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http://hdl.handle.net/10609/99207
Title: Clasificación automática de objetos astronómicos por fotometría en series históricas recogidas por el Large Synoptic Survey Telescope (LSST)
Author: Arribas Zapater, Luis Enrique
Director: Nuñez Do Río, Joan Manuel
Tutor: Ventura Royo, Carles  
Keywords: machine learning
LSST
data mining
photometry
Issue Date: 12-Jun-2019
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: This project addresses a problem of classification of astronomical objects from the data recorded by the LSST telescope, corresponding to historical flow series. First, we present the techniques of computational learning and data mining used in the project. Next, the astronomical concepts necessary for the work are described. We analyze the data using data mining techniques and group the samples according to two of their characteristics. We transform the data, calculating the magnitude and color of the objects. We submit the data to a noise reduction process and to a Bayesian inference of its flow values. We convert the data into time series to which we perform a feature extraction process. We reduce the characteristics, through successive classifications iteratively, until we find the optimal dimensionality and build with these characteristics three classifiers of 1, 2 and 4 random forests. We validate the model and discuss the results. Finally, work lines are proposed for the future.
Language: Spanish
URI: http://hdl.handle.net/10609/99207
Appears in Collections:Bachelor thesis, research projects, etc.

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larribas24TFG0719memoria.pdfMemoria del TFG3.22 MBAdobe PDFView/Open
larribas24TFG0719presentación.pdfPresentación del TFG3.63 MBAdobe PDFView/Open
Luis_Arribas_Presentación_TFG.mp4208.09 MBMP4View/Open

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