SP(k) - a hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum

Upcoming large-scale structure surv e ys will measure the matter power spectrum to approximately per cent level accuracy with the aim of searching for evidence for new physics beyond the standard model of cosmology. In order to a v oid biasing our conclusions, the theoretical predictions need to be at least as accurate as the measurements for a given choice of cosmological parameters. Ho we ver, recent theoretical work has shown that complex physical processes associated with galaxy formation (particularly energetic feedback processes associated with stars and especially supermassive black holes) can alter the predictions by many times larger than the required accuracy. Here we present SP(k) , a model for the effects of baryon physics on the non-linear matter power spectrum based on a new large suite of hydrodynamical simulations. Specifically, the ANTILLES suite consists of 400 simulations spanning a very wide range of the feedback landscape' and show that the effects of baryons on the matter power spectrum can be understood at approaching the per cent level in terms of the mean baryon fraction of haloes, at scales of up to k less than or similar to 10 h Mpc (-1) and redshifts up to z = 3. For the range of scales and redshifts that will be probed by forthcoming cosmic shear measurements, most of the effects are driven by galaxy group mass haloes (M similar to 10 (13-14) M-circle dot). We present a simple PYTHON implementation of our model, available at https://github.com/jemme07/pyspk, which can be used to incorporate baryon effects in standard gravity-only predictions, allowing for marginalization o v er baryon physics within cosmological pipelines.

Automated summary

Upcoming large-scale structure surveys aim to measure the matter power spectrum with high accuracy to search for new physics. However, recent theoretical work shows that complex physical processes associated with galaxy formation can significantly alter the predictions beyond the required accuracy.