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- W4387209060 abstract "Abstract In General Relativity approximations based on the spherical collapse model such as Press–Schechter theory and its extensions are able to predict the number of objects of a certain mass in a given volume. In this paper we use a machine learning algorithm to test whether such approximations hold in screened modified gravity theories. To this end, we train random forest classifiers on data from N-body simulations to study the formation of structures in ΛCDM as well as screened modified gravity theories, in particular f(R) and nDGP gravity. The models are taught to distinguish structure membership in the final conditions from spherical aggregations of density field behaviour in the initial conditions. We examine the differences between machine learning models that have learned structure formation from each gravity, as well as the model that has learned from ΛCDM. We also test the generalisability of the ΛCDM model on data from f(R) and nDGP gravities of varying strengths, and therefore the generalisability of Extended-Press-Schechter spherical collapse to these types of modified gravity." @default.
- W4387209060 created "2023-09-30" @default.
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- W4387209060 date "2023-09-29" @default.
- W4387209060 modified "2023-10-01" @default.
- W4387209060 title "Machine learning and structure formation in modified gravity" @default.
- W4387209060 doi "https://doi.org/10.1093/mnras/stad2915" @default.
- W4387209060 hasPublicationYear "2023" @default.
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