PROBABILISTIC CROSS-IDENTIFICATION IN CROWDED FIELDS AS AN ASSIGNMENT PROBLEM

Journal Article · Sat Oct 01 00:00:00 EDT 2016 · Astronomical Journal (Online)

DOI:https://doi.org/10.3847/0004-6256/152/4/86· OSTI ID:22662958

Budavári, Tamás; Basu, Amitabh ^[1]

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1. Dept. of Applied Mathematics and Statistics, Johns Hopkins University, 3400 N. Charles St., MD 21218 (United States)

One of the outstanding challenges of cross-identification is multiplicity: detections in crowded regions of the sky are often linked to more than one candidate associations of similar likelihoods. We map the resulting maximum likelihood partitioning to the fundamental assignment problem of discrete mathematics and efficiently solve the two-way catalog-level matching in the realm of combinatorial optimization using the so-called Hungarian algorithm. We introduce the method, demonstrate its performance in a mock universe where the true associations are known, and discuss the applicability of the new procedure to large surveys.

• OSTI ID: 22662958
• Journal Information: Astronomical Journal (Online), Vol. 152, Issue 4; Other Information: Country of input: International Atomic Energy Agency (IAEA); ISSN 1538-3881
• Country of Publication: United States
• Language: English

Related Subjects

79 ASTROPHYSICS
COSMOLOGY AND ASTRONOMY
ALGORITHMS
ALLOCATIONS
CATALOGS
DETECTION
MAXIMUM-LIKELIHOOD FIT
MULTIPLICITY
OPTIMIZATION
PERFORMANCE
PROBABILISTIC ESTIMATION
SKY
UNIVERSE