Looking more into deep learning and missing power on large scales

Our Dark Sector group at JPL has recently started an initiative to expand our skillsets towards deep learning, eventually coming up with new ideas to tackle the challenges that we'll face in the current and upcoming generation of cosmological surveys. I've thus started to dig into the deep learning book by Francois Chollet, which I … Continue reading Looking more into deep learning and missing power on large scales

First look into the scale-dependent bias and non-Gaussianity

This afternoon, I worked on a model implementation of the scale-dependent bias, following the ansatz of De Putter & Doré (2014, their Eq. 1). The goal is to constrain the fNL parameter which quantifies the amplitude of the primordial non-Gaussianity. These primordial density fluctuations are very Gaussian, but a small departure from this Gaussianity or limits on … Continue reading First look into the scale-dependent bias and non-Gaussianity

Python packaging, astropy, and Travis CI

Last week, I started to re-visited quickspeck, a python package by Duncan Hanson that allows to create angular power spectra for cosmic fields under the Limber approximation (see this paper for a nice overview and an extension). In light of the upcoming CMB lensing workshop at Stanford and our ongoing work on the cross correlation between … Continue reading Python packaging, astropy, and Travis CI