In recent years, many physicists have begun to investigate the role of Lorentz symmetry in
physics and whether it could be broken. One mechanism by which this could be broken is with
a spacetime vector field; but the energy and pressure of this field would likely result in a slight
anisotropic expansion of the Universe, which could be observable through statistical analysis of
cosmological signals such as supernova data. However, allowing for this anisotropy also
increases the dimension of the parameter space involved.
In 2019, two students performed an initial analysis and found some intriguing results. Their
code involved running an MCMC code in Mathematica to sample a restricted parameter
subspace, and find the most likely values of various cosmological parameters in this restricted
space. Complicating matters, the objective function being optimized in this analysis was itself a
non-linear function of the parameters, and required the solution of numerical integrals at each
step.
I would like to extend this initial analysis to the full parameter space. This will require
optimization of code and porting to a language with less computational “overhead” (probably
Python, but other options might be viable as well.)
Project Information
Project Status: Finishing Up Project Region: CAREERS Submitted By:Michael Seifert Project Email:mseifer1@conncoll.edu Project Institution: Connecticut College Anchor Institution: CR-Yale
In recent years, many physicists have begun to investigate the role of Lorentz symmetry in
physics and whether it could be broken. One mechanism by which this could be broken is with
a spacetime vector field; but the energy and pressure of this field would likely result in a slight
anisotropic expansion of the Universe, which could be observable through statistical analysis of
cosmological signals such as supernova data. However, allowing for this anisotropy also
increases the dimension of the parameter space involved.
In 2019, two students performed an initial analysis and found some intriguing results. Their
code involved running an MCMC code in Mathematica to sample a restricted parameter
subspace, and find the most likely values of various cosmological parameters in this restricted
space. Complicating matters, the objective function being optimized in this analysis was itself a
non-linear function of the parameters, and required the solution of numerical integrals at each
step.
I would like to extend this initial analysis to the full parameter space. This will require
optimization of code and porting to a language with less computational “overhead” (probably
Python, but other options might be viable as well.)