grburgess/popsynth

A generic flux/parameter population synthesis code

popsynth

popsynth core function is to create observed surveys from latent population models.

First, let's define what a population of objects is in terms of a generative model. The two main ingredients are the objects' spatial distribution () and the distribution of their inherent properties (). Here, are the latent population parameters, are the spatial locations of the objects, and are the properties of the individual objects (luminosity, spin, viewing angle, mass, etc.). The spatial distribution is defined such that:

is the intensity of objects for a given set of population parameters. With these definitions we can define the probability for an object to have position and properties as

popsynth allows you to specify these spatial and property distributions in an object-oriented way to create surveys. The final ingredient to creating a sample for a survey is knowing how many objects to sample from the population (before any selection effects are applied). Often, we see this number in simulation frameworks presented as "we draw N objects to guarantee we have enough." This is incorrect. A survey takes place over a given period of time () in which observed objects are counted. This is a description of a Poisson process. Thus, the number of objects in a simulation of this survey is a draw from a Poisson distribution:

Thus, popsynth first numerically integrates the spatial distribution to determine the Poisson rate parameter for the given $\vec{\psi}$, then makes a Poisson draw for the number of objects in the population survey. For each object, positions and properties are drawn with arbitrary dependencies between them. Finally, selection functions are applied to either latent or observed (with or without measurement error) properties.

Note: If instead we draw a preset number of objects, as is done in many astrophysical population simulation frameworks, it is equivalent to running a survey up until that specific number of objects is detected. This process is distributed as a negative binomial process, i.e, wait for a number of successes and requires a different statistical framework to compare models to data.

Installation

pip install popsynth

Note: This is not synth pop! If you were looking for some hard driving beats out of a yamaha keyboard with bells... look elsewhere

Contributing

Contributions to popsynth are always welcome. They can come in the form of:

Bug reports

Please use the Github issue tracking system for any bugs, for questions, and or feature requests.

Code and more distributions

While it is easy to create custom distributions in your local setup, if you would like to add them to popsynth directly, go ahead. Please include tests to ensure that your contributions are compatible with the code and can be maintained in the long term.

Documentation

Additions or examples, tutorials, or better explanations are always welcome. To ensure that the documentation builds with the current version of the software, I am using jupytext to write the documentation in Markdown. These are automatically converted to and executed as jupyter notebooks when changes are pushed to Github.