Mapping Lyman-alpha forest three-dimensional large scale structure in real and redshift space

Abstract

This work presents a new physically-motivated supervised machine learning method, hydro-bam, to reproduce the three-dimensional Lyman-α forest field in real and in redshift space learning from a reference hydrodynamic simulation, thereby saving about 7 orders of magnitude in computing time. We show that our method is accurate up to k ∼ 1 h Mpc −1 in the one-(PDF), two-(power-spectra) and three-point (bi-spectra) statistics of the reconstructed fields. When compared to the reference simulation including redshift space distortions, our method achieves deviations of 2% up to k = 0.6 h Mpc −1 in the monopole, 5% up to k = 0.9 h Mpc −1 in the quadrupole. The bi-spectrum is well reproduced for triangle configurations with sides up to k = 0.8 h Mpc −1 . In contrast, the commonly-adopted Fluctuating Gunn-Peterson approximation shows significant deviations already neglecting peculiar motions at configurations with sides of k = 0.2 − 0.4 h Mpc −1 in the bi-spectrum, being also significantly less accurate in the power-spectrum (within 5% up to k = 0.7 h Mpc −1 ). We conclude that an accurate analysis of the Lyman-α forest requires considering the complex baryonic thermodynamical large-scale structure relations. Our hierarchical domain specific machine learning method can efficiently exploit this and is ready to generate accurate Lyman-α forest mock catalogues covering large volumes required by surveys such as DESI and WEAVE.

Fast, High-fidelity Lyα Forests with Convolutional Neural Networks

“…The range of scales accurately mapped with this method is well beyond the resolution of next-generation observations (Walther et al 2021), as well as that of alternate machinelearning-based approaches for modeling Lyα forest (Sinigaglia et al 2021). Our method provides an alternative to running costly hydrodynamical simulations for mock catalog generation and the construction of Lyα power-spectra emulators.…”

LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields

“…Note that in a companion paper (Horowitz et al 2021), convolutional neural networks have also been used to synthesize hydrodynamic fields conditioned on dark matter fields from N-body simulations, which might be very useful for the rapid generation of mocks. Similarly, Sinigaglia et al (2021) has developed a new physically-motivated supervised machine learning method (HYDRO-BAM) from a reference hydrodynamical simulation of comoving side 100 Mpc/h. The PDF, 3d power spectrum and bi-spectra can be reconstructed with error of a few percent up to modes k= 0.9 Mpc/h.…”

“…Fig. 5 of Sinigaglia et al (2021)). But 3d Ly-α forest surveys also enable precise measurements of flux correlations at much smaller scales where the FGPA might not be adequate.…”

“…Both methods have proved to be again more accurate than the FGPA approach (which strongly relies on the DM smoothing scale) when reproducing line-of-sight observables, such as the PDF and power spectrum as well as the 3d flux power spectrum (5-20%). Finally, Machine Learning based methods start to be considered and lead to promising results (Harrington et al 2021;Sinigaglia et al 2021;Chopitan et al 2021).…”

The hierarchical cosmic web and assembly bias

“…The study by [23], using high mass halos (halos with ≳ 10 12 M ⊙ h −1 in a 1,5 h −1 Gpc side cubical volume), showed that a proper modelling of the higher order statistics requires going to at least third-order non-local bias, and that this can be done through the cosmic web classification. These findings were exploited in the context of effective baryonic physics bias mapping and Lyman-α forest modelling (see [36][37][38]). This paper extends previous PATCHY versions with a novel hierarchical cosmic web classification, indirectly accounting for both long-and short-range non-local bias at least up to third order.…”