Search for: All records

Aims.We cross-correlated galaxies from the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) second data release (DR2) radio source with the extended Baryon Oscillation Spectroscopic Survey (eBOSS) luminous red galaxy (LRG) sample to extract the baryon acoustic oscillation (BAO) signal and constrain the linear clustering bias of radio sources in LoTSS DR2. Methods.In the LoTSS DR2 catalogue, employing a flux density limit of 1.5 mJy at the central LoTSS frequency of 144 MHz and a signal-to-noise ratio (S/N) of 7.5, additionally considering eBOSS LRGs with redshifts between 0.6 and 1, we measured both the angular LoTSS-eBOSS cross-power spectrum and the angular eBOSS auto-power spectrum. These measurements were performed across various eBOSS redshift tomographic bins with a width of Δz = 0.06. By marginalising over the broadband shape of the angular power spectra, we searched for a BAO signal in cross-correlation with radio galaxies, and determine the linear clustering bias of LoTSS radio sources for a constant-bias and an evolving-bias model. Results.Using the cross-correlation, we measured the isotropic BAO dilation parameter asα = 1.01 ± 0.11 atz_eff = 0.63. By combining four redshift slices atz_eff = 0.63, 0.69, 0.75, and 0.81, we determined a more constrained value ofα = 0.968_−0.095^+0.060. For the entire redshift range ofz_eff = 0.715, we measuredb_C = 2.64 ± 0.20 for the constant-bias model,b(z0) =b_C, and thenb_D = 1.80 ± 0.13 for the evolving-bias model,b(z) =b_D/D(z), withD(z) denoting the growth rate of linear structures. Additionally, we measured the clustering bias for individual redshift bins. Conclusions.We detected the cross-correlation of LoTSS radio sources and eBOSS LRGs at a 9.2σstatistical significance for one single redshift bin and at a 14.7σsignificance when the four redshift bins were combined. For the BAO signal, we achieved a significance of 2.2σfor a single redshift bin, 2.7σfor the combined cross-correlation and eBOSS auto-correlation, and 4σfor the combined analysis of four redshift bins in the cross-correlation, when assuming a Gaussian distribution for the BAO dilation parameter. 

Context.We study the flux density dependence of the redshift distribution of low-frequency radio sources observed in the LOFAR Two-metre Sky Survey (LoTSS) deep fields and apply it to estimate the clustering length of the large-scale structure of the Universe, examining flux density limited samples (1 mJy, 2 mJy, 4 mJy and 8 mJy) of LoTSS wide field radio sources. Methods.We utilise and combine the posterior probability distributions of photometric redshift determinations for LoTSS deep field observations from three different fields (Boötes, Lockman hole and ELAIS-N1, together about 26 square degrees of sky), which are available for between 91% to 96% of all sources above the studied flux density thresholds and observed in the area covered by multi-frequency data. We estimate uncertainties by a bootstrap method. We apply the inferred redshift distribution on the LoTSS wide area radio sources from the HETDEX field (LoTSS-DR1; about 424 square degrees) and make use of the Limber approximation and a power-law model of three dimensional clustering to measure the clustering length,r₀, for various models of the evolution of clustering. Results.We find that the redshift distributions from all three LoTSS deep fields agree within expected uncertainties. We show that the radio source population probed by LoTSS at flux densities above 1 mJy has a median redshift of at least 0.9. At 2 mJy, we measure the clustering length of LoTSS radio sources to ber₀ = (10.1 ± 2.6) h⁻¹Mpc in the context of the comoving clustering model. Conclusions.Our findings are in agreement with measurements at higher flux density thresholds at the same frequency and with measurements at higher frequencies in the context of the comoving clustering model. Based on the inferred flux density limited redshift distribution of LoTSS deep field radio sources, the full wide area LoTSS will eventually cover an effective (source weighted) comoving volume of about 10 h⁻³Gpc³. 

Abstract Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star formation rate. We quantify the amount of cosmological and astrophysical information that the internal properties of individual galaxies and their host dark matter halos contain. We train neural networks using hundreds of thousands of galaxies from 2000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood-free inference on the value of the cosmological and astrophysical parameters. We find that knowing the internal properties of a single galaxy allows our models to infer the value of Ω m , at fixed Ω b , with a ∼10% precision, while no constraint can be placed on σ 8 . Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all considered redshifts, z ≤ 3, and they incorporate uncertainties in astrophysics as modeled in CAMELS. However, our models are not robust to changes in subgrid physics due to the large intrinsic differences the two considered models imprint on galaxy properties. We find that the stellar mass, stellar metallicity, and maximum circular velocity are among the most important galaxy properties to determine the value of Ω m . We believe that our results can be explained by considering that changes in the value of Ω m , or potentially Ω b /Ω m , affect the dark matter content of galaxies, which leaves a signature in galaxy properties distinct from the one induced by galactic processes. Our results suggest that the low-dimensional manifold hosting galaxy properties provides a tight direct link between cosmology and astrophysics.