Statistical analysis of the trigger algorithm for the NEMO project

We discuss the performances of a trigger implemented for the planned neutrino telescope NEMO. This trigger seems capable to discriminate between the signal and the strong background introduced by atmospheric muons and by the beta decay of the K-40 nuclei present in the water. The performances of the trigger, as evaluated on simulated data are analyzed in detail.Comment: Published in the Proceedings of the "I Workshop of Astronomy and Astrophysics for Students", Eds. N.R. Napolitano & M. Paolillo, Naples, 19-20 April 2006 (astro-ph/0701577

Implementation of the trigger algorithm for the NEMO project

We describe the implementation of trigger algorithm specifically tailored on the characteristics of the neutrino telescope NEMO. Extensive testing against realistic simulations shows that, by making use of the uncorrelated nature of the noise produced mainly by the decay of K-40 beta-decay, this trigger is capable to discriminate among different types of muonic events.Comment: Published in the Proceedings of the "I Workshop of Astronomy and Astrophysics for Students", Eds. N.R. Napolitano & M. Paolillo, Naples, 19-20 April 2006 (astro-ph/0701577

Data Driven Discovery in Astrophysics

We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" science, with exponentially growing data volumes and data rates, and an ever-increasing complexity, now entering the Petascale regime. Telescopes and observatories from both ground and space, covering a full range of wavelengths, feed the data via processing pipelines into dedicated archives, where they can be accessed for scientific analysis. Most of the large archives are connected through the Virtual Observatory framework, that provides interoperability standards and services, and effectively constitutes a global data grid of astronomy. Making discoveries in this overabundance of data requires applications of novel, machine learning tools. We describe some of the recent examples of such applications.Comment: Keynote talk in the proceedings of ESA-ESRIN Conference: Big Data from Space 2014, Frascati, Italy, November 12-14, 2014, 8 pages, 2 figure

Star Formation Rates for photometric samples of galaxies using machine learning methods

Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised Machine Learning models. We demonstrate that, with such approach, accurate multi-band photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky survey Data Release 7. The catalogue is available through the Vizier facility at the following link ftp://cdsarc.u-strasbg.fr/pub/cats/J/MNRAS/486/1377

The VST telescope control software in the ESO VLT environment

The VST (VLT Survey Telescope) is a 2.6 m Alt-Az telescope to be installed at Mount Paranal in Chile, in the European Southern Observatory (ESO) site. The VST is a wide-field imaging facility planned to supply databases for the ESO Very Large Telescope (VLT) science and carry out stand-alone observations in the UV to I spectral range. This paper will focus mainly on control software aspects, describing the VST software architecture in the context of the whole ESO VLT control concept. The general architecture and the main components of the control software will be described.Comment: 3 pages, 2 figures, ICALEPCS 2001 Conference, PSN#THAP05

Photometric redshifts for Quasars in multi band Surveys

MLPQNA stands for Multi Layer Perceptron with Quasi Newton Algorithm and it is a machine learning method which can be used to cope with regression and classification problems on complex and massive data sets. In this paper we give the formal description of the method and present the results of its application to the evaluation of photometric redshifts for quasars. The data set used for the experiment was obtained by merging four different surveys (SDSS, GALEX, UKIDSS and WISE), thus covering a wide range of wavelengths from the UV to the mid-infrared. The method is able i) to achieve a very high accuracy; ii) to drastically reduce the number of outliers and catastrophic objects; iii) to discriminate among parameters (or features) on the basis of their significance, so that the number of features used for training and analysis can be optimized in order to reduce both the computational demands and the effects of degeneracy. The best experiment, which makes use of a selected combination of parameters drawn from the four surveys, leads, in terms of DeltaZnorm (i.e. (zspec-zphot)/(1+zspec)), to an average of DeltaZnorm = 0.004, a standard deviation sigma = 0.069 and a Median Absolute Deviation MAD = 0.02 over the whole redshift range (i.e. zspec <= 3.6), defined by the 4-survey cross-matched spectroscopic sample. The fraction of catastrophic outliers, i.e. of objects with photo-z deviating more than 2sigma from the spectroscopic value is < 3%, leading to a sigma = 0.035 after their removal, over the same redshift range. The method is made available to the community through the DAMEWARE web application.Comment: 38 pages, Submitted to ApJ in February 2013; Accepted by ApJ in May 201

Integration of the VIMOS control system

The VIRMOS consortium of French and Italian Institutes (PI: O. Le Fevre, co-PI: G. Vettolani) is manufacturing two wide field imaging multi-object spectrographs for the European Southern Observatory Very Large Telescope (VLT), with emphasis on the ability to carry over spectroscopic surveys of large numbers of sources: the VIsible Multi-Object Spectrograph, VIMOS, and the Near InfraRed Multi-Object Spectrograph, NIRMOS. There are 52 motors to be controlled in parallel in the spectrograph, making VIMOS a complex machine to be handled. This paper will focus on the description of the control system, designed in the ESO VLT standard control concepts, and on some integration issues and problem solving strategies.Comment: 3 pages, 3 figures, ICALEPCS 2001 Conference, PSN#TUBT00

Astrophysics in S.Co.P.E

S.Co.P.E. is one of the four projects funded by the Italian Government in order to provide Southern Italy with a distributed computing infrastructure for fundamental science. Beside being aimed at building the infrastructure, S.Co.P.E. is also actively pursuing research in several areas among which astrophysics and observational cosmology. We shortly summarize the most significant results obtained in the first two years of the project and related to the development of middleware and Data Mining tools for the Virtual Observatory

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