Baryonic feedback biases on fundamental physics from lensed CMB power spectra

Upcoming measurements of the small-scale primary cosmic microwave background (CMB) temperature and polarization power spectra ($TT/TE/EE$) are anticipated to yield transformative constraints on new physics, including the effective number of relativistic species in the early universe (${N}_{\mathrm{eff}}$). However, at multipoles $\ensuremath{\ell}\ensuremath{\gtrsim}3000$, the CMB power spectra receive significant contributions from gravitational lensing. While these modes still carry primordial information, their theoretical modeling requires knowledge of the CMB lensing convergence power spectrum, ${C}_{L}^{\ensuremath{\kappa}\ensuremath{\kappa}}$, including on small scales where it is affected by nonlinear gravitational evolution and baryonic feedback processes. Thus, the high-$\ensuremath{\ell}$ (lensed) CMB is sensitive to these late-time, nonlinear effects. Here, we show that inaccuracies in the modeling of ${C}_{L}^{\ensuremath{\kappa}\ensuremath{\kappa}}$ can yield surprisingly large biases on cosmological parameters inferred from the lensed CMB power spectra measured by the upcoming Simons Observatory and CMB-S4 experiments. For CMB-S4, the biases can be as large as $1.6\ensuremath{\sigma}$ on the Hubble constant ${H}_{0}$ in a fit to $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ and $1.2\ensuremath{\sigma}$ on ${N}_{\mathrm{eff}}$ in a fit to $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}+{N}_{\mathrm{eff}}$. We show that these biases can be mitigated by explicitly discarding all $TT$ data at $\ensuremath{\ell}>3000$ or by marginalizing over parameters describing baryonic feedback processes, both at the cost of slightly larger error bars. We also discuss an alternative, data-driven mitigation strategy based on delensing the CMB $T$ and $E$-mode maps. Finally, we show that analyses of upcoming data will require Einstein-Boltzmann codes to be run with much higher numerical precision settings than is currently standard, so as to avoid similar--- or larger---parameter biases due to inaccurate theoretical predictions.

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