Models of modified gravity offer promising alternatives to the concordance cold dark matter ( CDM) cosmology to explain the late-time acceleration of the universe.A popular such model is f(R) gravity, in which the Ricci scalar in the Einstein-Hilbert action is replaced by a general function of it.We study the f(R) model of Hu & Sawicki, which recovers standard general relativity in high-density regimes, while reproducing the desired late time acceleration at cosmological scales.We run a suite of high-resolution zoom simulations using the ECOSMOG code to examine the effect of f(R) gravity on the properties of a halo that is analogous to the Virgo cluster.We show that the velocity dispersion profiles can potentially discriminate between f(R) models and CDM, and provide complementary analysis of lensing signal profiles to explore the possibility to further distinguish the different f(R) models.Our results confirm the techniques explored by Cabré et al. to quantify the effect of environment in the behaviour of f(R) gravity, and we extend them to study halo satellites at various redshifts.We find that the modified gravity effects in our models are most observable at low redshifts, and that effects are generally stronger for satellites far from the centre of the main halo.We show that the screening properties of halo satellites trace very well that of dark matter particles, which means that low-resolution simulations in which subhaloes are not very well resolved can in principle be used to study satellite properties.We discuss observables, particularly for halo satellites, that can potentially be used to constrain the observational viability of f(R) gravity.
The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot , a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.
Within a sufficiently large cosmic volume, conservation of baryons implies a simple `closed box' view in which the sum of the baryonic components must equal a constant fraction of the total enclosed mass. We present evidence from Rhapsody-G hydrodynamic simulations of massive galaxy clusters that the closed-box expectation may hold to a surprising degree within the interior, non-linear regions of haloes. At a fixed halo mass, we find a significant anti-correlation between hot gas mass fraction and galaxy mass fraction (cold gas + stars), with a rank correlation coefficient of -0.69 within $R_{500c}$. Because of this anti-correlation, the total baryon mass serves as a low-scatter proxy for total cluster mass. The fractional scatter of total baryon fraction scales approximately as $0.02 (\Delta_c/100)^{0.6}$, while the scatter of either gas mass or stellar mass is larger in magnitude and declines more slowly with increasing radius. We discuss potential observational tests using cluster samples selected by optical and hot gas properties; the simulations suggest that joint selection on stellar and hot gas has potential to achieve 5% scatter in total halo mass.
We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim 10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every halo in the catalogue is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a mean relative error of $\sim6\%$, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer $\Omega_{\rm m}$ also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters.
LensMC is a weak lensing shear measurement method developed for Euclid and Stage-IV surveys. It is based on forward modelling to deal with convolution by a point spread function with comparable size to many galaxies; sampling the posterior distribution of galaxy parameters via Markov Chain Monte Carlo; and marginalisation over nuisance parameters for each of the 1.5 billion galaxies observed by Euclid. The scientific performance is quantified through high-fidelity images based on the Euclid Flagship simulations and emulation of the Euclid VIS images; realistic clustering with a mean surface number density of 250 arcmin$^{-2}$ ($I_{\rm E}<29.5$) for galaxies, and 6 arcmin$^{-2}$ ($I_{\rm E}<26$) for stars; and a diffraction-limited chromatic point spread function with a full width at half maximum of $0.^{\!\prime\prime}2$ and spatial variation across the field of view. Objects are measured with a density of 90 arcmin$^{-2}$ ($I_{\rm E}<26.5$) in 4500 deg$^2$. The total shear bias is broken down into measurement (our main focus here) and selection effects (which will be addressed elsewhere). We find: measurement multiplicative and additive biases of $m_1=(-3.6\pm0.2)\times10^{-3}$, $m_2=(-4.3\pm0.2)\times10^{-3}$, $c_1=(-1.78\pm0.03)\times10^{-4}$, $c_2=(0.09\pm0.03)\times10^{-4}$; a large detection bias with a multiplicative component of $1.2\times10^{-2}$ and an additive component of $-3\times10^{-4}$; and a measurement PSF leakage of $\alpha_1=(-9\pm3)\times10^{-4}$ and $\alpha_2=(2\pm3)\times10^{-4}$. When model bias is suppressed, the obtained measurement biases are close to Euclid requirement and largely dominated by undetected faint galaxies ($-5\times10^{-3}$). Although significant, model bias will be straightforward to calibrate given the weak sensitivity.
Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale structure. The aim of the Higher-Order Weak Lensing Statistics (HOWLS) project is to assess, compare, and combine the constraining power of $10$ different HOS on a common set of $Euclid$-like mocks, derived from N-body simulations. In this first paper of the HOWLS series we compute the non-tomographic ($\Omega_{\rm m}$, $\sigma_8$) Fisher information for one-point probability distribution function, peak counts, Minkowski functionals, Betti numbers, persistent homology Betti numbers and heatmap, and scattering transform coefficients, and compare them to the shear and convergence two-point correlation functions in the absence of any systematic bias. We also include forecasts for three implementations of higher-order moments, but these cannot be robustly interpreted as the Gaussian likelihood assumption breaks down for these statistics. Taken individually, we find that each HOS outperforms the two-point statistics by a factor of around $2$ in the precision of the forecasts with some variations across statistics and cosmological parameters. When combining all the HOS, this increases to a $4.5$ times improvement, highlighting the immense potential of HOS for cosmic shear cosmological analyses with $Euclid$. The data used in this analysis are publicly released with the paper.
Abstract This chapter gives an introduction to the main concepts of computational cosmology. It describes the equations governing the nonlinear dynamics of self-gravitating fluids evolving in the expanding universe, namely dark matter, baryons, and radiation. While the first of these is modelled using N-body techniques, the other two can be simulated using traditional fluid solvers. Basic numerical techniques are introduced and their limitations are discussed. The goal is to familiarize the reader with the terminology of computational cosmology, and to give insight into the strengths and weaknesses of cosmological simulations. Finally, the chapter discusses the future role of computational cosmology in the post-Planck era of high-precision cosmology.
We use 500 pc resolution cosmological simulations of a Virgo-like galaxy cluster to study the properties of the brightest cluster galaxy (BCG) that forms at the center of the halo. We compared two simulations; one incorporating only supernovae feedback and a second that also includes prescriptions for black hole growth and the resulting AGN feedback from gas accretion. As previous work has shown, with supernovae feedback alone we are unable to reproduce any of the observed properties of massive cluster ellipticals. The resulting BCG is rotating quickly, has a high Sersic index, a strong mass excess in the center and a total central density profile falling more steeply than isothermal. Furthermore, it is far too efficient at converting most of the available baryons into stars which is strongly constrained by abundance matching. With a treatment of black hole dynamics and AGN feedback the BCG properties are in good agreement with data: they rotate slowly, have a cored surface density profile, a flat or rising velocity dispersion profile and a low stellar mass fraction. The AGN provides a new mechanism to create cores in luminous elliptical galaxies; the core expands due to the combined effects of heating from dynamical friction of sinking massive black holes and AGN feedback that ejects gaseous material from the central regions.
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