Stellar formation rates in galaxies using Machine Learning models

Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot match the needs of modern precision cosmology. We therefore propose a novel method to estimate SFRs for large samples of galaxies using a variety of supervised ML models.Comment: ESANN 2018 - Proceedings, ISBN-13 978287587048

Seeking for the Rational Basis of the Median Model: The Optimal Combination of Multi-model ENSEMBLE Results

In this paper we present an approach for the statistical analysis of multi-model ENSEMBLE results. The models considered here are operational long-range transport and dispersion models, also used for the real-time simulation of pollutant dispersion or the accidental release 5 of radioactive nuclides. We first introduce the theoretical basis (with its roots sinking into the Bayes theorem) and then apply this approach to the analysis of model results obtained during the ETEX-1 exercise. We recover some interesting results, supporting the heuristic approach called ‘median model’, originally introduced in Galmarini et al., 2004 a,b. This approach also provides a way to systematically reduce (and quantify) model uncertainties, thus supporting the decision-making process and/or regulatory-purpose activities in a very effective manner.JRC.H.4-Transport and air qualit

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

C3C^{3} : A Command-line Catalogue Cross-matching tool for modern astrophysical survey data

In the current data-driven science era, it is needed that data analysis techniques has to quickly evolve to face with data whose dimensions has increased up to the Petabyte scale. In particular, being modern astrophysics based on multi-wavelength data organized into large catalogues, it is crucial that the astronomical catalog cross-matching methods, strongly dependant from the catalogues size, must ensure efficiency, reliability and scalability. Furthermore, multi-band data are archived and reduced in different ways, so that the resulting catalogues may differ each other in formats, resolution, data structure, etc, thus requiring the highest generality of cross-matching features. We present $C^{3}$ (Command-line Catalogue Cross-match), a multi-platform application designed to efficiently cross-match massive catalogues from modern surveys. Conceived as a stand-alone command-line process or a module within generic data reduction/analysis pipeline, it provides the maximum flexibility, in terms of portability, configuration, coordinates and cross-matching types, ensuring high performance capabilities by using a multi-core parallel processing paradigm and a sky partitioning algorithm.Comment: 6 pages, 4 figures, proceedings of the IAU-325 symposium on Astroinformatics, Cambridge University pres

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

Machine learning based data mining for Milky Way filamentary structures reconstruction

We present an innovative method called FilExSeC (Filaments Extraction, Selection and Classification), a data mining tool developed to investigate the possibility to refine and optimize the shape reconstruction of filamentary structures detected with a consolidated method based on the flux derivative analysis, through the column-density maps computed from Herschel infrared Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present methodology is based on a feature extraction module followed by a machine learning model (Random Forest) dedicated to select features and to classify the pixels of the input images. From tests on both simulations and real observations the method appears reliable and robust with respect to the variability of shape and distribution of filaments. In the cases of highly defined filament structures, the presented method is able to bridge the gaps among the detected fragments, thus improving their shape reconstruction. From a preliminary "a posteriori" analysis of derived filament physical parameters, the method appears potentially able to add a sufficient contribution to complete and refine the filament reconstruction.Comment: Proceeding of WIRN 2015 Conference, May 20-22, Vietri sul Mare, Salerno, Italy. Published in Smart Innovation, Systems and Technology, Springer, ISSN 2190-3018, 9 pages, 4 figure

Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data

Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities.Comment: Preprint version of the manuscript to appear in the Volume "Intelligent Astrophysics" of the series "Emergence, Complexity and Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature Switzerland, ISSN: 2194-728

Anomaly Detection in Astrophysics: A Comparison Between Unsupervised Deep and Machine Learning on KiDS Data

Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities

Normal Maps vs. Visible Images: Comparing Classifiers and Combining Modalities

This work investigates face recognition based on normal maps, and the performance improvement that can be obtained when exploiting it within a multimodal system, where a further independent module processes visible images. We first propose a technique to align two 3D models of a face by means of normal maps, which is very fast while providing an accuracy comparable to well-known and more general techniques such as Iterative Closest Point (ICP). Moreover, we propose a matching criterion based on a technique which exploits difference maps. It does not reduce the dimension of the feature space, but performs a weighted matching between two normal maps. In the second place, we explore the range of performance soffered by different linear and non linear classifiers, when applied to the normal maps generated from the above aligned models. Such experiments highlight the added value of chromatic information contained in normal maps. We analyse a solid list of classifiers which we reselected due to their historical reference value (e.g. Principal Component Analysis) or to their good performances in the bidimensional setting (Linear Discriminant Analysis, Partitioned Iterated Function Systems). Last but not least, we perform experiments to measure how different ways of combining normal maps and visible images can enhance the results obtained by the single recognition systems, given that specific characteristics of the images are taken into account. For these last experiments we only consider the classifier giving the best average results in the preceding ones, namely the PIFS-based one