GAIA Based Astrometry

Querying the GAIA Catalogue

The first task is to allow creating lists of sources in a rectangular or circular area of the sky down to some limiting magnitude, optionally imposing cuts based on other factors, e.g. color quality flags etc. The minimum information required is:

• GAIA Source Identifier

• Sky position (RA, Dec), optionally corrected for proper motion

• Some indicator of the brightness of the source in a relevant filter

• Any relevant quality flags for the reliability of the position or brightness.

It is worth considering if more efficient queries can be accomplished by re-packaging the GAIA data release files into another format, e.g. one splitting the data based on HEALPix of a level coarser than 12, sorting by flux etc. Some options include:

Format	Advantages	Disadvantages/Concerns
mysql	Can add indices by sub-HEALPix, brightness, color etc. Can partition table by super- HEALPix.	Could result in big data volume.
sqlite	Can add indices by sub-HEALPix, brightness, color etc. Each batch of data can be in a separate file	Could result in big data volume.
FITS	Very standard Each batch of data can be in a separate file	More work to perform queries. Possibly slower queries (no indices).
HDF5	Good compression options. Each batch of data can be in a separate file	More work to perform queries. Possibly slower queries. (no indices).

Queries will be accomplished in three or four steps depnending on how the data is organized:

1. Using the healpy module get a list of HEALPix indices covering the query area.

2. If not using a database, get a list of the GAIA data release files containing the identified indices.

3. Extract all sources from the selected pixels and apply the specified magnitude, color etc. cuts.

4. Apply proper motion corrections to the desired epoch.

Source Extraction

There are four possible source extractor tools:

• fistar (from HATpipe)

• hatphot (from HATpipe)

• sextractor

• simplexy (from astrometry.net)

In practice simplexy seems to misbehave if sources are over-sampled, requiring binning, which necessarily degrades the precision of the extracted source positions, os it is probably not competitive with the other options. All other options will eventually be supported by the pipeline. The result of source extraction will always consist of (x, y) image positons, an estiamte of the source brightness and some indicators for the properties of the source (e.g. extendend vs point source, signal to noise, etc.

Two Step Plate Solving

Step 1: Astrometry.net

Astrometry.net provides unparalleled robustness in finding the location on sky of a collection of sources. To take advantage of that we will first use a locally compiled copy of solve-field along with the standard indices shipped by astrometry.net to find an approximate astrometric solution for the extracted sources from the Source Extraction step, producing a WCS file.

Step 2: Refinement based on GAIA

The approximate solution derived above is refined as follows:

1. The sky to frame transformaion from the WCS file above will be used to project the list of catalogue sources obtained by Querying the GAIA Catalogue, using the astropy.wcs module <http://docs.astropy.org/en/stable/wcs/index.html>

2. Build a cKDtree from the projected sources, and query it for the neighbours within some tolerance to each extracted source.

3. Resolve multiple candidate matches are using brightness informatino or discard matches if no clear determination can be made.

4. Derive a polynomial transformation between tan-projected (RA, Dec) positions of the matched sources and the corresponding extracted image (x, y) positions. This will be done iteratively, re-deriving the center used for tan projection by inverting the last solution for the central image position, until the center changes no more than some tolerance. Usually just a single re-fit is necessary.