Clustering in the Simulated Hα Galaxy Redshift Survey from Nancy Grace Roman Space Telescope

We present a realistic 2000 deg2 Hα galaxy mock catalog with 1 < z < 2 for the Nancy Grace Roman Space Telescope galaxy redshift survey, the High Latitude Spectroscopic Survey (HLSS), created using Galacticus, a semi-analytical galaxy formation model, and high resolution cosmological N-body simulations. Galaxy clustering can probe dark energy and test gravity via baryon acoustic oscillation (BAO) and redshift space distortion (RSD) measurements. Using our realistic mock as the simulated Roman HLSS data, and a covariance matrix computed using a large set of approximate mocks created using EZmock, we investigate the expected precision and accuracy of the BAO and RSD measurements using the same analysis techniques used in analyzing real data. We find that the Roman Hα galaxy survey alone can measure the angular diameter distance with 2% uncertainty, the Hubble parameter with 3-6% uncertainty, and the linear growth parameter with 7% uncertainty, in each of four redshift bins. Our realistic forecast illustrates the power of the Roman galaxy survey in probing the nature of dark energy and testing gravity.

Periodicity of quasar and galaxy redshift

Context. An approach for studying the large-scale structure of the Universe lies in the detection and analysis of periodicity of redshift values of extragalactic objects, galaxies, and quasi stellar objects (QSO), in particular. Moreover, the hypothesis of the existence of multiple periodicities in the redshift distributions deserves exploration. The task is compounded by the presence of confounding effects and measurement noise. Aims. Studies of periodicity detection in redshift values of extragalactic objects obtained from the Sloan Digital Sky Survey (SDSS) have been conducted in the past, largely based on the Fourier transform. The present study aims to revisit the same thing using the singular value decomposition (SVD) as the primary tool. Methods. Periodicity detection and the determination of the fundamental period have been performed using a standard spectral approach as well as a SVD-based approach for a variety of simulated datasets. The analysis of the quasar redshift data from DR12 and galaxy redshift dataset of DR10 from SDSS data has been carried out. Results. A wide range of periodicities are observed in different redshift ranges of the quasar datasets. For redshifts greater than 0.03, a period length of 0.2094 was determined while periodicities of 0.1210 and 0.0654 were obtained for redshift ranges (0.03, 1) and (3, 4), respectively. Twin periodicities of 0.1153 and 0.0807 were obtained for the redshift range (1, 3). Determining the ranges to be examined has been done based on histogram computation; the binwidths of which have been obtained by employing a kernel density estimation. The redshift sequence for the galaxy samples exhibits a somewhat different nature, but still contains periodic components. Twin periodicities of 0.0056 and 0.0079 were observed for a redshift range greater than 0.03. Conclusions. Galaxy and quasar redshift values form sequences, which are not only discrete in amplitude but also contain periodic components. The superiority of the singular value decomposition method over the spectral estimation approach, in redshift periodicity analysis especially from the point of view of robustness, is demonstrated through simulations. The existence of periodicity for quasar and galaxy families is thus firmly established, lending further support to the Hoyle-Narlikar variable mass theory.

Cosmic flows in the nearby Universe: new peculiar velocities from SNe and cosmological constraints

ABSTRACT The peculiar velocity field offers a unique way to probe dark matter density field on large scales at low redshifts. In this work, we have compiled a new sample of 465 peculiar velocities from low redshift ($z$ < 0.067) Type Ia supernovae. We compare the reconstructed velocity field derived from the 2M++ galaxy redshift compilation to the supernovae, the SFI++ and the 2MTF Tully–Fisher distance catalogues. We used a forward method to jointly infer the distances and the velocities of distance indicators by comparing the observations to the reconstruction. Comparison of the reconstructed peculiar velocity fields to observations allows us to infer the cosmological parameter combination fσ8, and the bulk flow velocity arising from outside the survey volume. The residual bulk flow arising from outside the 2M++ volume is inferred to be $171^{+11}_{-11}$ km s−1 in the direction l = 301° ± 4° and b = 0° ± 3°. We obtain fσ8 = 0.400 ± 0.017, equivalent to S8 ≈ σ8(Ωm/0.3)0.55 = 0.776 ± 0.033, which corresponds to an approximately $4{{\ \rm per\ cent}}\,$ statistical uncertainty on the value of fσ8. Our inferred value is consistent with other low redshift results in the literature.

The cosmic web through the lens of graph entropy

ABSTRACT We explore the information theory entropy of a graph as a scalar to quantify the cosmic web. We find entropy values in the range between 1.5 and 3.2 bits. We argue that this entropy can be used as a discrete analogue of scalars used to quantify the connectivity in continuous density fields. After showing that the entropy clearly distinguishes between clustred and random points, we use simulations to gauge the influence of survey geometry, cosmic variance, redshift space distortions, redshift evolution, cosmological parameters, and spatial number density. Cosmic variance shows the least important influence while changes from the survey geometry, redshift space distortions, cosmological parameters, and redshift evolution produce larger changes of the order of 10−2 bits. The largest influence on the graph entropy comes from changes in the number density of clustred points. As the number density decreases, and the cosmic web is less pronounced, the entropy can diminish up to 0.2 bits. The graph entropy is simple to compute and can be applied both to simulations and observational data from large galaxy redshift surveys; it is a new statistic that can be used in a complementary way to other kinds of topological or clustering measurements.