Related papers: Deep learning for Directional Dark Matter search
Gas-filled Time Projection Chambers (TPCs) with Gas Electron Multipliers (GEMs) and pixels appear suitable for direction-sensitive WIMP dark matter searches. We present the background and motivation for our work on this technology, past and…
The dark matter directional detection opens a new field in cosmology bringing the possibility to build a map of nuclear recoils that would be able to explore the galactic dark matter halo giving access to a particle characterization of such…
Weakly Interacting Massive Particles (WIMPs) are among the main candidates for the relic dark matter (DM). The idea of the direct DM detection relies on elastic spin-dependent (SD) and spin-independent (SI) interaction of WIMPs with target…
It is usually assumed that dark matter direct detection is sensitive to a large fraction of the dark matter (DM) velocity distribution. We propose an alternative form of dark matter-nucleus scattering which only probes a narrow range of DM…
A neutral dark matter (DM) particle with a magnetic dipole moment has a very different direct detection phenomenology with respect to standard candidates. This is due to the peculiar functional form of the differential cross section for…
An array of powerful neutrino-beam experiments will study the fundamental properties of neutrinos with unprecedented precision in the coming years. Along with their primary neutrino-physics motivations, there has been growing recognition…
This article gives an overview on the status of experimental searches for dark matter at the end of 2014. The main focus is on direct searches for weakly interacting massive particles (WIMPs) using underground-based low-background…
Recently, deep learning approach has achieved promising results in various fields of computer vision. In this paper, a new framework called Hierarchical Depth Motion Maps (HDMM) + 3 Channel Deep Convolutional Neural Networks (3ConvNets) is…
A concept to search for a neutron electric dipole moment (nEDM) is presented, which employs a pulsed neutron beam instead of the nowadays established use of storable ultracold neutrons (UCN). The technique takes advantage of the high peak…
The analysis of signals in directional dark matter (DM) detectors typically assumes that the directions of nuclear recoils can be measured in the Galactic rest frame. However, this is not possible with all directional detection…
Future multi-tonne Direct Detection experiments will be sensitive to solar neutrino induced nuclear recoils which form an irreducible background to light Dark Matter searches. Indeed for masses around 6 GeV the spectra of neutrinos and Dark…
The direct detection rate for supersymmetric cold dark matter (CDM) particles is calculated for a number of suitable nuclear targets. Both the coherent and spin contributions are considered. By considering representative phenomenologically…
Models of nuclear dark matter propose that the dark sector contains large composite states consisting of dark nucleons in analogy to Standard Model nuclei. We examine the direct detection phenomenology of a particular class of nuclear dark…
Dark photons can be the ultralight dark matter candidate, interacting with Standard Model particles via kinetic mixing. We propose to search for ultralight dark photon dark matter (DPDM) through the local absorption at different radio…
DARWIN (dark matter wimp search with noble liquids) is a design study for a next-generation, multi-ton dark matter detector in Europe. Liquid argon and/or liquid xenon are the target media for the direct detection of dark matter candidates…
Dark matter detection experiments are getting ever closer to the sensitivity needed to detect the primary particle physics candidates for nonbaryonic dark matter. Indirect detection methods include searching for antimatter and gamma rays,…
The ArDM project aims at operating a large noble liquid detector to search for direct evidence of Weakly Interacting Massive Particles (WIMP) as Dark Matter in the universe. Background sources relevant to ton-scale liquid and gaseous argon…
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…
Despite their unprecedented performance in various domains, utilization of Deep Neural Networks (DNNs) in safety-critical environments is severely limited in the presence of even small adversarial perturbations. The present work develops a…
The problem of the dark matter in the universe is reviewed. A short history of the subject is given, and several of the most obvious particle candidates for dark matter are identified. Particular focus is given to weakly interacting,…