Spectral analysis of stellar light curves by means of neural 
networks

Tagliaferri, Roberto; Ciaramella, Angelo; Milano, L.; Barone, F.; Longo, Giuseppe

doi:10.1051/aas:1999254

Cited by 30 publications

(28 citation statements)

References 20 publications

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“…Also, tests carried out by the Authors of this method (Tagliaferri et al 1999) have shown that low-frequency drifts of the baseline flux of sources do not affect the derived periodicities.…”

Section: Autocorrelation Matrix Based Analysismentioning

confidence: 95%

“…In a previous paper (Tagliaferri et al 1999, hereafter T99) some of us introduced the so called STIMA algorithm, based on a particular type of the MUlty SIgnal Classifier (MUSIC) (Oppennheim & Schafer 1965) estimator specifically tailored to work with unevenly sampled data and on a robust nonlinear PCA Neural Network used to extract the principal components of the autocorrelation matrix of the input sources. Without entering into details (which may be found in T99) we now briefly summarize its main features.…”

Section: Autocorrelation Matrix Based Analysismentioning

confidence: 99%

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A multifrequency analysis of radio variability of blazars

Ciaramella

Bongardo

Aller

et al. 2004

A&A

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Abstract.We have carried out a multifrequency analysis of the radio variability of blazars, exploiting the data obtained during the extensive monitoring programs carried out at the University of Michigan Radio Astronomy Observatory (UMRAO, at 4.8, 8, and 14.5 GHz) and at the Metsähovi Radio Observatory (22 and 37 GHz). Two different techniques detect, in the Metsähovi light curves, evidence of periodicity at both frequencies for 5 sources (0224 + 671, 0945 + 408, 1226 + 023, 2200 + 420, and 2251 + 158). For the last three sources, consistent periods are found also at the three UMRAO frequencies and the Scargle (1982) method yields an extremely low false-alarm probability. On the other hand, the 22 and 37 GHz periodicities of 0224+671 and 0945 + 408 (which were less extensively monitored at Metsähovi and for which we get a significant false-alarm probability) are not confirmed by the UMRAO database, where some indications of ill-defined periods of about a factor of two longer are retrieved. We have also investigated the variability index, the structure function, and the distribution of intensity variations of the most extensively monitored sources. We find a statistically significant difference in the distribution of the variability index for BL Lac objects compared to flat-spectrum radio quasars (FSRQs), in the sense that the former objects are more variable. For both populations the variability index steadily increases with increasing frequency. The distribution of intensity variations also broadens with increasing frequency, and approaches a log-normal shape at the highest frequencies. We find that variability enhances by 20-30% the high frequency counts of extragalactic radio-sources at bright flux densities, such as those of the WMAP and P surveys. In all objects with detected periodicity we find evidence for the existence of impulsive signals superimposed on the periodic component.

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Section: Autocorrelation Matrix Based Analysismentioning

confidence: 95%

Section: Autocorrelation Matrix Based Analysismentioning

confidence: 99%

A multifrequency analysis of radio variability of blazars

Ciaramella

Bongardo

Aller

et al. 2004

A&A

Self Cite

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“…[7,10,23], examine the light curves of variable stars (see e.g. [3,6,22]) and make morphological classification of galaxies (see e.g. [2,11,21]).…”

Section: Introductionmentioning

confidence: 99%

Removing cosmic-ray hits from CCD images in real-time mode by means of an artificial neural network

Waniak

2007

Exp Astron

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A feed-forward artificial neural network has been implemented to the problem of removing cosmic-ray hits (CRH) from CCD images. The results of a number of tests demonstrate the effectiveness of this method especially for undersampled stellar profiles. The problem of optimal and low price preparing of training data, which could enable realtime or at least fast post-processing filtering out of CRH is discussed. The training and test ensembles were composed of a number of synthetic stellar profiles involving different S/N ratios and CRH images taken from real data. Certain aspects of the network's architecture and its training efficiency for different modes of the back-propagation procedure as well as for the pre-process normalization of data have been examined. It is shown that for training set composed of stellar images and CRH at a ratio of 1:2 recognition can reach 99% in the case of stars and 96% for CRH. To determine the extent to which the cognition power of a network trained using an ensemble of circular symmetric stellar profiles of a given radius can be generalised the test data included stellar profiles of different radii, as well as elongated profiles. The goal was to mimic temporal changes in seeing as well as such problems as image defocusing, the lack of isoplanatism and improper sideral tracking of a telescope. The experiments provided us with the conclusion that for S/N>10 excellent classification property is maintained in cases where the change in the radius of a circular profile is up to 30%, as well as for elongated profiles where the longest dimension is almost double that of the shortest one. Moreover, the generalization capability has been investigated for test images of synthetic pairs of overlapping stars with different distances between components. Almost 99% recognition efficiency was achieved even if the separation was nearly three times the radius of the stellar profile, a case when two stars could be analyzed by appropriate software as separate objects. The example of removal of CRH from real CCD images is presented to give an idea of how an algorithm based on a neural network can work in practice. The result of such an experiment appears fully consistent with the conclusions drawn from the tests made on synthetic data.

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“…In our approach we extract the features by using an approach based on a non-linear PCA method that permit to extract the eigenvectors directly from the unevenly sampled data. The approach is based on a novel periodicity estimator, STIMA algorithm, described in [5], [17], [18].…”

Section: Paranmeter Intcidence Of Latent Variable Computationmentioning

confidence: 99%

“…The features extraction process can be divided in the following two steps: * Preprocessing: we first calculate and subtract the average pattern to obtain zero mean process; * Neural computing: the fundamental learning parameters are: i) the initial W weight matrix; ii) the number of neurons, which is the number p of principal eigenvectors that we need; iii) y, the nonlinear learning function parameter; iv) the learning rate p; v) early stopping parmeter £ (see [5], [17], [18] for a different early stopping); We then initialize the weight matrix W assigning the classical small random values. Otherwise we can use the first patterns of the signal as the columns of the matrix.…”

Section: B Non-linear Pca Based Feature Extractormentioning

confidence: 99%

Data visualization methodologies for data mining systems in bioinformatics

Staiano

Ciaramella

Raiconi

et al.

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

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Bioinformatics systems benefit from the use of data mining strategies to locate interesting and pertinent relationships within massive information. For example, data mining methods can ascertain and summarize the set of genes responding to a certain level of stress in an organism. Even a cursory glance through the literature in journals, reveals the persistent role of data mining in experimental biology. Integrating data mining within the context of experimental investigations is central to bioinformatics software. In this paper we describe the framework of Probabilistic Principal Surfaces, a latent variable model which offers a large variety of appealing visualization capabilities and which can be successfully integrated in the context of microarray analysis. A preprocessing phase consisting of a nonlinear PCA neural network which seems to be very useful to deal with noisy and time dependent nature of microarray data has been added to this framework.

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Spectral analysis of stellar light curves by means of neural networks

Cited by 30 publications

References 20 publications

A multifrequency analysis of radio variability of blazars

A multifrequency analysis of radio variability of blazars

Removing cosmic-ray hits from CCD images in real-time mode by means of an artificial neural network

Data visualization methodologies for data mining systems in bioinformatics

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