Combinando Métodos de Aprendizado Supervisionado para a Melhoria da Previsão do Redshift de Galáxias

Rafael Izbicki, Marcela Musetti

Abstract


Um problema fundamental em cosmologia é estimar redshifts de galáxias com base em dados fotométricos. Por exemplo a Sloan Digital Sky Survey (SDSS) já coletou dados fotométricos relativos a cerca de um bilhão de objetos para os quais é necessário estimar os respectivos redshifts. Tradicionalmente, essa tarefa é resolvida utilizando-se métodos de aprendizado de máquina. Neste trabalho, mostramos como métodos existentes podem ser combinados de forma a se obter estimativas ainda mais precisas para os redshifts de galáxias. Abordamos este problema sob duas óticas: (i) estimação da regressão do redshift y nas covariáveis fotométricas x, E[Y|x], e (ii) estimação da função densidade condicional f(y|x). Aplicamos as técnicas propostas para um banco de dados provenientes do SDSS e concluímos que as predições combinadas são de fato mais precisas que os métodos individuais.


Keywords


Aprendizado de máquina; stacking; densidades condicionais; cosmologia

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DOI: https://doi.org/10.5540/tema.2020.021.01.117

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TEMA - Trends in Applied and Computational Mathematics

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