Exploring galaxy evolution with generative models
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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.
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