D3^3PO - Denoising, Deconvolving, and Decomposing Photon Observations

The analysis of astronomical images is a non-trivial task. The D3PO algorithm addresses the inference problem of denoising, deconvolving, and decomposing photon observations. Its primary goal is the simultaneous but individual reconstruction of the diffuse and point-like photon flux given a single photon count image, where the fluxes are superimposed. In order to discriminate between these morphologically different signal components, a probabilistic algorithm is derived in the language of information field theory based on a hierarchical Bayesian parameter model. The signal inference exploits prior information on the spatial correlation structure of the diffuse component and the brightness distribution of the spatially uncorrelated point-like sources. A maximum a posteriori solution and a solution minimizing the Gibbs free energy of the inference problem using variational Bayesian methods are discussed. Since the derivation of the solution is not dependent on the underlying position space, the implementation of the D3PO algorithm uses the NIFTY package to ensure applicability to various spatial grids and at any resolution. The fidelity of the algorithm is validated by the analysis of simulated data, including a realistic high energy photon count image showing a 32 x 32 arcmin^2 observation with a spatial resolution of 0.1 arcmin. In all tests the D3PO algorithm successfully denoised, deconvolved, and decomposed the data into a diffuse and a point-like signal estimate for the respective photon flux components.Comment: 22 pages, 8 figures, 2 tables, accepted by Astronomy & Astrophysics; refereed version, 1 figure added, results unchanged, software available at http://www.mpa-garching.mpg.de/ift/d3po

Wavenet based low rate speech coding

Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used. We describe how a WaveNet generative speech model can be used to generate high quality speech from the bit stream of a standard parametric coder operating at 2.4 kb/s. We compare this parametric coder with a waveform coder based on the same generative model and show that approximating the signal waveform incurs a large rate penalty. Our experiments confirm the high performance of the WaveNet based coder and show that the speech produced by the system is able to additionally perform implicit bandwidth extension and does not significantly impair recognition of the original speaker for the human listener, even when that speaker has not been used during the training of the generative model.Comment: 5 pages, 2 figure

The Effelsberg-Bonn HI Survey (EBHIS)

The Effelsberg-Bonn HI survey (EBHIS) comprises an all-sky survey north of Dec = -5 degrees of the Milky Way and the local volume out to a red-shift of z ~ 0.07. Using state of the art Field Programmable Gate Array (FPGA) spectrometers it is feasible to cover the 100 MHz bandwidth with 16.384 spectral channels. High speed storage of HI spectra allows us to minimize the degradation by Radio Frequency Interference (RFI) signals. Regular EBHIS survey observations started during the winter season 2008/2009 after extensive system evaluation and verification tests. Until today, we surveyed about 8000 square degrees, focusing during the first all-sky coverage of the Sloan-Digital Sky Survey (SDSS) area and the northern extension of the Magellanic stream. The first whole sky coverage will be finished in 2011. Already this first coverage will reach the same sensitivity level as the Parkes Milky Way (GASS) and extragalactic surveys (HIPASS). EBHIS data will be calibrated, stray-radiation corrected and freely accessible for the scientific community via a web-interface. In this paper we demonstrate the scientific data quality and explore the expected harvest of this new all-sky survey.Comment: 19 pages, 11 figures, accepted for publication by Astronomical Note