Probabilistic Cross-Identification of Cosmic Events

We discuss a novel approach to identifying cosmic events in separate and independent observations. In our focus are the true events, such as supernova explosions, that happen once, hence, whose measurements are not repeatable. Their classification and analysis have to make the best use of all the available data. Bayesian hypothesis testing is used to associate streams of events in space and time. Probabilities are assigned to the matches by studying their rates of occurrence. A case study of Type Ia supernovae illustrates how to use lightcurves in the cross-identification process. Constraints from realistic lightcurves happen to be well-approximated by Gaussians in time, which makes the matching process very efficient. Model-dependent associations are computationally more demanding but can further boost our confidence.Comment: 5 pages, 2 figures, accepted to Ap

Catalog Matching with Astrometric Correction and its Application to the Hubble Legacy Archive

Object cross-identification in multiple observations is often complicated by the uncertainties in their astrometric calibration. Due to the lack of standard reference objects, an image with a small field of view can have significantly larger errors in its absolute positioning than the relative precision of the detected sources within. We present a new general solution for the relative astrometry that quickly refines the World Coordinate System of overlapping fields. The efficiency is obtained through the use of infinitesimal 3-D rotations on the celestial sphere, which do not involve trigonometric functions. They also enable an analytic solution to an important step in making the astrometric corrections. In cases with many overlapping images, the correct identification of detections that match together across different images is difficult to determine. We describe a new greedy Bayesian approach for selecting the best object matches across a large number of overlapping images. The methods are developed and demonstrated on the Hubble Legacy Archive, one of the most challenging data sets today. We describe a novel catalog compiled from many Hubble Space Telescope observations, where the detections are combined into a searchable collection of matches that link the individual detections. The matches provide descriptions of astronomical objects involving multiple wavelengths and epochs. High relative positional accuracy of objects is achieved across the Hubble images, often sub-pixel precision in the order of just a few milli-arcseconds. The result is a reliable set of high-quality associations that are publicly available online.Comment: 9 pages, 9 figures, accepted for publication in the Astrophysical Journa

A Unified Framework for Photometric Redshifts

We present a rigorous mathematical solution to photometric redshift estimation and the more general inversion problem. The challenge we address is to meaningfully constrain unknown properties of astronomical sources based on given observables, usually multicolor photometry, with the help of a training set that provides an empirical relation between the measurements and the desired quantities. We establish a formalism that blurs the boundary between the traditional empirical and template-fitting algorithms, as both are just special cases that are discussed in detail to put them in context. The new approach enables the development of more sophisticated methods that go beyond the classic techniques to combine their advantages. We look at the directions for further improvement in the methodology, and examine the technical aspects of practical implementations. We show how training sets are to be constructed and used consistently for reliable estimation.Comment: 9 pages, 2 figures, accepted to the Ap

Cross-Identification Performance from Simulated Detections: GALEX and SDSS

We investigate the quality of associations of astronomical sources from multi-wavelength observations using simulated detections that are realistic in terms of their astrometric accuracy, small-scale clustering properties and selection functions. We present a general method to build such mock catalogs for studying associations, and compare the statistics of cross-identifications based on angular separation and Bayesian probability criteria. In particular, we focus on the highly relevant problem of cross-correlating the ultraviolet Galaxy Evolution Explorer (GALEX) and optical Sloan Digital Sky Survey (SDSS) surveys. Using refined simulations of the relevant catalogs, we find that the probability thresholds yield lower contamination of false associations, and are more efficient than angular separation. Our study presents a set of recommended criteria to construct reliable cross-match catalogs between SDSS and GALEX with minimal artifacts.Comment: 7 pages, 9 figures; ApJ in pres

More than just halo mass: Modelling how the red galaxy fraction depends on multiscale density in a HOD framework

The fraction of galaxies with red colours depends sensitively on environment, and on the way in which environment is measured. To distinguish competing theories for the quenching of star formation, a robust and complete description of environment is required, to be applied to a large sample of galaxies. The environment of galaxies can be described using the density field of neighbours on multiple scales - the multiscale density field. We are using the Millennium simulation and a simple HOD prescription which describes the multiscale density field of Sloan Digital Sky Survey DR7 galaxies to investigate the dependence of the fraction of red galaxies on the environment. Using a volume limited sample where we have sufficient galaxies in narrow density bins, we have more dynamic range in halo mass and density for satellite galaxies than for central galaxies. Therefore we model the red fraction of central galaxies as a constant while we use a functional form to describe the red fraction of satellites as a function of halo mass which allows us to distinguish a sharp from a gradual transition. While it is clear that the data can only be explained by a gradual transition, an analysis of the multiscale density field on different scales suggests that colour segregation within the haloes is needed to explain the results. We also rule out a sharp transition for central galaxies, within the halo mass range sampled.Comment: 24 pages, 21 figures, accepted for publication by MNRA

Cross-Identification of Stars with Unknown Proper Motions

The cross-identification of sources in separate catalogs is one of the most basic tasks in observational astronomy. It is, however, surprisingly difficult and generally ill-defined. Recently Budav\'ari & Szalay (2008) formulated the problem in the realm of probability theory, and laid down the statistical foundations of an extensible methodology. In this paper, we apply their Bayesian approach to stars that, we know, can move measurably on the sky, with detectable proper motion, and show how to associate their observations. We study models on a sample of stars in the Sloan Digital Sky Survey, which allow for an unknown proper motion per object, and demonstrate the improvements over the analytic static model. Our models and conclusions are directly applicable to upcoming surveys such as PanSTARRS, the Dark Energy Survey, Sky Mapper, and the LSST, whose data sets will contain hundreds of millions of stars observed multiple times over several years.Comment: 10 pages, 5 figure