Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation

With the availability of the huge amounts of data produced by current and future large multi-band 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 to derive photometric redshifts through a combination of data mining techniques. \noindent 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 \sigma^2(\Delta z)=2.3x10^{-4} (\sigma^2(\Delta z) =0.08), where \Delta z = z_{phot} - z_{spec}) for the reconstruction of the photometric redshifts for the optical galaxies from the SDSS and for the optical quasars respectively, while the Root Mean Square (RMS) of the \Delta z 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 catalogs obtained for the optical SDSS galaxies, for the optical candidate quasars extracted from the DR7 SDSS photometric dataset {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 UV range. The WGE method exploits the new technological paradigm provided by the Virtual Observatory and the emerging field of Astroinformatics.Comment: 36 pages, 22 figures and 8 table

The Observational Signatures of Cosmic Strings

Cosmic strings were postulated by Kibble in 1976 and, from a theoretical point of view, their existence finds support in modern superstring theories, both in compactification models and in theories with extended additional dimensions. One of the best observational evidences for cosmic strings is the gravitational lensing effects they produce. A first effect is produced by an intervening string along the line of sight which splits in two components (double images) faint background galaxies, thus forming a chain of lensed galaxies along the path of the string. The second optical method is the serendipity discovery through anomalous lensing of extended objects. The huge ratio existing between the string width and length leads to a sort of step function signature on the gravitationally lensed images of background sources. The optical research of cosmic strings signatures suffers from many spurious effects mainly induced by the fact that, in order to be effective, the detection of background galaxies needs to be pushed down to very low flux limits. At these flux levels photometric errors, as well as noise statistics increase the number of spurious detections and, for instance, an application to the Sloan Digital Sky Survey leads to an huge and unrealistic number of candidate pairs. One way to minimize the contamination introduced in the catalogues by the spurious detection, is to increase the contrast by selecting pairs in the 3D space, i.e. by attributing to each galaxy a redshift estimate. At this purpose, a new method for photometric redshifts estimation has been created. The method is based on multiwavelength photometry and on a combination of various data mining techniques developed under the EuroVO and NVO frameworks for data gathering, pre-processing and mining, while relying on the scaling capabilities of the computing grid. This method allowed us to obtain photometric redshifts with an increased accuracy (up to 30%) with respect to the literature. The second fundamental observational evidence for cosmic strings is the signature they are expected to leave in the CMB a signature which may be sought for in the available WMAP data and in the soon to come Planck data. Theory shows that a moving string should produce a step-like discontinuity of low S/N ratio in the CMB, as a consequence of the Doppler shift due to the relative velocity between the string and the observer, thus causing the temperature distribution to deviate from a Gaussian. In the simplifying assumption that the string is a straight discontinuity in space time, we used the S.Co.P.E. computational grid to produce a large number of simulations covering a wide range of values for the velocity of the string, its direction and its distance from the observer. Simulations are produced using a C++ code that generates realistic maps of the CMB temperature distribution in presence of a straight cosmic string. By varying its characteristic parameters, it is possible to explore the signatures left by various types of moving strings. In order to amplify the step-like discontinuity and smooth the noise, maps are then subjected to a “squeezing” procedure. Successively, on the “squeezed” maps, we tested some filters that recognizes high value differences between close pixels. The excellent results of our filter on simulations prompted us to apply it on WMAP 5 years data

Stellar formation rates in galaxies using Machine Learning models

Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot match the needs of modern precision cosmology. We therefore propose a novel method to estimate SFRs for large samples of galaxies using a variety of supervised ML models.Comment: ESANN 2018 - Proceedings, ISBN-13 978287587048

Astrophysical Data Analytics based on Neural Gas Models, using the Classification of Globular Clusters as Playground

