Observations of the post shock break-out emission of SN 2011dh with XMM-Newton

After the occurrence of the type cIIb SN 2011dh in the nearby spiral galaxy M 51 numerous observations were performed with different telescopes in various bands ranging from radio to gamma-rays. We analysed the XMM-Newton and Swift observations taken 3 to 30 days after the SN explosion to study the X-ray spectrum of SN 2011dh. We extracted spectra from the XMM-Newton observations, which took place ~7 and 11 days after the SN. In addition, we created integrated Swift/XRT spectra of 3 to 10 days and 11 to 30 days. The spectra are well fitted with a power-law spectrum absorbed with Galactic foreground absorption. In addition, we find a harder spectral component in the first XMM-Newton spectrum taken at t ~ 7 d. This component is also detected in the first Swift spectrum of t = 3 - 10 d. While the persistent power-law component can be explained as inverse Compton emission from radio synchrotron emitting electrons, the harder component is most likely bremsstrahlung emission from the shocked stellar wind. Therefore, the harder X-ray emission that fades away after t ~ 10 d can be interpreted as emission from the shocked circumstellar wind of SN 2011dh.Comment: Accepted for publication as a Research Note in Astronomy and Astrophysic

Riemannian Sparse Coding for Positive Definite Matrices

International audienceInspired by the great success of sparse coding for vector valued data, our goal is to represent symmetric positive definite (SPD) data matrices as sparse linear combinations of atoms from a dictionary, where each atom itself is an SPD matrix. Since SPD matrices follow a non-Euclidean (in fact a Riemannian) geometry, existing sparse coding techniques for Euclidean data cannot be directly extended. Prior works have approached this problem by defining a sparse coding loss function using either extrinsic similarity measures (such as the log-Euclidean distance) or kernelized variants of statistical measures (such as the Stein divergence, Jeffrey's divergence, etc.). In contrast, we propose to use the intrinsic Riemannian distance on the manifold of SPD matrices. Our main contribution is a novel mathematical model for sparse coding of SPD matrices; we also present a computationally simple algorithm for optimizing our model. Experiments on several computer vision datasets showcase superior classification and retrieval performance compared with state-of-the-art approaches