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Abstract:

We investigate the clustering of Lyman-break galaxies at redshifts of 3 5 within the COSMOS field by measuring the angular two-point correlation function. Our robust sample of 60 000 bright () Lyman-break galaxies was selected based on spectral energy distribution fitting across 14 photometric bands spanning optical and near-infrared wavelengths. We constrained both the 1- and 2-halo terms at separations up to 300 arcsec, finding an excess in the correlation function at scales corresponding to kpc, consistent with enhancement due to clumps in the same galaxy or interactions on this scale. We then performed Bayesian model fits on the correlation functions to infer the Halo Occupation Distribution parameters, star formation duty cycle, and galaxy bias in three redshift bins. We examined several cases where different combinations of parameters were varied, showing that our data can constrain the slope of the satellite occupation function, which previous studies have fixed. For an -limited sub-sample, we found galaxy bias values of at , at , at . The duty cycle values are , , and , respectively. These results suggest that, as the redshift increases, there is a slight decrease in the host halo masses and a shorter time-scale for star formation in bright galaxies, at a fixed rest-frame UV luminosity threshold.

Abstract:

Abstract This study investigates the estimation of the neutral hydrogen (H i) mass function (HiMF) using a Bayesian stacking approach with simulated data for the Five-hundred-meter Aperture Spherical radio Telescope (FAST) H i intensity mapping (HiIM) drift-scan surveys. Using data from the IllustrisTNG simulation, we construct H i sky cubes at redshift z ∼ 0.1 and the corresponding optical galaxy catalogs, simulating FAST observations under various survey strategies, including pilot, deep-field, and ultradeep-field surveys. The HiMF is measured for distinct galaxy populations—classified by optical properties into red, blue, and bluer galaxies—and injected with systematic effects such as observational noise and flux confusion caused by the FAST beam. The results show that Bayesian stacking significantly enhances HiMF measurements. For red and blue galaxies, the HiMF can be well constrained with pilot surveys, while deeper surveys are required for the bluer galaxy population. Our analysis also reveals that sample variance dominates over observational noise, emphasizing the importance of wide-field surveys to improve constraints. Furthermore, flux confusion shifts the HiMF toward higher masses, which we address using a transfer function for correction. Finally, we explore the effects of intrinsic sample incompleteness and propose a framework to quantify its impact. This work lays the groundwork for future HiMF studies with FAST HiIM, addressing key challenges and enabling robust analyses of H i content across galaxy populations.

Abstract:

Using deep observations across three of the LOFAR Two-metre Sky Survey Deep Fields, this work measures the angular clustering of star-forming galaxies (SFGs) and low-excitation radio galaxies (LERGs) to 1.5 for faint sources, 200 Jy. We measure the angular auto-correlation of LOFAR sources in redshift bins and their cross-correlation with multiwavelength sources to measure the evolving galaxy bias for SFGs and LERGs. Our work shows the bias of the radio-selected SFGs increases from = at 0.2 to = at 1.2; faster than the assumed models adopted in previous LOFAR cosmology studies (at sensitivities where active galactic nuclei dominate), but in broad agreement with previous work. We further study the luminosity dependence of bias for SFGs and find little evidence for any luminosity dependence at fixed redshift, although uncertainties remain large for the sample sizes available. The LERG population instead shows a weaker redshift evolution with = at 0.7 to = at 1.2, though it is also consistent with the assumed bias evolution model () within the measured uncertainties. For those LERGs that reside in quiescent galaxies, there is weak evidence that they are more biased than the general LERG population and evolve from = at 0.7 to = at 1.2. This suggests the halo environment of radio sources may be related to their properties. These measurements can help constrain models for the bias evolution of these source populations, and can help inform multi-tracer analyses.

Abstract:

Context. Baryonic physics has a considerable impact on the distribution of matter in our Universe on scales probed by current and future cosmological surveys, acting as a key systematic in such analyses. Aims. We seek simple symbolic parametrisations for the impact of baryonic physics on the matter power spectrum for a range of physically motivated models, as a function of wavenumber, redshift, cosmology, and parameters controlling the baryonic feedback. Methods. We used symbolic regression to construct analytic approximations for the ratio of the matter power spectrum in the presence of baryons to that without such effects. We obtained separate functions of each of four distinct sub-grid prescriptions of baryonic physics from the CAMELS suite of hydrodynamical simulations (Astrid, IllustrisTNG, SIMBA, and Swift-EAGLE) as well as for a baryonification algorithm. We also provide functions that describe the uncertainty on these predictions, due to both the stochastic nature of baryonic physics and the errors on our fits. Results. The error on our approximations to the hydrodynamical simulations is comparable to the sample variance estimated through varying initial conditions, and our baryonification expression has a root mean squared error of better than one percent, although this increases on small scales. These errors are comparable to those of previous numerical emulators for these models. Our expressions are enforced to have the physically correct behaviour on large scales and at high redshift. Due to their analytic form, we are able to directly interpret the impact of varying cosmology and feedback parameters, and we can identify parameters that have little to no effect. Conlcusions. Each function is based on a different implementation of baryonic physics, and can therefore be used to discriminate between these models when applied to real data. We provide a publicly available code for all symbolic approximations found.

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