Cross-Matching Multiple Spatial Observations and Dealing with Missing Data

TL;DR

This paper presents a novel method for cross-matching astronomical observations across multiple surveys, effectively handling missing data by classifying observation misses and constructing comprehensive spatial libraries and match tables.

Contribution

It introduces an advanced framework that classifies different types of observation misses and uses spatial libraries to improve cross-matching accuracy in astronomical data.

Findings

Effective classification of misses improves matching accuracy

Construction of match and bundle tables enhances data organization

Evolution of SDSS cross-match design for better handling of missing data

Abstract

Cross-match spatially clusters and organizes several astronomical point-source measurements from one or more surveys. Ideally, each object would be found in each survey. Unfortunately, the observation conditions and the objects themselves change continually. Even some stationary objects are missing in some observations; sometimes objects have a variable light flux and sometimes the seeing is worse. In most cases we are faced with a substantial number of differences in object detections between surveys and between observations taken at different times within the same survey or instrument. Dealing with such missing observations is a difficult problem. The first step is to classify misses as ephemeral - when the object moved or simply disappeared, masked - when noise hid or corrupted the object observation, or edge - when the object was near the edge of the observational field. This classification and a spatial library to represent and manipulate observational footprints help construct a Match table recording both hits and misses. Transitive closure clusters friends-of-friends into object bundles. The bundle summary statistics are recorded in a Bundle table. This design is an evolution of the Sloan Digital Sky Survey cross-match design that compared overlapping observations taken at different times. Cross-Matching Multiple Spatial Observations and Dealing with Missing Data.