Signatures of Alternative Gravities from Galaxy Simulations

Lead Research Organisation: University of Cambridge
Department Name: Institute of Astronomy

Abstract

In this project, I will be performing computer simulations of galaxy formation under modified gravity
theories, such as the f(R) theories of gravity. This will be with a view towards searching for observable
signatures of these alternative theories, which can then be compared with existing observations of
distant galaxies. Thus, the modified gravity theories in question can be favoured or disfavoured.
An example of such an observable could be, for instance, the galactic 'rotation curve'. The effect of f(R)
gravity on galactic rotation curves is hitherto unexplored, so an investigation thereof could well yield
illuminating results about the nature of gravity.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/N503988/1 01/10/2015 31/03/2021
1791664 Studentship ST/N503988/1 01/10/2016 31/03/2020 Aneesh Naik
 
Title smoggy 
Description smoggy is a Python 3 restricted N-body code under chameleon gravity. It was used to generate the results in Naik et al., (in prep.) 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
Impact smoggy was used to generate the results for a paper shortly to be submitted to Physical Review D, of which I am first author. 
URL https://github.com/aneeshnaik/smoggy
 
Title spam 
Description spam is a python-3 package designed to search for imprints of Hu-Sawicki f(R) gravity on the rotation curves of the SPARC sample, using the MCMC sampler emcee. This code was used to generate the results in Naik et al., (2019). Please direct any comments/questions to the author, Aneesh Naik, at an485@[Cambridge University]. 
Type Of Technology Software 
Year Produced 2019 
Open Source License? Yes  
Impact This code was used to generate the results for a publication in Monthly Notices of the Royal Astronomical Society (doi:10.1093/mnras/stz2131). 
URL https://ui.adsabs.harvard.edu/abs/2019MNRAS.489..771N/abstract