132 research outputs found
Cosmic backreaction and the mean redshift drift from symbolic regression
The possibility of obtaining symbolic expressions for cosmic backreaction is
explored through a case study of so-called 2-region models. By using the
publicly available symbolic regression algorithm AI Feynman, it is shown that
the kinematical backreaction from a single 2-region model can be well described
as a function of the mean redshift (or, equivalently, the volume averaged scale
factor). A single expression depending on the redshift/scale factor as well as
a model parameter, , that can accurately describe the backreaction for a
significant range of models is naturally more complicated but is also achieved
with percent-level accuracy. \newline\indent Data sets of redshift drift in the
2-region models are also considered. Again utilizing AI Feynman, expressions
for the redshift drift are found. In particular, an expression for the
difference between the mean redshift drift and the drift of the mean redshift
in terms of the kinematical backreaction is easily obtained for a single
2-region model. An accurate symbolic expression that describes this difference
for an array of 2-region models is achieved by using the redshift as a feature
instead of the kinematical backreaction.Comment: 20 pages incl. 16 captioned figures. Accepted for publication in PR
Cosmological parameter constraints using phenomenological symbolic expressions: On the significance of symbolic expression complexity and accuracy
Phenomenological models are widely used in cosmology in relation to
constraining different cosmological models, with two common examples being
cosmographic expansions and modeling the equation-of-state parameter of dark
energy. This work presents a study of how using different phenomenological
expressions for observables and physical quantities versus using physically
motivated, derived expressions affects cosmological parameter constraints. The
study includes the redshift-distance relation and Hubble parameter as
observables, and the dark energy equation-of-state parameter as a physical
quantity, and focuses on constraining the cosmological parameter
. The observables and equation-of-state parameter are all
modeled both using the physical, derived expressions and a variety of
phenomenological models with different levels of accuracy and complexity. The
results suggest that the complexity of phenomenological expressions only has
minor impact on the parameter constraints unless the complexity is very high.
The results also indicate that statistically significantly different results
can be expected from parameter constraints using different phenomenological
models if the models do not have very similar accuracy. This suggests that a
good practice is to use multiple phenomenological models when possible, in
order to assess the model dependence of results. Straightforward examples of
this is that results obtained using cosmographic expansions should always be
checked against similar results obtained with expansions of other order, and
when using phenomenological models such as for the equation-of-state
parameters, robustness of results could be assessed using fitted models from
symbolic regression, similar to what is done in this study.Comment: 17 pages, 8 captioned figures. To be published in PR
Machine learning cosmic backreaction and its effects on observations
Symbolic expressions for cosmic backreaction and mean redshift drift in a
range of 2-region models in terms of average quantities are presented. The
demonstration that these expressions can be obtained constitutes the opening of
a new avenue towards understanding the effects of cosmic backreaction in our
universe: With a symbolic expression for the redshift drift at hand, the
redshift drift can be used to constrain cosmological parameters including the
large-scale expansion rate and backreaction. In addition, by introducing
symbolic expressions for cosmic backreaction, this quantity can be constrained
with observations such as redshift-distance measures.Comment: 6 pages, 3 captioned figures. Accepted for publication in PR
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