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Previous works have shown that a dense cluster environment affects satellite galaxy properties and accelerates or truncates their evolutionary processes. In this work, we use the EAGLE simulation to study this effect, dissecting the galaxies in two halves: the one that is falling directly to the cluster (leading half) and the one behind (trailing half). Considering all galaxies within the virial radius of the most massive groups and clusters of the simulation ( M h a l o > 10 13 . 8 [ M ⊙ ] ), we find that on average the leading half presents an enhancement of the star formation rate with respect to the trailing half. We conclude that galaxies falling into the intra-cluster medium experience a boost in star-formation in their leading half due to ram pressure. Sparse observations of jellyfish galaxies have revealed visually the enhancement of the star formation in the leading half. In order to confirm this effect statistically using observations, different cases must be investigated using the simulation as a test dataset.

We present a study of the evolution of early-type galaxies (ETGs) that combines luminosity function and clustering measurements. This technique shows that ETGs at a given redshift evolve into brighter galaxies in the rest-frame passively evolved optical at lower redshifts. Notice that this indicates that a stellar-mass selection at different redshifts does not necessarily provide samples of galaxies in a progenitor-descendant relationship. The comparison between high redshift ETGs and their likely descendants at z = 0 points to a higher number density for the progenitors by a factor 3 to 11, implying the need for mergers to decrease their number density by today. Because the progenitor-to-descendant ratios of luminosity density are consistent with the unit value, our results show no need for strong star-formation episodes in ETGs since z = 1, which indicates that the needed mergers are dry, i.e. gas free.

Cosmic voids are becoming key players in testing the physics of our Universe. Here we concentrate on the abundances and the dynamics of voids as these are among the best candidates to provide information on cosmological parameters. Cai, Padilla & Li (2014) use the abundance of voids to tell apart Hu & Sawicki f(R) models from General Relativity. An interesting result is that even though, as expected, voids in the dark matter field are emptier in f(R) gravity due to the fifth force expelling away from the void centres, this result is reversed when haloes are used to find voids. The abundance of voids in this case becomes even lower in f(R) compared to GR for large voids. Still, the differences are significant and this provides a way to tell apart these models. The velocity field differences between f(R) and GR, on the other hand, are the same for halo voids and for dark matter voids. Paz et al. (2013), concentrate on the velocity profiles around voids. First they show the necessity of four parameters to describe the density profiles around voids given two distinct void populations, voids-in-voids and voids-in-clouds. This profile is used to predict peculiar velocities around voids, and the combination of the latter with void density profiles allows the construction of model void-galaxy cross-correlation functions with redshift space distortions. When these models are tuned to fit the measured correlation functions for voids and galaxies in the Sloan Digital Sky Survey, small voids are found to be of the void-in-cloud type, whereas larger ones are consistent with being void-in-void. This is a novel result that is obtained directly from redshift space data around voids. These profiles can be used to remove systematics on void-galaxy Alcock-Pacinsky tests coming from redshift-space distortions.

Lagos, Padilla & Cora (2009) show that if alignments between the galaxy kinematics and the AGN system were to occur, massive galaxies should host BHs with high spin values, regardless of the detailed physics of the BH. Since the BH spin regulates the mass-to-energy conversion (Marconi et al. 2004) and possibly the existence of radio jets (Sikora et al. 2007), this study has a strong impact in our understanding of galaxy formation.

This paper explores the impact of primordial black holes (PBHs) on the abundance of low-mass haloes and subhaloes in the dark and low-stellar-mass regime, and examines how these effects can be measured through fluctuations in strong lensing and brightness fluctuations in clusters of galaxies, providing potential ways to constrain the fraction of dark matter in PBHs. Various dark matter candidates leave unique imprints on the low-mass range of the halo mass function, which can be challenging to detect. Among these are hot and warm dark matter models, which predict a reduced abundance of low-mass structures compared to the \\(\\Lambda\\)CDM model. Models with PBHs also affect this mass range, but in the opposite direction, producing an increase in low-mass objects. By examining lensing perturbations in galaxy clusters, constraints can be placed on the low-mass subhalo abundance and, therefore, on these different dark matter models. We aim to provide predictions useful for this type of perturbation in the PBH case. Additionally, we examine the abundance of haloes and subhaloes in the range where the stellar-to-halo mass relation rises steeply, which could be contrasted with brightness fluctuations in clusters caused by low-luminosity satellites. To do this, we run cosmological simulations using the {\\small SWIFT} code, comparing a fiducial model with alternative inflationary models, both with and without PBHs. We find a significant excess of substructure in the presence of PBHs compared to \\(\\Lambda\\)CDM, without altering the abundance of high-mass haloes at redshift zero. This increase reaches factors of \\(\\sim6\\) for extended PBH mass functions with exponential cutoffs at \\(M_{\\rm PBH}=10^2M_\\odot\\) in the range of parameter space where they could make up all of the dark matter, and persists even for sub-percent PBH fractions with cutoffs at \\(M_{\\rm PBH}=10^4M_\\odot\\).

