Related papers: Inferring Warm Dark Matter Masses with Deep Learni…

We present a Fisher matrix forecast for the sensitivity on the mass of a thermal warm dark matter (WDM) particle from current (DES-like) and future (LSST-like) photometric galaxy surveys using the galaxy angular power spectrum. We model the…

Well-motivated elementary particle candidates for the dark matter, such as the sterile neutrino, behave as warm dark matter (WDM).For particle masses of order a keV, free streaming produces a cutoff in the linear fluctuation power spectrum…

The matter power spectrum at comoving scales of (1-40) h^{-1} Mpc is very sensitive to the presence of Warm Dark Matter (WDM) particles with large free streaming lengths. We present constraints on the mass of WDM particles from a combined…

Deep learning (DL) models have received particular attention in medical imaging due to their promising pattern recognition capabilities. However, Deep Neural Networks (DNNs) require a huge amount of data, and because of the lack of…

Dark Matter (DM) is generally assumed to be massive, cold and collisionless from the structure formation point of view. A more correct statement however is that DM indeed experiences collisional damping, but on a scale which is supposed to…

We propose counting peaks in weak lensing (WL) maps, as a function of their height, to probe models of dark energy and to constrain cosmological parameters. Because peaks can be identified in two-dimensional WL maps directly, they can…

We use large-scale cosmological observations to place constraints on the dark-matter pressure, sound speed and viscosity, and infer a limit on the mass of warm-dark-matter particles. Measurements of the cosmic microwave background (CMB)…

Analytic formulas reproducing the warm dark matter (WDM) primordial spectra are obtained for WDM particles decoupling in and out of thermal equilibrium which provide the initial data for WDM non-linear structure formation. We compute and…

We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool commonly used in image recognition tasks. The CNN is trained…

We have developed a machine learning algorithm capable of detecting ``out-of-domain data'' for trustworthy cosmological inference. By using data from two separate suites of cosmological simulations, we show that our algorithm is able to…

We examine the cosmology of warm dark matter (WDM), both stable and decaying, from the point of view of structure formation. We compare the matter power spectrum associated to WDM masses of 1.5 keV and 0.158 keV, with that expected for the…

We report a series of high-resolution cosmological N-body simulations designed to explore the formation and properties of dark matter halos with masses close to the damping scale of the primordial power spectrum of density fluctuations. We…

We present a series of four simulations of Cold Dark Matter (CDM) and Cold + Hot Dark Matter (CHDM) cosmologies. We discuss the power spectrum and correlation functions in real and redshift space, with comparisons to the CfA2 and IRAS…

Warm dark matter (WDM) might more easily account for small scale clustering measurements than the heavier particles typically invoked in Lambda cold dark matter (LCDM) cosmologies. In this paper, we consider a Lambda WDM cosmology in which…

Several direct detection experiments, including recently CDMS-II, have reported signals consistent with 5 to 10 GeV dark matter (DM) that appear to be in tension with null results from XENON and LUX experiments; these indicate a careful…

Sterile neutrinos with the mass in the keV range are interesting warm dark matter (WDM) candidates. The restrictions on their parameters (mass and mixing angle) obtained by current X-ray missions (XMM-Newton or Chandra) can only be improved…

We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM),…

State-of-the-art Deep Neural Networks can be easily fooled into providing incorrect high-confidence predictions for images with small amounts of adversarial noise. Does this expose a flaw with deep neural networks, or do we simply need a…

In this paper, we formulate a new model of density distribution for halos made of warm dark matter (WDM) particles. The model is described by a single microphysics parameter - the mass (or, equivalently, the maximal value of the initial…

Any successful model of dark matter must explain the diversity of observed Milky Way (MW) satellite density profiles, from very dense ultrafaints to large, low density satellites such as Crater~II that appear to be larger their anticipated…