On the road to percent accuracy V: the non-linear power spectrum beyond ΛCDM with massive neutrinos and baryonic feedback

In the context of forthcoming galaxy surveys, to ensure unbiased constraints on cosmology and gravity when using non-linear structure information, percent-level accuracy is required when modelling the power spectrum. This calls for frameworks that can accurately capture the relevant physical effects, while allowing for deviations from ΛCDM. Massive neutrino and baryonic physics are two of the most relevant such effects. We present an integration of the halo model reaction frameworks for massive neutrinos and beyond-ΛCDM cosmologies. The integrated halo model reaction, combined with a pseudo power spectrum modelled by HMCode2020 is then compared against N-body simulations that include both massive neutrinos and an f(R) modification to gravity. We find that the framework is 5% accurate down to at least k≈3h/Mpc for a modification to gravity of |fR0|≤10−5 and for the total neutrino mass Mν≡∑mν≤0.15 eV. We also find that the framework is 4(1)% consistent with the Bacco (EuclidEmulator2) emulator for νwCDM cosmologies down to at least k≈3h/Mpc. Finally, we compare against hydrodynamical simulations employing HMCode2020’s baryonic feedback modelling on top of the halo model reaction. For νΛCDM cosmologies we find 2% accuracy for Mν≤0.48eV down to at least k≈5h/Mpc. Similar accuracy is found when comparing to νwCDM hydrodynamical simulations with Mν=0.06eV. This offers the first non-linear and theoretically general means of accurately including massive neutrinos for beyond-ΛCDM cosmologies, and further suggests that baryonic effects can be reliably modelled independently of massive neutrino and dark energy physics. These extensions have been integrated into the publicly available ReACT code.

On the road to percent accuracy V: the non-linear power spectrum beyond ΛCDM with massive neutrinos and baryonic feedback, Benjamin BoseBill S. WrightMatteo CataneoAlkistis PourtsidouCarlo GiocoliLucas LombriserIan G. McCarthyMarco BaldiSimon PfeiferQianli Xia, arXiv:2105.12114
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