Systematic effects in cosmic shear measurements for future ground-based optical surveys
Abstract/Contents
- Abstract
- Cosmic shear measurements with large optical surveys have the largest potential power in the near future for revealing the nature of dark energy -- the greatest mystery in modern cosmology. This is one of the main science goals for the Large Synoptic Survey Telescope (LSST) project. The 10-year survey from LSST will image $\sim$ 20,000 square degrees of sky in six filter bands to a final survey depth of $r$-band magnitude $\sim27.5$ and median redshift $\sim1.2$. To take full advantage of this unprecedented statistical power, however, the systematic errors associated with cosmic shear measurements need to be controlled to a level similar to the statistical errors. In this thesis, I estimate the statistical and systematic errors in the cosmic shear dataset expected for LSST and investigate methods to mitigate some of the major systematic errors. The analyses are based on high-fidelity simulated images generated by a fast Monte Carlo ray-tracing code. The code models the interactions of the photons including major physical effects from the top of the atmosphere down to the charge-coupled devices, or CCDs. I first calculate the number density of % detectors? galaxies expected for a cosmic shear analysis with LSST, which provides a quantitative measure of the statistical power of the survey for these topics of investigation. Accounting for the realistic measurement errors for shear and blending/masking effects, I show that $\sim26$ arcmin$^{-2}$ well-measured galaxies is expected using conventional algorithms, but the number can increase by as much as $20\%$ if future, more sophisticated algorithms allow for the use of faint galaxies with lower signal-to-noise ratio. Next, I identify and isolate the different sources of additive systematic errors on shear measurements for LSST, and predict their impact on the final cosmic shear results using standard analysis techniques. I find that the main source of the errors comes from an inability to adequately characterize the spatial variation of the point spread function. With the large multi-epoch dataset that will be acquired by LSST, however, these stochastic errors average to a level very close to the statistical errors. Finally, I devise and evaluate a better point spread function interpolation technique and reduce the residual systematic errors by a factor of 3--10 on small scales. I conclude by describing a set of ongoing and future projects, which include an evaluation of the systematic errors associated with chromaticity of the point spread function and the development of optimal schemes for combining shear measurements from a multi-epoch dataset.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Chang, Chihway |
|---|---|
| Associated with | Stanford University, Department of Physics. |
| Primary advisor | Kahn, Steven Michael |
| Primary advisor | Schindler, Rafe |
| Thesis advisor | Kahn, Steven Michael |
| Thesis advisor | Schindler, Rafe |
| Thesis advisor | Blandford, Roger D |
| Thesis advisor | Kuo, Chao-Lin |
| Advisor | Blandford, Roger D |
| Advisor | Kuo, Chao-Lin |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Chihway Chang. |
|---|---|
| Note | Submitted to the Department of Physics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Chihway Chang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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