Difference between revisions of "Astronomy: Bulk Source Association"

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One of their common and main goals is to find transient and variable events on these short time scales.
 
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.  
 
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.  
Building time series (light curves) and finding changing patterns the sources are cross matched with potential counterparts in known catalogues of about 500 million to 1 billion sources.
+
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, that is the most challenging query from a database point of view.  
+
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 more queries need to run and the next  
+
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 the next and the next...
+
image comes in pretty soon, and then the next and the next...
A typical bulk association time might should be below the 10% of the overall time to process the image.
+
A typical bulk association time should be below the 10% of the overall time to process the image.
  
 
== Existing Solutions ==
 
== Existing Solutions ==

Revision as of 11:25, 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.

SQL

An example the MonetDB SQL implementation

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
                      ,decmin
                      ,decmax
                      ,ramin
                      ,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
;

Table

C