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