Description
An alternative approach to resolving the Hubble tension that relies on minimal assumptions of cosmology and does not propagate the uncertainty associated with calibrations of the distance ladder is based on the relationship between the Hubble parameter $H(z)$ and the differential age-redshift relation. This relationship allows us to quantify the expansion between two coevolving populations of galaxies as $H(z) = -dz/dt(1+z)$ thus allowing us to constrain the value of the Hubble constant $H_0$. By taking the differential age dt and redshift dz rather than absolute values of enough pairs, the uncertainties associated with their calculation can be overcome to provide an accurate measurement of the Hubble parameter.
Populations of passively evolving galaxies are used as standard clocks, or cosmic chronometers (CCs), however due to their intrinsic nature they are rare. Their selection relies on spectroscopic observation and full spectral fitting which limits the number of detected candidates. Therefore, we present a machine learning based method to flag cosmic chronometer candidates based on their images so future spectroscopic surveys can target them for observation in order to confirm CC status, determine their ages and use them to calculate $H_0$. Our method utilises outlier detection with a convolutional autoencoder concurrently with a KMeans clustering algorithm to identify CC candidates from the Galaxy and Mass Assembly (GAMA) survey. Using this approach, we determine a novel value of $H_0$ at the redshift regime of $0.03
