分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Galaxy formation theories predict that galaxy shapes and angular momenta have intrinsic alignments (IA) with the cosmic web, which provides an observational test of various theories, and is important to quantify as a nuisance parameter for weak lensing. We study galaxy IA in the IllustrisTNG suite of hydrodynamical simulations at redshifts 1 and 2, finding that alignment trends are consistent with previous studies. However, we find that the magnitude of the spin alignment signal is $\sim 2.4 \times$ weaker than seen in previous studies of the Horizon-AGN simulation, suggesting that IA may have significant dependence on subgrid physics. Based on IllustrisTNG, we then construct mock observational spectroscopic surveys that can probe shape-cosmic web IA at $z \sim 1-2$, modeled on the low-$z$ galaxy redshift and IGM tomography surveys on the upcoming Subaru Prime Focus Spectrograph Galaxy Evolution (PFS GE) survey. However, even over box sizes of $L=205 h^{-1} \mathrm{Mpc}$, we find that global anisotropies induce a sample variance in the 2D projected IA signal that depend on the projected direction -- this induces significant errors in the observed alignment. We predict a $5.3\sigma$ detection of IlustrisTNG's shape IA signal at $z \sim 1$ from Subaru PFS GE, although a detection would be challenging at $z \sim 2$. However, a rough rescaling of the relative IA signal strengths between the TNG and HorizonAGN simulations suggests that PFS GE should be able to constrain the latter's stronger signal.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： In this work, we propose an improved approach to reconstruct the three-dimensional intergalactic medium from observed Lyman-$\alpha$ forest absorption features. We present our new method, the Optimized Reconstruction with Constraints on Absorption (ORCA), which outperforms the current baseline Wiener Filter (WF) when tested on mock Lyman Alpha forest data generated from hydrodynamical simulations. We find that both reconstructed flux errors and cosmic web classification improve substantially with ORCA, equivalent to 30-40\% additional sight-lines with the standard WF. We use this method to identify and classify extremal objects, i.e. voids and (proto)-clusters, and find improved reconstruction across all summary statistics explored. We apply ORCA to existing Lyman Alpha forest data from the COSMOS Lyman Alpha Mapping and Tomography Observations (CLAMATO) Survey and compare it to the WF reconstruction.
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