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Full-Text Articles in Physical Sciences and Mathematics
Disk Heating, Galactoseismology, And The Formation Of Stellar Halos, Kathryn V. Johnston, Adrian M. Price-Whelan, Maria Bergemann, Chervin F. P. Laporte, Ting S. Li, Allyson A. Sheffield, Steven R. Majewski, Rachael S. Beaton, Branimir Sesar, Sanjib Sharma
Disk Heating, Galactoseismology, And The Formation Of Stellar Halos, Kathryn V. Johnston, Adrian M. Price-Whelan, Maria Bergemann, Chervin F. P. Laporte, Ting S. Li, Allyson A. Sheffield, Steven R. Majewski, Rachael S. Beaton, Branimir Sesar, Sanjib Sharma
Publications and Research
Deep photometric surveys of the MilkyWay have revealed diffuse structures encircling our Galaxy far beyond the “classical” limits of the stellar disk. This paper reviews results from our own and other observational programs, which together suggest that, despite their extreme positions, the stars in these structures were formed in our Galactic disk. Mounting evidence from recent observations and simulations implies kinematic connections between several of these distinct structures. This suggests the existence of collective disk oscillations that can plausibly be traced all the way to asymmetries seen in the stellar velocity distribution around the Sun. There are multiple interesting implications …
How To Measure Metallicity From Five-Band Photometry With Supervised Machine Learning Algorithms, Viviana Acquaviva
How To Measure Metallicity From Five-Band Photometry With Supervised Machine Learning Algorithms, Viviana Acquaviva
Publications and Research
We demonstrate that it is possible to measure metallicity from the SDSS five-band photometry to better than 0.1 dex using supervised machine learning algorithms. Using spectroscopic estimates of metallicity as ground truth, we build, optimize and train several estimators to predict metallicity. We use the observed photometry, as well as derived quantities such as stellar mass and photometric redshift, as features, and we build two sample data sets at median redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3, respectively. We find that ensemble methods, such as random forests of trees and extremely randomized trees and …