Journal
ASTRONOMICAL JOURNAL
Volume 150, Issue 1, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/0004-6256/150/1/4
Keywords
Galaxy: stellar content; Galaxy: structure; stars: distances; surveys
Categories
Funding
- U.S. National Science Foundation [AST 09-37523, AST 14-09421]
- Strategic Priority Research Program The Emergence of Cosmological Structures of the Chinese Academy of Sciences [XDB09000000]
- National Key Basic Research Program of China [2014CB845700]
- National Science Foundation of China [11373032, 11333003, 11403056]
- Physics Frontiers Center/Joint Institute for Nuclear Astrophysics (JINA) - U.S. National Science Foundation [PHY 08-22648]
- Physics Frontier Center/JINA Center for the Evolution of the Elements (JINA-CEE) - U.S. National Science Foundation [PHY 14-30152]
- National Development and Reform Commission
- National Aeronautics and Space Administration
- National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Astronomical Sciences [1412648, 1409421] Funding Source: National Science Foundation
- Division Of Physics
- Direct For Mathematical & Physical Scien [1430152] Funding Source: National Science Foundation
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We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and returns a posterior probability density function for each star's absolute magnitude. This technique is tailored specifically to data from the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey. Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each similar to 5 degrees diameter plate that is observed, we can use the stellar parameters of the observed stars to account for the stellar luminosity function and target selection effects. This removes biasing assumptions about the underlying populations, both due to predictions of the luminosity function from stellar evolution modeling, and from Galactic models of stellar populations along each line of sight. Using calibration data of stars with known distances and stellar parameters, we show that our method recovers distances for most stars within similar to 20%, but with some systematic overestimation of distances to halo giants. We apply our code to the LAMOST database, and show that the current precision of LAMOST stellar parameters permits measurements of distances with similar to 40% error bars. This precision should improve as the LAMOST data pipelines continue to be refined.
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