Related papers: Measuring the Dark Matter Self-Interaction Cross-S…
The cold dark matter (CDM) paradigm provides a remarkably good description of the Universe's large-scale structure. However, some discrepancies exist between its predictions and observations at very small sub-galactic scales. To address…
Astrophysical observations provide compelling evidence for gravitationally interacting dark matter in the universe that cannot be explained by the standard model of particle physics. The extraordinary amount of data from the CERN LHC…
We suggest that two-to-two dark matter fusion may be the relaxation process that resolves the small-scale structure problems of the cold collisionless dark matter paradigm. In order for the fusion cross section to scale correctly across…
We constrain the physical nature of dark matter using the newly identified massive merging galaxy cluster MACSJ0025.4-1222. As was previously shown by the example of the Bullet Cluster (1E0657-56), such systems are ideal laboratories for…
Assuming that dark matter (DM) efficiently clusters on various scales we analyse the possible impact on direct DM searches. For certain sizes and densities of DM clusters, mutual detector-cluster encounters may occur only once a year or…
Both generative learning and discriminative learning have recently witnessed remarkable progress using Deep Neural Networks (DNNs). For structured input synthesis and structured output prediction problems (e.g., layout-to-image synthesis…
Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data. These deep clustering methods mainly focus on the correlation among samples, e.g., selecting high precision pairs to gradually…
We present a study of the weak lensing inferred matter profiles $\Delta\Sigma(R)$ of 698 South Pole Telescope thermal Sunyaev-Zel'dovich effect selected and MCMF optically confirmed galaxy clusters in the redshift range $0.25 <z< 0.94$ that…
The formation and evolution of galaxies is known to be sensitive to tidal processes leading to intrinsic correlations between their shapes and orientations. Such correlations can be measured to high significance today, suggesting that…
Current dark matter detection strategies are based on the assumption that the dark matter is a gas of non-interacting particles with a reasonably large number density. This picture is dramatically altered if there are significant self…
Self-interacting dark matter offers an interesting alternative to collisionless dark matter because of its ability to preserve the large-scale success of the cold dark matter model, while seemingly solving its challenges on small scales. We…
Robust measurements of cosmological parameters from galaxy surveys rely on our understanding of systematic effects that impact the observed galaxy density field. In this paper we present, validate, and implement the idea of adopting the…
Besides Standard Model measurements and other Beyond Standard Model studies, the ATLAS and CMS experiments at the LHC will search for Supersymmetry, one of the most attractive explanation for dark matter. The SUSY discovery potential with…
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and…
We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. Effectively, this is a novel approach to determining the scaling relation between a cluster's Sunyaev-Zel'dovich…
If dark matter (DM) is composed by particles which are non-gravitationally coupled to ordinary matter, their annihilations or decays in cosmic structures can result in detectable radiation. We show that the most powerful technique to detect…
Cold interstellar gas clouds provide an exciting new method to discover dark matter. Their immense size makes them uniquely sensitive to interactions from the heaviest, most rarefied dark matter models. Using gas cloud observations, we…
Action recognition and detection in the context of long untrimmed video sequences has seen an increased attention from the research community. However, annotation of complex activities is usually time consuming and challenging in practice.…
We present a new suite of over 1,500 cosmological N-body simulations with varied Warm Dark Matter (WDM) models ranging from 2.5 to 30 keV. We use these simulations to train Convolutional Neural Networks (CNNs) to infer WDM particle masses…
The lack of tangible evidence for non-gravitational interactions between dark and visible sectors drives the need for exploring new avenues of inferring dark matter properties through purely gravitational probes. In particular, addressing…