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Cosmological numerical simulations of galaxy formation have led to the cuspy density profile of a pure cold dark matter halo toward the center, which is in sharp contradiction with the observations of the rotation curves of cold dark matter-dominated dwarf and low surface brightness disk galaxies, with the latter tending to favor mass profiles with a flat central core. Many efforts have been devoted to resolve this cusp-core problem in recent years, among them, baryon-cold dark matter interactions are considered to be the main physical mechanisms erasing the cold dark matter (CDM) cusp into a flat core in the centers of all CDM halos. Clearly, baryon-cold dark matter interactions are not customized only for CDM-dominated disk galaxies, but for all types, including giant ellipticals. We first fit the most recent high resolution observations of rotation curves with the Burkert profile, then use the constrained core size-halo mass relation to calculate the lensing frequency, and compare the predicted results with strong lensing observations. Unfortunately, it turns out that the core size constrained from rotation curves of disk galaxies cannot be extrapolated to giant ellipticals. We conclude that, in the standard cosmological paradigm, baryon-cold dark matter interactions are not universal mechanisms for galaxy formation, and therefore, they cannot be true solutions to the cusp-core problem.
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This paper gives an overview of the attempts to determine the distribution of dark matter in low surface brightness disk and gas-rich dwarf galaxies, both through observations and computer simulations. Observations seem to indicate an approximately constant dark matter density in the inner parts of galaxies, while cosmological computer simulations indicate a steep power-law-like behaviour. This difference has become known as the core/cusp problem, and remains one of the unsolved problems in small-scale cosmology.
Standard cosmology has many successes on large scales, but faces some fundamental difficulties on small, galactic scales. One such difficulty is the cusp/core problem. High resolution observations of the rotation curves for dark matter dominated low surface brightness (LSB) galaxies imply that galactic dark matter halos have a density profile with a flat central core, whereas N-body structure formation simulations predict a divergent (cuspy) density profile at the center. It has been proposed that this problem can be resolved by stellar feedback driving turbulent gas motion that erases the initial cusp. However, strong gravitational lensing prefers a cuspy density profile for galactic halos. In this paper, we use the most recent high resolution observations of the rotation curves of LSB galaxies to fit the core size as a function of halo mass, and compare the resultant lensing probability to the observational results for the well defined combined sample of the Cosmic Lens All-Sky Survey (CLASS) and Jodrell Bank/Very Large Array Astrometric Survey (JVAS). The lensing probabilities based on such density profiles are too low to match the observed lensing in CLASS/JVAS. High baryon densities in the galaxies that dominate the lensing statistics can reconcile this discrepancy, but only if they steepen the mass profile rather than making it more shallow. This places contradictory demands upon the effects of baryons on the central mass profiles of galaxies.
The difficult task of observing Dark Matter subhaloes is of paramount importance since it would constrain Dark Matter particle properties (cold or warm relic) and confirm once again the longstanding $Lambda$CDM model. In the near future the new generation of ground and space surveys will observe thousands of strong gravitational lensing systems providing a unique probe of Dark Matter substructures. Here, we describe a new strong lensing analysis pipeline that combines deep Convolutional Neural Networks with physical models and exploits traditional sampling techniques such as Hamiltonian Monte Carlo. Using simulated strong gravitational lensing systems, we discuss first results and characterize the accuracy of the reconstruction of the main lensing parameters.
We study how well halo properties of galaxy clusters, like mass and concentration, are recovered using lensing data. In order to generate a large sample of systems at different redshifts we use the code MOKA. We measure halo mass and concentration using weak lensing data alone (WL), fitting to an NFW profile the reduced tangential shear profile, or by combining weak and strong lensing data, by adding information about the size of the Einstein radius (WL+SL). For different redshifts, we measure the mass and the concentration biases and find that these are mainly caused by the random orientation of the halo ellipsoid with respect to the line-of-sight. Since our simulations account for the presence of a bright central galaxy, we perform mass and concentration measurements using a generalized NFW profile which allows for a free inner slope. This reduces both the mass and the concentration biases. We discuss how the mass function and the concentration mass relation change when using WL and WL+SL estimates. We investigate how selection effects impact the measured concentration-mass relation showing that strong lens clusters may have a concentration 20-30% higher than the average, at fixed mass, considering also the particular case of strong lensing selected samples of relaxed clusters. Finally, we notice that selecting a sample of relaxed galaxy clusters, as is done in some cluster surveys, explain the concentration-mass relation biases.
Strong gravitational lensing, which can make a background source galaxy appears multiple times due to its light rays being deflected by the mass of one or more foreground lens galaxies, provides astronomers with a powerful tool to study dark matter, cosmology and the most distant Universe. PyAutoLens is an open-source Python 3.6+ package for strong gravitational lensing, with core features including fully automated strong lens modeling of galaxies and galaxy clusters, support for direct imaging and interferometer datasets and comprehensive tools for simulating samples of strong lenses. The API allows users to perform ray-tracing by using analytic light and mass profiles to build strong lens systems. Accompanying PyAutoLens is the autolens workspace (see https://github.com/Jammy2211/autolens_workspace), which includes example scripts, lens datasets and the HowToLens lectures in Jupyter notebook format which introduce non experts to strong lensing using PyAutoLens. Readers can try PyAutoLens right now by going to the introduction Jupyter notebook on Binder (see https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/master) or checkout the readthedocs (see https://pyautolens.readthedocs.io/en/latest/) for a complete overview of PyAutoLenss features.
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