SITP Wine and Cheese Seminar: Fast and Differentiable Big Bang Nucleosynthesis

May 17, 2024 - 3:00 pm to 4:00 pm

Campus, Varian 312

Hongwan Liu (KICP, UChicago, & Fermilab ) In Person

The process of Big Bang Nucleosynthesis (BBN) is a crucial test of cosmology. In this talk, I will describe a new code for predicting the primordial elemental abundance due to BBN. This code takes advantage of JAX, a machine learning framework, to enable fast and differentiable predictions of elemental abundances. This allows us to put BBN calculations on the same level of rigor and ease-of-use as cosmic microwave background analyses, taking nuclear rate uncertainties fully into account. The differentiable nature of the code will also allow the use of more sophisticated and efficient methods of parameter estimation beyond e.g. traditional MCMC techniques.  Post-inflation axion models also face a potential problem from fractionally charged relics; solving this problem often leads to low-energy Landau poles for Standard Model gauge couplings, reintroducing the quality problem. We study several examples, finding that models that solve the quality problem face cosmological problems, and vice versa. This is not a no-go theorem; nonetheless, we argue that it is much more difficult than generally appreciated to find a viable post-inflation QCD axion model. Successful examples may have a nonstandard cosmological history (e.g., multiple types of cosmic axion strings of different tensions), undermining the widespread expectation that the post-inflation QCD axion scenario predicts a unique mass for axion dark matter.