The growth of structure

The growth of structure by gravitational collapse from initially small perturbations is described. The Jeans instability is calculated. The structure equations are obtained and solved in various cases (radiation-dominated, matter-dominated and others) via a linearized treatment. Hence the main features of the growth of density perturbations are obtained. The observed spectrum in the present is used to infer the primordial spectrum. The scale-invariant (Harrison-Zol’dovich) spectrum is described. The process of baryon acoustic oscillations is outlined and the sound horizon is defined. The chapter concludes with brief notes on galaxy formatiom.

Can f(R) gravity relieve $$H_0$$ and $$\sigma _8$$ tensions?

To investigate whether f(R) gravity can relieve current $$H_0$$ H 0 and $$\sigma _8$$ σ 8 tensions, we constrain the Hu-Sawicki f(R) gravity with Planck-2018 cosmic microwave background and redshift space distortions observations. We find that this model fails to relieve both $$H_0$$ H 0 and $$\sigma _8$$ σ 8 tensions, and that its two typical parameters $$\log _{10}f_{R0}$$ log 10 f R 0 and n are insensitive to other cosmological parameters. Combining the cosmic microwave background, baryon acoustic oscillations, Type Ia supernovae, cosmic chronometers with redshift space distortions observations, we give our best constraint $$\log _{10}f_{R0}<-6.75$$ log 10 f R 0 < - 6.75 at the $$2\sigma $$ 2 σ confidence level.

Baryon acoustic oscillations in the projected cross-correlation function between the eBOSS DR16 quasars and photometric galaxies from the DESI Legacy Imaging Surveys

ABSTRACT We search for the baryon acoustic oscillations in the projected cross-correlation function binned into transverse comoving radius between the SDSS-IV DR16 eBOSS quasars and a dense photometric sample of galaxies selected from the DESI Legacy Imaging Surveys. We estimate the density of the photometric sample of galaxies in this redshift range to be about 2900 deg−2, which is deeper than the official DESI emission line galaxy selection, and the density of the spectroscopic sample is about 20 deg−2. In order to mitigate the systematics related to the use of different imaging surveys close to the detection limit, we use a neural network approach that accounts for complex dependences between the imaging attributes and the observed galaxy density. We find that we are limited by the depth of the imaging surveys that affects the density and purity of the photometric sample and its overlap in redshift with the quasar sample, which thus affects the performance of the method. When cross-correlating the photometric galaxies with quasars in the range 0.6 ≤ z ≤ 1.2, the cross-correlation function can provide better constraints on the comoving angular distance DM (6 per cent precision) compared to the constraint on the spherically averaged distance DV (9 per cent precision) obtained from the autocorrelation. Although not yet competitive, this technique will benefit from the arrival of deeper photometric data from upcoming surveys that will enable it to go beyond the current limitations we have identified in this work.

Dynamical dark energy after Planck CMB final release and H0 tension

In this article we compare a variety of well known dynamical dark energy models using the cosmic microwave background measurements from the 2018 Planck legacy and 2015 Planck data releases, the baryon acoustic oscillations measurements and the local measurements of H0 obtained by the SH0ES (Supernovae, H0, for the Equation of State of Dark energy) collaboration analysing the Hubble Space Telescope data. We discuss the alleviation of H0 tension, that is obtained at the price of a phantom-like dark energy equation of state. We perform a Bayesian evidence analysis to quantify the improvement of the fit, finding that all the dark energy models considered in this work are preferred against the ΛCDM scenario. Finally, among all the possibilities analyzed, the CPL model is the best one in fitting the data and solving the H0 tension at the same time. However, unfortunately, this dynamical dark energy solution is not supported by the baryon acoustic oscillations (BAO) data, and the tension is restored when BAO data are included for all the models.

Baryon acoustic oscillations reconstruction using convolutional neural networks

ABSTRACT We propose a new scheme to reconstruct the baryon acoustic oscillations (BAO) signal, which contains key cosmological information, based on deep convolutional neural networks (CNN). Trained with almost no fine tuning, the network can recover large-scale modes accurately in the test set: the correlation coefficient between the true and reconstructed initial conditions reaches $90{{\ \rm per\ cent}}$ at $k\le 0.2 \, h\mathrm{Mpc}^{-1}$, which can lead to significant improvements of the BAO signal-to-noise ratio down to $k\simeq 0.4\, h\mathrm{Mpc}^{-1}$. Since this new scheme is based on the configuration-space density field in sub-boxes, it is local and less affected by survey boundaries than the standard reconstruction method, as our tests confirm. We find that the network trained in one cosmology is able to reconstruct BAO peaks in the others, i.e. recovering information lost to non-linearity independent of cosmology. The accuracy of recovered BAO peak positions is far less than that caused by the difference in the cosmology models for training and testing, suggesting that different models can be distinguished efficiently in our scheme. It is very promising that our scheme provides a different new way to extract the cosmological information from the ongoing and future large galaxy surveys.