A5103160785- Astronomy and Astrophysical Research
- Dark Matter and Cosmic Phenomena
- Adaptive optics and wavefront sensing
- Distributed and Parallel Computing Systems
- Galaxies: Formation, Evolution, Phenomena
- Diabetes Management and Research
- Diabetes Management and Education
- Mobile Health and mHealth Applications
- Big Data Technologies and Applications
- Advanced Data Storage Technologies
- Advanced Image Processing Techniques
- Statistical and numerical algorithms
- History and Developments in Astronomy
Stanford University
2019-2025
Kavli Institute for Particle Astrophysics and Cosmology
2019
The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. This Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually together, with conservative assumptions about analysis methodology follow-up observational resources based on our current understanding evolution within field in coming...
We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons detection deblending algorithms based on a suite metrics. The package, named Blending Toolkit (BTK), serves as modular, flexible, easy-to-install, simple-to-use interface exploring analyzing systematic effects related to blended in cosmological surveys such the Vera Rubin Observatory Legacy Survey Space Time (LSST). BTK has three main components:...
Diabetes management is complex, and program personalization has been identified to enhance engagement clinical outcomes in diabetes programs. However, 50% of individuals living with are unable achieve glycemic control, presenting a gap the delivery self-management education behavior change. Machine learning recommender systems, which have used within health care setting, could be feasible application for programs provide personalized user experience improve outcomes.This study aims evaluate...
Abstract Galaxy color gradients (CGs)—i.e., spectral energy distributions that vary across the galaxy profile—will impact shape measurements when modeled point-spread function (PSF) corresponds to for a with spatially uniform color. This paper describes techniques and results of study expected CGs on weak lensing Large Synoptic Survey Telescope (LSST) PSF size depends wavelength. The bias cosmic shear from is computed both parametric bulge+disk simulations more realistic chromatic surface...
We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons detection deblending algorithms based on a suite metrics. The package, named Blending Toolkit (BTK), serves as modular, flexible, easy-to-install, simple-to-use interface exploring analyzing systematic effects related to blended in cosmological surveys such the Vera Rubin Observatory Legacy Survey Space Time (LSST). BTK has three main components:...
Galaxy color gradients - i.e., spectral energy distributions that vary across the galaxy profile will impact shape measurements when modeled point spread function (PSF) corresponds to for a with spatially uniform color. This paper describes techniques and results of study expected on weak lensing Large Synoptic Survey Telescope (LSST) PSF size depends wavelength. The bias cosmic shear from is computed both parametric bulge+disk simulations more realistic chromatic surface brightness profiles...
<sec> <title>BACKGROUND</title> Diabetes management is complex, and program personalization has been identified to enhance engagement clinical outcomes in diabetes programs. However, 50% of individuals living with are unable achieve glycemic control, presenting a gap the delivery self-management education behavior change. Machine learning recommender systems, which have used within health care setting, could be feasible application for programs provide personalized user experience improve...