Machine Learning and Structure Formation in Modified Gravity

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 $\Lambda$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 $\Lambda$CDM. We also test the generalisability of the $\Lambda$CDM 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.

arXiv comment9 pages, 8 figures

References in corpus (24)

• Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
• Measurements of Omega and Lambda from 42 High-Redshift Supernovae
• Planck 2015 results. XIII. Cosmological parameters
• The Cosmological Constant and Dark Energy
• 4D Gravity on a Brane in 5D Minkowski Space
• Large scale bias and the peak background split
• Populating a cluster of galaxies - I. Results at z=0
• E pur si muove: Galiliean-invariant cosmological hydrodynamical simulations on a moving mesh
• Toward a halo mass function for precision cosmology: the limits of universality
• Models of f(R) Cosmic Acceleration that Evade Solar-System Tests
• Mass function of dark matter halos
• Non-linear Evolution of f(R) Cosmologies III: Halo Statistics
• Astrophysical Tests of Modified Gravity: Constraints from Distance Indicators in the Nearby Universe
• New constraints on $f(R)$ gravity from clusters of galaxies
• Cosmological Constraints on DGP Braneworld Gravity with Brane Tension
• Cosmological Constraints from Multiple Probes in the Dark Energy Survey
• Spherical collapse model in dark energy cosmologies
• Machine learning cosmological structure formation
• Astrophysical Tests of Modified Gravity: the Morphology and Kinematics of Dwarf Galaxies
• Galaxy formation in the brane world I: overview and first results
• A general framework to test gravity using galaxy clusters IV: Cluster and halo properties in DGP gravity
• Constraints on $f(R)$ and nDGP Modified Gravity Model Parameters with Cluster Abundances and Galaxy Clustering
• High-redshift test of gravity using enhanced growth of small structures probed by the neutral hydrogen distribution
• Universality of the halo mass function in screened gravity theories

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