1,826 research outputs found
Hybrid Neural Networks for Frequency Estimation of Unevenly Sampled Data
In this paper we present a hybrid system composed by a neural network based
estimator system and genetic algorithms. It uses an unsupervised Hebbian
nonlinear neural algorithm to extract the principal components which, in turn,
are used by the MUSIC frequency estimator algorithm to extract the frequencies.
We generalize this method to avoid an interpolation preprocessing step and to
improve the performance by using a new stop criterion to avoid overfitting.
Furthermore, genetic algorithms are used to optimize the neural net weight
initialization. The experimental results are obtained comparing our methodology
with the others known in literature on a Cepheid star light curve.Comment: 5 pages, to appear in the proceedings of IJCNN 99, IEEE Press, 199
Neural Nets and Star/Galaxy Separation in Wide Field Astronomical Images
One of the most relevant problems in the extraction of scientifically useful
information from wide field astronomical images (both photographic plates and
CCD frames) is the recognition of the objects against a noisy background and
their classification in unresolved (star-like) and resolved (galaxies) sources.
In this paper we present a neural network based method capable to perform both
tasks and discuss in detail the performance of object detection in a
representative celestial field. The performance of our method is compared to
that of other methodologies often used within the astronomical community.Comment: 6 pages, to appear in the proceedings of IJCNN 99, IEEE Press, 199
On the detection of very high redshift Gamma Ray Bursts with Swift
We compute the probability to detect long Gamma Ray Bursts (GRBs) at z>5 with
Swift, assuming that GRBs form preferentially in low-metallicity environments.
The model fits well both the observed BATSE and Swift GRB differential peak
flux distribution and is consistent with the number of z>2.5 detections in the
2-year Swift data. We find that the probability to observe a burst at z>5
becomes larger than 10% for photon fluxes P<1 ph s^{-1} cm^{-2}, consistent
with the number of confirmed detections. The corresponding fraction of z>5
bursts in the Swift catalog is ~10%-30% depending on the adopted metallicity
threshold for GRB formation. We propose to use the computed probability as a
tool to identify high redshift GRBs. By jointly considering promptly-available
information provided by Swift and model results, we can select reliable z>5
candidates in a few hours from the BAT detection. We test the procedure against
last year Swift data: only three bursts match all our requirements, two being
confirmed at z>5. Other three possible candidates are picked up by slightly
relaxing the adopted criteria. No low-z interloper is found among the six
candidates.Comment: 5 pages, 2 figures, MNRAS in pres
Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification
[Abriged] Astronomical Wide Field Imaging performed with new large format CCD
detectors poses data reduction problems of unprecedented scale which are
difficult to deal with traditional interactive tools. We present here NExt
(Neural Extractor): a new Neural Network (NN) based package capable to detect
objects and to perform both deblending and star/galaxy classification in an
automatic way. Traditionally, in astronomical images, objects are first
discriminated from the noisy background by searching for sets of connected
pixels having brightnesses above a given threshold and then they are classified
as stars or as galaxies through diagnostic diagrams having variables choosen
accordingly to the astronomer's taste and experience. In the extraction step,
assuming that images are well sampled, NExt requires only the simplest a priori
definition of "what an object is" (id est, it keeps all structures composed by
more than one pixels) and performs the detection via an unsupervised NN
approaching detection as a clustering problem which has been thoroughly studied
in the artificial intelligence literature. In order to obtain an objective and
reliable classification, instead of using an arbitrarily defined set of
features, we use a NN to select the most significant features among the large
number of measured ones, and then we use their selected features to perform the
classification task. In order to optimise the performances of the system we
implemented and tested several different models of NN. The comparison of the
NExt performances with those of the best detection and classification package
known to the authors (SExtractor) shows that NExt is at least as effective as
the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at
http://www.na.astro.it/~andreon/listapub.htm
The BMW (Brera-Multiscale-Wavelet) Catalogue of Serendipitous X-ray Sources
In collaboration with the Observatories of Palermo and Rome and the SAX-SDC
we are constructing a multi-site interactive archive system featuring specific
analysis tools. In this context we developed a detection algorithm based on the
Wavelet Transform (WT) and performed a systematic analysis of all ROSAT-HRI
public data (~3100 observations +1000 to come). The WT is specifically suitable
to detect and characterize extended sources while properly detecting point
sources in very crowded fields. Moreover, the good angular resolution of HRI
images allows the source extension and position to be accurately determined.
This effort has produced the BMW (Brera Multiscale Wavelet) catalogue, with
more than 19,000 sources detected at the 4.2 sigma level. For each source
detection we have information on the X-ray flux and extension, allowing for
instance to select complete samples of extended X-ray sources such as candidate
clusters of galaxies or SNR's. Here we present an overview of first results
from several undergoing projects which make use of the BMW catalogue.Comment: 7 pages, 6 postscript files, 2 gif images, to appear in the
proceedings of the conference "Mining the Sky", August 2000, Garching,
German
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