Bayesian Analysis for Stellar Evolution with Nine Parameters (BASE-9) v9.4.3 User’s Manual¶
- Ted von Hippel
- Embry-Riddle Aeronautical University, Daytona Beach, FL, USA; ted.vonhippel@erau.edu
- Elliot Robinson
- Argiope Technical Solutions, Ft White, FL, USA; elliot.robinson@argiopetech.com
- Elizabeth Jeffery
- Brigham Young University, Provo, UT, USA; ejeffery@byu.edu
- Rachel Wagner-Kaiser
- University of Florida, Gainesville, FL, USA; rawagnerkaiser@gmail.com
- Steven DeGennaro
- Studio 42, Austin, TX, USA; studiofortytwo@yahoo.com
- Nathan Stein
- University of Pennsylvania, Philadelphia, PA, USA; nathanmstein@gmail.com
- David Stenning
- University of California, Irvine, CA, USA; dstennin@uci.edu
- William H Jefferys
- University of Texas, Austin, TX, USA and University of Vermont, Burlington, VT, USA; bill@astro.as.utexas.edu
- David van Dyk
- Imperial College London, London, UK; d.van-dyk@imperial.ac.uk
BASE-9 is a Bayesian software suite that recovers star cluster and stellar parameters from photometry. BASE-9 is useful for analyzing single-age, single-metallicity star clusters, binaries, or single stars, and for simulating such systems. BASE-9 uses Markov chain Monte Carlo and brute-force numerical integration techniques to estimate the posterior probability distributions for the age, metallicity, helium abundance, distance modulus, and line-of-sight absorption for a cluster, and the mass, binary mass ratio, and cluster membership probability for every stellar object. BASE-9 is provided as open source code on a version-controlled web server. The executables are also available as Amazon Elastic Compute Cloud images. This manual provides potential users with an overview of BASE-9, including instructions for installation and use.