2019/02/28 by Andrew Everall, Payel Das · 11 citations
Chemistry · Physics and Astronomy · #Astrophysics #Biology #Computer science #Evolutionary biology #Function (biology) #Galaxy #Machine learning #Milky Way #Physics #Selection (genetic algorithm) #Sky #Spectroscopy and Chemometric Analyses #Star (game theory) #Stellar, planetary, and galactic studies #astro-ph.GA
paper · pdf · doi:10.1093/mnras/staa283
published in Monthly Notices of the Royal Astronomical Society 493(2), 2042-2058 (Oxford University Press) · MNRAS, revised version contains significant improvements to the model and more rigorous statistical tests
openalex publication_date 2020/01/30 · arxiv created 2020/02/06 · arxiv updated 2020/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
ABSTRACT Selection functions are vital for understanding the observational biases of spectroscopic surveys. With the wide variety of multiobject spectrographs currently in operation and becoming available soon, we require easily generalizable methods for determining the selection functions of these surveys. Previous work, however, has largely been focused on generating individual, tailored selection functions for every data release of each survey. Moreover, no methods for combining these selection functions to be used for joint catalogues have been developed. We have developed a Poisson likelihood estimation method for calculating selection functions in a Bayesian framework, which can be generalized to any multiobject spectrograph. We include a robust treatment of overlapping fields within a survey as well as selection functions for combined samples with overlapping footprints. We also provide a method for transforming the selection function that depends on the sky positions, colour, and apparent magnitude of a star to one that depends on the galactic location, metallicity, mass, and age of a star. This ‘intrinsic’ selection function is invaluable for chemodynamical models of the Milky Way. We demonstrate that our method is successful at recreating synthetic spectroscopic samples selected from a mock galaxy catalogue.