# Joint Theoretical High Energy and Discovery Seminar – University of Copenhagen

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# Joint Theoretical High Energy and Discovery Seminar

Speaker: Juan Garcia Bellido,

Titel: A new perspective on Dark Energy modeling via Genetic Algorithms

Abstract:

The nature of Dark Energy is still a total mystery. We are beginning to accumulate data from type Ia supernovae (SnIa), the microwave background (CMB) and large scale structure (LSS), but lack a general framework that can accommodate all possible hypothesis without any prejudices about its nature. We propose to use Genetic Algorithms (GA) as a new method for such a model independent analysis, and extract information from several cosmological probes, such as the SnIa, the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This information consists of a model-independent and bias-free reconstruction of the functional form of the cosmological distances that characterize the data, like the luminosity $d_L(z)$ and the angular diameter distance $d_A(z)$ in the SnIa and BAO data, or the redshift-dependence of the matter density $\Om_m(z)$ in the growth rate data, $f\sigma_8(z)$. This information can then be used to recover the expansion history of the Universe $H(z)$, and the acceleration parameter $q(z)$, in the context of FRW models, or the radial mass function $\Om_M(r)$ in LTB models. In this way, the reconstruction is completely independent of our prior bias. We also give a prescription on the estimated error of the GA method based on the path integral formalism.