Riccardo Campisano
A5076114174- Astronomy and Astrophysical Research
- Gamma-ray bursts and supernovae
- Data Management and Algorithms
- Data Mining Algorithms and Applications
- Galaxies: Formation, Evolution, Phenomena
- Time Series Analysis and Forecasting
- Big Data and Business Intelligence
- Stellar, planetary, and galactic studies
- Blind Source Separation Techniques
- Machine Learning and Algorithms
- Calibration and Measurement Techniques
- Dark Matter and Cosmic Phenomena
- Imbalanced Data Classification Techniques
- Astronomical Observations and Instrumentation
- Scientific Computing and Data Management
- Digital Radiography and Breast Imaging
Laboratório Interinstitucional de e-Astronomia
2018
Federal Center for Technological Education of Minas Gerais
2016-2018
Federal Center for Technological Education Celso Suckow da Fonseca
2016-2018
Abstract We describe the first public data release of Dark Energy Survey, DES DR1, consisting reduced single-epoch images, co-added source catalogs, and associated products services assembled over 3 yr science operations. DR1 is based on optical/near-infrared imaging from 345 distinct nights (2013 August to 2016 February) by Camera mounted 4 m Blanco telescope at Cerro Tololo Inter-American Observatory in Chile. wide-area survey covering ∼5000 deg 2 southern Galactic cap five broad...
For recent or planned deep astronomical surveys, it is important to tell stars and galaxies apart, a task known as Star/Galaxy Separation Problem (SGSP). At faint magnitudes, the separation between pointy extended sources fuzzy, which makes SGSP hard task. This problem even harder for large surveys like Dark Energy Survey (DES) and, in near future, Large Synoptic Telescope (LSST) due their data volume. Hence, search classification methods that are both accurate efficient highly relevant. In...
A myriad of applications from different domains collects time series data for further analysis. In many them, such as seismic datasets, the observed is also associated to a space dimension, which corresponds, in fact, spatial-time series. The analysis these datasets difficult due both continuous nature and relationship between spatial dimensions. Meanwhile, sequential patterns mining techniques have been successfully used large volume transactional databases obtain insights data. this work,...
Descrevemos como o LIneA foi criado, e que produziu em termos de software hardware nos últimos 10 anos trabalho no Dark Energy Survey preparação para vários outros levantamentos, tal LSST, quando um centro e-ciência Brasil será necessário atender seu grande volume, velocidade, variabilidade dados. Paralelismo, proveniência, visualização são alguns dos desafios encarados pelo a fim gerar produtos alcançar os resultados científicos.
O Dark Energy Survey, após um período proprietário, disponibiliza para a comunidade seus dados processados na forma de imagens, catálogos, e mapas. portal científico do LIneA permite que esses diferentes produtos necessários análise científica sejam integrados distribuídos colaboração o público. Descrevemos query builder juntar eficientemente produtos.