Related papers: Deep Learning the Morphology of Dark Matter Substr…
One of the most popular classes of candidates for dark matter are Weakly Interacting Massive Particles (WIMPs), i.e. particles possessing masses and couplings falling roughly within the electroweak scale. Apart from offering a natural…
Cosmological structure formation predicts that our galactic halo contains an enormous hierarchy of substructures and streams, the remnants of the merging hierarchy that began with tiny Earth mass microhalos. If these structures persist…
Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and…
Astrometry -- the precise measurement of positions and motions of celestial objects -- has emerged as a promising avenue for characterizing the dark matter population in our Galaxy. By leveraging recent advances in simulation-based…
Next-generation surveys will provide photometric and spectroscopic data of millions to billions of galaxies with unprecedented precision. This offers a unique chance to improve our understanding of the galaxy evolution and the unresolved…
Dark matter is an important ingredient of galaxies, as was recognised early on by Ken Freeman himself! Evidence for dark matter halos is still indirect, based on analysing motions of tracers such as gas and stars. In a sense the visible…
Higgsino in supersymmetric standard models can play the role of dark matter particle. In conjunction with the naturalness criterion, the higgsino mass parameter is expected to be around the electroweak scale. In this work, we explore the…
We provide an analysis of a convolutional neural network's ability to identify the lensing signal of single dark matter subhalos in strong galaxy-galaxy lenses in the presence of increasingly complex source light morphology. We simulate a…
Dark matter haloes play a fundamental role in cosmological structure formation. The most common approach to model their assembly mechanisms is through N-body simulations. In this work we present an innovative pathway to predict dark matter…
We present a novel method to infer the Dark Matter (DM) content and spatial distribution within galaxies, based on convolutional neural networks trained within state-of-the-art hydrodynamical simulations (Illustris TNG100). The framework we…
We consider a machine learning algorithm to detect and identify strong gravitational lenses on sky images. First, we simulate different artificial but very close to reality images of galaxies, stars and strong lenses, using six different…
The complementarity of direct, indirect and collider searches for dark matter has improved our understanding concerning the properties of the dark matter particle. I will review the basic concepts that these methods rely upon and highlight…
Dark matter halos are typically defined as spheres that enclose some overdensity, but these sharp, somewhat arbitrary boundaries introduce non-physical artifacts such as backsplash halos, pseudo-evolution, and an incomplete accounting of…
The next decade will bring massive new data sets from experiments of the direct detection of weakly interacting massive particle (WIMP) dark matter. The primary goal of these experiments is to identify and characterize the dark-matter…
Strong gravitational lensing is a powerful probe for studying the fundamental properties of dark matter on sub-galactic scales. Detailed analyses of galaxy-scale lenses have revealed localized gravitational perturbations beyond the smooth…
Characterising the population and internal structure of sub-galactic halos is critical for constraining the nature of dark matter. These halos can be detected near galaxies that act as strong gravitational lenses with extended arcs, as they…
Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior…
Lensed gravitational waves (GWs) provide a new window into the study of dark matter substructures, yet the faint interference signatures they produce are buried in detector noise. To address this challenge, we develop a deep learning…
Similar to light, gravitational waves (GWs) can be lensed. Such lensing phenomena can magnify the waves, create multiple images observable as repeated events, and superpose several waveforms together, inducing potentially discernible…
Strong gravitational lensing offers a unique probe of dark matter (DM) on sub-galactic scales, where the abundance and distribution of low-mass halos are highly sensitive to the underlying properties of DM particles. In this work, we…