1,571 research outputs found
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
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