Is $Λ$CDM on the run? Reconciling the CMB with the Lyman-$α$ Forest
Malcolm Fairbairn, Lucien Heurtier, María Olalla Olea-Romacho
TL;DR
This work confronts the tension between CMB and Ly-$\alpha$ forest data by constraining the scale dependence of the primordial power spectrum beyond a simple power law. By combining Planck, ACT DR6, SPT-3G, and eBOSS Ly-$\alpha$ data, the authors constrain both the running $\alpha_s$ and the running of the running $\beta_s$, finding that eBOSS dramatically tightens these constraints and favors nonzero higher-order runnings, challenging vanilla slow-roll inflation. They test inflationary potentials with localized features (bumps, dips, and axion-monodromy) using a new public tool PIPE, showing that such features can reproduce the observed small-scale suppression while remaining compatible with CMB constraints. The results imply a persistent CMB–Ly-$\alpha$ tension that may point to high-energy structures in the inflaton potential, and PIPE enables rapid testing of arbitrary potentials against current data. If confirmed by future surveys, these findings could provide indirect evidence for nontrivial inflationary dynamics beyond standard slow-roll models.
Abstract
We present new constraints on the scale dependence of the primordial power spectrum by combining Planck, ACT DR6, SPT-3G and eBOSS Lyman-$α$ forest data, extending sensitivity to smaller comoving scales. While ACT results previously indicated a mild preference for positive running of the spectral index, our joint analysis constrains both the running $α_s$ and its running $β_s$. Including eBOSS markedly tightens these constraints, yielding a $>2σ$ indication of nonzero $α_s$ and/or $β_s$, challenging predictions from vanilla slow-roll inflation potentials. Comparing reconstructed spectral parameters with theoretical models, we find that inflationary potentials with localised dips, bumps, or oscillations better reproduce the observed scale dependence. We release the public PIPE code to test arbitrary inflationary potentials against these datasets.
