Related papers: Measuring the Dark Matter Self-Interaction Cross-S…
We examine how the statistics of the quadrupoles of (projected) cluster masses can discriminate between flat cold dark matter (CDM) universes with or without a cosmological constant term. Even in the era of high precision cosmology that…
Deep subspace clustering based on auto-encoder has received wide attention. However, most subspace clustering based on auto-encoder does not utilize the structural information in the self-expressive coefficient matrix, which limits the…
We use present cosmological observations and forecasts of future experiments to illustrate the power of large-scale structure (LSS) surveys in probing dark matter (DM) microphysics and unveiling potential deviations from the standard…
Laboratory experiments, large-scale computer simulations and observational cosmology have begun to make progress in the campaign to identify the particle responsible for gravitationally-inferred dark matter. In this contribution we discuss…
Mergers of galaxy clusters are promising probes of dark matter (DM) physics. For example, an offset between the DM component and the galaxy distribution can constrain DM self-interactions. We investigate the role of the intracluster medium…
We show, by using an extensive sample of viable supersymmetric models as templates, that indirect detection of dark matter through gamma rays may have a large potential for identifying the nature of dark matter. This is in particular true…
Discovering and clustering subspaces in high-dimensional data is a fundamental problem of machine learning with a wide range of applications in data mining, computer vision, and pattern recognition. Earlier methods divided the problem into…
While dark matter (DM) is the key ingredient for a successful theory of structure formation, its microscopic nature remains elusive. Indirect detection may provide a powerful test for some strongly motivated DM particle models.…
We compare the statistics and morphology of giant arcs in galaxy clusters using N-body and non-radiative SPH simulations within the standard cold dark matter model and simulations where dark matter has a non-negligible probability of…
Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…
In this work, we discuss the relation between the strength of the self-interaction of dark matter particles and the predicted properties of the inner density distributions of dark matter haloes. We present the results of $N$-body…
Galaxy clusters are powerful probes of astrophysics and cosmology through gravitational lensing: the clusters' mass, dominated by 85% dark matter, distorts background light. Yet, mass reconstruction lacks the scalability and large-scale…
Galaxy-scale strong gravitational lensing is not only a valuable probe of the dark matter distribution of massive galaxies, but can also provide valuable cosmological constraints, either by studying the population of strong lenses or by…
The core-cusp problem remains as a challenging discrepancy between observations and simulations in the standard $\Lambda$CDM model for the formation of galaxies. The problem is that $\Lambda$CDM simulations predict a steep power-law mass…
Strong lensing studies can provide detailed mass maps of the inner regions even in dynamically active galaxy clusters. It is shown that proper modelling of the intracluster medium, i.e. the main baryonic component, can play an important…
We investigate the prospect of reconstructing the ''cosmic distance ladder'' of the Universe using a novel deep learning framework called LADDER - Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the…
Multicomponent dark matter with self-interactions, which allows for inter-conversions of species into one another, is a promising paradigm that is known to successfully and simultaneously resolve major problems of the conventional…
Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensive. In this work, we attempt to address these challenges in…
Clustering is a fundamental unsupervised representation learning task with wide application in computer vision and pattern recognition. Deep clustering utilizes deep neural networks to learn latent representation, which is suitable for…
We present a very large high-resolution cosmological N-body simulation, the Millennium-XXL or MXXL, which uses 303 billion particles to represent the formation of dark matter structures throughout a 4.1Gpc box in a LambdaCDM cosmology. We…