Prospects for Annihilating Dark Matter in the inner Galactic halo by the Cherenkov Telescope Array

We compute the sensitivity to dark matter annihilations for the forthcoming large Cherenkov Telescope Array (CTA) in several primary channels and over a range of dark matter masses from 30 GeV up to 80 TeV. For all channels, we include inverse Compton scattering of e$^\pm$ by dark matter annihilations on the ambient photon background, which yields substantial contributions to the overall gamma-ray flux. We improve the analysis over previous work by: i) implementing a spectral and morphological analysis of the gamma-ray emission; ii) taking into account the most up-to-date cosmic ray background obtained from a full CTA Monte Carlo simulation and a description of the diffuse astrophysical emission; and iii) including the systematic uncertainties in the rich observational CTA datasets. We find that our spectral and morphological analysis improves the CTA sensitivity by roughly a factor 2. For the hadronic channels, CTA will be able to probe thermal dark matter candidates over a broad range of masses if the systematic uncertainties in the datasets will be controlled better than the percent level. For the leptonic modes, the CTA sensitivity will be well below the thermal value of the annihilation cross-section. In this case, even with larger systematics, thermal dark matter candidates up to masses of a few TeV will be easily studied.Comment: 15 pages, 4 figures, v2: Jfactors for two different DM profiles in Tab.1 added; two new plots added; some clarifications and some references added; results unchanged; matches version published on Phys. Rev.

Caractérisation d'un transect de sols de la station agronomique de Misamfu (Zambie)

Cet article décrit en détail, le long d'un transect de 485 m. situé sur la zone du plateau de la station de recherche de Misamfu, les variations concernant les sols, les horizons indurés associés, la végétation et les termitières. Les auteurs discutent les contraintes physiques des sols pour l'agriculture et, plus particulièrement, la présence et la formation de deux types de croûtes superficielles

On moments of twisted LL-functions

We study the average of the product of the central values of two $L$-functions of modular forms $f$ and $g$ twisted by Dirichlet characters to a large prime modulus $q$. As our principal tools, we use spectral theory to develop bounds on averages of shifted convolution sums with differences ranging over multiples of $q$, and we use the theory of Deligne and Katz to estimate certain complete exponential sums in several variables and prove new bounds on bilinear forms in Kloosterman sums with power savings when both variables are near the square root of $q$. When at least one of the forms $f$ and $g$ is non-cuspidal, we obtain an asymptotic formula for the mixed second moment of twisted $L$-functions with a power saving error term. In particular, when both are non-cuspidal, this gives a significant improvement on M.~Young's asymptotic evaluation of the fourth moment of Dirichlet $L$-functions. In the general case, the asymptotic formula with a power saving is proved under a conjectural estimate for certain bilinear forms in Kloosterman sums.Comment: final version; to appear in American Journal of Mat

An investigation of discrete-state discriminant approaches to single-sensor source separation

International audienceThis paper investigated a new scheme for single-sensor audio source separation. This framework is introduced comparatively to the existing Gaussian mixture model generative approach and is focusing on the mixture states rather than on the source states, resulting in a discrete, joint state discriminant approach. The study establishes the theoretical performance bounds of the proposed scheme and an actual source separation system is designed. The performance is computed on a set of musical recordings and a discussion is proposed, including the question of the source correlation and the possible drawbacks of the method

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model.Comment: Accepted for publication in 41st International Engineering in Medicine and Biology Conference (EMBC), July 23-27, 201