Mission Overview

A First Catalog of Variable Stars Measured by ATLAS ("ATLAS-VAR")

Primary Investigator: Aren N. Heinze

HLSP AuthorsAren N. Heinze

Released: 2018-03-28

Updated: 2018-03-28

Primary Reference(s): Heinze et al. 2018

DOI: 10.17909/T9H98D

Overview

The ATLAS-VAR data release is a catalog of variable stars discovered from ATLAS data. The first data release, presented here, consists of variable stars identified from 1.4 x 108 stars down to a limiting magnitude of r ~ 18 that obtained a minimum of 100 observations during the first two years of ATLAS operations. A total of 4.7 million variable star candidates are detected, using a Lomb-Scargle periodogram and several variability metrics described in Heinze et al. (2018).

Data Products

The data products contain three catalog tables: object, observation, and detection.

The catalog of candidate variable stars has the following name:

hlsp_atlas-var_atlas_ccd_all_cyan-orange_dr1_object.fits

The catalog of all ATLAS images for DR1 has the following name:

hlsp_atlas-var_atlas_ccd_all_cyan-orange_dr1_observation.fits

Data file types:

 _object.fits The catalog of candidate stars _observation.fits The catalog of all images used for DR1

The "detection" table is only available through in the "HLSP_ATLAS_VAR" Context within MAST CasJobs. See Data Access below.

Data Access

The primary way to interact with the ATLAS-VAR catalog tables is through MAST CasJobs. For convenience, we also provide the "object" and "observation" tables as binary FITS files below, but the "detection" table is only available through MAST CasJobs.

MAST CasJobs

A web-based SQL interface to query and cross-match between catalogs. Export tables, saved queries, long query submissions.

Files Size Description

hlsp_atlas-var_atlas_ccd_all_cyan-orange_dr1_object.fits

~74 MB Object Catalog

hlsp_atlas-var_atlas_ccd_all_cyan-orange_dr1_observation.fits

~8.5 GB Image Catalog

CasJobs

The catalog is also available through in the "HLSP_ATLAS_VAR" Context within MAST CasJobs. The "detection" table (all ATLAS photometric measurements of all the candidate variables) is only available via the MAST CasJobs SQL interface.

Tutorial 1: Select 10 Targets Classified As "Pulse" With Period Between 0.3 and 0.9 Days And Return The Cyan Filter Light Curves

WITH ctePLUS (ATO_ID, class, fp_period, objid) as (select top 10 ATO_ID, class, fp_period, objid from object where class='PULSE' and fp_period between 0.3 and 0.9)

select o.ATO_ID, o.class, o.fp_period, d.filter, d.m, d.mjd

from ctePLUS as o inner join detection as d on o.objid=d.objid

where d.filter='c'

order by o.ATO_ID, d.mjd

Line 1: randomly selects 10 targets from the "object" table that have class="PULSE" and period between 0.3 and 0.9 days. The ATO_ID, class, fp_period, and objid columns are selected for these 10 targets and assigned to the "ctePLUS" variable.

Line 2: selects the ATO_ID, class, and fp_period values from the "ctePLUS" variable we created in Line 1, and selects the filter, m, and mjd columns from the "detection" table.

Line 3: assigns the "ctePLUS" variable to "o" and the "detection" table as "d" so that they can be used as short-hand elsewhere, for example, Lines 2, 4, and 5. It also performs a "join" between the "ctePLUS" variable and the "detection" table using the "objid" column from both. The "objid" column is a common key that allows SQL to combine the information in the "ctePLUS" and "detection" tables using this column as a common link between the two.

Line 4: applies a further constraint to only return the "cyan" ATLAS filter data.

Line 5: orders the output table by first the ATO_ID, then the MJD.

Tutorial 2: Do A Cone Search Centered On A Specified RA, Dec Coordinate And Return Light Curves For Both Cyan And Orange Filters

select o.objid, o.ra, o.dec, d.mjd, d.m, d.dm, d.filter

from fGetNearbyObjEq(295.85266, 19.82466, 300./3600.) as nb

inner join object as o on o.objid = nb.ObjID

inner join detection as d on o.objid = d.objid

order by o.objid, d.filter, d.mjd

into myDB.cone_search_results
Line 1: selects the columns we want to output, specifically, the object ID, RA, and Dec columns from the "observation" table and the MJD, mag, and mag uncertainty columns from the "detection" table.

Line 2: executes a cone search centered on a specified RA and Dec coordinate (in degrees). The search radius is the third argument, and must be given in degrees (in our example, the search radius is set to 300 arcseconds). The result of this cone search is given a nickname of "nb" for shorthand use later.

Line 3: performs a "join" combining the columns we want from the "observation" table with the output columns of our cone search using the object ID columns from both as a common key.

Line 4: performs another "join", this time combining the columns we want from the "detection" table with the "observation" table, again using the object ID columns from both as a common key.

Line 5: orders the output to be sorted first by object ID, then by filter, then by timestamp.

Line 6: then stores the result in your output space (MyDB) in a table called "cone_search_results". You can then examine, download, or even use the output table in other queries. You can visit the MyDB tab in the top menu and choose "MyDB" to access any output tables you've made. Make sure the Context drop-down menu in the upper left of the "MyDB" tab page is set to "MyDB" in order to see your output tables.

Citations

Please remember to cite the appropriate paper(s) below and the DOI if you use these data in a published work.

Note: These HLSP data products are licensed for use under CC BY 4.0.