Related papers: Deep learning insights into cosmological structure…
Understanding the large-scale structure of the universe remains a fundamental challenge in cosmology, with computational simulations providing critical insights into non-linear structure growth. Particularly, computational simulations…
The set-up of the initial conditions in cosmological N-body simulations is usually implemented by rescaling the desired low-redshift linear power spectrum to the required starting redshift consistently with the Newtonian evolution of the…
We present a machine learning (ML) approach for the prediction of galaxies' dark matter halo masses that achieves an improved performance over conventional methods. We train three ML algorithms (\texttt{XGBoost}, Random Forests, and neural…
The annihilation of dark matter (DM) particles is expected to produce Standard Model particles, providing a potential indirect signature of DM. The clumpy substructure of DM haloes amplifies the expected annihilation signal, an effect…
Halos, filaments, sheets and voids in the cosmic web can be defined in terms of the eigenvalues of the smoothed shear tensor and a threshold $\lambda_{\rm th}$. Using analytic methods, we construct mean maps centered on these types of…
The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the…
The density profiles of dark matter halos are typically modeled using empirical formulae fitted to the density profiles of relaxed halo populations. We present a neural network model that is trained to learn the mapping from the raw density…
We develop an analysis pipeline for characterizing the topology of large scale structure and extracting cosmological constraints based on persistent homology. Persistent homology is a technique from topological data analysis that quantifies…
The potential of deep learning-based image-to-image translations has recently attracted significant attention. One possible application of such a framework is as a fast, approximate alternative to cosmological simulations, which would be…
The development of methods and algorithms to solve the $N$-body problem for classical, collisionless, non-relativistic particles has made it possible to follow the growth and evolution of cosmic dark matter structures over most of the…
Adaptive SPH and N-body simulations were carried out to study the collapse and evolution of dark matter halos that result from the gravitational instability and fragmentation of cosmological pancakes. Such halos resemble those formed by…
We numerically construct an one-parameter family of initial data of an expanding inhomogeneous universe model which is composed of regularly aligned black holes with an identical mass. They are initial data for vacuum solutions of the…
We use the universal mass accretion history recently reported for simulations of halo formation in the cold dark matter model (CDM) to analyze the formation and growth of a single halo. We derive the time-dependent density profile three…
Cosmological data probe massive neutrinos via their effects on the geometry of the Universe and the growth of structure, both of which are degenerate with the late-time expansion history. We clarify the nature of these degeneracies and the…
The simple, conventional dark matter halo mass definitions commonly used in cosmological simulations ("virial" mass, FoF mass, $M_{50,100,200,...}$) only capture part of the collapsed material and are therefore inconsistent with the halo…
We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately approximating the Green's function expansion that relates the…
We present two new diagnostics based on the intrinsic shape alignments of group/cluster size dark matter halos to disentangle the effect of $f(R)$ gravity from that of massive neutrinos. Using the snapshot data from a series of the {\small…
The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets becoming available in the near future, we develop a likelihood-free Deep Learning technique…
We construct exact initial data for closed cosmological models filled with regularly arranged black holes in the presence of $\Lambda$. The intrinsic geometry of the 3-dimensional space described by this data is a sum of simple closed-form…
Measurement of accelerated expansion in the Universe led to propose a new cosmic fluid as its cause: dark energy. Its various incarnations offer a wealth of models whose relevance it is important to discriminate via contacts with…