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Star/galaxy separation at faint magnitudes: application to a simulated Dark Energy Survey


Soumagnac, M T; Abdalla, F B; Lahav, O; Kirk, D; Sevilla, I; Bertin, E; Rowe, B T P; Annis, J; Busha, M T; Da Costa, L N; Frieman, J A; Gaztanaga, E; Jarvis, M; Lin, H; Percival, W J; Santiago, B X; Sabiu, C G; Wechsler, R H; Wolz, L; Yanny, B (2015). Star/galaxy separation at faint magnitudes: application to a simulated Dark Energy Survey. Monthly Notices of the Royal Astronomical Society, 450(1):666-680.

Abstract

We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the gravitational weak lensing and large-scale structure probes. These requirements are dictated by the need to control both the statistical and systematic errors on the cosmological parameters, and by point spread function calibration. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements at faint magnitudes, we propose a new method for star/galaxy separation in the second part of the paper. We first use principal component analysis to outline the correlations between the objects parameters and extract from it the most relevant information. We then use the reduced set of parameters as input to an Artificial Neural Network. This multiparameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SExtractor), especially at faint magnitudes: it increases the purity by up to 20 per cent for stars and by up to 12 per cent for galaxies, at i-magnitude fainter than 23

Abstract

We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the gravitational weak lensing and large-scale structure probes. These requirements are dictated by the need to control both the statistical and systematic errors on the cosmological parameters, and by point spread function calibration. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements at faint magnitudes, we propose a new method for star/galaxy separation in the second part of the paper. We first use principal component analysis to outline the correlations between the objects parameters and extract from it the most relevant information. We then use the reduced set of parameters as input to an Artificial Neural Network. This multiparameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SExtractor), especially at faint magnitudes: it increases the purity by up to 20 per cent for stars and by up to 12 per cent for galaxies, at i-magnitude fainter than 23

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Item Type:Journal Article, refereed, original work
Communities & Collections:National licences > 142-005
Dewey Decimal Classification:530 Physics
Language:English
Date:11 June 2015
Deposited On:08 Nov 2018 17:43
Last Modified:12 Nov 2018 13:42
Publisher:Oxford University Press
ISSN:0035-8711
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/mnras/stu1410
Related URLs:https://www.swissbib.ch/Search/Results?lookfor=nationallicenceoxford101093mnrasstu1410 (Library Catalogue)

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