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Brute-force Bayesian inference for photometric distances, reddenings, and stellar properties

Project description

# 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](https://www.dropbox.com/sh/ozq9tk8iyy8fhte/AAC_G0wA9eQ8shHbZzAKwLe-a?dl=0). 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](http://waps.cfa.harvard.edu/MIST/) and [Bayestar](https://arxiv.org/abs/1401.1508).

#### 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)](https://arxiv.org/abs/1905.02734).

#### Generating SEDs brutus contains built-in SED generation utilities based on the MIST stellar models, modeled off of [minesweeper](https://github.com/pacargile/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.

Project details


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