The terms Eisenstein and Hu missed
Date:
The matter power spectrum of cosmology, $P(k)$, is of fundamental importance in cosmological analyses, yet solving the Boltzmann equations can be computationally prohibitive if required several thousand times, e.g. in a MCMC. Emulators for $P(k)$ as a function of cosmology have therefore become popular, whether they be neural network or Gaussian process based. Yet one of the oldest emulators we have is an analytic, physics-informed fit proposed by Eisenstein and Hu (E&H). Given this is already accurate to within a few percent, does one really need a large, black-box, numerical method for calculating $P(k)$, or can one simply add a few terms to E&H? In this talk I demonstrate that Symbolic Regression can obtain such a correction, yielding sub-percent level predictions for $P(k)$.
- Given at:
- Debating the potential of machine learning in astronomical surveys ML-IAP/CCA-2023 (Nov. 2023)