The Bias from Hydrodynamic Simulations: Mapping Baryon Physics onto Dark Matter Fields

Sinigaglia, Francesco; Kitaura, Francisco-Shu; Balaguera-Antolínez, A.; Nagamine, Kentaro; Ata, M.; Shimizu, Ikkoh; Sánchez-Benavente, Manuel

doi:10.3847/1538-4357/ac158b

Cited by 17 publications

(3 citation statements)

References 62 publications

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“…with i = {knots, filaments, sheets, voids}. 10 To include the effect of peculiar velocities and obtain the Lyα forest in redshift space (as is observed), we perform a celllevel mapping from real to redshift space (Sinigaglia et al 2021(Sinigaglia et al , 2022(Sinigaglia et al , 2024, including a velocity bias contribution in the customary RSD formula (Kaiser 1984):…”

Section: Fast Lyα Forest Simulationsmentioning

confidence: 99%

The Negative Baryon Acoustic Oscillation Shift in the Lyα Forest from Cosmological Simulations

Sinigaglia,

Kitaura,

Nagamine

et al. 2024

ApJL

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We present the first measurement of the Lyα forest baryon acoustic oscillations (BAO) shift parameter from cosmological simulations. In particular, we generate a suite of 1000 accurate effective field-level bias-based Lyα forest simulations of volume V = ( 1 h − 1 Gpc ) 3 at z = 2, both in real and redshift space, calibrated upon two fixed-and-paired cosmological hydrodynamic simulations. To measure the BAO, we stack the 3D power spectra of the 1000 different realizations, compute the average, and use a model accounting for a proper smooth-peak component decomposition of the power spectrum, to fit it via an efficient Markov Chain Monte Carlo scheme estimating the covariance matrices directly from the simulations. We report the BAO shift parameters to be α = 0.9969 − 0.0014 + 0.0014 and α = 0.9905 − 0.0027 + 0.0027 in real and redshift space, respectively. We also measure the bias b lya and the BAO broadening parameter Σnl, finding b lya = − 0.1786 − 0.0001 + 0.0001 and Σ nl = 3.87 − 0.20 + 0.20 in real space, and b lya = − 0.073 − 0.004 + 0.005 and Σ nl = 6.55 − 0.22 + 0.23 in redshift space. Moreover, we measure the linear Kaiser factor β lya = 1.39 − 0.18 + 0.24 from the isotropic redshift space fit. Overall, we find evidence for a negative shift of the BAO peak at the ∼2.2σ and ∼3.5σ levels in real and redshift space, respectively. This work sets new important theoretical constraints on the Lyα forest BAO scale and offers a potential solution to the tension emerging from previous observational analysis, in light of ongoing and upcoming Lyα forest spectroscopic surveys, such as DESI, the Prime Focus Spectrograph Survey, and WEAVE-QSO.

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Section: Fast Lyα Forest Simulationsmentioning

confidence: 99%

The Negative Baryon Acoustic Oscillation Shift in the Lyα Forest from Cosmological Simulations

Sinigaglia,

Kitaura,

Nagamine

et al. 2024

ApJL

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“…The concept of the bias as a multidimensional probability distribution has been shown to be very versatile and powerful. It has allowed not only to assign halo masses but also halo positions in order to create realistic mock catalogues (Balaguera-Antolínez et al 2019 and even assign baryonic properties to dark matter catalogues (Sinigaglia et al 2021).…”

Section: Hadron Codementioning

confidence: 99%

A machine learning approach to correct for mass resolution effects in simulated halo clustering statistics

Forero-Sánchez

Chuang

Rodríguez-Torres

et al. 2022

Monthly Notices of the Royal Astronomical Society

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The increase in the observed volume in cosmological surveys imposes various challenges on simulation preparations. Firstly, the volume of the simulations required increases proportionally to the observations. However, large-volume simulations are quickly becoming computationally intractable. Secondly, on-going and future large-volume survey are targeting smaller objects, e.g. emission line galaxies, compared to the earlier focus, i.e. luminous red galaxies. They require the simulations to have higher mass resolutions. In this work we present a machine learning (ML) approach to calibrate the halo catalogue of a low-resolution (LR) simulation by training with a paired high-resolution (HR) simulation with the same background white noise, thus we can build the training data by matching HR haloes to LR haloes in a one-to-one fashion. After training, the calibrated LR halo catalogue reproduces the mass-clustering relation for mass down to 2.5 × 1011 h−1M⊙ within 5 per cent at scales $k<1~h\, \rm Mpc^{-1}$. We validate the performance of different statistics including halo mass function, power spectrum, two-point correlation function, and bispectrum in both real and redshift space. Our approach generates high-resolution-like halo catalogues (>200 particles per halo) from low-resolution catalogues (>25 particles per halo) containing corrected halo masses for each object. This allows to bypass the computational burden of a large-volume real high-resolution simulation without much compromise in the mass resolution of the result. The cost of our ML approach (∼1 CPU-hour) is negligible compared to the cost of a N-body simulation (e.g. millions of CPU-hours), The required computing time is cut a factor of 8.

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“…More recently, the BAM code has been designed to learn the complex bias relation from reference simulations (Balaguera-Antolínez et al 2019Kitaura et al 2022). These techniques can also accurately map the Lyman-α forest (see, e.g., Sinigaglia et al 2021Sinigaglia et al , 2022. All the methods mentioned above require a dark matter field defined on a mesh.…”

Section: Introductionmentioning

confidence: 99%

The cosmic web from perturbation theory

Kitaura,

Sinigaglia,

Balaguera-Antolínez

et al. 2024

A&A

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Analysing the large-scale structure (LSS) in the Universe with galaxy surveys demands accurate structure formation models. Such models should ideally be fast and have a clear theoretical framework in order to rapidly scan a variety of cosmological parameter spaces without requiring large training data sets. This study aims to extend Lagrangian perturbation theory (LPT), including viscosity and vorticity, to reproduce the cosmic evolution from dark matter $N$-body calculations at the field level. We extend LPT to a Eulerian framework, which we dub eALPT. An ultraviolet regularisation through the spherical collapse model provided by Augmented LPT turns out to be crucial at low redshifts. This iterative method enables modelling of the stress tensor and introduces vorticity. The eALPT model has two free parameters apart from the choice of cosmology, redshift snapshots, cosmic volume, and the number of particles. Mpc black and $z=0$ from $ with the Zel'dovich approximation ($ with ALPT), to sim 95<!PCT!> with the three-timestep eALPT, and the power spectra show percentage accuracy up to $k Mpc

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The Bias from Hydrodynamic Simulations: Mapping Baryon Physics onto Dark Matter Fields

Cited by 17 publications

References 62 publications

The Negative Baryon Acoustic Oscillation Shift in the Lyα Forest from Cosmological Simulations

The Negative Baryon Acoustic Oscillation Shift in the Lyα Forest from Cosmological Simulations

A machine learning approach to correct for mass resolution effects in simulated halo clustering statistics

The cosmic web from perturbation theory

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