Version 2.0! (in progress)

This is a major refactor to allow for multiple datasets (i.e. multiple spectra) with different noise models and instrument parameters to constrain a single galaxy model. Other updates may include cleaner stored outputs, interfaces with additional nested samplers, and improved tretments of smoothing.

Work to do includes:

• Convert to/from old style observation dictionaries
• Put responsibility for Noise Models including outlier modeling in individual Observation instances
• Make predictions even when there is no fittable data (e.g. model spectra when fitting photometry only)
• Store Observation objects in HDF5, FITS, etc as structured arrays with metadata
• Catch (and handle) emission line marginalization if spectra overlap.
• Structured ndarray for output chains and lnlikehoods
• Update docs
• Update demo scripts
• Account for undersampled spectra via explicit rebinning.
• Account for undersampled spectra via a square convolution in pixel space.
• Implement UltraNest and Nautilus backends
• Test i/o with structured arrays
• Structured ndarray for derived parameters
• Store samples of spectra, photometry, and mfrac (blobs)
• Update notebooks
• Update plotting module
• Test multi-spectral calibration, smoothing, and noise modeling
• Test smoothing accounting for library, instrumental & physical smoothing
• Implement an emulator-based SpecModel class

Migration from < v2.0

For many users the primary difference from v1.X will be that the data to predict and fit a model to is now specified as a list of prospect.observation.Observation() instances, instead of a single 'obs' dictionary. There is a new convenience method to convert from the old 'obs' dictionary format to the new specification. This can be used with existing scripts as follows:

# old build_obs function giving a dictionary
obs_dict = build_obs(**run_params)
# get convenience method
from prospect.observation import from_oldstyle
# make a new list of Observation instances from the dictionary
observations = from_oldstyle(obs_dict)
# verify and prepare for fitting; similar to 'obsutils.fix_obs()'
[obs.rectify() for obs in observations]
print(observations)

It is recommended to do the conversion within the build_obs() method, if possible. This list of observations is then supplied to fit_model. Because noise models are now attached explicitly to each observation, they do not need to be generated separately or supplied to fit_model(), which no longer accepts a noise= argument. For outlier models, the the noise model should be instantiated with names for the outlier model that correspond to fixed or free parameters of the model.

from prospect.fitting import fit_model
output = fit_model(observations, model, sps, **config)

Another change is that spectral response functions (i.e. calibration vectors) are now handled by specialized sub-classes of these Observation classes. See the spectroscopy docs for details.

The interface to write_model has been changed and simplified. See usage for details.

Finally, the output chain or samples is now stored as a structured array, where each row corresponds to a sample, and each column is a parameter (possibly multidimensional). Additional information (such as sample weights, likelihoods, and poster probabilities) are stored as additional datasets in the output. The unstructured_chain dataset of the output contains an old-style simple numpy.ndarray of shape (nsample, ndim)

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 multiple photometric, spectroscopic, and/or line flux datasets 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.