Primordial non-Gaussianity with the Dark Energy Survey

In this thesis defense, I'll summarise the current status and future perspectives on measuring primordial non-Gaussianity within the Dark Energy Survey collaboration. Primordial non-Gaussianity (PNG) is a promising observable of the underlying physics of cosmic inflation, characterised by a parameter denoted fNL. I'll focus on presenting the methodology to measure local fNL from the Dark Energy Survey (DES) data using the 2-point angular correlation function (ACF) via the induced scale-dependent bias. One of the main focuses is the treatment of the integral constraint, a condition that arises when estimating the number density of galaxies from the data itself. This is particularly relevant for PNG analyses and is key to obtaining unbiased fNL constraints. The methods are analysed using two types of simulations: GOLIAT N-body simulations with non-Gaussian initial conditions and the Gaussian ICE-COLA mocks that follow the DES-Y3 BAO angular and redshift distribution. Using the ICE-COLA mocks, we forecast constraints in fNL when using the BAO sample. To assess the impact that using different galaxy samples might have on the measurement of fNL, I'll also mention the current efforts to search for an optimised galaxy sample using the final DES-Y6 data.

pie de foto: 

pie de foto: