Fernando Garcia Cortez

Ongoing work: Neural methods for cosmological differential equations

One aspect that makes cosmological problems difficult to tackle is that variety of scales. Big numbers coexisting with small numbers, large scales and small scales, extremely fast processes parallel with slow processes, and the list goes on. As such, many numerical methods fail at modeling them. I am studying different neural approaches to replace numerical methods, in the hope of producing efficient and as-precise model surrogates.

Ongoing work: CMB differentiable code

I am in the initial stages of developing a fully differentiable CMB cosmology code. Differentiability opens the door to many possibilities, such as faster parameter estimation, but most importantly, it allows us to constrain models with an immense number of parameters.

Ongoing work: BCS inspired dark matter

Following the work in my undergraduate honors thesis, I am running statistical analysis of a model of dark matter based on the BCS theory of superconductivity.

Previous work

In the past, I have worked on thermal field theory, neutrino physics in a cosmological context, applications of representation theory to quantum field theory, as well as some geophysics and photonics.