Sowmya Kamath

ORCID: 0000-0003-0443-8221
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About
Contact & Profiles
Research Areas
  • 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...

10.48550/arxiv.1809.01669 preprint EN other-oa arXiv (Cornell University) 2018-01-01

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:...

10.33232/001c.129699 article EN cc-by The Open Journal of Astrophysics 2025-02-14

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...

10.2196/33329 article EN cc-by JMIR Formative Research 2022-03-21

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...

10.3847/1538-4357/ab54cb article EN cc-by The Astrophysical Journal 2019-12-30

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:...

10.48550/arxiv.2409.06986 preprint EN arXiv (Cornell University) 2024-09-10

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...

10.48550/arxiv.1907.04459 preprint EN other-oa arXiv (Cornell University) 2019-01-01

<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...

10.2196/preprints.33329 preprint EN 2021-09-03
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