Welcome to the CDS Tutorial Repository. Here we collect all Jupyter notebook tutorials on how to interact with Virtual Observatory compliant archives and CDS archives in particular. To have an interactive look at these tutorials without the need to install anything have a look at our Binder:

All tutorials in this repository are based on EURO-VO tutorials usually of the same or similar name. The aim of these tutorials is to enable astronomers to search, find and access the data they need from archives across the world and at the CDS. We focus in particular on archives that are compliant with Virtual Observatory (VO) protocols and standards as defined by the International Virtual Observatory Alliance (IVOA)

You can download the notebooks to your personal machine and use them there. Note that you might have to install a few packages to do so. In the requirements.txt file you will find a list of all packages that we use in our tutorials.

We are particularly happy to present examples for the usage of the ipyaladin and MOCPy packages, which were developed at the CDS. Furthermore, we will make extensive use of the PyVO and astroquery packages.

For the CDS tutorial two versions exist: a short (The_CDS_tutorial.ipynb) and an extended one (The_CDS_tutorial_extended.ipynb). In the short version we use ipyaladin to look at the Antennae galaxy pair. Then we demonstrate how to access catalogues and tables in VizieR using both astroquery and PyVO. Here we are searching for a table of Arp's peculiar galaxies. We close by visualising the downloaded table in the AladinLite widget create with ipyaladin.

In the extended version we start off as in the short version. After visualising the table of Arp's peculiar galaxies in the AladinLite widget, we move on to investigate which galaxies in the table are within the footprint of both the SDSS and the GALEX survey. For this step we download the SDSS and GALEX MOCs from the CDS MOC server with astroquery and use implicitly MOCPy to handle the MOCs.

This tutorial focuses on the Coma Cluster of Galaxies. In particular, we search for redshift information of galaxies in the spatial vicinity of the cluster centre. To get this information we get catalogues with measured redshifts and a spectrum, from which we measure a redshift.

In this tutorial we obtain catalogue data from SDSS and 2MASS in a given sky region. Then we cross-match the results of these searches and filter the resulting table for brown dwarfs. In the end we verify our sample of brown dwarfs by getting more information about our candidates from Simbad.

For this tutorial we focus on gamma-ray and high energy spectral bands and start by querying suitable catalogues using VO tools. After that we cross-correlate catalogues to find entries for objects in different photon energy bands. We then apply selection criteria to extract sources from a catalogue and use the resulting observational measures of the selected objects to explore possible correlations. We close by displaying spectral energy distributions obtained from different photometric data sets.

This tutorial is centred on using HiPS and MOC to efficiently reach your science goals. In particular it will help you answer questions such as: What is the footprint of my imaging survey? Where on the sky do we have low foreground extinction? Where do these two regions overlap? How can I get interesting catalogue entries within these regions? We build our own MOCs from a set of images and for an all-sky survey, where pixel values are within a certain range. We then move on to query catalogues within an intersection of these MOCs.

• MOC: MultiOrder Coverage map - efficient way of describing arbitrary patches of the sky, logical operations such as union, intersection of MOC can be calculated, tables can be filtered by MOCs, more information here
• HiPS: Hierarchical Progressive Surveys - efficient tiling systems to serve (all-sky) imaging surveys, cubes or catalogues, the user only downloads and sees what they are interested in and the more they zoom the more they see, more information here