Data Model

VASCA uses a hierarchical data model which wich defines cosmic sources, individual fields and whole regions in the celestial sky.

The VASCA data model.

This model aims to abstract the input data, astronomical photometric detection lists, in in the most general way possible in order to allow integration of data from multiple instruments and parallel processing of the data.

Input Data

Typically, astronomical photometric surveys observe the sky by segmenting it into fields which correspond to the field of view as defined by the instrument’s telescope optics. A field is then defined by a central coordinate and the diameter of the field of view.

VASCA relies as its input on the science data products that missions/organizations create from their observational raw data. Specifically this means VASCA takes tables of photometric detections that have a field and visit ID. A reference of the full list of required columns can be found below VASCA Columns or in the API reference: BaseField, Region.

In the case of GALEX, the detection lists created by the mission pipeline (“mcat” files) have a large number of different observables and parameters (columns) per detection (see GALEX docs here). A subset is used by VASCA, where GALEX specific parameters are added to the ones used by BaseField. This is implemented in the instrument specific TableCollection object GALEXField and its corresponding column definitions.

Data Structures

All data structures in VASCA are based on TableCollection. As the name suggests these objects describe a collection of astropy (inv:astropy:std:doc#*.Table) objects. A general API is provided to add and remove tables to and from these objects. This is used when, for instance, an individual VASCA Source is extracted out of a Region.

VASCA Columns

See the full list of data columns defined by VASCA below:

	name	dtype	unit	default	description
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VASCA Tables

Below is a short description of the most important tables that are used in VASCA:

tt_visits

Table that stores metadata about observational visits such as the visit ID (vis_id), the associated field ID (field_id) and observation start time (time_bin_start) as well as exposure time (time_bin_size).

Note

All parameters related to obersvational timing follow the naming scheme to be compatible with astropy BinnedTimeSeries. This ist then utilized when creating and analyzing light curves for specific sources.

tt_fields

Table storing metadata about all fields such as the field ID (field_id), the center coordinates (ra, dec), total exposure time (time_bin_size_sum) and the size of the field of view (r_fov).

tt_detections

Visit-level detections table. All detections are listed that pass the quality cuts on signal-to-noise (s2n), artifacts (artifacts) and more. Both Region and BaseField hold this table. So all VASCA-generated ID parameters are limited to the respective scope. Especially this also stores the region/field-level cluster ID for all detections, i.e., the ID that associates all detections belonging to one cosmic source (rg_src_id, fd_src_id).

tt_sources

Source information table where all cosmic sources are listed that pass the cuts applied used for variability detection. Important variability parameters (flux_cpval, flux_nxv) and cross-matching results (gaiadr3_match_id, ogrp) are given for each source.

Tip

VASCA supports field-level reference data that might be provided by the instrument’s mission pipelines. This includes field-averaged (co-added) intensity sky maps (load_sky_map) and detection lists. This data is stored in tt_coadd_detections based on which tt_coadd_sources is created during the region-level clustering stage. This information is useful when visualizing pipeline results and for consistency checks on the source clustering.

tt_coadd_detections

Reference detections table. This data is provided at the input stage of the pipeline and contains no visit-to-visit information.

tt_coadd_sources

Reference source information table. This table is created in the region-level clustering stage where spatially overlapping detections in tt_coadd_sources are merged.

tt_src_id_map

Map between region and field source IDs (rg_src_id, fd_src_id).

tt_filters

Instrument filters, their IDs and index.

tt_coverage_hp

Region observations properties in healpix binning. RING ordering and equatorial coordinates

Tip

Additional tables are created during catalog cross-matching (tt_simbad or tt_gaiadr3) and post-processing stages (tt_lombscargle, tt_sed).

VASCA, by design, handles visit-to-visit variability. In post-processing, users can investigate inter-visit variability with the help of gPhoton

tt_sed

Spectral Energy Distribution from VizieR database.

tt_gphoton_lc

Light curve from gPhoton.gAperture

tt_spectrum

Spectrum table

tt_lombscargle

LombScargle results information