  Context. Generative models open up the possibility to interrogate scientific
data in a more data-driven way. Aims: We propose a method that uses generative
models to explore hypotheses in astrophysics and other areas. We use a neural
network to show how we can independently manipulate physical attributes by
encoding objects in latent space. Methods: By learning a latent space
representation of the data, we can use this network to forward model and
explore hypotheses in a data-driven way. We train a neural network to generate
artificial data to test hypotheses for the underlying physical processes.
Results: We demonstrate this process using a well-studied process in
astrophysics, the quenching of star formation in galaxies as they move from
low-to high-density environments. This approach can help explore astrophysical
and other phenomena in a way that is different from current methods based on
simulations and observations.
