Query Source Search

Finding sources in the VAST Pilot Survey¶

This notebook gives an example of how to use vast-tools in a notebook environment to perform a search of known sources or coordinates.

Below are the imports required for this example. The main import required from vast-tools is the Query object, with which queries can be initialised and run. Astropy objects are also imported as they are required as input to the query.

Note for large queries it is recommened to use the script version of find_sources.py included with vast-tools.

In this example we will search for three sources of interest:

• 1AXG J134412+0016
• SN 2012dy
• PSR J2129-0429

If you don't enter any coordiantes then SIMBAD will be queried to find a match to the name entered. You cannot mix the SIMBAD search with coordinates - i.e. if you supply coordinates to search to need to supply the correct number of names.

The first steps is to set up our source names and then initialise the query. The input settings are similar to the find_sources.py.

Note: we provide the argument use_tiles=False to use the COMBINED dataset, which was the default for the pilot survey. The default for the full survey is to use the post-processed tiles data - your query settings should only differ from the default with good reason!

To check what will be searched you can look at the query_df attribute. Below we can see the sources we have set up to search.

Note that 1AXG J134412+0016 has been changed to 2SLAQ J134414.15+001642.2 as this is the identifier returned by SIMBAD.

If we didn't use SIMBAD and entered coordinates we would build the query as below.

The settings can be double checked by looking at the settings attribute, which is a dictionary that contains the requested settings.

The query can now be run, there are two main methods available:

1. find_fields()
2. find_sources()

find_fields() will only locate the fields that the contains the queried sources/coordinates. find_sources() will build upon find_fields() and perform the source crossmatching from the selavy catalogues.

While find_sources() can be run directly (it will also run find_fields() for you) for the purposes of this example we'll run them separately.

Running find_fields¶

Call the find_fields() method, it may take a few seconds to perform the search.

If you would like to check what has been found, you can look at the fields_df attribute that will show all the information.

The results of find_fields can be written to a file if you you would like to:

Running find_sources¶

Now we would like to find the actual crossmatches (if any) to the sources/coordiantes. Now we can call find_sources(), which again will take a few seconds to complete:

Looking at the find_sources results¶

The results of find_sources() are stored in the results attribute:

It is a pandas series object, which contains a vast-tools Source object for each result, accessed by using the souce name as the index.

For example lets look at the PSR J2129-04 result. For convenience we'll assign the object to the variable my_source.

Below will show you all the plotting and searching you can do with a source object, which includes:

• Viewing and saving the measurements.
• Plotting a lightcurve.
• Calculate variability metrics.
• Displaying the source cutout from the FITS file per epoch.
• Display all the cutouts together.
• Create an astroquery 'SkyView' plot with ASKAP contours per epoch.
• Search other services such as SIMBAD or CASDA.

You can show individual cutouts using show_png_cutouts and passing the index of the observation of interest.

You can force resizing the cutout data by including force=True and a new size value (which is an Astropy Angle)