Results on Low-Mass Weakly Interacting Massive Particles from an 11 kg d Target Exposure of DAMIC at SNOLAB.

We present constraints on the existence of weakly interacting massive particles (WIMPs) from an 11 kg d target exposure of the DAMIC experiment at the SNOLAB underground laboratory. The observed energy spectrum and spatial distribution of ionization events with electron-equivalent energies >200 eV_{ee} in the DAMIC CCDs are consistent with backgrounds from natural radioactivity. An excess of ionization events is observed above the analysis threshold of 50 eV_{ee}. While the origin of this low-energy excess requires further investigation, our data exclude spin-independent WIMP-nucleon scattering cross sections σ_{χ-n} as low as 3×10^{-41} cm^{2} for WIMPs with masses m_{χ} from 7 to 10 GeV c^{-2}. These results are the strongest constraints from a silicon target on the existence of WIMPs with m_{χ}<9 GeV c^{-2} and are directly relevant to any dark matter interpretation of the excess of nuclear-recoil events observed by the CDMS silicon experiment in 2013.

Constraints on Light Dark Matter Particles Interacting with Electrons from DAMIC at SNOLAB.

We report direct-detection constraints on light dark matter particles interacting with electrons. The results are based on a method that exploits the extremely low levels of leakage current of the DAMIC detector at SNOLAB of 2-6×10^{-22} A cm^{-2}. We evaluate the charge distribution of pixels that collect <10e^{-} for contributions beyond the leakage current that may be attributed to dark matter interactions. Constraints are placed on so-far unexplored parameter space for dark matter masses between 0.6 and 100 MeV c^{-2}. We also present new constraints on hidden-photon dark matter with masses in the range 1.2-30 eV c^{-2}.

A self-organizing algorithm for vector quantizer design applied to signal processing.

Vector quantization plays an important role in many signal processing problems, such as speech/speaker recognition and signal compression. This paper presents an unsupervised algorithm for vector quantizer design. Although the proposed method is inspired in Kohonen learning, it does not incorporate the classical definition of topological neighborhood as an array of nodes. Simulations are carried out to compare the performance of the proposed algorithm, named SOA (self-organizing algorithm), to that of the traditional LBG (Linde-Buzo-Gray) algorithm. The authors present an evaluation concerning the codebook design for Gauss-Markov and Gaussian sources, since the theoretic optimal performance bounds for these sources, as described by Shannon's Rate-Distortion Theory, are known. In speech and image compression, SOA codebooks lead to reconstructed (vector-quantized) signals with better quality as compared to the ones obtained by using LBG codebooks. Additionally, the influence of the initial codebook in the algorithm performance is investigated and the algorithm ability to learn representative patterns is evaluated. In a speaker identification system, it is shown that the the codebooks designed by SOA lead to higher identification rates when compared to the ones designed by LBG.