In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and reliability, demonstrating the capability to improve the traditional approaches. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning, on the classification of Globular Clusters, extracted from the NGC1399 HST data. Main focus of this work was to use a well-tested playground to scientifically validate such kind of models for further extended experiments in astrophysics and using other standard Machine Learning methods (for instance Random Forest and Multi Layer Perceptron neural network) for a comparison of performances in terms of purity and completeness.Comment: Proceedings of the XIX International Conference "Data Analytics and Management in Data Intensive Domains" (DAMDID/RCDL 2017), Moscow, Russia, October 10-13, 2017, 8 pages, 4 figure

An analysis of feature relevance in the classification of astronomical transients with machine learning methods

The exploitation of present and future synoptic (multi-band and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) and K-Nearest Neighbors, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extra-galactic objects and identification of supernovae.Comment: Accepted by MNRAS, 11 figures, 18 page

AIDA, a Modular Web Application for Astronomical Data Analysis and Instrument Monitoring Services

In the last decade, Astronomy has been the scene of the realization of panchromatic surveys, with sophisticated instruments acquiring a huge quantity of exceptional quality data. This poses the need to integrate advanced data-driven science methodologies for the automatic exploration of huge data archives, and the need for efficient short- and long-term monitoring and diagnostics systems. The goal is to keep the quality of the observations under control and to detect and circumscribe anomalies and malfunctions, facilitating rapid and effective corrections, ensuring correct maintenance of all components and the good health of scientific data over time. In particular, this requirement is crucial for space-borne observation systems, both in logistical and economic terms. AIDA (Advanced Infrastructure for Data Analysis) is a portable and modular web application, designed to provide an efficient and intuitive software infrastructure to support monitoring of data acquiring systems over time, diagnostics and both scientific and engineering data quality analysis, particularly suited for astronomical instruments. Given its modular system prerogative, it is possible to extend its functionalities, by integrating and customizing monitoring and diagnostics systems, as well as scientific data analysis solutions, including machine/deep learning and data mining techniques and methods. A specialized version of AIDA has been recently appointed as focal plane instrument operation diagnostics, analytics and monitoring service within the Science Ground Segment of the Euclid space mission

C3C^{3} : A Command-line Catalogue Cross-matching tool for modern astrophysical survey data

In the current data-driven science era, it is needed that data analysis techniques has to quickly evolve to face with data whose dimensions has increased up to the Petabyte scale. In particular, being modern astrophysics based on multi-wavelength data organized into large catalogues, it is crucial that the astronomical catalog cross-matching methods, strongly dependant from the catalogues size, must ensure efficiency, reliability and scalability. Furthermore, multi-band data are archived and reduced in different ways, so that the resulting catalogues may differ each other in formats, resolution, data structure, etc, thus requiring the highest generality of cross-matching features. We present $C^{3}$ (Command-line Catalogue Cross-match), a multi-platform application designed to efficiently cross-match massive catalogues from modern surveys. Conceived as a stand-alone command-line process or a module within generic data reduction/analysis pipeline, it provides the maximum flexibility, in terms of portability, configuration, coordinates and cross-matching types, ensuring high performance capabilities by using a multi-core parallel processing paradigm and a sky partitioning algorithm.Comment: 6 pages, 4 figures, proceedings of the IAU-325 symposium on Astroinformatics, Cambridge University pres

Railways' stability observed in Campania (Italy) by InSAR data

Campania region is characterized by intense urbanization, active volcanoes, subsidence, and landslides; therefore, the stability of public transportation structures is highly concerned. We have app..

Railways’ Stability Observation by Satellite Radar Images

Remote sensing has many vital civilian applications. Space-borne Interferometric Synthetic Aperture Radar has been used to measure the Earth’s surface deformation widely. In particular, Persistent Scatterer Interferometry (PSI) is designed to estimate the temporal characteristics of the Earth’s deformation rates from multiple InSAR images acquired over time. This chapter reviews the space-borne Differential Interferometric Synthetic Aperture Radar techniques that have shown their capabilities in monitoring of railways displacements. After description of the current state of the art and potentials of the available radar remote sensing techniques, one case study is examined, pertaining to a railway bridge in the Campania region, Italy