Inverse-k Primordial Oscillations from a Symbolic Regression Search

Oscillatory features in the primordial power spectrum, potential signatures of new physics in the early universe, are usually searched for using fixed templates. In this work, we perform a template-free search for primordial features using symbolic regression. We find that both Planck and the combined Planck+ACT+SPT-3G datasets independently select an inverse-$k$ oscillation, $\cos(B/k)$ with $B\simeq4\,\mathrm{Mpc}^{-1}$, as the leading low-complexity feature. Comparing this inverse-$k$ template with standard linear and logarithmic oscillating templates, we find that it fits the data best, showing a weak preference for a non-zero amplitude. Our results show that symbolic regression as a powerful machine learning technique can provide an interpretable, model-independent approach to cosmological discovery.