Email this article Combinando Métodos de Aprendizado Supervisionado para a Melhoria da Previsão do Redshift de Galáxias
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.
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Almosallam, Ibrahim A. ; Jarvis, Matt J. ; Roberts, Stephen J.: GPZ: nonstationary sparse Gaussian processes for heteroscedastic uncertainty estimation in photometric redshifts. In: Monthly Notices of the Royal Astronomical Society 462 (2016), Nr. 1, S. 726–739
Brammer, Gabriel B. ; Dokkum, Pieter G. ; Coppi, Paolo: EAZY: a fast,
public photometric redshift code. In: The Astrophysical Journal 686 (2008),
Nr. 2, S. 1503
Breiman, Leo: Random forests. In: Machine learning 45 (2001), Nr. 1, S.
–32
Chen, Tianqi ; Guestrin, Carlos: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining ACM, 2016, S. 785–794
Csabai, Istvan ; Budavari, Tamas ; Connolly, Andrew J. ; Szalay, Alexander S. ; Győry, Zsuzsanna ; Benitez, Narciso ; Annis, Jim ; Brinkmann, Jon ; Eisenstein, Daniel ; Fukugita, Masataka u. a.: The application of photometric redshifts to the SDSS early data release. In: The Astronomical Journal 125 (2003), Nr. 2, S. 580
Freeman, Peter E. ; Izbicki, Rafael ; Lee, Ann B.: A unified framework for constructing, tuning and assessing photometric redshift density estimates in a selection bias setting. In: Monthly Notices of the Royal Astronomical Society 468 (2017), Nr. 4, S. 4556–4565
Friedman, Jerome ; Hastie, Trevor ; Tibshirani, Robert: The elements of
statistical learning. Bd. 1. Springer series in statistics New York, 2001
Izbicki, R. ; Santos, T. M. d.: Machine Learning sob a ótica estatística. 2019 http://www.rizbicki.ufscar.br/sml
Izbicki, Rafael ; Lee, Ann B. u. a.: Converting high-dimensional regression to high-dimensional conditional density estimation. In: Electronic Journal of Statistics 11 (2017), Nr. 2, S. 2800–2831
Izbicki, Rafael ; Lee, Ann B. ; Freeman, Peter E.: Photo-z estimation: An example of nonparametric conditional density estimation under selection bias. In: The Annals of Applied Statistics 11 (2017), Nr. 2, S. 698–724
Sheldon, Erin S. ; Cunha, Carlos E. ; Mandelbaum, Rachel ; Brinkmann, J ; Weaver, Benjamin A.: Photometric redshift probability distributions for galaxies in the SDSS DR8. In: The Astrophysical Journal Supplement Series 201 (2012), Nr. 2, S. 32
Tibshirani, Robert: Regression shrinkage and selection via the lasso. In: Journal of the Royal Statistical Society: Series B (Methodological) 58 (1996), Nr. 1, S. 267–288
Turlach, Berwin A. ; Weingessel, Andreas: quadprog: Functions to solve quadratic programming problems. In: CRAN-Package quadprog (2007)
Wadadekar, Yogesh: Estimating photometric redshifts using support vector machines. In: Publications of the Astronomical Society of the Pacific 117 (2004), Nr. 827, S. 79
Wittman, D: What lies beneath: Using p(z) to reduce systematic photometric redshift errors. In: The Astrophysical Journal Letters 700 (2009), Nr. 2, S. L174
Yèche, Ch ; Petitjean, P ; Rich, J ; Aubourg, E ; Hamilton, J-Ch ;
Le Goff, J-M ; Paris, I ; Peirani, S ; Pichon, Ch ; Rollinde, E u. a.:
Artificial neural networks for quasar selection and photometric redshift determination. In: Astronomy & Astrophysics 523 (2010), S. A14
York, Donald G. ; Adelman, J ; Anderson Jr, John E. ; Anderson,
Scott F. ; Annis, James ; Bahcall, Neta A. ; Bakken, JA ; Barkhouser,
Robert ; Bastian, Steven ; Berman, Eileen u. a.: The sloan digital sky survey: Technical summary. In: The Astronomical Journal 120 (2000), Nr. 3, S. 1579
Zhang, Min-Ling ; Zhou, Zhi-Hua: ML-KNN: A lazy learning approach to multi-label learning. In: Pattern recognition 40 (2007), Nr. 7, S. 2038–2048
Zhou, Zhi-Hua: Ensemble methods: foundations and algorithms. CRC press, 2012
DOI: https://doi.org/10.5540/tema.2020.021.01.117
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