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Rule-based Cross-matching of Very Large Catalogs Patrick Ogle and the NED Team IPAC, California Institute of Technology.

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Presentation on theme: "Rule-based Cross-matching of Very Large Catalogs Patrick Ogle and the NED Team IPAC, California Institute of Technology."— Presentation transcript:

1 Rule-based Cross-matching of Very Large Catalogs Patrick Ogle and the NED Team IPAC, California Institute of Technology

2 NASA Extragalactic Database (NED) A fusion of multi-wavelength extragalactic data from journal articles and large catalogs

3 NED Holdings (October 2014) 2MASS PSC And much more, including classifications, notes, images, spectra…

4 New Cross-matching Algorithm Very Large Catalogs (VLCs, >10 7 sources) Find candidate matches in NED Select best match – Rule-based – Statistical analysis Match data recorded in DB Reversible and iterable GALEX ASC (NUV) vs. SDSS DR6 (gri, 6’x6’)

5 Cross-match Inputs VLC Source and NED Object Positions (RA, Dec, ±)  Source-Object Separation (s, ±σ) Source and Object Types (galaxy, galaxy cluster, star, UV source, etc…) Background Object Density (measured for each source) Instrumental Beam Size Other: redshift, photometry, diameters

6 NED Pipeline for Very Large Catalogs Source Loader – Load Very Large Catalog (VLC) source names and positions into NED. CSearch (PostgreSQL) – Find match candidates with NED near position search – Count background objects – Spatial indexing will speed up search (e.g. Q3C, HTM) MatchExpert (python) – Select best match from CSearch match candidates – Object associations for no-matches – Record match statistics for each match – Match statistic distributions and integrals – Code migration to DBMS for speed Object Loader (PostgreSQL) – Create NED cross-IDs – new objects – associations CSearch MatchEx Object Loader Source Loader

7 MatchEx Logic Match List from Csearch SPcut Type Match Name Prefix Match Error Circles Overlap S1/S2 <0.33 NED dup. No Match Single Good Match Match Create NED object and associations NED Cross-ID Thresholds

8 Associations Where a match is not made to a nearby object, an association record may be created. Association types: – Source and object position error circles overlap (  ) – Object is within the beam (PSF) of the source (  ) No Match Error Circles Overlap S

9 Application to GALEX ASC Catalog GALEX ASC (NUV) vs. NED Background region NED object GALEX search region SDSS DR6 (g,r,i) SDSS DR6 (gri, 6’x6’) GALEX All-Sky Catalog of ~40 milllion unique NUV sources created by M. Seibert (2012) Matched against ~180 million NED objects (2013)

10 Poisson Match Probability Search radius: r s = 7.5″ for GALEX Background radius: r b = 46.5″ for GALEX Density of background NED objects: n = N/(πr b 2 ) Expected number inside s: = N(s/r b ) 2, s = separation Poisson probability of x = k objects closer than s: – P s (x=k) = k exp(- )/k! – For k=0, simplifies to: P s (x=0) = exp(- ) = exp(-N(s/r b ) 2 ) False-match probability: P f = 1-P s (0) s rsrs rbrb Example: N = 4, s/r b = 0.08 P s (0) = P f = 0.025

11 Optimizing Match Selection Optimize on 100K subsample in SDSS region False-positive rate decreases with increasing Poisson cutoff. False negative rate increases with Poisson cutoff. Give 10x weight to false positives--it’s worse to make an incorrect match than to miss a match. Poisson cutoff value of 90% minimizes the combined, weighted error rate.

12 39,570,031 input GALEX ASC UV sources NED (2013) contained ~180 million distinct objects 10,595,382 (26.8%) of the ASC sources matched NED objects  Cross-IDs 28,974,649 (73.2%) are not matched  new NED objects – 68.2% of GASC sources are in blank NED fields – 5.0% have multiple match candidates GALEX ASC Match Results: Totals Image credit : GALEX NASA/JPL-Caltech/SSC

13 GALEX ASC Match Results: Background Rejection and False-Negative Rate Uncorrelated background out to 15 arcsec fit by straight line: dN/ds ~ s MatchEx is successful at filtering out this background. False-negative rate f n = 2.4% estimated by comparison to background-subtracted match candidates (red line). Separation (arcsec) false negatives

14 GALEX ASC Results: False Positive Rate The false-positive match rate is estimated by summing the Poisson statistic (1-P) over all matches and dividing by the total number of sources : f p =0.25% Number

15 GALEX ASC Results: Position Error Distribution The distribution of normalized separation r=s/σ deviates from a Gaussian. The peak is at 0.9 instead of 1.0, and the tail is stronger. r=s/σ Derivative of a Gaussian Important Lessons Learned: 1.Do not assume reported catalog position errors are correct. 2.Do not assume position error distributions are Gaussian. 3.A 3.5σ threshold on match separation rejected more candidates than expected. Number

16 Comparison to SDSS Photometry While no color criteria were used to select matches to GALEX sources, the NUV-g colors of GALEX-SDSS matches were checked: Most matches have -721.7

17 Results by Object Type Object Types ordered by candidate match frequency Most GALEX sources matched to galaxies (G) and stars (*) QSO, Galactic star (!*), UV excess object (UvES), and WD* matches overrepresented, as might be expected for a UV-selected catalog. Matches to RadioS, XrayS, GGroup, and GPair candidates were disallowed.

18 GALEX Photometry in NED GALEX ASC photometry added to NED spectral energy distribution of 3C 382 (CGCG ) Over 145 million GALEX ASC NUV and FUV photometry records added to NED (2 extraction methods per band)

19 VLCs in NED, now and future GALEX ASC: ~40,000,000 UV sources loaded and matched (2013) GALEX MSC: ~22,000,000 UV sources loaded and matched (2014) Spitzer Source List: ~42,000,000 MIR sources (2014) 2MASS PSC: ~471,000,000 NIR sources loaded (2015 finish) AllWISE: ~748,000,000 MIR sources (2015 start) SDSS DR10: ~469,000,000 Vis sources (2015 start) SDSS DR6: ~154,000,000 Vis sources loaded and matched (out of 217M), excluding sources with undesirable flag values (2008) NED aims to quadruple its object holdings in the next year!

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