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
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)
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.
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
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
Inference with mock broadband data, showing the change in posteriors as the
number of photometric bands is increased.