Astrophysical Data Analytics based on Neural Gas Models, using the Classification of Globular Clusters as Playground

In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and reliability, demonstrating the capability to improve the traditional approaches. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning, on the classification of Globular Clusters, extracted from the NGC1399 HST data. Main focus of this work was to use a well-tested playground to scientifically validate such kind of models for further extended experiments in astrophysics and using other standard Machine Learning methods (for instance Random Forest and Multi Layer Perceptron neural network) for a comparison of performances in terms of purity and completeness.Comment: Proceedings of the XIX International Conference "Data Analytics and Management in Data Intensive Domains" (DAMDID/RCDL 2017), Moscow, Russia, October 10-13, 2017, 8 pages, 4 figure

Humanoid odometric localization integrating kinematic, inertial and visual information

We present a method for odometric localization of humanoid robots using standard sensing equipment, i.e., a monocular camera, an inertial measurement unit (IMU), joint encoders and foot pressure sensors. Data from all these sources are integrated using the prediction-correction paradigm of the Extended Kalman Filter. Position and orientation of the torso, defined as the representative body of the robot, are predicted through kinematic computations based on joint encoder readings; an asynchronous mechanism triggered by the pressure sensors is used to update the placement of the support foot. The correction step of the filter uses as measurements the torso orientation, provided by the IMU, and the head pose, reconstructed by a VSLAM algorithm. The proposed method is validated on the humanoid NAO through two sets of experiments: open-loop motions aimed at assessing the accuracy of localization with respect to a ground truth, and closed-loop motions where the humanoid pose estimates are used in real-time as feedback signals for trajectory control

A New Calibration Procedure for 3-D Shape Measurement System Based on Phase-Shifting Projected Fringe Profilometry

An original procedure is presented for the calibration of fringe-projection-based 3-D vision systems. The proposed approach estimates both the phase-to-depth and transverse relationships by directly measuring the phase maps for only three planes placed within the calibration volume and then estimating the phase maps for a number of other ldquovirtual planes.rdquo Experimental tests conducted on a fringe projection system show the effectiveness of the proposed procedure

Metrological Characterization of a Vision-Based Measurement System for the Online Inspection of Automotive Rubber Profile

This paper deals with the metrological characterization of a stereovision-based measurement system for the inspection of automotive rubber profiles in an industrial plant. The characterization of this class of measurement systems introduces new challenges due to both the unavailability of reference measurement instruments and the complexity of the measurement system itself, which does not allow a straightforward application of the standard procedures for uncertainty evaluation. To assign optimum values to a number of design parameters, the followed approach focuses not only on evaluating the total uncertainty but also on analyzing systematic effects and influence quantities

Photometric classification of emission line galaxies with machine-learning methods

In this paper, we discuss an application of machine-learning-based methods to the identification of candidate active galactic nucleus (AGN) from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine-learning algorithms, namely the Multi Layer Perceptron, trained, respectively, with the Conjugate Gradient, the Scaled Conjugate Gradient, the Quasi Newton learning rules and the Support Vector Machines, to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs versus non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features, we discuss also the behaviour of the classifiers on finer AGN classification tasks, namely Seyfert I versus Seyfert II, and Seyfert versus LINER. Furthermore, we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self-adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations

Neural Gas based classification of Globular Clusters

Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multi-dimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts.Comment: 15 pages, 3 figures, to appear in the Volume of Springer Communications in Computer and Information Science (CCIS). arXiv admin note: substantial text overlap with arXiv:1710.0390

The Luminosity Function of Low Mass X-Ray Binaries in the Globular Cluster System of NGC 1399

We present a study of the faint-end of the X-ray Luminosity Function of Low Mass X-ray binaries in the Globular Cluster system of the cD galaxy NGC 1399 by performing a stacking experiment on 618 X-ray undetected GCs, in order to verify the presence of faint LMXBs and to constrain the faint-end slope of the GC-LMXBs XLF below the individual detection threshold of $8\times10^{37}$ erg s$^{-1}$ in the $0.5-8$ keV band. We obtain a significant X-ray detection for the whole GC sample, as well as for the red and blue GC subpopulations, corresponding to an average luminosity per GC $_{GC}$ of $(3.6\pm1.0)\times10^{36}\ erg\ s^{-1}$, $(6.9\pm2.1)\times10^{36}\ erg\ s^{-1}$and$(1.7\pm0.9)\times10^{36}\ erg\ s^{-1}$, respectively for all, red and blue GCs. If LMXBs in red and blue GCs have the same average intrinsic luminosity, we derive a red/blu ratio$\simeq 3$of GCs hosting LMXBs ($2.5\pm1.0$or$4.1\pm2.5$depending on the surveyed region); alternatively, assuming the fractions observed for brighter sources, we measure an average X-ray luminosity of$L_{X}=(4.3\pm1.3)\times10^{37}\ erg\ s^{-1}$and$L_{X}=(3.4\pm1.7)\times10^{37}\ erg\ s^{-1}$per red and blue GC-LMXBs respectively. In the assumption that the XLF follows a power-law distribution, we find that a low-luminosity break is required at$L_{X}\leq 8\times10^{37}$erg s$^{-1}$both in the whole, as well as in the color-selected (red and blue) subsamples. Given the bright-end slopes measured above the X-ray completeness limit, this result is significant at$>3\sigma$level. Our best estimates for the faint end slope are$\beta_{L}=-1.39/-1.38/-1.36$for all/red/blue GC-LMXBs. We also find evidence that the luminosity function becomes steeper at luminosities$L_X\gtrsim 3\times 10^{39}$erg s$^{-1}$, as observed in old ellipticals.Comment: In press on A&

The detection of globular clusters in galaxies as a data mining problem

We present an application of self-adaptive supervised learning classifiers derived from the Machine Learning paradigm, to the identification of candidate Globular Clusters in deep, wide-field, single band HST images. Several methods provided by the DAME (Data Mining & Exploration) web application, were tested and compared on the NGC1399 HST data described in Paolillo 2011. The best results were obtained using a Multi Layer Perceptron with Quasi Newton learning rule which achieved a classification accuracy of 98.3%, with a completeness of 97.8% and 1.6% of contamination. An extensive set of experiments revealed that the use of accurate structural parameters (effective radius, central surface brightness) does improve the final result, but only by 5%. It is also shown that the method is capable to retrieve also extreme sources (for instance, very extended objects) which are missed by more traditional approaches.Comment: Accepted 2011 December 12; Received 2011 November 28; in original form 2011 October 1