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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.


See installation for requirements and dependencies. The documentation includes a tutorial and demos.

To install to a conda environment with dependencies, see To install just Prospector (stable release):

python -m pip install astro-prospector

To install the latest development version:

cd <install_dir>
git clone
cd prospector
python -m pip install .

Then, in Python

import prospect


If you use this code, please reference this paper:

       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 = {},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}

and make sure to cite the dependencies as listed in installation


Inference with mock broadband data, showing the change in posteriors as the number of photometric bands is increased. Demonstration of posterior inference with increasing number of photometric bands