Title: Predicting the impact of feedback on matter clustering with machine learning in CAMELS Show Chinese title

Title: 用机器学习预测反馈对物质聚集的影响在CAMELS中

Abstract: Extracting information from the total matter power spectrum with the precision needed for upcoming cosmological surveys requires unraveling the complex effects of galaxy formation processes on the distribution of matter. We investigate the impact of baryonic physics on matter clustering at $z=0$ using a library of power spectra from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, containing thousands of $(25\,h^{-1}{\rm Mpc})^3$ volume realizations with varying cosmology, initial random field, stellar and AGN feedback strength and sub-grid model implementation methods. We show that baryonic physics affects matter clustering on scales $k \gtrsim 0.4\,h\,\mathrm{Mpc}^{-1}$ and the magnitude of this effect is dependent on the details of the galaxy formation implementation and variations of cosmological and astrophysical parameters. Increasing AGN feedback strength decreases halo baryon fractions and yields stronger suppression of power relative to N-body simulations, while stronger stellar feedback often results in weaker effects by suppressing black hole growth and therefore the impact of AGN feedback. We find a broad correlation between mean baryon fraction of massive halos ($M_{\rm 200c} > 10^{13.5}$\,\Msun) and suppression of matter clustering but with significant scatter compared to previous work owing to wider exploration of feedback parameters and cosmic variance effects. We show that a random forest regressor trained on the baryon content and abundance of halos across the full mass range $10^{10} \leq M_\mathrm{halo}/$\Msun$< 10^{15}$ can predict the effect of galaxy formation on the matter power spectrum on scales $k = 1.0$--20.0\,$h\,\mathrm{Mpc}^{-1}$. Show Chinese abstract

Abstract: 从总物质功率谱中提取出满足即将到来的宇宙学调查所需精度的信息，需要解开星系形成过程对物质分布的复杂影响。 我们利用来自宇宙学与机器学习模拟项目（CAMELS）的功率谱库，研究了在$z=0$尺度下重子物理对物质聚集的影响，该库包含数千个$(25\,h^{-1}{\rm Mpc})^3$体积实现，具有不同的宇宙学参数、初始随机场、恒星和活动星系核反馈强度以及亚网格模型实现方法。 我们表明，重子物理在$k \gtrsim 0.4\,h\,\mathrm{Mpc}^{-1}$尺度上影响物质聚集，这种影响的大小取决于星系形成实现的细节以及宇宙学和天体物理参数的变化。 增强活动星系核反馈强度会减少晕中的重子分数，并相对于N体模拟产生更强的功率抑制，而更强的恒星反馈通常由于抑制黑洞增长从而减少活动星系核反馈的影响，导致效果较弱。 我们发现大质量晕的平均重子分数（$M_{\rm 200c} > 10^{13.5}$ \Msun ）与物质聚集的抑制之间存在广泛的相关性，但由于对反馈参数的更广泛探索和宇宙方差效应，与之前的工作相比存在显著的散射。 我们证明，一个在全质量范围内的晕的重子含量和丰度上训练的随机森林回归器$10^{10} \leq M_\mathrm{halo}/$\Msun $< 10^{15}$ 可以预测星系形成对物质功率谱的影响在尺度$k = 1.0$--20.0$h\,\mathrm{Mpc}^{-1}$。