In this talk, I'll summarize the current status and future perspectives on the measurement of Primordial non-Gaussianity within the Dark Energy Survey collaboration.
Primordial non-Gaussianity (PNG) is a promising observable of the underlying physics of inflation, characterized by a parameter denoted by 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 on the treatment of integral constraint, a condition that appears when estimating the number density of galaxies from the data and is especially relevant for PNG analyses, which is key in obtaining unbiased fNL constraints.
The methods are analyzed and validated using two types of simulations: GOLIAT-PNG N-body simulations with non-Gaussian initial conditions and the Gaussian ICE-COLA mocks that follow the Y3 DES 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 could have when measuring fNL, I'll also briefly mention the current efforts to look for an optimal galaxy sample using DES Y6 data.
Seminario CFP, hibrido: Edificio 2, Sala María de Maeztu / Zoom
Coordenadas zoom: https://cern.zoom.us/j/98768015328, pass 092020