The Excursion set approach: Stratonovich approximation and Cholesky decomposition

Nikakhtar, Farnik; Ayromlou, Mohammadreza; Baghram, Shant; Rahvar, S.; Tabar, M. Reza Rahimi; Sheth, Ravi K.

doi:10.1093/mnras/sty1415

Cited by 21 publications

(21 citation statements)

References 15 publications

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“…We will produce Markov trajectories by the method developed in our previous works [35,36,37] where the distribution of DM halos in the (extended) standard model are addressed in more detail. It is worth to mention that there is an important and fundamental difference between the statistics of DM halos and PBHs.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Primordial Black Holes in the Excursion Set Theory

Erfani,

Kameli,

Baghram

2021

Preprint

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We study primordial black holes (PBHs) formation in the excursion set theory (EST) in a vast range of PBHs masses with and without confirmed constraints on their abundance. In this work, a new concept of the first touch in the EST is introduced for PBHs formation which takes into account the earlier horizon reentry of smaller masses. Our study shows that in the EST, it is possible to produce PBHs in different mass ranges which could make up all dark matter. We also show that in a broad blue-tilted power spectrum, the production of PBHs is dominated towards a smaller mass. Our analysis put upper limit ∼ 0.1 on the amplitude of the curvature power spectrum at length scales relevant for PBHs formation.

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…Note that the non-Markov extension of EST has great importance in halo formation due to more physical smoothing window functions which lead to correction of statistics of FUs, especially in small scales[35,36,38].…”

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confidence: 99%

Primordial Black Holes in the Excursion Set Theory

Erfani,

Kameli,

Baghram

2021

Preprint

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“…It worths to mention, using other window functions, which results to non-Markov trajectories there is no analytical expression for f FU . So the first up-crossing of the trajectories should be counted numerically (for more discussion see Nikakhtar et al (2018)).…”

Section: Non-linear Structure Formationmentioning

confidence: 99%

Mass assembly history of dark matter halos in the light of $H_0$ tension

Kameli,

Baghram

2020

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The Hubble tension may introduce a new course of action to revise the standard ΛCDM model to unravel dark energy and dark matter physics. The Hubble parameter can be reconstructed by late-time observations of the background evolution model independently to reconciles the Hubble tension. We relate the reconstructed Hubble parameter to the structure formation and large scale structure observables in this work. We use the excursion set theory to calculate the number density of dark matter halos, the distribution of sub-halo progenitors, and dark matter halos' merger rate. We obtain the results for both the Markov and non-Markov extension of the excursion set theory. We show that the number density of dark matter halos in the reconstructed model has a ∼ 2σ difference in comparison to the Planck-2018 ΛCDM in the mass range of M < 10 12 M . We also compare the dark matter halo merger rate with the pair-galaxy statistics and their merger rate from observational data of HST, CANDEL survey. We assert a ∼ 5 times more accurate observations in the redshift range of z 0.75 − 2.5 can distinguish the reconstructed model and the Planck-2018 ΛCDM.

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“…The two ideas of peak theory (Bardeen et al 1986) and excursion set theory (EST) (Bond et al 1991) are theoretical developments to use the idea of using initial conditions to predict the late time distribution of matter. The two main features of these analytical models are the collapse model (spherical collapse, ellipsoidal collapse (Sheth et al 2001;Sheth & Tormen 2002)) and the smoothing functions of the initial density field (Nikakhtar et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Unravelling the role of cosmic velocity field in dark matter halo mass function using deep learning

Etezad-Razavi¹,

Erfan²,

Sotoudeh³

et al. 2021

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We discuss an implementation of a deep learning framework to gain insight into the dark matter structure formation. We investigate the impact of velocity and density field information on the construction of halo mass function through cosmological 𝑁-body simulations. In this direction, we train a Convolutional Neural Network (CNN) on the initial snapshot of an only dark matter simulation to predict the halo mass that individual particles fall into at 𝑧 = 0, in the halo mass range of 10.5 < log(𝑀/𝑀 ) < 14. Our results show a negligible improvement from including the velocity in addition to the density information when considering simulations based on the standard model of cosmology (ΛCDM) with the amplitude of initial scalar perturbations 𝐴 𝑠 = 2 × 10 −9 . In order to investigate the ellipsoidal collapse models and to study the effect of velocity in smaller mass ranges, we increase the initial power spectrum such that we see the effect of velocities in larger halos which are in the resolution of our simulation. The CNN model trained on the simulation snapshots with large 𝐴 𝑠 shows a considerable improvement in the halo mass prediction when adding the velocity field information. Eventually, for the simulation with 𝐴 𝑠 = 8 × 10 −8 , the model trained with only density information shows at least 80% increase in the mean squared error relative to the model with both velocity and density information at almost all mass scales, which indicates the failure of the density-only model to predict the halo masses in this case. Our results indicate that the effect of velocity field on the halo collapse is scale-dependent with a negligible effect for the standard model of cosmology in mass scales 10.5 < log(𝑀/𝑀 ) < 14.

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The Excursion set approach: Stratonovich approximation and Cholesky decomposition

Cited by 21 publications

References 15 publications

Primordial Black Holes in the Excursion Set Theory

Primordial Black Holes in the Excursion Set Theory

Mass assembly history of dark matter halos in the light of $H_0$ tension

Unravelling the role of cosmic velocity field in dark matter halo mass function using deep learning

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