Purpose
Conduct principled inference of stellar population properties from photometric
and/or spectroscopic data. Prospector allows you to:
-
Infer high-dimensional stellar population properties using parametric or
highly flexible SFHs (with nested or ensemble Monte Carlo sampling)
-
Combine photometric and spectroscopic data from the UV to Far-IR rigorously
using a flexible spectroscopic calibration model and forward modeling many
aspects of spectroscopic data analysis.
Read the documentation and the
code paper.
Installation
See installation for requirements and dependencies.
The documentation includes a tutorial and demos.
To install to a conda environment with dependencies, see conda_install.sh
.
To install just Prospector (stable release):
python -m pip install astro-prospector
To install the latest development version:
cd <install_dir>
git clone https://github.com/bd-j/prospector
cd prospector
python -m pip install .
Then, in Python
import prospect
Citation
If you use this code, please reference this paper:
@ARTICLE{2021ApJS..254...22J,
author = {{Johnson}, Benjamin D. and {Leja}, Joel and {Conroy}, Charlie and {Speagle}, Joshua S.},
title = "{Stellar Population Inference with Prospector}",
journal = {\apjs},
keywords = {Galaxy evolution, Spectral energy distribution, Astronomy data modeling, 594, 2129, 1859, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2021,
month = jun,
volume = {254},
number = {2},
eid = {22},
pages = {22},
doi = {10.3847/1538-4365/abef67},
archivePrefix = {arXiv},
eprint = {2012.01426},
primaryClass = {astro-ph.GA},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021ApJS..254...22J},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
and make sure to cite the dependencies as listed in installation
Example
Inference with mock broadband data, showing the change in posteriors as the
number of photometric bands is increased.