Difference between revisions of "Astronomy: Bulk Source Association"

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=== SQL ===
 
=== SQL ===
  
An example of the SQL implementation (any DB compliant)
+
==== MonetDB ====
 +
 
 +
Or any other database
  
 
   DECLARE iradius, isint2 DOUBLE;
 
   DECLARE iradius, isint2 DOUBLE;
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   WHERE t1.dist < isint2
 
   WHERE t1.dist < isint2
 
  ;
 
  ;
 +
 +
==== PostgreSQL (GiST) ====
  
 
Table
 
Table
  
 
=== C ===
 
=== C ===

Revision as of 13:15, 12 January 2016

Introduction

In the near future several optical and radio telescopes will produce large field-of-view images at second to minute cadence. One of their common and main goals is to find transient and variable events on these short time scales. As a consequence of the high resolution and large images the number of sources is larger than ever before and may peak up to 300,000 sources per image. Analysis of time series data, called light curves in astronomy, of all the sources is essential to find new, varying and patterns in the source properties. Constructing these light curves in near real time requires fast cross matching of source lists with potential counterparts in known catalogues, having about 500 million to 1 billion sources.

It is this last part, the bulk association, where you need to cross match 300,000 sources with 1 billion sources, that is the most challenging query from a database point of view. We want to keep the processing time as short as possible, because above this we need more queries to run and the next image comes in pretty soon, and then the next and the next... A typical bulk association time should be below the 10% of the overall time to process the image.

Existing Solutions

Solutions exist, both implemented in C and SQL, whereas the latter works for MonetDB using the zone algorithm and for PostgreSQL using GiST indexing. However, the C function, using kdtree indexing is roughly an order of magnitude faster, when we do not take into account the time to build of tree.

SQL

MonetDB

Or any other database

 DECLARE iradius, isint2 DOUBLE;
 SET iradius = CAST(0.5 AS DOUBLE)/3600; /* [degrees] */
 SET isint2 = 4 * SIN(RADIANS(0.5 * iradius)) * SIN(RADIANS(0.5 * iradius));
SELECT runcatid
      ,xtrsrcid
      ,3600*DEGREES(2*ASIN(SQRT(dist)/2)) AS dist_arcsec
  FROM (SELECT z0.id AS runcatid
              ,t0.id AS xtrsrcid
              ,  (z0.x - t0.x) * (z0.x - t0.x)
               + (z0.y - t0.y) * (z0.y - t0.y)
               + (z0.z - t0.z) * (z0.z - t0.z)
               AS dist
          FROM rc_zone z0 
              ,(SELECT id
                      ,dec_deg - iradius AS decmin
                      ,dec_deg + iradius AS decmax
                      ,ra_deg - alpha(dec_deg, iradius) AS ramin
                      ,ra_deg + alpha(dec_deg, iradius) AS ramax
                      ,x
                      ,y
                      ,z
                  FROM xtrsrc_548 
               ) t0
         WHERE z0."dec" BETWEEN t0.decmin AND t0.decmax
           AND z0.ra BETWEEN t0.ramin AND t0.ramax
       ) t1
  WHERE t1.dist < isint2
;

PostgreSQL (GiST)

Table

C