Searching for local features in primordial power spectrum using genetic algorithms

Understand

We present a novel methodology for exploring local features directly in the primordial power spectrum using a genetic algorithm (GA) pipeline coupled with a Boltzmann solver and Cosmic Microwave Background data (CMB).

• After testing the robustness of our pipeline using mock data, we apply it to the latest CMB data, including Planck 2018 and CamSpec PR4.
• Our model-independent approach provides an analytical reconstruction of the power spectra that best fits the data, with the unsupervised machine learning algorithm exploring a functional space built off simple ``grammar'' functions.
• We find significant improvements upon the simple power-law behaviour, by $\Delta \chi^2 \lesssim -21$, consistently with more traditional model-based approaches.