Testing dark energy models with $H(z)$ data

Understand

$Om(z)$ is a diagnostic approach to distinguish dark energy models.

• However, there are few articles to discuss what is the distinguishing criterion.
• In this paper, firstly we smooth the latest observational $H(z)$ data using a model-independent method -- Gaussian processes, and then reconstruct the $Om(z)$ and its fist order derivative $\mathcal{L}^{(1)}_m$.
• Such reconstructions not only could be the distinguishing criteria, but also could be used to estimate the authenticity of models.