Ridgway, Stephen and Akeson, Rachel and Baines, Ellyn and Creech-Eakman, Michelle and Boyajian, Tabetha and De Beck, Elvire and Dupree, Andrea and Gies, Doug and Hinkle, Kenneth and Humphreys, Elizabeth and Humphreys, Roberta and Joyce, Richard and Matthews, Lynn and Monnier, John and Norris, Ryan and Roettenbacher, Rachael and Stanghellini, Letizia and ten Brummelaar, Theo and van Belle, Gerard and Vlemmings, Wouter and Wheeler, J. Craig and White, Russell and Ziurys, Lucy (2019) Precision Analysis of Evolved Stars. Astro2020 Science White Paper, . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20191126-132032525
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Abstract
Evolved stars dominate galactic spectra, enrich the galactic medium, expand to change their planetary systems, eject winds of a complex nature, produce spectacular nebulae and illuminate them, and transfer material between binary companions. While doing this, they fill the HR diagram with diagnostic loops that write the story of late stellar evolution. Evolved stars sometimes release unfathomable amounts of energy in neutrinos, light, kinetic flow, and gravitational waves. During these late-life times, stars evolve complexly, with expansion, convection, mixing, pulsation, mass loss. Some processes have virtually no spatial symmetries, and are poorly addressed with low-resolution measurements and analysis. Even a "simple" question as how to model mass loss resists solution. However, new methods offer increasingly diagnostic tools. Astrometry reveals populations and groupings. Pulsations/oscillations support study of stellar interiors. Optical/radio interferometry enable 2-3d imagery of atmospheres and shells. Bright stars with rich molecular spectra and velocity fields are a ripe opportunity for imaging with high spatial and spectral resolution, giving insight into the physics and modeling of later stellar evolution.
Item Type: | Report or Paper (White Paper) | ||||||||||||||||
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Group: | Infrared Processing and Analysis Center (IPAC) | ||||||||||||||||
Series Name: | Astro2020 Science White Paper | ||||||||||||||||
Record Number: | CaltechAUTHORS:20191126-132032525 | ||||||||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20191126-132032525 | ||||||||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||||
ID Code: | 100068 | ||||||||||||||||
Collection: | CaltechAUTHORS | ||||||||||||||||
Deposited By: | Tony Diaz | ||||||||||||||||
Deposited On: | 26 Nov 2019 23:15 | ||||||||||||||||
Last Modified: | 27 Nov 2019 18:53 |
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