# Exploring galaxy evolution with generative models

## TL;DR

This paper introduces a neural network-based generative modeling approach to explore astrophysical hypotheses, specifically galaxy evolution, by manipulating physical attributes in latent space to generate and analyze artificial data.

## Contribution

It presents a novel method using generative models to independently manipulate physical attributes and explore hypotheses in astrophysics, offering an alternative to traditional simulation-based approaches.

## Key findings

- Successfully modeled galaxy quenching in different environments
- Demonstrated hypothesis testing via generated artificial data
- Showed potential for broader applications in astrophysics

## Abstract

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.