Electrode level Monte Carlo model of radiation damage effects on astronomical CCDs

Current optical space telescopes rely upon silicon Charge Coupled Devices (CCDs) to detect and image the incoming photons. The performance of a CCD detector depends on its ability to transfer electrons through the silicon efficiently, so that the signal from every pixel may be read out through a single amplifier. This process of electron transfer is highly susceptible to the effects of solar proton damage (or non-ionizing radiation damage). This is because charged particles passing through the CCD displace silicon atoms, introducing energy levels into the semi-conductor bandgap which act as localized electron traps. The reduction in Charge Transfer Efficiency (CTE) leads to signal loss and image smearing. The European Space Agency's astrometric Gaia mission will make extensive use of CCDs to create the most complete and accurate stereoscopic map to date of the Milky Way. In the context of the Gaia mission CTE is referred to with the complementary quantity Charge Transfer Inefficiency (CTI = 1-CTE). CTI is an extremely important issue that threatens Gaia's performances. We present here a detailed Monte Carlo model which has been developed to simulate the operation of a damaged CCD at the pixel electrode level. This model implements a new approach to both the charge density distribution within a pixel and the charge capture and release probabilities, which allows the reproduction of CTI effects on a variety of measurements for a large signal level range in particular for signals of the order of a few electrons. A running version of the model as well as a brief documentation and a few examples are readily available at http://www.strw.leidenuniv.nl/~prodhomme/cemga.php as part of the CEMGA java package (CTI Effects Models for Gaia).Comment: Accepted by MNRAS on 13 February 2011. 15 pages, 7 figures and 5 table

A Fractal Analysis of the HI Emission from the Large Magellanic Cloud

A composite map of HI in the LMC using the ATCA interferometer and the Parkes multibeam telescope was analyzed in several ways in an attempt to characterize the structure of the neutral gas and to find an origin for it. Fourier transform power spectra in 1D, 2D, and in the azimuthal direction were found to be approximate power laws over 2 decades in length. Delta-variance methods also showed the same power-law structure. Detailed models of these data were made using line-of-sight integrals over fractals that are analogous to those generated by simulations of turbulence with and without phase transitions. The results suggested a way to measure directly for the first time the line-of-sight thickness of the cool component of the HI disk of a nearly face-on galaxy. The signature of this thickness was found to be present in all of the measured power spectra. The character of the HI structure in the LMC was also viewed by comparing positive and negative images of the integrated emission. The geometric structure of the high-emission regions was found to be filamentary, whereas the geometric structure of the low-emission (intercloud) regions was found to be patchy and round. This result suggests that compressive events formed the high-emission regions, and expansion events, whether from explosions or turbulence, formed the low-emission regions. The character of the structure was also investigated as a function of scale using unsharp masks. All of these results suggest that most of the ISM in the LMC is fractal, presumably the result of pervasive turbulence, self-gravity, and self-similar stirring.Comment: 30 pages, 21 figures, scheduled for ApJ Vol 548n1, Feb 10, 200

Brain Tumor Segmentation with Deep Neural Networks

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster