brutus
Et tu, Brute?
brutus is a Pure Python package whose core modules involve using
"brute force" Bayesian inference to derive distances, reddenings, and
stellar properties from photometry using a grid of stellar models.
The package is designed to be highly modular, with current modules including utilities for modeling individual stars, star clusters, and stellar-based 3-D dust mapping.
Documentation
Currently nonexistent.
Data
Various files needed to run different brutus modules can be downloaded
here.
Various components of these are described below.
Stellar Models
Note that while brutus can (in theory) be run over an arbitrary set of
stellar models, it is configured for two by default:
MIST
and Bayestar.
Zero-points
Zero-point offsets in several bands have been estimated using Gaia data and can be included during runtime. These are currently not thoroughly vetted, so use at your own risk.
Dust Map
brutus is able to incorporate a 3-D dust prior. The current prior is
based on the "Bayestar19" dust map from
Green et al. (2019).
Generating SEDs
brutus contains built-in SED generation utilities based on the MIST
stellar models, modeled off of
minesweeper.
These are optimized for either generating photometry from stellar mass
tracks or for a single-age stellar isochrone based on
artificial neural networks trained on bolometric correction tables.
Empirical corrections to the MIST models derived using several clusters are implemented by default, which improves main sequence behavior down to ~0.5 solar masses. These can be easily disabled by users using the appropriate flag. These are currently not thoroughly vetted.
Please contact Phil Cargile (pcargile@cfa.harvard.edu) and Josh Speagle (jspeagle@cfa.harvard.edu) for more information on the provided data files.
Installation
brutus can be installed by running
python setup.py install
from inside the repository.
Demos
Several Jupyter notebooks currently outline very basic usage of the code. Please contact Josh Speagle (jspeagle@cfa.harvard.edu) with any questions.
