Probability density estimation of photometric redshifts based on machine learning

Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data have proven to be very effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) is a novel method designed to provide a reliable PDF (Probability density Function) of the error distribution of photometric redshifts predicted by ML methods. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. After a short description of the software, we present a summary of results on public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison with a completely different method based on Spectral Energy Distribution (SED) template fitting.Comment: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 784995

METAPHOR: Probability density estimation for machine learning based photometric redshifts

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).Comment: proceedings of the International Astronomical Union, IAU-325 symposium, Cambridge University pres

Dynamic resources of the Sanità spring at Caposele (South Italy)

The Sanità Spring at Caposele (NE of Campania – South Italy) is the main spring of the Sele river. Since the end of '800, it was indicated and was prospected as the possible base of the Acquedotto Pugliese for drinking the dry Apulia zone. The spring was effective tapped in 1911. A research on the source dynamic resources was began to start in 2009 and is terminated today on the base of data collected of Spring discharge during more of 100 y. A great quantities of specific work about the Spring are collected since the last occur in the Cervialto massif (the cross gallery to connect to Aqueduct the Sorbo Serpico springs). The hydrogeological characteristics of the massif was surveyed and a hydrogeological map was completed. The everyday discharge data are studied with numerous mathematic models of the Sanità empting curves. The models, prepared for the tapping work management, gain to divide the spring trend in various operative cycles and in two different kind of the emptying curves, everyone exanimated and modulated. The research produce relevant conclusions to the actual tapping work and their protection areas against pollution. This work is concluded by a complete discussion of research finality and the models utilized for a complete operation by tapping work operator

DAMEWARE - Data Mining & Exploration Web Application Resource

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms.Comment: User Manual of the DAMEWARE Web Application, 51 page

Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies

Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Density Functions (PDFs) is still an open problem. First, because it is difficult to accurately assess the contribution from different sources of errors, namely internal to the method itself and from the photometric features defining the available parameter space. Second, because the problem of defining a robust statistical method, always able to quantify and qualify the PDF estimation validity, is still an open issue. We present a comparison among PDFs obtained using three different methods on the same data set: two ML techniques, METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) and ANNz2, plus the spectral energy distribution template fitting method, BPZ. The photometric data were extracted from the KiDS (Kilo Degree Survey) ESO Data Release 3, while the spectroscopy was obtained from the GAMA (Galaxy and Mass Assembly) Data Release 2. The statistical evaluation of both individual and stacked PDFs was done through quantitative and qualitative estimators, including a dummy PDF, useful to verify whether different statistical estimators can correctly assess PDF quality. We conclude that, in order to quantify the reliability and accuracy of any zphot PDF method, a combined set of statistical estimators is required.Comment: Accepted for publication by MNRAS, 20 pages, 14 figure

Aluminium blunts the proliferative response and increases apoptosis of cultured human cells: putative relationship to alzheimer's disease

Aluminium (Al) has been investigated as a neurotoxic substance. Al ranks among the potential environmental risk factors for Alzheimer's disease (AD). Epidemiological studies tested the relationship between Al in drinking water and AD, showing a significant correlation between elevated levels of monomeric Al in water and AD, although data to date remain inconclusive with respect to total Al. The aim of this study was to test whether or not Al exacerbates cellular toxicity mediated by the amyloid β (Aβ) peptide. We evaluated the role of Al in modulating programmed cell death (apoptosis) in human cell cultures. We used the osteosarcoma cell line monolayer (SaOs-2) to demonstrate that treatment of SaOs-2 cultures with the Aβ peptide mid-fragment (25 to 35) at nano M, followed by co-incubation with physiological concentrations of aluminium chloride, which release monomeric Al in solution, led to marked expression of caspase 3, but not caspase 9, key markers of the apoptotic process. The same experimental conditions were shown to blunt significantly the proliferative response of normal human peripheral blood mononuclear cells (PBMC) to phytohemagglutinin (PHA) stimulation. Our observations support the hypothesis that Al significantly impairs certain cellular immune responses, and confirm that Al-mediated cell toxicity may play an important role in AD

Implementation of Radio-Frequency Deflecting Devices for Comprehensive High-Energy Electron Beam Diagnosis

In next-generation light sources, high-brightness electron beams are used in a free-electron laser configuration to produce light for use by scientists and engineers in numerous fields of research. High-brightness beams are described for such light sources as having low transverse and longitudinal emittances, high peak currents, and low slice emittance and energy spread. The optimal generation and preservation of such high-brightness electron beams during the acceleration process and propagation to and through the photon-producing element is imperative to the quality and performance of the light source. To understand the electron beam's phase space in the accelerating section of a next-generation light source machine, we employed radio-frequency cavities operating in a deflecting mode in conjunction with a magnetic spectrometer and imaging system for both low (250 MeV) and high (1.2 GeV) electron energies. This high-resolution, high-energy system is an essential diagnostic for the optimization and control of the electron beam in the FERMI light source generating fully transversely and longitudinally coherent light in the VUV to soft x-ray wavelength regimes. This device is located at the end of the linear accelerator in order to provide the longitudinal phase space nearest to the entrance of the photon-producing beam-lines. Here, we describe the design, fabrication, characterization, commissioning, and operational implementation of this transverse deflecting cavity structure diagnostic system for the high-energy (1.2 GeV) regime

The third data release of the Kilo-Degree Survey and associated data products

The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope. It aims to image 1500 square degrees in four filters (ugri). The core science driver is mapping the large-scale matter distribution in the Universe, using weak lensing shear and photometric redshift measurements. Further science cases include galaxy evolution, Milky Way structure, detection of high-redshift clusters, and finding rare sources such as strong lenses and quasars. Here we present the third public data release (DR3) and several associated data products, adding further area, homogenized photometric calibration, photometric redshifts and weak lensing shear measurements to the first two releases. A dedicated pipeline embedded in the Astro-WISE information system is used for the production of the main release. Modifications with respect to earlier releases are described in detail. Photometric redshifts have been derived using both Bayesian template fitting, and machine-learning techniques. For the weak lensing measurements, optimized procedures based on the THELI data reduction and lensfit shear measurement packages are used. In DR3 stacked ugri images, weight maps, masks, and source lists for 292 new survey tiles (~300 sq.deg) are made available. The multi-band catalogue, including homogenized photometry and photometric redshifts, covers the combined DR1, DR2 and DR3 footprint of 440 survey tiles (447 sq.deg). Limiting magnitudes are typically 24.3, 25.1, 24.9, 23.8 (5 sigma in a 2 arcsec aperture) in ugri, respectively, and the typical r-band PSF size is less than 0.7 arcsec. The photometric homogenization scheme ensures accurate colors and an absolute calibration stable to ~2% for gri and ~3% in u. Separately released are a weak lensing shear catalogue and photometric redshifts based on two different machine-learning techniques.Comment: small modifications; 27 pages, 12 figures, accepted for publication in Astronomy & Astrophysic