Speaker
Description
We present a simulation-based inference analysis framework for a higher-order weak lensing observable called the integrated 3-point correlation function. For this we have created a forward model based on N-body simulations. This forward model can create realistic shear maps including survey masks, realistic shape noise, and relevant systematic effects. Furthermore, I present a Python package for the efficient evaluation of the integrated 3-point correlation function.
Combining this I have created a set of measurements of correlation functions that have been validated against theoretical predictions.
Based on these, a simulation-based inference pipeline has been established. For this, generative models are trained to learn the likelihood of the chosen summary statistics given the set of parameters. This ensemble is shown to be able to recover the correct cosmological parameters at the fiducial cosmology, as well as to pass coverage tests.
Applying this to the Dark Energy Survey year 3 galaxy shape catalogue, in our preliminary analysis we find $S_8 = 0.76 \pm 0.03$, agreeing with other late-time cosmological probes.
| Abstract title | Weak Lensing Cosmology using Simulation Based Inference |
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