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Register a new userProbabilistic record linkage in astronomy: Directional cross-identification and beyond
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Modern astronomy increasingly relies upon systematic surveys, whose dedicated telescopes continuously observe the sky across varied wavelength ranges of the electromagnetic spectrum; some surveys also observe non-electromagnetic messengers, such as high-energy particles or gravitational waves. Stars and galaxies look different through the eyes of different instruments, and their independent measurements have to be carefully combined to provide a complete, sound picture of the multicolor and eventful universe. The association of an objects independent detections is, however, a difficult problem scientifically, computationally, and statistically, raising varied challenges across diverse astronomical applications. The fundamental problem is finding records in survey databases with directions that match to within the direction uncertainties. Such astronomic
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We lay the foundations of a statistical framework for multi-catalogue cross-correlation and cross-identification based on explicit simplified catalogue models. A proper identification process should rely on both astrometric and photometric data. Under some conditions, the astrometric part and the photometric part can be processed separately and merged a posteriori to provide a single global probability of identification. The present paper addresses almost exclusively the astrometrical part and specifies the proper probabilities to be merged with photometric likelihoods. To select matching candidates in n catalogues, we used the Chi (or, indifferently, the Chi-square) test with 2(n-1) degrees of freedom. We thus call this cross-match a chi-match. In order to use Bayes formula, we considered exhaustive sets of hypotheses based on combinatorial analysis. The volume of the Chi-test domain of acceptance -- a 2(n-1)-dimensional acceptance ellipsoid -- is used to estimate the expected numbers of spurious associations. We derived priors for those numbers using a frequentist approach relying on simple geometrical considerations. Likelihoods are based on standard Rayleigh, Chi and Poisson distributions that we normalized over the Chi-test acceptance domain. We validated our theoretical results by generating and cross-matching synthetic catalogues. The results we obtain do not depend on the order used to cross-correlate the catalogues. We applied the formalism described in the present paper to build the multi-wavelength catalogues used for the science cases of the ARCHES (Astronomical Resource Cross-matching for High Energy Studies) project. Our cross-matching engine is publicly available through a multi-purpose web interface. In a longer term, we plan to integrate this tool into the CDS XMatch Service.
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.
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.
Astronomy plays a major role in the scientific landscape of Namibia. Because of its excellent sky conditions, Namibia is home to ground-based observatories like the High Energy Spectroscopic System (H.E.S.S.), in operation since 2002. Located near the Gamsberg mountain, H.E.S.S. performs groundbreaking science by detecting very-high-energy gamma rays from astronomical objects. The fascinating stories behind many of them are featured regularly in the ``Source of the Month, a blog-like format intended for the general public with more than 170 features to date. In addition to other online communication via social media, H.E.S.S. outreach activities have been covered locally, e.g. through `open days and guided tours on the H.E.S.S. site itself. An overview of the H.E.S.S. outreach activities are presented in this contribution, along with discussions relating to the current landscape of astronomy outreach and education in Namibia. There has also been significant activity in the country in recent months, whereby astronomy is being used to further sustainable development via human capacity-building. Finally, as we take into account the future prospects of radio astronomy in the country, momentum for a wider range of astrophysics research is clearly building -- this presents a great opportunity for the astronomy community to come together to capitalise on this movement and support astronomy outreach, with the overarching aim to advance sustainable development in Namibia.
Aspects ([asp{epsilon}], ASsociation PositionnellE/ProbabilistE de CaTalogues de Sources in French) is a Fortran 95 code for the cross-identification of astrophysical sources. Its source files are freely available. Given the coordinates and positional uncertainties of all the sources in two catalogs K and K, Aspects computes the probability that an object in K and one in K are the same or that they have no counterpart. Three exclusive assumptions are considered: (1) Several-to-one associations: a K-source has at most one counterpart in K, but a K-source may have several counterparts in K; (2) One-to-several associations: the same with K and K swapped; (3) One-to-one associations: a K-source has at most one counterpart in K and vice versa. To compute the probabilities of association, Aspects needs the a priori (i.e. ignoring positions) probability that an object has a counterpart. The code obtains estimates of this quantity by maximizing the likelihood to observe all the sources at their effective positions under each assumption. The likelihood may also be used to determine the most appropriate model, given the data, or to estimate the typical positional uncertainty if unknown.
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