Navegando por Assunto "Surveys"

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    On the behavior of the black hole candidate 1E 1740.7-2942’scorona based on long-term INTEGRAL database
    (Wiley) Stecchini, Paulo Eduardo; Leão, Jurandi; Castro, Manuel; D'Amico, Flavio
    One of the most straightforward ways to explain the hard X-ray spectra observed in X-ray binaries is to assume that comptonization of soft photons from the accretion disk is occurring. The region where this process takes place, called the corona, is characterized by only two parameters: its thermal energy kT and its optical depth τ. Hard X-ray spectra analysis is, thus, an imperative tool in diagnosing the behavior of these parameters. The lack of consistency in obtain-ing/analyzing long-term databases, however, may have been hindering this kind of characterization from being attained. With the aim of better understanding the corona behavior in the black hole candidate 1E 1740.7-2942, we performed a homogeneous analysis for a large hard X-ray data set from the ISGRI telescope on-board the INTEGRAL satellite. Results from modeling the spectra show that, for most of our sample, unsaturated thermal comptonization is the main mechanism responsible for the hard X-ray spectra observed in 1E 1740.7-2942.Moreover, such extensive database allowed us to produce what is probably the longest hard X-ray light curve of 1E 1740.7-2942 and whose units—due to recent findings regarding dynamical quantities of the system—could be expressed in %of Eddington’s luminosity.
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    Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?
    (Elsevier) Lima, Erik Vinicius Rodrigues; Sodré Junior, Laerte; Bom, Clécio Roque; Teixeira, Gabriel da Silva Moreira; Nakazono, Lilianne Mariko Izuti; Buzzo, Maria Luisa; Queiroz, Carolina Queiroz de Abreu; Herpich, Fábio R.; Castellon, José Luis Nilo; Dantas, Maria Luiza Linhares; Dors Junior, Oli Luiz; Souza, Rodrigo Clemente Thom de; Akras, Stavros; Jiménez-Teja, Yolanda; Kanaan, Antonio; Ribeiro, Tiago; Schoennell, William
    The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. It achieves single-point photometric redshifts with scatter ( ) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r_auto magnitudes between 16 and