Searching for Local Features in Primordial Power Spectrum using Genetic Algorithms

Abstract: 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. These best-fits always address both the low$\ell$ anomaly in the TT spectrum and the residual high$\ell$ oscillations in the TT, TE and EE spectra. The proposed pipeline provides an adaptable tool for exploring features in the primordial power spectrum in a model-independent way, providing valuable hints to theorists for constructing viable inflationary models that are consistent with the current and upcoming CMB surveys.