Calibration and Commissioning of the LSST
- Univ. of Washington, Seattle, WA (United States)
Understanding the nature of dark energy and dark matter remains one of the fundamental questions in physics today; impacting our understanding of particle physics, cosmology, and possibly theories of gravity. Given the scale and complexity of the next generation of cosmology experiments (e.g., the Rubin Observatory, the Euclid satellite mission, and the Roman space telescope) we are entering an era where statistical noise no longer determines the accuracy to which we can measure cosmological parameters. Our ability to control and correct for systematics will ultimately determine the scientific impact of these experiments. This award addressed the challenge of how we determine what limits the accuracy of our cosmological measures, what techniques are appropriate for measuring and calibrating the properties of galaxies to best constrain cosmological models, how to develop statistical techniques that are insensitive to systematic errors, and how to optimize survey strategies in order to minimize systematics while maximizing the speed at which an experiment can achieve its science objectives. In this final technical report for award DE-SC0011635 we describe a set of open-source frameworks that simulate the characteristics and properties of current and planned cosmology surveys and the application of these frameworks to the development of new methodologies for estimating the properties and distances to galaxies that are robust to noisy and incomplete data.
- Research Organization:
- Univ. of Washington, Seattle, WA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Contributing Organization:
- Dark Energy Science Collaboration
- DOE Contract Number:
- SC0011635
- OSTI ID:
- 1889087
- Report Number(s):
- DOE-Washington-0011635; TRN: US2313658
- Country of Publication:
- United States
- Language:
- English
Similar Records
Towards Precision Photometric Type ia Supernova Cosmology With Machine Learning
Galaxy Clustering with LSST: Effects of Number Count Bias from Blending