Machine learning and structure formation in modified gravity

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 lambda cold dark matter (Lambda CDM) 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 similar to CDM. We also test the generalizability of the Lambda CDM model on data from f(R) and nDGP gravities of varying strengths, and therefore the generalizability of extended Press-Schechter spherical collapse to these types of modified gravity.