Impact of Large-Scale Structure Systematics on Cosmological Parameter Estimation
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| Format: | Preprint |
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2024
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| _version_ | 1866911179961532416 |
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| author | Awan, Humna Gawiser, Eric Sanchez, Javier Sevilla-Noarbe, Ignacio Collaboration, the LSST Dark Energy Science |
| author_facet | Awan, Humna Gawiser, Eric Sanchez, Javier Sevilla-Noarbe, Ignacio Collaboration, the LSST Dark Energy Science |
| contents | Large near-future galaxy surveys offer sufficient statistical power to make our cosmology analyses data-driven, limited primarily by systematic errors. Understanding the impact of systematics is therefore critical. We perform an end-to-end analysis to investigate the impact of some of the systematics that affect large-scale structure studies by doing an inference analysis using simulated density maps with various systematics; these include systematics caused by photometric redshifts (photo-$z$s), Galactic dust, structure induced by the telescope observing strategy and observing conditions, and incomplete covariance matrices. Specifically, we consider the impacts of incorrect photo-$z$ distributions (photometric biases, scatter, outliers; spectroscopic calibration biases), dust map resolution, incorrect dust law, selecting none or only some contaminant templates for deprojection, and using a diagonal covariance matrix instead of a full one. We quantify the biases induced by these systematics on cosmological parameter estimation using tomographic galaxy angular power spectra, with a focus on identifying whether the maximum plausible level of each systematic has an adverse impact on the estimation of key cosmological parameters from a galaxy clustering analysis with Rubin Observatory Legacy Survey of Space and Time (LSST). We find photo-$z$ systematics to be the most pressing out of the systematics investigated, with spectroscopic calibration biases leading to the greatest adverse impact while helpfully being flagged by a high $χ^2$ value for the best fit model. Larger-than-expected photo-$z$ scatter, on the other hand, has a significant impact without necessarily indicating a poor fit. In contrast, in the analysis framework used in this work, biases from observational systematics and incomplete covariance matrices are comfortably subdominant. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_14265 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Impact of Large-Scale Structure Systematics on Cosmological Parameter Estimation Awan, Humna Gawiser, Eric Sanchez, Javier Sevilla-Noarbe, Ignacio Collaboration, the LSST Dark Energy Science Cosmology and Nongalactic Astrophysics Large near-future galaxy surveys offer sufficient statistical power to make our cosmology analyses data-driven, limited primarily by systematic errors. Understanding the impact of systematics is therefore critical. We perform an end-to-end analysis to investigate the impact of some of the systematics that affect large-scale structure studies by doing an inference analysis using simulated density maps with various systematics; these include systematics caused by photometric redshifts (photo-$z$s), Galactic dust, structure induced by the telescope observing strategy and observing conditions, and incomplete covariance matrices. Specifically, we consider the impacts of incorrect photo-$z$ distributions (photometric biases, scatter, outliers; spectroscopic calibration biases), dust map resolution, incorrect dust law, selecting none or only some contaminant templates for deprojection, and using a diagonal covariance matrix instead of a full one. We quantify the biases induced by these systematics on cosmological parameter estimation using tomographic galaxy angular power spectra, with a focus on identifying whether the maximum plausible level of each systematic has an adverse impact on the estimation of key cosmological parameters from a galaxy clustering analysis with Rubin Observatory Legacy Survey of Space and Time (LSST). We find photo-$z$ systematics to be the most pressing out of the systematics investigated, with spectroscopic calibration biases leading to the greatest adverse impact while helpfully being flagged by a high $χ^2$ value for the best fit model. Larger-than-expected photo-$z$ scatter, on the other hand, has a significant impact without necessarily indicating a poor fit. In contrast, in the analysis framework used in this work, biases from observational systematics and incomplete covariance matrices are comfortably subdominant. |
| title | Impact of Large-Scale Structure Systematics on Cosmological Parameter Estimation |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2409.14265 |