User Guide

This guide aims to introduce VASCA’s basic functionality and central building blocks of the package’s internals. Good understanding of the latter will help expanding VASCA to more instruments. For more detailed guides and descriptions see the respective API reference, tutorials and Jupyter examples on post-processing listed here.

Intro

VASCA is based on a very simple data model as you can see illustrated by the image below. Photometric detections from repeated observations (visits) are taken as input. They must be associated to a uniquely defined patch on the sky, a field. These detections are then clustered on the field level to form uniquely defined sources. Multiple fields are combined to form a region. Overlapping fields undergo a second clustering step to assure the uniqueness of sources also on the region level. The final output of VASCA, the variable source catalog, is based on variability detection, cross-matching and classification of the region-level sources.

data_model

The VASCA data model.

On the implementation side, VASCA provides three main data objects that inherit VASCA’s base data structure, the TableCollection:

Source

Unique cosmic source with an ID, sky coordinates, and a (multi-wavelength) light curve from which variability parameters are computed in addition to possible IDs associating the source to known objects from external catalogs.

BaseField

Unique field defined on a sky area which holds all sources including their multi-visit detections. Note that a field is uniquely specific in the instrument and the filter (band-pass) it used to make the observation.

Region

Region on the sky composed of multiple fields where sources are combined from observations in different filters and instruments.

Instrument independence

VASCA’s instrument independence is largely owed to the fact that BaseField is indeed specific to a given instrument and filter. This allows to treat field-level processing in parallel and allows instrument- and filter-specific configuration of the pipeline.

Expanding VASCA to new instruments

The only task required to expand VASCA to operate on data for a new instrument is to create its own field class. This is necessary in order to map observational parameters like flux, spatial coordinates and their uncertainties to the corresponding parameters in VASCA. Fields my include co-added sky maps, or intensity images, as reference images and also visit-level images. The field class must also provide a load() method which handles data I/O together with the ResourceManager.

All objects inheriting from TableCollection can be written to storage as FITS files. These hold images and tables compatible with the FITS version 4.0 standard so that users my use tools like DS9 and TOPCAT for data exploration and debugging. To see what kind of Tables are stored in VASCA’s data structures, see the glossary here.

Using VASCA

More detailed information on using the package is provided on separate pages, listed below.