Related papers: Measuring the Dark Matter Self-Interaction Cross-S…
Deep subspace clustering (DSC) networks based on self-expressive model learn representation matrix, often implemented in terms of fully connected network, in the embedded space. After the learning is finished, representation matrix is used…
Dark matter (DM) self-interactions have important implications for the formation and evolution of structure, from dwarf galaxies to clusters of galaxies. We study the dynamics of self-interacting DM via a light mediator, focusing on the…
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale…
We perform a general computational analysis of possible post-collision mass distributions in high-speed galaxy cluster collisions in the presence of weakly self-interacting dark matter. Using this analysis, we show that weakly…
We propose a low-rank transformation-learning framework to robustify subspace clustering. Many high-dimensional data, such as face images and motion sequences, lie in a union of low-dimensional subspaces. The subspace clustering problem has…
In two-component self-interacting dark matter (SIDM) models with inter-species interactions, mass segregation arises naturally from collisional relaxation, enhancing central densities and gravothermal evolution. We demonstrate that models…
We demonstrate the potential of Deep Learning methods for measurements of cosmological parameters from density fields, focusing on the extraction of non-Gaussian information. We consider weak lensing mass maps as our dataset. We aim for our…
The predicted mass function of dark matter halos is essential in connecting observed galaxy cluster counts and models of galaxy clustering to the properties of the primordial density field. We determine the mass function in the concordance…
We investigate the possibility of applying machine learning techniques to images of strongly lensed galaxies to detect a low mass cut-off in the spectrum of dark matter sub-halos within the lens system. We generate lensed images of systems…
Despite the Milky Way's proximity to us, our knowledge of its dark matter halo is fairly limited, and there is still considerable uncertainty in its halo mass. Many past techniques have been limited by assumptions such as the Galaxy being…
We study the phase-space structure of a dark-matter halo formed in a high resolution simulation of a Lambda CDM cosmology. Our goal is to quantify how much substructure is left over from the inhomogeneous growth of the halo, and how it may…
We study the geometry and topology of the large-scale structure traced by galaxy clusters in numerical simulations of a box of side 320 $h^{-1}$ Mpc, and compare them with available data on real clusters. The simulations we use are…
We show that compatibility between the DAMA modulation result (as well as less statistically significant excesses such as the CDMS Silicon effect and the excess claimed by CRESST) with constraints from other experiments can be achieved by…
It has been suggested that cold dark matter (CDM) has difficulties in explaining tentative evidence for noncuspy halo profiles in small galaxies, and the low velocity dispersions observed in the largest Milky Way satellites ("too big to…
In the cold dark matter model of structure formation, galaxies are assembled hierarchically from mergers and the accretion of subclumps. This process is expected to leave residual substructure in the Galactic dark halo, including partially…
Strong lensing galaxy clusters provide a unique and powerful way to test simulation-derived structure predictions that follow from $\Lambda$ Cold Dark Matter ($\Lambda$CDM) cosmology. Specifically, the relative alignments of the dark matter…
The number density of galaxy clusters across mass and redshift has been established as a powerful cosmological probe. Cosmological analyses with galaxy clusters traditionally employ scaling relations. However, many challenges arise from…
The detection of Dark Matter (DM) remains a significant challenge in particle physics. This study exploits advanced machine learning models to improve detection capabilities of liquid xenon time projection chamber experiments, utilizing…
The concordance Cold Dark Matter model for the formation of structure in the Universe, while remarkably successful at describing observations on large scales, has a number of problems on galaxy scales. The Milky Way and its satellite system…
The high dimensionality of hyperspectral images often results in the degradation of clustering performance. Due to the powerful ability of deep feature extraction and non-linear feature representation, the clustering algorithm based on deep…