to see more examples, we recommend our tutorials on GitHub. Tell us how the tools work for you and contact our helpdesk for questions. We also welcome issues on GitHub, and are on Twitter, Facebook, and YouTube.
As a final note before I finish this series, I would like to say a big thank you to M. Allen, M. Baumann, T. Boch, C. Bot, S. Derriere, and A. Nebot for their valuable contributions to this series of articles and to the AstroBetter team for their wonderful support publishing this series.