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• W3105655676 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." @default.
• W3105655676 created "2020-11-23" @default.
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• W3105655676 date "2018-08-01" @default.
• W3105655676 modified "2023-10-16" @default.
• W3105655676 title "Exploring galaxy evolution with generative models" @default.
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• W3105655676 doi "https://doi.org/10.1051/0004-6361/201833800" @default.
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