With the availability of the huge amounts of data produced by current and future large multiband photometric surveys, photometric redshifts have become a crucial tool for extragalactic astronomy and cosmology. In this paper we present a novel method, called Weak Gated Experts (WGE), which allows us to derive photometric redshifts through a combination of data mining techniques. The WGE, like many other machine learning techniques, is based on the exploitation of a spectroscopic knowledge base composed by sources for which a spectroscopic value of the redshift is available. This method achieves a variance σ2(Δz) = 2.3 × 10−4 [σ2(Δz) = 0.08, where Δz=zphot−zspec] for the reconstruction of the photometric redshifts for the optical galaxies from the Sloan Digital Sky Survey (SDSS) and for the optical quasars, respectively, while the root mean square (rms) of the Δz variable distributions for the two experiments is, respectively, equal to 0.021 and 0.35. The WGE provides also a mechanism for the estimation of the accuracy of each photometric redshift. We also present and discuss the catalogues obtained for the optical SDSS galaxies, for the optical candidate quasars extracted from the Data Release 7 of SDSS photometric data set (the sample of SDSS sources on which the accuracy of the reconstruction has been assessed is composed of bright sources, for a subset of which spectroscopic redshifts have been measured) and for optical SDSS candidate quasars observed by GALEX in the ultraviolet range. The WGE method exploits the new technological paradigm provided by the virtual observatory and the emerging field of astroinformatics.
The Flexible Image Transport System (FITS) standard has been a great boon to astronomy, allowing observatories, scientists and the public to exchange astronomical information easily. The FITS standard, however, is showing its age. Developed in the late 1970s, the FITS authors made a number of implementation choices that, while common at the time, are now seen to limit its utility with modern data. The authors of the FITS standard could not anticipate the challenges which we are facing today in astronomical computing. Difficulties we now face include, but are not limited to, addressing the need to handle an expanded range of specialized data product types (data models), being more conducive to the networked exchange and storage of data, handling very large datasets, and capturing significantly more complex metadata and data relationships.There are members of the community today who find some or all of these limitations unworkable, and have decided to move ahead with storing data in other formats. If this fragmentation continues, we risk abandoning the advantages of broad interoperability, and ready archivability, that the FITS format provides for astronomy. In this paper we detail some selected important problems which exist within the FITS standard today. These problems may provide insight into deeper underlying issues which reside in the format and we provide a discussion of some lessons learned. It is not our intention here to prescribe specific remedies to these issues; rather, it is to call attention of the FITS and greater astronomical computing communities to these problems in the hope that it will spur action to address them.
In this paper we present the Clustering-Labels-Score Patterns Spotter (CLaSPS), a new methodology for the determination of correlations among astronomical observables in complex datasets, based on the application of distinct unsupervised clustering techniques. The novelty in CLaSPS is the criterion used for the selection of the optimal clusterings, based on a quantitative measure of the degree of correlation between the cluster memberships and the distribution of a set of observables, the labels, not employed for the clustering. CLaSPS has been primarily developed as a tool to tackle the challenging complexity of the multi-wavelength complex and massive astronomical datasets produced by the federation of the data from modern automated astronomical facilities. In this paper we discuss the applications of CLaSPS to two simple astronomical datasets, both composed of extragalactic sources with photometric observations at different wavelengths from large area surveys. The first dataset, CSC+, is composed of optical quasars spectroscopically selected in the SDSS data, observed in the Xrays by Chandra and with multi-wavelength observations in the near-infrared, optical and ultraviolet spectral intervals. One of the results of the application of CLaSPS to the CSC+ is the re-identification of a well-known correlation between the α OX parameter and the near ultraviolet color, in a subset of CSC+ sources with relatively small values of the near-ultraviolet colors. The other dataset consists of a sample of blazars for which photometric observations in the optical, mid and near infrared are available, complemented for a subset of the sources, by Fermi γ-ray data. The main results of the application of CLaSPS to such datasets have been the discovery of a strong correlation between the multi-wavelength color distribution of blazars and their optical spectral classification in BL Lacs and Flat Spectrum Radio Quasars (FSRQs) and a peculiar pattern followed by blazars in the WISE midinfrared colors space. This pattern and its physical interpretation have been discussed in details in other papers by one of the authors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.