We apply the marked correlation function test proposed by Armijo et al. (Paper I) to samples of luminous red galaxies (LRGs) from the final data release of the Sloan Digital Sky Survey (SDSS) III. The test assigns a density-dependent mark to galaxies in the estimation of the projected marked correlation function. Two gravity models are compared: general relativity (GR) and \\(f(R)\\) gravity. We build mock catalogues which, by construction, reproduce the measured galaxy number density and two-point correlation function of the LRG samples, using the halo occupation distribution model (HOD). A range of HOD models give acceptable fits to the observational constraints, and this uncertainty is fed through to the error in the predicted marked correlation functions. The uncertainty from the HOD modelling is comparable to the sample variance for the SDSS-III LRG samples. Our analysis shows that current galaxy catalogues are too small for the test to distinguish a popular \\(f(R)\\) model from GR. However, upcoming surveys with a better measured galaxy number density and smaller errors on the two-point correlation function, or a better understanding of galaxy formation, may allow our method to distinguish between viable gravity models.

We introduce a new scheme based on the marked correlation function to probe gravity using the large-scale structure of the Universe. We illustrate our approach by applying it to simulations of the metric-variation \\(f(R)\\) modified gravity theory and general relativity (GR). The modifications to the equations in \\(f(R)\\) gravity lead to changes in the environment of large-scale structures that could, in principle, be used to distinguish this model from GR. Applying the Monte Carlo Markov Chain algorithm, we use the observed number density and two-point clustering to fix the halo occupation distribution (HOD) model parameters and build mock galaxy catalogues from both simulations. To generate a mark for galaxies when computing the marked correlation function we estimate the local density using a Voronoi tessellation. Our approach allows us to isolate the contribution to the uncertainty in the predicted marked correlation function that arises from the range of viable HOD model parameters, in addition to the sample variance error for a single set of HOD parameters. This is critical for assessing the discriminatory power of the method. In a companion paper we apply our new scheme to a current large-scale structure survey.

We investigate void properties in f(R) models using N-body simulations, focusing on their differences from General Relativity (GR) and their detectability. In the Hu-Sawicki f(R) modified gravity (MG) models, the halo number density profiles of voids are not distinguishable from GR. In contrast, the same f(R) voids are more empty of dark matter, and their profiles are steeper. This can in principle be observed by weak gravitational lensing of voids, for which the combination of a spectroscopic redshift and a lensing photometric redshift survey over the same sky is required. Neglecting the lensing shape noise, the f(R) model parameter amplitudes fR0=10-5 and 10-4 may be distinguished from GR using the lensing tangential shear signal around voids by 4 and 8 σ for a volume of 1 (Gpc/h)3. The line-of-sight projection of large-scale structure is the main systematics that limits the significance of this signal for the near future wide angle and deep lensing surveys. For this reason, it is challenging to distinguish fR0=10-6 from GR. We expect that this can be overcome with larger volume. The halo void abundance being smaller and the steepening of dark matter void profiles in f(R) models are unique features that can be combined to break the degeneracy between fR0 and σ8.

Context. Galaxies in the Universe show a conformity in the fraction of quenched galaxies out to large distances, being much larger around quenched central galaxies than for star-forming ones. On the other hand, simulations have shown that the clustering of halos and the galaxies within them depends on secondary properties other than halo mass, a phenomenon termed assembly bias. Aims. Our aim is to study whether samples that show galactic conformity also show assembly bias and to see if the amplitude of these two effects is correlated. Methods. We use synthetic galaxies at \\(z = 0\\) from the semi-analytical model SAG run on the MultiDark Planck 2 (MDPL2) cosmological simulation and measure both conformity and galaxy assembly bias for different samples of central galaxies at fixed host halo mass. We focus on central galaxies hosted by low-mass halos of 10\\(^{11.6}\\) \\(\\leq\\) \\(M_{\\rm h}\\)/\\(h^{-1}\\) M\\(_{\\odot}\\) \\(<\\) 10\\(^{11.8}\\) because it is a mass range where the assembly bias has been reported to be strong. The samples of central galaxies are separated according to their specific star formation rate and stellar age. Results. We find that the level of conformity shown by our different samples is correlated with the level of assembly bias measured for them. We also find that removing central galaxies around massive halos diminishes the conformity signal and lowers the amount of assembly bias. Conclusions. The high correlation in the amplitude of conformity and assembly bias for different samples with and without removing galaxies near massive halos clearly indicates the strong relationship between both phenomena.

Using data from TNG300-2, we train a neural network (NN) to recreate the stellar mass (\\(M^*\\)) and star formation rate (SFR) of central galaxies in a dark-matter-only simulation. We consider 12 input properties from the halo and sub-halo hosting the galaxy and the near environment. \\(M^*\\) predictions are robust, but the machine does not fully reproduce its scatter. The same happens for SFR, but the predictions are not as good as for \\(M^*\\). We chained neural networks, improving the predictions on SFR to some extent. For SFR, we time-averaged this value between \\(z=0\\) and \\(z=0.1\\), which improved results for \\(z=0\\). Predictions of both variables have trouble reproducing values at lower and higher ends. We also study the impact of each input variable in the performance of the predictions using a leave-one-covariate-out approach, which led to insights about the physical and statistical relation between input variables. In terms of metrics, our machine outperforms similar studies, but the main discoveries in this work are not linked with the quality of the predictions themselves, but to how the predictions relate to the input variables. We find that previously studied relations between physical variables are meaningful to the machine. We also find that some merger tree properties strongly impact the performance of the machine. %We highlight the value of machine learning (ML) methods in helping understand the information contained in different variables, since with its help we were able to obtain useful insights resulting from studying the impact of input variables on the resulting behaviour of galaxy properties. We conclude that ML models are useful tools to understand the significance of physical different properties and their impact on target characteristics, as well as strong candidates for potential simulation methods.