Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -I. Methodology and a Fast Fitting Algorithm
The principles of measuring the shapes of galaxies by a model-fitting approach are discussed in the context of shape-measurement for surveys of weak gravitational lensing. It is argued that such an approach should be optimal, allowing measurement with maximal signal-to-noise, coupled with estimation...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
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2007
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| _version_ | 1797103024261300224 |
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| author | Miller, L Kitching, T Heymans, C Heavens, A Waerbeke, L |
| author_facet | Miller, L Kitching, T Heymans, C Heavens, A Waerbeke, L |
| author_sort | Miller, L |
| collection | OXFORD |
| description | The principles of measuring the shapes of galaxies by a model-fitting approach are discussed in the context of shape-measurement for surveys of weak gravitational lensing. It is argued that such an approach should be optimal, allowing measurement with maximal signal-to-noise, coupled with estimation of measurement errors. The distinction between likelihood-based and Bayesian methods is discussed. Systematic biases in the Bayesian method may be evaluated as part of the fitting process, and overall such an approach should yield unbiased shear estimation without requiring external calibration from simulations. The principal disadvantage of model-fitting for large surveys is the computational time required, but here an algorithm is presented that enables large surveys to be analysed in feasible computation times. The method and algorithm is tested on simulated galaxies from the Shear TEsting Program (STEP). |
| first_indexed | 2024-03-07T06:14:09Z |
| format | Journal article |
| id | oxford-uuid:f0886b71-a0b5-42d1-84eb-0ad74a861ceb |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:14:09Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | oxford-uuid:f0886b71-a0b5-42d1-84eb-0ad74a861ceb2022-03-27T11:48:41ZBayesian Galaxy Shape Measurement for Weak Lensing Surveys -I. Methodology and a Fast Fitting AlgorithmJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f0886b71-a0b5-42d1-84eb-0ad74a861cebEnglishSymplectic Elements at Oxford2007Miller, LKitching, THeymans, CHeavens, AWaerbeke, LThe principles of measuring the shapes of galaxies by a model-fitting approach are discussed in the context of shape-measurement for surveys of weak gravitational lensing. It is argued that such an approach should be optimal, allowing measurement with maximal signal-to-noise, coupled with estimation of measurement errors. The distinction between likelihood-based and Bayesian methods is discussed. Systematic biases in the Bayesian method may be evaluated as part of the fitting process, and overall such an approach should yield unbiased shear estimation without requiring external calibration from simulations. The principal disadvantage of model-fitting for large surveys is the computational time required, but here an algorithm is presented that enables large surveys to be analysed in feasible computation times. The method and algorithm is tested on simulated galaxies from the Shear TEsting Program (STEP). |
| spellingShingle | Miller, L Kitching, T Heymans, C Heavens, A Waerbeke, L Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -I. Methodology and a Fast Fitting Algorithm |
| title | Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -I.
Methodology and a Fast Fitting Algorithm |
| title_full | Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -I.
Methodology and a Fast Fitting Algorithm |
| title_fullStr | Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -I.
Methodology and a Fast Fitting Algorithm |
| title_full_unstemmed | Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -I.
Methodology and a Fast Fitting Algorithm |
| title_short | Bayesian Galaxy Shape Measurement for Weak Lensing Surveys -I.
Methodology and a Fast Fitting Algorithm |
| title_sort | bayesian galaxy shape measurement for weak lensing surveys i methodology and a fast fitting algorithm |
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