arXiv subjects

Ciro Donalek

Publications and source records attributed to Ciro Donalek.

22 records · Page 2Linked to original sources

arXiv2010

DAME: A Web Oriented Infrastructure for Scientific Data Mining & Exploration

Nowadays, many scientific areas share the same need of being able to deal with massive and distributed datasets and to perform on them complex knowledge extraction tasks. This simple consideration is behind the international efforts to build virtual organizations such as, for instance, the Virtual Observatory (VObs). DAME (DAta Mining & Exploration) is an innovative, general purpose, Web-based, VObs compliant, distributed data mining infrastructure specialized in Massive Data Sets exploration with machine learning methods. Initially fine tuned to deal with astronomical data only, DAME has evolved in a general purpose platform which has found applications also in other domains of human endeavor. We present the products and a short outline of a science case, together with a detailed description of main features available in the beta release of the web application now released.

astro-ph.IM↗

arXiv2009

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.

astro-ph.IM↗

arXiv2009

Highly Variable Objects in the Palomar-QUEST Survey: A Blazar Search using Optical Variability

We identify 3,113 highly variable objects in 7,200 square degrees of the Palomar-QUEST Survey, which each varied by more than 0.4 magnitudes simultaneously in two broadband optical filters on timescales from hours to roughly 3.5 years. The primary goal of the selection is to find blazars by their well-known violent optical variability. Because most known blazars have been found in radio and/or X-ray wavelengths, a sample discovered through optical variability may have very different selection effects, elucidating the range of behavior possible in these systems. A set of blazars selected in this unusual manner will improve our understanding of the physics behind this extremely variable and diverse class of AGN. The object positions, variability statistics, and color information are available using the Palomar-QUEST CasJobs server. The time domain is just beginning to be explored over large sky areas; we do not know exactly what a violently variable sample will hold. About 20% of the sample has been classified in the literature; over 70% of those objects are known or likely AGN. The remainder largely consists of a variety of variable stars, including a number of RR Lyrae and cataclysmic variables.

astro-ph.CO↗

arXiv2002

Neural Networks and Photometric Redshifts

We present a neural network based approach to the determination of photometric redshift. The method was tested on the Sloan Digital Sky Survey Early Data Release (SDSS-EDR) reaching an accuracy comparable and, in some cases, better than SED template fitting techniques. Different neural networks architecture have been tested and the combination of a Multi Layer Perceptron with 1 hidden layer (22 neurons) operated in a Bayesian framework, with a Self Organizing Map used to estimate the accuracy of the results, turned out to be the most effective. In the best experiment, the implemented network reached an accuracy of 0.020 (interquartile error) in the range 0<zphot<0.3, and of 0.022 in the range 0<zphot<0.5.

astro-ph↗